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1
Tadas, Harikrishna, Jeevan Nagarkar, Sushant Malik, Dharmesh K. Mishra, and Dipen Paul. "The effectiveness of technical trading strategies: Evidence from Indian equity markets." Investment Management and Financial Innovations 20, no. 2 (2023): 26–40. http://dx.doi.org/10.21511/imfi.20(2).2023.03.
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The purpose of the study was to analyze the effectiveness of technical trading strategies in trading stocks of selected Indian companies represented in the Nifty 50 Index. The research was done using secondary data from January 2022 to August 2022. Hourly share prices of 14 largest companies as per market capitalization from 14 different sectors from the Nifty 50 Index were considered as a part of the study. Simple Moving Average, Exponential Moving Average – Relative Strength Index and Bollinger Bands – Relative Strength Index – strategies considered in the study. It was found that strategy based on Bollinger Bands and Relative Strength Index performed the best. Performance was considered with respect to both the number of stocks having a net profit and the number of stocks that were able to outperform the buy-and-hold strategy for the time period considered. The study considered several combined strategies and performance indicators, whereas previous studies used limited indicators. Out of the 14 stocks considered, the Simple Moving Average strategy was able to generate net profit for 8 stocks and it outperformed the buy-and-hold strategy for 6 stocks, Exponential Moving Average – Relative Strength Index strategy generated net profit for 6 stocks and it outperformed the buy-and-hold strategy for 5 stocks, and the Bollinger Bands – Relative Strength Index generated net profit for 11 stocks and it outperformed the buy-and-hold strategy for 10 stocks. The Bollinger Bands – Relative Strength Index strategy was able to outperform as it was more dynamic and entered and exited positions actively.
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Atkins, Allen, Kevin C. H. Chiang, and Ming-Long Lee. "Chasing Housing Prices?" Journal of Applied Business Research (JABR) 28, no. 2 (2012): 237. http://dx.doi.org/10.19030/jabr.v28i2.6844.
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If a person or organization is planning to buy real estate in the future but is unable or unwilling to buy it now, how can they best hedge this purchase? In what class of asset should they invest their money until they are ready to purchase the real estate? This paper uses Monte Carlo simulation and bootstrap techniques to investigate the effectiveness of using traditional asset classes in managing the long-term risks associated with the future purchase of real estate. We find that the best purchase early hedge for both residential and commercial real estate is small value stocks. Small value stocks would be the most likely to provide returns at least as good as real estate and they would be least likely to suffer losses relative to real estate. The effectiveness of the hedge increases the longer the time horizon of the investor. Large value stocks and equity REITs are also quite good but not as good as small value stocks. Other asset classes are not nearly as effective. The least effective asset class is T-Bills.
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Balik, Robert, and Jamshid Mehran. "Benjamin Graham Revisited." Journal of Finance Issues 6, no. 2 (2008): 181–86. http://dx.doi.org/10.58886/jfi.v6i2.2399.
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Warren Buffett read the first edition of Benjamin Graham’s book, The Intelligent Investor, in 1950. According to Buffett (Graham, Revised, 2003) “I thought then that it was by far the best book about investing ever written. I still think it is.” Can a buy and hold defensive investor use this stock selection criteria and earn a positive abnormal risk adjusted rate of return? To test this hypothesis data for 2000 and prior years are used to select stocks that meet the defensive investor criteria. Nine stocks from an original list of more than 9,000 satisfy the criteria. Buy and hold is the investment strategy and these stocks are held from the end of June 2001 until the end of December 2007. The performance measure is the time-series regression intercept, or alpha, for the four-factor model. The estimated alpha is negative but not statistically significant.
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Sandri, Siti Hanifa, Siti Samsiah, Misral Misral, Bakaruddin Bakaruddin, Sri Rahmayanti, and Hendri Ali Ardi. "INVESTASI SAHAM BAGI PEMULA." Jurnal Pengabdian UntukMu NegeRI 3, no. 1 (2019): 40–45. http://dx.doi.org/10.37859/jpumri.v3i1.1105.
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Investing is the best option for maintaining financial security in the future. For most people especially beginners, investing is a very complicated thing and will make it confused to take the right investment decision. All types of investments contain elements of risk. Investor decisions are based on consideration of risk factors and expected gains. The purpose of this activity is to socialize the investment measures for beginners who are teachers and learners. Acquire the right knowledge of trends to buy stocks in the capital market for beginners i.e. teachers and students through live practice in the field. This method of community dedication uses lecture methods, direct demonstrations by the speaker, and questions and answers. The lecture method is used to convey knowledge on how to invest stocks for beginners. Demonstrations are used to provide direct examples of how to buy stocks on the capital markets. The result of this activity shows the participants very enthusiastic during the activities until the training is completed. Participants hope the next year can be given the opportunity to get similar training. Therefore, the investment will not be enough if done once.
 Keywords: Investing, stocks and beginners
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Bhavanagarwala, Mustafa Shabbir, Nagarjun K N, Tanzim Abbas Charolia, Vishal M, and Ashwini M. "STOCK AND CRYPTOCURRENCY PREDICTION." International Journal of Innovative Research in Advanced Engineering 9, no. 8 (2022): 182–86. http://dx.doi.org/10.26562/ijirae.2022.v0908.06.
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In our project, the point is to anticipate long term esteem of the money related stocks of a company and crypto coins individually with fine precision. The future prices of stock and cryptocurrency are predicted by using the past available values. “Buy low, sell high" is a good saying but it is not a good choice for making speculations. Investment is best stock or crypto currency in awful time can have bad results, while investment in best stock or cryptocurrency at right time can have best benefits. Prediction for long term values is easy as compared to day-to-day basis as prices fluctuate a lot. So, our model predicts the price of stocks and cryptocurrencies, which helps the investors to invest in appropriate stocks and cryptocoins. The dataset used is taken from yahoo finance and twelve data using web scraping. The dataset retrieved is in raw format. It consists of collection of values of stock market data of various companies, and also data of various cryptocurrencies. First, raw data is converted into processed data, which is done using feature extraction. Then the dataset is splitted into training and test sets. We use the training dataset to train the model, and use test dataset to predict the future prices of stocks and cryptocurrencies. Now user can gain best knowledge about stock price trends of various companies and also cryptocurrency price trends, and can decide on for best investments in respective fields and gain best benefits.
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Harahap, Kartini. "How to Get Daily Capital Gain in Covid-19 Pandemic Period? (Case study: Indonesia Stock Exchange)." International Journal of Business Studies 6, SI (2022): 44–53. http://dx.doi.org/10.32924/ijbs.v6i1.185.
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The purpose of this research is to find out how to obtain daily capital gains in the midst of the Covid-19 pandemic using the day trade strategy and the Stochastic Oscillator as a technical indicator case study of stocks in the telecommunications industry sector. This research is descriptive in nature and uses secondary data, namely: data on daily stock prices and hourly charts, per 10 minutes on the stock price movements of PT Telekomunikasi Indonesia Tbk (TLKM) and PT Smartfren Telecom Tbk (FREN). The result of this study are finding TLKM and FREN stocks as the right stocks for day trade, day trading from the market opening or in the morning and completing transactions during the day because the volume of shares decreases reflected in the volume of TLKM shares, but not on FREN shares. Stochastic Oscillator indicator can be used to obtain daily capital gains at TLKM and FREN stocks; (a) the red box area is the best momentum for "sell" positions, the green box area is the best momentum for "buy" positions. (b) the arrow in the red box there is a% K line that crosses% D upwards/ downwards, indicating a sell/buy signal at TLKM and during this period the FREN shares did not have the momentum to sell. The results of this study are expected to give consideration to investors not to panic selling and provide knowledge about how to earn daily profits on the stock market during the Covid-19 Pandemic with a simple method so that the Indonesian capital market remains excited amid the Covid-19 Pandemic.
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Delrinata, Winanda, and Fernando B. Siahaan. "Implementasi Algoritma Apriori Untuk Menentukan Stok Obat." Jurnal Sisfokom (Sistem Informasi dan Komputer) 9, no. 2 (2020): 222. http://dx.doi.org/10.32736/sisfokom.v9i2.875.
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The supply of drugs in a pharmacy is very important to maintain the fulfillment of consumer needs based on a doctor's prescription. Problems arise due to limitations on the expiry date of each drug, this needs to be overcome so that there is no buildup of drug stocks at the pharmacy so that it causes losses because there are types of drugs that have expired in sufficient quantities, therefore we need data mining that can determine which pattern of drug type works best, using a priori algorithm. The association method is needed to see the correlation between a number of attributes for example if a consumer buys drug A then he will buy drug B as well. A priori analysis to determine the minimum conditions for support and confidence. The conclusion of this research is that if you buy amlodipine 5 mg, you will buy sanmol, this is obtained from 33.33% support and 66.66% confidence, if you buy 500 mg amoxan, you will buy sanmol with a support value of 41.66% and confidence 71, 42% and if you buy sanmol, you will buy amoxan 500 mg with a support value of 41.66% and confidence 62.50%.
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Rickett, Laura, and Pratim Datta. "Beauty-Contests in the Age of Financialization: Information Activism and Retail Investor Behavior." Journal of Information Technology 33, no. 1 (2018): 31–49. http://dx.doi.org/10.1057/s41265-016-0026-2.
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Keynes (The General Theory of Employment, Interest and Money, Harcourt Brace and Co., New York, 1936) had rightfully argued that picking stocks is akin to a beauty contest. The chances of winning are amplified if one's choice matches the likelihood of the panel's choice. In this era of financialization, where profit-making has shifted to speculative sways rather than fundamental trade and commodity production measures (Krippner, Socio-Econ Rev 3(2): 173-208, 2005), similar beauty contests have become even more acute. Online, real-time media channels along with pervasive investments applications have ushered in unprecedented online financial information and retail investor interest, ranging from dealing in penny stocks to sentiment-based trading. More than information sources, similar investment sites compete to recommend investment directions and strategies, not driven by strict fundamentals used by “arbitrageurs” or rational speculators but on pseudo-signals proffered by various information investment channels with varying degrees of credibility. This behavior, referred to herein as information activism, concomitantly adds a sociopsychological dimension to the concept of financialization (Lagoarde-Segot, Int Rev Financial Anal 2016) – wherein technology-driven information reach and range contribute to financial dominance of financial actors and practices. Using information activism as a lens, this research empirically evidences the extent to which information activism affects retail investor behavior under various market conditions. This study examines the differential effects of two primary, albeit reputable, sources of information activism: an investment news channel (CNBC – Mad Money) and an online financial blog (SeekingAlpha), and the effect on investor behavior during the 2008 financial crisis. In identifying the specific downstream effects of information activism on capital markets and investor behavior, factors related to investor behavior, such as trading volume and price reaction, are analyzed surrounding information activism events. Results indicate that retail investors appear to rely on online information activists during uncertain economic conditions. Findings denote that abnormal returns are associated with information activism during uncertain economic conditions and for buy recommendations when information asymmetry is high. Abnormal trading volume is also associated with information activism during economic uncertainty and with buy recommendations when information asymmetry is high particularly for stocks exchanges where unsophisticated investors tend to trade more heavily.
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Gomathi, M., and Dr S. Nirmala. "Analysis of Nifty Movement on Share Prices of Selected Construction Companies." INTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY 1, no. 3 (2012): 59–65. http://dx.doi.org/10.24297/ijmit.v1i3.1421.
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This study aims at analyzing and predicting the price movements of construction companies stocks contributing to the NIFTY50 Index. To analyze the volatility of telecom stock and understand the behavior of stock prices in construction sector stocks i.e. (JP ASSOCIATES LIMITED, DLF LIMITED, GAMMON INDIA LIMITED, PUNJ LLOYD LIMITED, HCC LIMITED). The data for these stocks are collected from magazines, newspaper and websites. The stocks are analyzed by monitoring their respective price movements using technical tools. The technical tools used in this study are Exponential moving average, Relative strength index, Rate of change, MACD. Using these tools the trend over the recent past was deciphered. The expected trend in the immediate future was also predicted. Technical Analysis studies the price and volume movement in the market and predicts the future. It helps in identifying that the best time to buy and sell equity. Technical Analysis is a method of evaluating equities by analyzing the statistics generated by market activity, such as past prices and volume.
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Iriyadi, Iriyadi, Meiryani Meiryani, Ahmad Syamil, et al. "The analysis of chasing returns strategy in equity funds." Corporate and Business Strategy Review 5, no. 1 (2024): 66–76. http://dx.doi.org/10.22495/cbsrv5i1art7.
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Most of the new investors, who are dominated by millennial investors, do not understand the basics of the capital market, so they have to suffer losses. Therefore, a strategy for investing in mutual funds is needed. This study aims to compare the performance of return-chasing investments with the buy-and-hold strategy in providing the best return to stock mutual fund investors. Beers (2020) states that “buy-and-hold” is a strategy in which investors buy stocks (or other types of securities, such as exchange-traded funds) and hold them for a long time regardless of market fluctuations. The data used is the net asset value of mutual funds which is then processed to obtain rank one based on annual returns. Simulations will be carried out to see the investment results of the two strategies and then the Wilcoxon signed-rank statistical test will be carried out on the profit/loss percentage to see the significance of the difference. The results of statistical tests show that there is no significant difference in investment returns between the chase return and buy and hold strategies. This result indicates the chasing return strategy provides much better investment returns than the buy-and-hold strategy for five periods on mutual fund instruments. The implication of this research for investors in using a chasing return strategy is that investors must use technical analysis, namely analyzing and finding out which mutual funds have the best prospects in that year.
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Hasan, Nurain, Frendy A. O. Pelleng, and Joanne V. Mangindaan. "Analisis Capital Asset Pricing Model (CAPM) Sebagai Dasar Pengambilan Keputusan Berinvestasi Saham (Studi pada Indeks Bisnis-27 di Bursa Efek Indonesia)." JURNAL ADMINISTRASI BISNIS 8, no. 1 (2019): 36. http://dx.doi.org/10.35797/jab.8.1.2019.23498.36-43.
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The purpose of this study were: (1) To help investors pick efficient and inefficient stocks, (2) Investors know which stocks that have an optimal return and appropriate risk, (3) Investors know about CAPM metodh in determining the best investment decisions. CAPM is a model for estimating returns earned on risky securities or as a benchmark in evaluating the rate of return on an investment. The samples were selected by purposive sampling technique, the samples were determined by the specific criteria: (1) Companies listed on the Indonesia Stock Exchange belonging to the Business-27 stock index (2) Companies whose shares are included in the Business-27 stock index consistenly. The selection criteria in this study is choosing the efficient stocks in which individual return > expected return (Ri>ERi). Efficient collection of shares must be a priority in investment decisions made only efficient stocks that can be purchased. The results of this study indicate that: There are 18 stocks included on Efficient shares ie AKRA, BBCA, BBNI, BBRI, BMRI, CPIN, GGRM, INDF, INTP, PGAS, SMGR. These shares have a Ri> ERi value, investment decisions should be taken by investors was to buy efficient stocks. Based on data analysis there is a non-linear relationship between systematic risk and expected stock returns.
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Dwi Permana, Hendi, and Subiakto Sukarno. "Implementation of Lo Kheng Hong Investment Strategy to Build Optimal Stock Portfolio." European Journal of Business and Management Research 8, no. 5 (2023): 13–17. http://dx.doi.org/10.24018/ejbmr.2023.8.5.2089.
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The favorite investment choice of the Indonesian people, gold is in first place with a survey of 60.2%, and stock investment is ranked sixth with a value of 22.5%. Compared to Indonesia’s total population, the number of individual investors has a relatively small percentage of 1.48% but continues to show significant growth yearly. Before entering the Indonesia Stock Exchange, every stock investor must have good knowledge and strategies to get optimal returns and risks. Lo Kheng Hong is an individual investor who has proven successful in the Indonesian stock exchange. The purpose of this research is to get an overview of the results of stock investment using the Lo Kheng Hong investment strategy on the Indonesian stock exchange. The method used to select stocks IDX is to use the PBV <0.5, PER <10, and Market Cap > 500 billion; after that, the top 4 stocks with the largest Total Annual Income were selected in 2012. The author uses the RTI Analysis Method (Read Think Analysis), which Lo Kheng Hong uses to verify that the stocks being screened are truly undervalued stocks with good fundamentals. Optimize portfolio and investment risk using the Excel Solver tool to obtain the composition of the stock weight with the best Sharpe Ratio. The author simulated purchasing shares within ten years using the buy hold, rebalancing method of buying shares every 1, 3, 6, and 12 months. Then, compare the Portfolio performance with the Composite Stock Price Index (IHSG). The results obtained from this study are the composition of the PTRO stock portfolio = 15.01%, INKP = 44.19%, SMDR = 35.90%, and TKIM = 5% by maximizing the Sharpe ratio of 0.2134. For the method of buying shares using the rebalancing method every year, it gets the best performance compared to the buy and holds or rebalancing plans for other periods, with a yield of 3400.61% and a Sharpe ratio of 0.1807. Portfolio performance using the Lo Kheng Hong investment strategy can exceed the Jakarta Composite Index (IHSG) performance within a 10-year investment period.
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Svoboda, Milan, and Pavla Říhová. "STOCK PRICE PREDICTION USING MARKOV CHAINS ANALYSIS WITH VARYING STATE SPACE ON DATA FROM THE CZECH REPUBLIC." E+M Ekonomie a Management 24, no. 4 (2021): 142–55. http://dx.doi.org/10.15240/tul/001/2021-4-009.
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The article describes empirical research that deals with short-term stock price prediction. The aim of this study is to use this prediction to create successful business models. A business model that outperforms the stock market, represented by the Buy and Hold strategy, is considered to be successful. A stochastic model based on Markov chains analysis with varying state space is used for short-term stock price prediction. The varying state spate is defined based on multiples of the moving standard deviation. A total of 80 state space models were calculated for the moving standard deviation with 5-step lengths from 10 to 30 in combination with the standard deviation multiples from 0.5 to 2.0 with the step of 0.1. The efficiency of the business models was verified for 3 long-term, liquid stocks of the Czech stock market, namely the stocks of KB, CEZ, and O2 within a 14-year period – from the beginning of 2006 to the end of 2019. Business models perform best when they use a state space defined on the length of a moving standard deviation between 15 and 30 in combination with multiples of the standard deviation between 1.1 and 1.2. Business models based on these parameters outperform the passive Buy and Hold strategy. In fact, they outperform the Buy and Hold strategy for both the entire period under review and the yielded five-year periods (including transaction fees). The only exception is the five-year periods covering 2015 for O2 stocks. After the end of the uncertainty period caused by unclear intentions of the new majority stockholder, the stock price rose sharply. These results are in conflict with the efficient markets theory and suggest that in the period under review, the Czech stock market was not effective in any form.
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Salim, Jana, and Hamparsum Bozdogan. "A Novel Approach to Forecasting High Dimensional S&P500 Portfolio Using VARX Model with Information Complexity." Journal of Economics and Technology Research 3, no. 2 (2022): p1. http://dx.doi.org/10.22158/jetr.v3n2p1.
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This study considers vector autoregressive models that allow for endogenous and exogeneous regressors VARX using multivariate OLS regression. For the model selection, we follow bozdogan’s entropic or information-theoretic measure of complexity ICOMP criterion of the estimated inverse Fisher information matrix IFIM in choosing the best VARX lag parameter and we established that ICOMP outperform the conventional information criteria. As an empirical illustration, we reduced the dimension of the S&P500 multivariate time series using Sparse Principal Component Analysis (SPCA) and chose the best subset of 37 stocks belonging to six sectors. We then performed a portfolio of stocks based on the highest SPC loading weight matrix, plus the S&P500 index. Furthermore, we applied the proposed VARX model to predict the price movements in the constructed portfolio, where the S&P500 index was treated as an exogeneous regressor of the VARX model. It has been deduced too that the buy-sell decision making in response to VARX (4,0) for a stock outperforms investing and holding the stock over the out-of-sample period.
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Hui, Eddie C. M., Sheung-Chi Phillip Yam, and Si-Wei Chen. "SHIRYAEV-ZHOU INDEX – A NOBLE APPROACH TO BENCHMARKING AND ANALYSIS OF REAL ESTATE STOCKS." International Journal of Strategic Property Management 16, no. 2 (2012): 158–72. http://dx.doi.org/10.3846/1648715x.2011.638946.
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Real estate markets and real estate stocks are interrelated and are important not only to the investors, but also to the academics. Real estate stocks are, in a sense, good measures of performance of the physical real estate market. The objective of this paper is to provide a preliminary study on gauging the performances of real estate stocks in Hong Kong using the Shiryaev-Zhou index. Evidence shows that the Shiryaev-Zhou index can gauge a real estate stock's performance, good or bad, according to the sign of the Shiryaev-Zhou index. Thus a trading strategy can be formulated as follows: buy a stock if its Shiryaev-Zhou index changes from negative to positive, then hold it until its Shiryaev-Zhou index turns negative, when it is time to sell the stock. We examine the Shiryaev-Zhou indices of the real estate stocks in Hong Kong, and from this we deduce the latest best selling dates of the stocks during the period of our study. The Shiryaev-Zhou index could be an indicator of whether the market is bullish or bearish and consequently tells an investor to hold a stock or not, and it naturally leads to an optimal selling strategy that maximize the average ratio of the selling price to the maximum stock price when the underlying coefficients are assumed to be constant over a definite period of time.
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Stewart, Robert EA, Erik W. Born, J. Blair Dunn, William R. Koski, and Anna K. Ryan. "Use of Multiple Methods to Estimate Walrus (Odobenus rosmarus rosmarus) Abundance in the Penny Strait-Lancaster Sound and West Jones Sound Stocks, Canada." NAMMCO Scientific Publications 9 (December 15, 2014): 95. http://dx.doi.org/10.7557/3.2608.
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Surveys to estimate walrus abundance at terrestrial haulout sites in the Penny Strait-Lancaster Sound (PS-LS) and West Jones Sound (WJS) stocks were conducted in 1977 and 1998-2009. The Minimum Counted Population (MCP) was similar in 1977 (565) to recent years (557) for the PS-LS stock. The MCP for the WJS stock was higher in recent surveys (404) than in 1977 (290). Regression analysis of MCP and density (number of walrus divided by number of haulouts surveyed) showed no significant trends over time. We also calculated bounded count estimates for comparison. Finally, we used broad-scale behavioural data to estimate the proportion of the total stock that could be considered countable, to produce two adjusted estimates. We selected recent surveys with good coverage and ignored adjusted estimates that were lower than MCP. For the PS-LS stock, the adjusted MCP (with 95% CL) was 672 (575-768) and 727 (623-831) walrus in 2007 and 2009, respectively. For WJS, the best estimates were the adjusted MCP of 503 (473-534) in 2008 and the adjusted bounded count of 470 (297-1732) in 2009. While both stocks appear to have remained stable over three decades, differences in survey coverage and possible differences in walrus distribution make precise population estimation difficult.
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Sumalpong, Felipe Jr Raypan, Michael Frondoza, and Noel Lito Sayson. "British Put Option On Stocks Under Regime-Switching Model." European Journal of Pure and Applied Mathematics 16, no. 3 (2023): 1830–47. http://dx.doi.org/10.29020/nybg.ejpam.v16i3.4830.
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In a plain vanilla option, its holder is given the right, but not the obligation, to buy or sell the underlying stock at a specified price (strike price) at a predetermined date. If the exercise date is at maturity, the option is called a European; if the option is exercised anytime prior to maturity, it is called an American. In a British option, the holder can enjoy the early exercise feature of American option whereupon his payoff is the ‘best prediction’ of the European payoff given all the information up to exercise date under the hypothesis that the true drift of the stock equals a specified contract drift. In this paper, in contrast to the constant interest rate and constant volatility assumptions, we consider the British option by assuming that the economic state of the world is described by a finite state continuous-time Markov chain. Also, we provide a solution to a free boundary problem by using PDE arguments. However, closed form expression for the arbitrage-free price are not available in our setting.
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Rajvanshi, Vivek, and Samit Paul. "What’s hidden behind bulk deals? A study on Indian stock market." Managerial Finance 48, no. 4 (2022): 557–76. http://dx.doi.org/10.1108/mf-08-2021-0374.
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Purpose Emerging market, like India, is characterised by poor institutional structure, weaker regulations and higher information asymmetry which may lead to stock price manipulation. This study shed some light on such manipulation by investigating front-running behaviour around the bulk deals of stocks traded at the National Stock Exchange (NSE) from 2010 to 2019.Design/methodology/approach The authors employ an event study methodology to identify front-running in pre-event period of bulk deals. The bulk deals are classified into Only Buy, Only Sell, Partial Buy and Partial Sell trades. They are further subsampled based on the category of investors. Through cross-sectional regression, the authors also identify factors explaining such front-running.Findings The results show that the front-runners can achieve 5%–7% returns within a week around the event day. Abnormal Returns (AR) before the deals are higher for “Buy” deals than “Sell” deals. The authors also examine the role of volume and delivery in explaining the AR and cumulative abnormal returns (CAR). Lagged CAR, change in volume and change in delivery explain the AR. The results are robust after controlling for Bullish and Bearish Periods.Originality/value To the best of authors’ knowledge, this is the first study that explores the front-running in “Partial Buy” and “Partial Sell” bulk deals. Further, it investigates whether the category of investors has any role in front running. It empirically tests the asymmetric market reaction between “Buy” and “Sell” trades. Finally, it examines the role of volume and delivery in front-running.
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Korade, Nilesh B., Mahendra B. Salunke, Amol Bhosle, et al. "Integrating deep learning and optimization algorithms to forecast real-time stock prices for intraday traders." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2254. https://doi.org/10.11591/ijece.v15i2.pp2254-2263.
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The number of stock investors is steadily increasing due to factors such as the availability of high-speed internet, smart trading platforms, lower trading commissions, and the perception that trading is an effective way of earning extra income to enhance financial stability. Accurate forecasting is crucial to earning profits in the stock market, as it allows traders to anticipate price changes and make strategic investments. The traders must skillfully negotiate short-term market changes to maximize gains and minimize losses, as intraday profit mostly depends on the timing of buy and sell decisions. In the presented work, we provide minute-by-minute forecasts that assist intraday traders in making the best decisions on when to buy and sell, consequently maximizing profits on each trade they make. We have implemented a one-dimensional convolutional neural network and bidirectional long-short-term memory (1DCNN-BiLSTM) optimized with particle swarm optimizer (PSO) to forecast the value of stocks for each minute using real-time data extracted from Yahoo Finance. The proposed method is evaluated against state-of-the-art technology, and the results demonstrate its strong potential to accurately forecast the opening price, stock movement, and price for the next timeframe. This provides valuable insights for intraday traders to make informed buy or sell decisions.
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Korade, Nilesh B., Mahendra B. Salunke, Amol A. Bhosle, et al. "Integrating deep learning and optimization algorithms to forecast real-time stock prices for intraday traders." International Journal of Electrical and Computer Engineering (IJECE) 15, no. 2 (2025): 2254–63. https://doi.org/10.11591/ijece.v15i2.pp2254-2263.
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The number of stock investors is steadily increasing due to factors such as the availability of high-speed internet, smart trading platforms, lower trading commissions, and the perception that trading is an effective way of earning extra income to enhance financial stability. Accurate forecasting is crucial to earning profits in the stock market, as it allows traders to anticipate price changes and make strategic investments. The traders must skillfully negotiate short-term market changes to maximize gains and minimize losses, as intraday profit mostly depends on the timing of buy and sell decisions. In the presented work, we provide minute-by-minute forecasts that assist intraday traders in making the best decisions on when to buy and sell, consequently maximizing profits on each trade they make. We have implemented a one-dimensional convolutional neural network and bidirectional long-short-term memory (1DCNN-BiLSTM) optimized with particle swarm optimizer (PSO) to forecast the value of stocks for each minute using real-time data extracted from Yahoo Finance. The proposed method is evaluated against state-of-the-art technology, and the results demonstrate its strong potential to accurately forecast the opening price, stock movement, and price for the next timeframe. This provides valuable insights for intraday traders to make informed buy or sell decisions.
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Agung, Ignatius Wiseto Prasetyo. "Input Parameters Comparison on NARX Neural Network to Increase the Accuracy of Stock Prediction." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 6, no. 1 (2022): 82–90. http://dx.doi.org/10.31289/jite.v6i1.7158.
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The trading of stocks is one of the activities carried out all over the world. To make the most profit, analysis is required, so the trader could determine whether to buy or sell stocks at the right moment and at the right price. Traditionally, technical analysis which is mathematically processed based on historical price data can be used. Parallel to technological development, the analysis of stock price and its forecasting can also be accomplished by using computer algorithms e.g. machine learning. In this study, Nonlinear Auto Regressive network with eXogenous inputs (NARX) neural network simulations were performed to predict the stock index prices. Experiments were implemented using various configurations of input parameters consisting of Open, High, Low, Closed prices in conjunction with several technical indicators for maximum accuracy. The simulations were carried out by using stock index data sets namely JKSE (Indonesia Jakarta index) and N225 (Japan Nikkei index). This work showed that the best input configurations can predict the future 13 days Close prices with 0.016 and 0.064 mean absolute error (MAE) for JKSE and N225 respectively.
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Saputra, Yustian Dwi, Di Asih I. Maruddani, and Abdul Hoyyi. "ANALISIS TEKNIKAL SAHAM DENGAN INDIKATOR GABUNGAN WEIGHTED MOVING AVERAGE DAN STOCHASTIC OSCILLATOR." Jurnal Gaussian 8, no. 1 (2019): 1–11. http://dx.doi.org/10.14710/j.gauss.v8i1.26617.
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The Stochastic Oscillator which is one of the leading indicators has the disadvantage of opening the gap for false signals. To minimize false signals, the smoothing process is carried out using the Moving Average. Stochastic Oscillator is usually combined with SMA (Simple Moving Average). But SMA has the disadvantage of giving the same weight to all data, even though in reality the data that best reflects the next value is the last data. This makes the basis of weighting the WMA (Weighted Moving Average) method.This study aims to test the combination of Stochastic Oscillator with SMA and WMA and use the best combination to predict the trends that will occur and trading decisions taken from the results of these predictions. The research samples were ANTM, BBRI, and GIAA stocks from November 9 2015 to November 9, 2018.The results show a combination of Stochastic Oscillator and WMA is a better combination of predictions than Stochastic Oscillator and SMA because it has a smaller MSE value. Based on the comparison of signal accuracy based on Overbought and Oversold, the best period of combination of Stochastic Oscillator and WMA is period 25. From the predicted trend that will occur with a combination of Stochastic Oscillator and WMA period 25 a decision is made to buy shares for ANTM shares, sell shares for BBRI shares, and waiting for a buy signal for GIAA shares.Keywords: Stochastic Oscillator, SMA, WMA, Predictions, Trends
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Alvin, Alvin Raditya, and Erman Arif Sumirat Erman. "Fundamental Analysis, Technical Analysis and Broker Summary Analysis in Investing in Indonesian Stock Market." COMSERVA Indonesian Jurnal of Community Services and Development 2, no. 3 (2022): 406–17. http://dx.doi.org/10.59141/comserva.v2i3.270.
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Investors, particularly beginner investors, are still uncertain about which stock to buy and when is the best moment to buy or sell the stock when they first begin investing in the capital market. Analysis is thus required to address the major issue so that investors do not select the incorrect stock and the incorrect moment to purchase or sell it. In order to derive financial ratios, fundamental analysis uses the company's financial statements. The results of the fundamental analysis will reveal whether or not the firm is doing well. However, technical analysis aids in determining when it is best to purchase or sell a stock. The broker summary analysis is an additional analysis that may be utilized in addition to these two that are both very often employed. It may be inferred from this report if the broker is stockpiling or dispersing goods.When performing fundamental research, a stock screener is utilized to calculate the ratios of Return on Assets, Return on Equity, Price to Book Value, Price to Earnings Ratio, and Earnings per Share. The Moving Average approach uses a Simple Moving Average, a Weighted Moving Average, and an Exponential Moving Average to establish the selling and purchasing locations. statistics on stocks from 2016 until 2022. The highest rate of return when using the Simple Moving Average approach is 112.03 percent.
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Singgih, Gabriella Maria, and Edwin Setiawan Nugraha. "Forecasting the Monthly Stock Price per Share of Taiwan Semiconductor Manufacturing Company Limited (TSM) using ARIMA Box-Jenkins Method." Journal of Actuarial, Finance, and Risk Management 2, no. 1 (2023): 1. http://dx.doi.org/10.33021/jafrm.v2i1.4560.
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Taiwan Semiconductor Manufacturing Company Limited is a Taiwanese multinational semiconductor contract manufacturing and design company. People can buy Stocks from Taiwan Semiconductor Manufacturing Company Limited. Stocks are one of the attractive investment instruments for companies and individuals. There are some theories and analyses to predict stock prices to help investors make wiser decisions when buying and selling stock portfolios. In this study, researchers will use ARIMA(p,d,q) technical analysis to predict the stock price of Taiwan Semiconductor Manufacturing Company Limited for the next 5 months from January 1, 2005 to May 1, 2005. For this forecasting, researchers used Taiwan Semiconductor Manufacturing Company Limited historical stock price data from January 1, 1998 to December 31, 2004 that was obtained from the Yahoo Finance website. Based on the test results of 8 ARIMA models, the best model that researchers got is model 2 ARIMA (2,1,1) with the equation Yt = 0.0759Yt-1 + 0.2706Yt-2 + et - 0.198et-1. This model is considered to be the best because it has the smallest MSE Value, which is 0.1076018; the smallest RMSE value, which is 0.0301156; the smallest MAE value, which is 0,2495926; and the smallest MAPE value, which is 3.0116%. This study shows that the stock price is predicted to rise for the next 5 months from January 1, 2005 to May 1, 2005.
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Novanti, Devi, Hajrul Multazam, Novira Laily Husna, Ossy Sanityasa Rahajeng, Selfina L, and Rani Nooraeni. "Pemodelan dan Peramalan Harga Penutupan Saham Perbankan dengan Metode ARIMA dan Family ARCH." ESTIMASI: Journal of Statistics and Its Application 1, no. 2 (2020): 94. http://dx.doi.org/10.20956/ejsa.v1i2.9637.
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Modelling the stock closing price stock is useful so that the investors are expected to be able to understand the situation of the stock, in order to make the right decision when they want to buy or sell their stocks. This study uses the ARIMA and Family ARCH methods in modelling the volatility of four banking stocks that are in high demand by the public, which are Bank BRI (BBRI), Bank BNI (BBNI), Bank Mandiri (BMRI), and Bank BCA (BBCA) from January 1st 2017 until January 31st 2020. Stock returns are modelled by using the ARIMA model, then proceeded with the heteroscedasticity testing. Based on the test, we obtained the results of BBRI, BMRI, and BBCA are heteroscedastic. While BBNI are homoscedastic. The volatility models obtained from the test are BBNI has ARIMA models ([6,13], 1, [6,13]), BBRI has ARI models ([2,24,28), 1,0) -ARCH (1), BMRI has an ARIMA (2,1,4) -GARCH (1,1) model, and BBCA has ARI ([1,2], 1,0) -GARCH (1,1) model. Based on the rising value of the stock price, we suggest the best stock for the investors is BBRI because it has the largest increase of 10% followed by BBCA and BMRI
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Bi, Yongxiang. "Research on Portfolio Model Based on LSTMIS-AMTM and Improved Markowitz." Highlights in Science, Engineering and Technology 12 (August 26, 2022): 197–203. http://dx.doi.org/10.54097/hset.v12i.1454.
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With the development of financial market, portfolio investment has become a new hotspot in the field of quantitative investment. We develop a model to propose the best strategy of gold and bitcoin portfolio investment. We first use LSTMIS to predict the value of gold and bitcoin with input sequence consisting of last 30 days data. Then we use the predicted data and the mean value of last 5 days as long- and short-term moving average input of AMTM model respectively, hence judging whether to buy or sell. Then we improve and modify the existing Markowitz portfolio model by treating gold and bitcoin as two different stocks and completing the matching of the two through the model, so as to make quantitative investments and reduce the risk while satisfying a higher rate of return.
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Malathi, M., and J. B. Simha Dr. "DIRECTION DETECTION OF SELECTED PHARMACEUTICAL STOCKS USING MACHINE LEARNING." Empirical Economics Letters 23, Special Issue 3 (2024): 21–36. https://doi.org/10.5281/zenodo.11301579.
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A direction is a trend that the market follows throughout a specific time period. Trends can be both upward and downward, corresponding to bullish and bearish markets. Direction detection is an excellent way to predict how the market will move in the future, and it can assist investors in shifting the odds in their favor while trading. Understanding the direction of the stock market is of the utmost importance for any approach that investors take in the market. An investor could suffer substantial losses if invested in a stock without knowing its direction and it happens to be in a prolonged slump. Investor may buy high and sell low if they do not grasp the stock's history and current direction. There are many algorithms available to investors that can pinpoint the exact closing price of any stock without revealing the direction of the closing price. This study uses a variety of machine learning classification methods to try and forecast the direction of the given stock. This study's goal is to create the best models possible by combining several modelling techniques and investigating innovative ways to reduce direction prediction mistakes. The dataset includes daily closing prices of a stock for the previous 22 years, and different models are employed to predict direction changes based on 2% and 4% differences in percentage change in close price. The rule classifies these changes as either positive or no change. To enhance prediction accuracy, technical analysis indicators like Average True Range (ATR) and Volume Weighted Average Price (VWAP) are also integrated as feature variables. The class Imbalance problem is solved using the SMOTE over-sampling technique. Multiple classification models are developed to assess their predictive accuracy, with the Random Forest (RF) model showing the highest accuracy of 91% for 2% variation and 96% for 4% variation in closing price. Also, Neural Networks provided the next best results in predicting stock price direction with 85% accuracy for 2% variation and 95% accuracy for 4% variation. The key takeaway from this is the significant utility of diverse classification modeling techniques in effectively forecasting the direction of closing prices for the stock in question.
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Zarika, Laila Marta, and R. A. Sista Paramita. "Analisis Sell in May and Go Away di Bursa Efek Indonesia dan Malaysia Periode 2017-2019." Jurnal Ilmu Manajemen 9, no. 1 (2021): 311. http://dx.doi.org/10.26740/jim.v9n1.p311-321.
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In May and Go Away (SMGA), Sell is a type of seasonal Anomaly, which historically originated in Europe and America that between May-October returns lower than the other periods from November to April. This research aims to determine the difference in abnormal return in the May-October (Worst period) period and November-April (Best period) in Indonesia and Malaysia Stock Exchange between 2017 to 2019. This test conducted using the company's stock price data samples listed on the LQ45 index in the Indonesia Stock Exchange and the FBMKLCI index in the Malaysia Stock Exchange period 2017 to 2019. Hypothesis testing using paired sample t-test to answer if there is a difference in return between the best period and the worst period, to prove the Sell's existence in May and Go Away. The results showed no difference returns between the best and worst periods in the Sell in May and Go Away phenomenon at the Indonesia and Malaysia Stock Exchange period 2017 to 2019. The Investor considers SMGA as not a phenomenon containing excellent or bad information that is capable of affecting the price movement of shares so that SMGA as a strategy to buy stocks in the best period and sell in the worst period is no longer relevant
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Fitriani, Ruli, Nur Rahmanti Ratih, and Siti Isnaniati. "Analisis Peramalan Harga Saham Dengan Metode Arima Terhadap Keputusan Investasi Pada Perusahaan Perbankan Dalam Indeks LQ45." JCA (Jurnal Cendekia Akuntansi) 5, no. 1 (2024): 75. http://dx.doi.org/10.32503/akuntansi.v5i1.5311.
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Every trader or investor in buying shares will always confused with the highly volatile stock price movements, this is causes traders or investors to be careful in choosing prices on when entering to buy shares. ARIMA price forecasting method is a forecasting method that traders and investors can use to help make decisions.This study is a descriptive study with a quantitative approach with the aim of analyzing, explaining, and concluding the analysis of price forecasting using the ARIMA (Autoregressive Integrated Moving Average) method on investment decisions in banking companies listed in the LQ45 index on the Indonesia Stock Exchange. The data collection technique in this study is documentation of the LQ45 Banking Index Stock Price on the Indonesian Stock Exchange.The results obtained in this study are that the model used in forecasting banking stock prices is the best model, the models include BBCA ARIMA (5.2.0), BBRI ARIMA (5.2.0), BMRI ARIMA (5.2, 0), BBNI ARIMA (2,2,0), and BBTN ARIMA (5,2,0). BBCA's share price decreased by -14.9%. BBRI's share price decreased by -5.8%. BMRI's share price decreased by -6.8%. BBNI's share price decreased by -58.5%. BBTN's share price decreased by -18.8%.106. Investors should not buy shares in banking stocks listed in the LQ45 index. This is because prices tend to fall and make investors suffer losses. However, if trading or investing in the near future, investors will buy shares of BBRI and BBTN companies, because within 1 month the shares will increase and investors or traders can get capital gains.
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Nor, Safwan Mohd, Nur Haiza Muhammad Zawawi, Guneratne Wickremasinghe, and Zairihan Abdul Halim. "Is Technical Analysis Profitable on Renewable Energy Stocks? Evidence from Trend-Reinforcing, Mean-Reverting and Hybrid Fractal Trading Systems." Axioms 12, no. 2 (2023): 127. http://dx.doi.org/10.3390/axioms12020127.
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Demand for power sources is gradually shifting from ozone-depleting-substances towards renewable and sustainable energy resources. The growth prospects of the renewable energy industry coupled with improved cost efficiency means that renewable energy companies offer potential returns for traders in stock markets. Nonetheless, there have been no studies investigating technical trading rules in renewable energy stocks by amalgamating fractal geometry with technical indicators that focus on different market phases. In this paper, we explore the profitability of technical analysis using a portfolio of 20 component stocks from the NASDAQ OMX Renewable Energy Generation Index using fractal dimension together with trend-reinforcing and mean-reverting (contrarian) indicators. Using daily prices for the period 1 July 2012 to 30 June 2022, we apply several tests to measure trading performance and risk-return dynamics of each form of technical trading system—both in isolation and simultaneously. Overall, trend (contrarian) trading system outperforms (underperforms) the naïve buy-and-hold policy on a risk-adjusted basis, while the outcome is further enhanced (reduced) by the fractal-reinforced strategy. Simultaneous use of both trend-reinforcing and mean-reverting indicators strengthened by fractal geometry generates the best risk-return trade-off, significantly outperforming the benchmark. Our findings suggest that renewable energy stock prices do not fully capture historical price patterns, allowing traders to earn significant profits from the weak form market inefficiency.
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Pathak, Dr Disha. "INTRINSIC VALUE IN ASSESSING THE FAIRNESS OF IT STOCK PRICE USING FUNDAMENTAL ANALYSIS." BSSS Journal of Management 12, no. 1 (2021): 23–34. http://dx.doi.org/10.51767/jm1203.
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Investors invest money in present with the expectation of making addition to the principal amount in the future. Each investor is willing to know the best instrument for investment and suitable time for that. Stock is one of the financial instruments used for making handsome return. Knowing the real value of the stock plays a vital role for gaining return, which is not an easy for the investors to compute real worth of company. Different techniques like Fundamental analysis and Technical analysis are used for deriving real value of the stock. This research focus on to assist the investor to know about the stock's worth value for investment. This research paper aims to arrive at the intrinsic value of shares for selected eight IT companies of India. It will help investors to know the intrinsic value and compare it with the market value to make decision related to buy or sell of those stocks.
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Saji, T. G., and S. Harikumar. "Earnings Growth and Value Premium: The Indian Experience." Vikalpa: The Journal for Decision Makers 40, no. 4 (2015): 444–54. http://dx.doi.org/10.1177/0256090915608542.
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Executive Summary This article addresses two main questions: Does a value premium exist in emerging market like India? If so, how pervasive is it in different market conditions? Value premium is assumed to be the difference in stock returns of undervalued and overvalued firms with a unique industry profile. The sample for the study consisted of 32 companies from the Information Technology (IT) sector, the stocks of which had traded continuously in the National Stock Exchange (NSE) during the period 2000–2010. Prowess and Capitaline constitute the sources for the firm-level financial data, and NSE web sources provide data related to share prices and market capitalization. The study involved a two-step empirical procedure: an exploratory factor analysis and a regression modelling under Ordinary Least Square (OLS) method. Exploratory factor analysis identified earnings growth and Earnings Price (E/P) rate as the prime determinants of stock returns. Expected earnings growth significantly explained E/P rate under OLS regression framework. The study then estimated normal E/P rate for the individual stock and compared the same with the actual E/P. If the actual E/P for a particular stock was greater than its estimated E/P, it was inferred that the stock was undervalued, the reverse being the case for overvaluation. The findings of this research provide empirical validity of use of E/P rate in identifying mispriced stocks in the Indian context. Undervalued stocks can produce better returns compared to overvalued stocks, and their success has been both persistent and impressive. E/P rate or P/E ratio is a valuable analytic device when properly interpreted. The publicly available E/P rate seems to possess information content and warrants an investor’s attention at the time of his portfolio formation or revision. The search process involving E/P rate suggests that the best buy would be the stock whose reported earnings per share is expected to grow most rapidly.
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Heliyani, Heliyani, and Helmi Hery Julianto. "ANALISIS KEPUTUSAN INVESTASI SAHAM BERDASARKAN PENILAIAN HARGA SAHAM PADA PERUSAHAAN PROPERTY DAN REAL ESTATE YANG TERDAFTAR DI BURSA EFEK INDONESIA." jurnal ekonomi 22, no. 2 (2019): 128–44. http://dx.doi.org/10.47896/je.v22i2.106.
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This study aims to analyze whether or not property and real estate stocks are worth buying as investments. The type of data is secondary data, which originates from the Indonesia Stock Exchange, Bank Indonesia and shares of Indonesian companies in the period 2016-2018. the population in this study are all property and real estate stocks listed on the Indonesia Stock Exchange. using the perpose sampling technique obtained 32 companies that were sampled. The analysis technique uses the Capital Asset Pricing Model (CAPM) method. worthy shares are stocks that have an individual return expected return (Ri ERi). The results of this study indicate that: (1) There are 7 shares of property and real estate companies that deserve to be used for, namely ASRI, BEST, BKSL, BSDE, CTRA, OMRE, SMRA. These shares have a Ri value greater than E (Ri) or [Ri E (Ri)]. The investment decision that must be taken by investors is to buy the shares. (2) There are 25 company shares that are not feasible. Inappropriate stocks have a Ri value smaller than E (Ri) or [Ri E (Ri)]. The investment decision that must be taken by the investor is to sell the stock before the price drops. Penelitian ini bertujuan untuk menganalisis layak atau tidak layaknya saham property dan real estat untuk dibeli sebagai sarana investasi. Jenis data adalah data sekunder, yang berasal dari Bursa Efek Indonesia, Bank Indonesia dan saham perusahaan Indonesia tahun periode 2016-2018. populasi dalam penelitian ini adalah seluruh saham properti dan real estate yang tedaftar pada Bursa Efek Indonesia. menggunakan teknik perpose sampling diperoleh 32 perusahaan yang dijadikan sampel. Teknik analisis menggunakan metode Capital Asset Pricing Model (CAPM). saham layak adalah saham yang memiliki return individu expected return (RiERi). Hasil penelitian ini menunjukkan bahwa: (1) Terdapat 7 saham-saham perusahaan property dan real estate yang layak dijadikan untuk yaitu ASRI, BEST, BKSL, BSDE, CTRA, OMRE, SMRA. Saham-saham tersebut memiliki nilai Ri lebih besar daripada E(Ri) atau [Ri E(Ri)]. Keputusan investasi yang harus diambil oleh investor adalah membeli saham tersebut. (2) Terdapat 25 saham-saham perusahaan yang tidak layak. Saham-saham tidak layak tersebut memiliki nilai Ri lebih kecil daripada E(Ri) atau [Ri E(Ri)]. Keputusan investasi yang harus diambil oleh investor adalah menjual saham tersebut sebelum harga turun.
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Xiu, BoWen. "Based on Baidu Index and GBDT Shanghai Index rise and fall forecast." BCP Business & Management 34 (December 14, 2022): 1559–66. http://dx.doi.org/10.54691/bcpbm.v34i.3212.
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The stock market reflects the country's economic conditions, and it is of great significance to have a good prediction effect on the stock market. But with the rapid rise of the Internet, big data, and machine learning, the prediction of the stock market trend is not limited to the traditional methods and data sets. The trend of the stock market is not only dependent on itself but also affected by some other factors. Therefore, based on the machine learning model, this paper studies the prediction of investors' attention to the Shanghai Composite Index trend. This paper crawled the relevant index data from the website of Baidu Index based on the selected keywords. The correlation coefficient is used to select the keyword data with the best lag order and data type and as the model's input data. Through the establishment of LSTM, LASSO, RF, and GBDT models, the rise and fall of the Shanghai Composite Index are predicted. That is to say. The paper takes the accuracy of the rise and fall prediction as the judgment standard. GBDT model has the best prediction effect on the Shanghai Stock Exchange Index and can best explain the rise and fall of the Shanghai Stock Exchange Index. So, people can use this research to buy stocks before they rise and sell them before they fall.
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Lukman, Radian, Mustafid Mustafid, and Sugito Sugito. "PENERAPAN DIAGRAM PENGENDALI NONPARAMETRIK EXPONENTIALLY WEIGHTED MOVING AVERAGE SIGN UNTUK ANALISIS PERGERAKAN HARGA SAHAM SEKTOR PROPERTI." Jurnal Gaussian 12, no. 1 (2022): 1–9. http://dx.doi.org/10.14710/j.gauss.12.1.1-9.
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Stocks are evidence of equity participation in a company. Investors need to know the quality of stock prices so that they can minimize losses when investing. Technical analysis can be used by investors to decide when to buy or sell a stock. One of the technical analysis that can be used on stock prices is using quality control. Control charts can be used to make decisions in stock trading activities. The Exponentially Weighted Moving Average control chart is very useful for detecting small shifts such as in financial data. The assumption that must be fulfilled in using the EWMA control chart is that the data is normally distributed. The non-fulfillment of the normal distribution assumption causes the EWMA control chart produces plots that are far from the control limits. This problem can be solved using the nonparametric EWMA Sign control chart. The construction of the nonparametric EWMA Sign control chart on stock prices is expected to overcome the limitations of the standard EWMA control chart and provide a signal to investors to know the best time to trade stocks. The data used in this study is the daily closing price data of PT Bumi Serpong Damai Tbk on March 1, 2021 to March 4, 2022 with a total of 250 data. The nonparametric EWMA Sign control chart shows that the daily closing price data is out of control because it produces plots that are spread out non-randomly and shows a relatively similar pattern.
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Han, Linyuan. "Analysis of Stock Price and Price Movement Prediction based on Machine Learning Models for E-Hualu." BCP Business & Management 44 (April 27, 2023): 404–13. http://dx.doi.org/10.54691/bcpbm.v44i.4849.
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At present, many multi-information input models are used in various stock forecasts, but for most Chinese investors, it is difficult to obtain effective stock information, and most of them buy and sell stocks only based on stock trends and stock prices. Therefore, in order to get better prediction results based on basic information, the author tries to use some machine learning methods to predict E-Hualu in the Chinese stock market, and the aim is to select the best prediction model. In this study, five machine learning models were applied to predict stock prices, namely linear regression, SVR, random forest, XGBoost, and LSTM, then the close price of the previous 14 days were converted into the input of each model to estimate the close price of the next day. After the price prediction results were obtained, secondary processing was carried out, transforming previous outputs into trend prediction results. The research results indicate that the LSTM model has outstanding performance in both price prediction and trend prediction. Hopefully, the results of this research can provide some suggestions for investing in E-Hualu.
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Rusmaningtyas, Rahmanita Febrianti, Neva Satyahadewi, and Setyo Wira Rizki. "Perbandingan Harga Opsi Saham Tipe Eropa Menggunakan Model Black Scholes dan Black Scholes Fraksional." Jurnal EurekaMatika 9, no. 2 (2022): 177–84. http://dx.doi.org/10.17509/jem.v10i1.44454.
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One of the investment is stocks. Stocks have derivative instrument in the form of stock options. Stocks option is a contract between two parties in the form of the right to sell and buy a stocks at a certain price and time. The method used in this study is the fractional Black Scholes and Black Scholes method with time of maturity fractioned by the Hurst parameter. The purpose od this study is to compare the prices of call and put options using the Black Scholes and Fractional Back Scholes. The data using close price of Apple Inc shares within period between October 1st 2020 until September 30th 2021. If the market option price is greater than the theoretical price using the Black Scholes or Fractional Black Scholes model, then the option is recommended to be sold and if the market option price is lower, the option is recommended to be purchased. Based on the smallest MAPE, the best model for call options is Fractional Black Scholes with Hurst Parameter 0,4 with 0,383% which means it is very accurate and the best model for put options is the Fractional Black Scholes Model with Hurst parameter 0,9 with 16,055% whick means accurate.Keywords: Black Scholes Fractional, Hurst Parameter, Stock Option. AbstrakSalah satu instrumen dalam berinvestasi adalah saham. Saham memiliki instrumen derivatif berupa opsi saham. Opsi saham adalah suatu pemberian kontrak antara dua pihak berupa hak untuk menjual dan membeli suatu saham dengan harga dan waktu tertentu. Harga opsi tipe Eropa dapat ditentukan dengan model Black Scholes dan Black Scholes Fraksional dengan waktu jatuh tempo yang difraksional menggunakan parameter Hurst. Penelitian ini bertujuan untuk membandingkan harga opsi beli dan opsi jual dengan model Black Scholes dan Black Scholes Fraksional. Data yang digunakan adalah harga penutupan saham Apple Inc periode 1 Oktober 2020 sampai 30 September 2021. Langkah dalam menghitung harga opsi adalah menghitung return saham, uji normalitas, menghitung volatilitas, menentukan parameter, menghitung harga opsi beli dan opsi jual, menghitung nilai MAPE dan melakukan perbandingan kedua model. Jika harga opsi pasar lebih besar daripada harga opsi teoritis menggunakan model Black Scholes atau Black Scholes Fraksional maka opsi direkomedasikan untuk dijual dan jika harga opsi pasar lebih kecil maka opsi direkomendasikan untuk dibeli. Berdasarkan nilai MAPE terkecil model terbaik untuk opsi beli saham adalah Black Scholes Fraksional dengan Parameter Hurst 0,4 dengan MAPE sebesar 0,383% yang berarti sangat akurat dan untuk opsi jual saham adalah Model Black Scholes Fraksional dengan parameter Hurst 0,9 dengan MAPE sebesar 16,055% yang berarti akurat.
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Jayawardena, Nirodha Imali, Akihiro Omura, and Bin Li. "The early bird and the late bird: which catches more worms in Australia?" International Journal of Managerial Finance 15, no. 4 (2019): 658–68. http://dx.doi.org/10.1108/ijmf-06-2018-0184.
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Purpose The purpose of this paper is to examine what the optimal time is in a typical trading day for investors to buy/sell stocks in the Australian stock market. Design/methodology/approach The study mainly focuses on the S&P/ASX200. Each trading day, between 10:00 a.m. and 4:00 p.m., is divided into 30-min blocks. The effectiveness of easily implementable trading strategy to purchase the index in the morning and sell at the close is tested. The study controls for the excess overnight price volatility to improve the effectiveness of the investment strategy. This trading strategy is compared against other 66 possible day-trading combinations. Findings The results show that the trading strategy of buying in the first 30 min of the trading session and close off the position during the last 30 min obtains higher returns than other 66 strategies. Practical implications The day-trading strategy proposed in this study is very simple and therefore can be easily implemented by investors including individual investors. Originality/value To the best of our knowledge, this is the first study which constructs a trading strategy using the J- or U-shaped intraday return pattern.
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FERNÁNDEZ-MARTÍNEZ, M., M. A. SÁNCHEZ-GRANERO, M. J. MUÑOZ TORRECILLAS, and BILL MCKELVEY. "A COMPARISON OF THREE HURST EXPONENT APPROACHES TO PREDICT NASCENT BUBBLES IN S&P500 STOCKS." Fractals 25, no. 01 (2017): 1750006. http://dx.doi.org/10.1142/s0218348x17500062.
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Since the pioneer contributions due to Vandewalle and Ausloos, the Hurst exponent has been applied by econophysicists as a useful indicator to deal with investment strategies when such a value is above or below [Formula: see text], the Hurst exponent of a Brownian motion. In this paper, we hypothesize that the self-similarity exponent of financial time series provides a reliable indicator for herding behavior (HB) in the following sense: if there is HB, then the higher the price, the more the people will buy. This will generate persistence in the stocks which we shall measure by their self-similarity exponents. Along this work, we shall explore whether there is some connections between the self-similarity exponent of a stock (as a HB indicator) and the stock’s future performance under the assumption that the HB will last for some time. With this aim, three approaches to calculate the self-similarity exponent of a time series are compared in order to determine which performs best to identify the transition from random efficient market behavior to HB and hence, to detect the beginning of a bubble. Generalized Hurst Exponent, Detrended Fluctuation Analysis, and GM2 algorithms have been tested. Traditionally, researchers have focused on identifying the beginning of a crash. We study the beginning of the transition from efficient market behavior to a market bubble, instead. Our empirical results support that the higher (respectively the lower) the self-similarity index, the higher (respectively the lower) the mean of the price change, and hence, the better (respectively the worse) the performance of the corresponding stock. This would imply, as a consequence, that the transition process from random efficient market to HB has started. For experimentation purposes, S&P500 stock Index constituted our main data source.
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Sitohang, Doan, H. M. Roy Sembel, and Melinda Malau. "Comparative Analysis of Sell-In-May-And-Go-Away and Monthly Effect Before and During COVID-19 Pandemic at LQ45 IDX." Keynesia : International Journal of Economy and Business 2, no. 2 (2023): 116–27. http://dx.doi.org/10.55904/keynesia.v2i2.980.
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In 2022, the number of investors in the Indonesian stock market experienced a four-fold growth compared to 2019. This surge occurred during the epidemic period. This financial research aims to analyze the impact of the COVID-19 pandemic on the monthly average return and risk pattern of LQ45, as well as the presence of the Sell-in-May-and-Go-Away (SIMAGA) effect and the optimal investment strategy for LQ45. The study uses a descriptive-comparative methodology and employs mathematical and statistical frameworks. The sample consists of LQ45 companies from 1997 to 2022. Data analysis techniques include the Normality Test, Wilcoxon Rank Test, F Test, and investment strategy simulation. The results indicate that COVID-19 did not have a negative effect on the monthly returns and risk patterns of LQ45, except in 2002. Additionally, the SIMAGA phenomenon is not present in LQ45, but the Sell in August-Buy in November (SIABIN) strategy has been identified as the most effective. These findings provide valuable insights for investors in allocating their investments and determining the best strategy for buying stocks. It is important to consider monthly return variance as a key metric for measuring investment risk and its impact on overall returns.
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Lolea, Iulian Cornel, and Simona Stamule. "Trading using Hidden Markov Models during COVID-19 turbulences." Management & Marketing. Challenges for the Knowledge Society 16, no. 4 (2021): 334–51. http://dx.doi.org/10.2478/mmcks-2021-0020.
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Abstract Obtaining higher than market returns is a difficult goal to achieve, especially in times of turbulence such as the COVID-19 crisis, which tested the resilience of many models and algorithms. We used a Hidden Markov Models (HMM) methodology based on monthly data (DAX returns, VSTOXX index Germany’s industrial production and Germany’s annual inflation rate) to calibrate a trading strategy in order to obtain higher returns than a buy-and-hold strategy for the DAX index., following Talla (2013) and Nguyen and Nguyen (2015). The stock selection was based on 26 stocks from DAX’s composition, which had enough data for this study, aiming to select the 15 best performing. The training period was January 2000 - December 2015, and the out-of-sample January 2016 - August 2021, including the period of high turbulence generated by COVID-19. Fitting the best model revealed that the following regimes are the most suitable: two regimes for DAX returns, two regimes for VSTOXX and three regimes for the inflation rate and for the industrial production, while the posterior transition probabilities were event-depending on the training sample. Furthermore, portfolios built using HMM strategy outperformed the DAX index for the out-of-sample period, both in terms of annualized returns and risk-adjusted returns. The results were in line with expectations and what other researchers like Talla (2013), Nguyen and Nguyen (2015) and Varenius (2020) found out. We managed to highlight that a strategy calibrated based on HMM methodology works well even in periods of extreme volatility such as the one generated in 2020 by COVID-19 pandemic.
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Khairani, Dita. "Analysis of Stock Based Investment Decisions Stock Price Assessment in Property and Companies Real Estate Listed on The Indonesian Stock Exchange." Jurnal Akuntansi Dan Keuangan West Science 3, no. 01 (2024): 47–60. http://dx.doi.org/10.58812/jakws.v3i01.793.
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Tujuan dari penelitian ini adalah untuk menganalisis apakah saham- saham real estate dan real estate layak untuk diinvestasikan. Kami menggunakan teknik Perpose sampling untuk mengidentifikasi 32 perusahaan untuk penelitian kami. Metode analisisnya menggunakan metodologi Capital Asset Pricing Model (CAPM). Saham yang berharga adalah saham yang return individualnya > return yang diharapkan (Ri > ERi). Hasil penelitian menunjukkan bahwa: (1) Terdapat tujuh saham perusahaan real estate dan real estate yang layak digunakan yaitu ASRI, BEST, BKSL, BSDE, CTRA, OMRE, dan SMRA. Nilai Ri bagian-bagian tersebut lebih besar dari E (Ri) atau [Ri>E (Ri)]. Keputusan investasi yang harus diambil seorang investor adalah membeli saham. (2) Ada 25 saham perusahaan kami yang tidak dapat direalisasikan. Saham yang tidak sesuai mempunyai nilai Ri yang lebih kecil dari E (Ri) atau [Ri].
 The aim of this research is to analyze whether real estate and real estate stocks are worthy of investment. We used Perpose sampling technique to identify 32 companies for our research. The analysis method uses the Capital Asset Pricing Model (CAPM) methodology. Valuable shares are shares whose individual return > expected return (Ri > ERi). The results of this research show that: (1) There are seven shares of real estate and real estate companies that are suitable for use : namely ASRI, BEST, BKSL, BSDE, CTRA, OMRE, and SMRA. The Ri
 value of these parts is greater than E (Ri) or [Ri>E (Ri)]. The investment decision that an investor must take is to buy a share. (2) There are 25 shares of our company that cannot be realized.
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Antad, Sonali, Saloni Khandelwal, Anushka Khandelwal, et al. "Stock Price Prediction Website Using Linear Regression - A Machine Learning Algorithm." ITM Web of Conferences 56 (2023): 05016. http://dx.doi.org/10.1051/itmconf/20235605016.
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The most valuable indicator of a company’s success is its stock price, which can rise in tandem with the price of a single share. For that reason, businesses advertise their stocks to their customers in an effort to get them to buy them. The volatility of stock prices makes it difficult for clients or stockholding companies to forecast the future value of a single stock. Therefore, stock market forecasting has emerged as the most well-liked topic in the corporate sector, and hence solving this problem has become so important for the betterment of the investors and buyers as many a times they have to face loss in their investment and this problem can be solve by various Machine learning algorithms. To solve this problem we are developing one stock price prediction website using Python and Linear Regression which is one of the best Machine Learning statistical method for predictive analysis. We are using historical Data for the prediction. Finding a method to use linear regression models to obtain more precise values is the major goal. To acquire more precise results from the linear regression models, it is possible to change the dataset that will be used to train the models. The purpose of this paper is to demonstrate that linear regression is the most appropriate and effective method for forecasting stock market analysis.
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Arshakian, Roza Araikovna, and Marina Vladimirovna Mironenkova. "Analysis of factors affecting the excess profitability of mutual funds in Russia for 2015-2022." Финансы и управление, no. 4 (April 2023): 33–47. http://dx.doi.org/10.25136/2409-7802.2023.4.41027.
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The relevance of the study of financial market instruments is undeniable and well aligned with the achievement of Russia's national development goals until 2030. The subject of this article is the excess return of Russian mutual funds (MF) in the period from 2015 to 2022. The authors analyzed in detail the scientific literature and took as a basis the conclusions and results obtained in previous studies on similar topics. The aim of the study was to find the factors determining the excess return of mutual funds, which is the difference between the return of the funds and the return of the selected benchmark. The study was based on data from Investfunds Internet portal, which provides information on investment assets for a wide range of people. Econometric analysis was conducted based on an unbalanced panel consisting of an average of 185 funds per year. To achieve the objective, we studied already existing econometric models for analyzing excess fund returns and used them to construct a pass-through regression model and a fixed-effects model. Analyzing the performance of mutual funds depending on their parameters will allow investors to get relevant recommendations about which fund should be preferred to buy a share in its portfolio, which corresponds to the scientific novelty of the study. The main conclusion of the study is that using econometric criteria, the fixed-effects model was selected as the best of the constructed models. A notable contribution of the authors to the study of the topic is the identification of the main aspects that investors should pay attention to when choosing a mutual fund, namely funds with a high net asset value, the object of investment of which are stocks and money, and the direction of investment - precious metals.
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Sikalo, Mirza, Almira Arnaut-Berilo, and Azra Zaimovic. "Efficient Asset Allocation: Application of Game Theory-Based Model for Superior Performance." International Journal of Financial Studies 10, no. 1 (2022): 20. http://dx.doi.org/10.3390/ijfs10010020.
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In this paper, we compared the models for selecting the optimal portfolio based on different risk measures to identify the periods in which some of the risk measures dominated over others. For decades, the best known return-risk model has been Markowitz’s mean-variance model. Based on the criticism of the classical Markowitz model, a whole series of risk measures and models for selecting the optimal portfolio have been developed, which are divided into two groups: symmetrical and downside risk measures. Based on the tools provided by game theory, we presented a minimax model for selecting the optimal portfolio based on the maximum loss as a measure of risk. Recent research has shown the adequacy of the application of this risk measure and its dominance concerning variance in certain circumstances. Theoretically, the model based on maximum loss as a measure of risk relies on a much smaller number of assumptions that must be satisfied. In the empirical part of the paper, we analyzed the real return performance, structure, correlation, stability, and predictive efficiency of the model based on maximum loss return as a measure of risk and compared it with the other famous models to determine whether the maximum loss-based risk measure model is more suitable for use in certain circumstances than conventional return-risk models. We compared portfolios created based on different models over the period of 2000–2020 from a selected sample of stocks that are components of the STOXX Europe 600 index, which covers 90% of the free market capitalization in the European capital market. The observed period included 3 bear market periods, including the period of market decline during the COVID-19 crisis. Our analysis showed that there was no significant difference in portfolio returns depending on the selected model using the “buy-and-hold” strategy, but there were crisis periods. The results showed a significantly higher stability of portfolios selected on the criterion of minimizing the maximum loss than others. In periods of market decline, this portfolio achieved the best performance and had a shorter recovery period than others. This allowed superior use of the minimax model at least for investors with a pronounced risk aversion.
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Ondiek, Samson Okoth, and Dr Ongoro. "FACTORS DETERMINING STOCK MARKET RETURNS: CASE OF NAIROBI STOCK EXCHANGE." International Journal of Finance and Accounting 1, no. 1 (2016): 108. http://dx.doi.org/10.47604/ijfa.122.
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AbstractPurpose: The study attempts to establish if the changing macroeconomic factors and the industry variables can predict the variation on the Nairobi Security Exchange stocks return Methodology: It adopted a regression model that related stock returns to various selected macro and micro economic factors and used data of 20 companies that constitute the NSE index. The study used monthly data spanning the year 2006 to 2010.Results: The regression results indicate that, four of the variables i.e. market return (NSEI), exchange rate for US/KSH, market to book value ratio have a positive and significant relationship with an individual company stock market returns. Risk Free rate (91 Treasury bill rate) also had a positive and significant relationship while industrial growth opportunity and inflation were found to be negative and significant. leverage on the other hand was found to be insignificant and therefore does not influence individual company stock market returns. Unique contribution to theory, practice and policy: These findings will have significant effects on investors’ investment decisions making as well as the Government and the capital markets authority (CMA) in the formulation of polices and guidelines. Once factor betas are estimated, we can describe the expected change in security returns with respect to changes in a given factor and thus giving the investors, CMA and the Government a better understanding on the effect of a change in the fiscal and monetary policies in the stock market. This is crucial to the Government as it seeks to promote the capital market as a source of alternative funding for economic growth. Investors wishing to construct portfolios should also consider the trends of the inflation rates, exchange rates, market to book value ratio, industrial production and the stock market. The rise of either of this micro and macroeconomic indicators may influence the returns positively or negatively and hence the investor may choose the best time to either buy or sell their securities
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Purnama, Eris Dwi, and Basuki Rakhim Setya Permana. "DESIGN PROTOTYPE ONLINE SHOP DAGING AYAM SEGAR BERBASIS WEBSITE PADA UD. KAZAIN BROILER DI KOTA SERANG." Jurnal Ilmiah Sistem Informasi 3, no. 1 (2023): 1–13. http://dx.doi.org/10.46306/sm.v3i1.24.
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Kazain Broiler is a Fresh Cut Chicken Supplier offering chicken pieces at affordable and cheap prices. This chicken shop, which is located in Serang City, has a complete list and list of chicken pieces. The development of digitalization is increasingly rapid and fulfills all aspects of human life throughout the world. Not only that, updates on the development of information technology are increasingly providing easy services to every user. Online shop or online business today is not something foreign to the court community in any corner of the world, especially the Indonesian people, whether they use the internet in their daily life or not. Computers are very popularly used as a tool for various activities, for example in business administration activities for trading businesses. UD. Kazain Broiler is the best poultry supplier in Serang City which offers high quality chicken meat at affordable prices. By having various types of fresh chicken that will keep the food healthy, nutritious and delicious. The quality chicken offered by UD.Kazain Broiler goes through various processes to make it hygienic and healthy for consumers. UD. Kazain Broiler is the best poultry supplier in Serang City which offers high quality chicken meat at affordable prices. By having various types of fresh chicken that will keep the food healthy, nutritious and delicious. The quality chicken offered by UD.Kazain Broiler goes through various processes to make it hygienic and healthy for consumers. Not only that, UD. Kazain Broiler also sells and supplies chicken meat to various food industries and big events such as celebrations, weddings, celebrations, and big day events. UD.Kazain Broiler in carrying out its main sales currently still uses conventional techniques, it still requires stationery, paper and calculators to manage buying and selling data. The owner wants every sale proceeds to have a recapitulation system and relevant financial reports. Since the implementation of the first PSBB and PPKM on April 28, 2020, the relatively quiet market conditions have caused stockpiles of chicken meat to accumulate and not be distributed. As a result, consumers cannot buy the desired meat if the stall selling the desired meat partners runs out and has to seek or wait for information from other partners. For this reason, an integrated information system is needed because it is considered to save more time and costs. So that consumers don't have to go around looking for meat stocks that are still there that consumers need. Based on the results of observations and interviews that have been carried out at UD. Kazain Broiler in Serang City, the researchers feel the need to build a system as well as a tool that is integrated with the inventory information system so that each partner can exchange information and overcome the problems above. In this design, the website prototype was chosen because it has easy access for anyone to do without the need to download it first
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Salehi, Mahdi, and Mojdeh Davoudi Pour. "Bankruptcy prediction of listed companies on the Tehran Stock Exchange." International Journal of Law and Management 58, no. 5 (2016): 545–61. http://dx.doi.org/10.1108/ijlma-05-2015-0023.
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Purpose Bankruptcy is the last phase of economic life of companies and has some impacts on all of the entity’s stakeholders. Thus, the prediction of bankruptcy is very important. The inherent aim of preparing and developing financial accounting information is to provide a basis for economic decision-making, and any decision requires information acquisition, processing and data analysis as well as logical and correct interpretation of information. Developing models for predicting financial crisis and comparing the capabilities of existing models can help to alert management about ongoing activities and investors about economic decision for purchase shares or granting loan facilities to companies. So, the purpose of this study is the predict bankruptcy of listed companies on the Tehran Stock Exchange. Design/methodology/approach From the statistical methods’ perspective, the present research is classified as modeling and with respect to research methodology, it is a correlative-descriptive study in which the relationship between variables is analyzed based on the research objective. Predictive variables are the best ratios of cost of goods sold, non-operating revenues, net sales, predicted earnings per share (EPS) and real EPS. Findings Prediction of corporate bankruptcy crisis is one of the vital research areas. Predictive models are means for estimating the company’s future situation. Investors and creditors are highly willing to predict the bankruptcy crisis because the high costs associated with bankruptcy crisis will spoil the economy as a whole. On the other hand, this raises concerns among owners, and they are always seeking to find ways to preserve their capital through prediction of stocks continuing operations in the future. Having knowledge about bankruptcy or non-bankruptcy of automotive parts companies makes it possible to recognize weaknesses and strengths in the companies’ current performance and to make investment decisions. Practical implications Development of financial markets and, subsequently, creation of fierce competition has resulted in bankruptcy of many companies. Investors are always looking for predicting possible bankruptcy of a firm to prevent their investments risks because bankruptcy costs are high for investors, creditors, lenders and government agencies. Hence, they are seeking ways to estimate corporate bankruptcy. For this reason, over the past four decades, bankruptcy prediction has been enumerated as a key issue in companies and consequently because of its importance, many studies have been conducted to achieve the best model to predict bankruptcy. Originality/value Bankruptcy forecast is an economically important issue in every organization and company. Financial and accounting researchers are trying to offer financial models using various combinations of financial ratios with better measuring ability for performance and dividends payments as well as company continued activities. Bankruptcy prediction models are among financial analysis techniques in which the purpose of financial analysis and bankruptcy forecasting is recognition of efficiency and management executive performances. Moreover, the analysis of stock value by shareholder is another application of such research results. Basically, shareholders are interested in knowing the future status of the companies that are going to buy. In this way, shareholders use this method of analysis to estimate future activity or inactivity of firms.
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Miranti, Miranti. "Interpretation of Literature Piracy in Tere Liye's Novel Title "Selamat Tinggal" A Study of Literature Sociology." International Journal of Science and Applied Science: Conference Series 6, no. 2 (2022): 129. https://doi.org/10.20961/ijsascs.v6i2.74078.
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<p class="Abstract">Sociology of literature is like an imitation, to see the real situation and social phenomena that occur. in the imaginative world, there are always events in the real world that then become part of the storytelling. Literature then blends and blends between real events and fictional events. With existing innovations, hidden realities can be brought back. The object of this research is a novel entitled <em>"Selamat Tinggal”</em>. This novel tells about Sintong, a student who works as a keeper of a pirated bookstore: "Berkah". Sintong is a graduate student from the faculty of literature. Ideally, he does not agree with his work as a mediator for pirated books to be sold. But conditionally, only selling pirated books, Sintong can continue her studies. Understandably, he is an overseas child who has not received any money from his parents. Even guarding a bootleg shop next to the station, because the owner of the blessing shop is his own Paklik. This research method is a qualitative research method. According to Moleong, for this qualitative research to understand the phenomena of what is experienced by the research subject, for example, perceptual behavior, motivation, action and others holistically, and by way of description in the form of words and language, in a special context that is natural and by utilizing various natural methods. With this the data presented in this study are in the form of words, phrases, sentences, and the context of the story in the novel. This qualitative descriptive research is descriptive and analyzes the problems contained in the novel objectively. This study is to compare the fictional events in the novel with the reality that occurs in the real world. One example that is closest to us is students who prefer to buy pirated books rather than original books. Of course, the advantage of pirated books is that they are much cheaper than original books. The student's mediocre economic situation supports this. Sintong even when selling pirated books, has a lot of subscribers who are among students. The name of the pirated bookstore is "Berkah". This becomes a kind of satire by the author towards the world of piracy. The word blessing has a positive connotation, meaning "a lot of good". This is because the market share of pirated books is the weak economy. The goal is that ordinary people can get a lot of good from reading pirated books. Of course, the hijacked books are the “best sellers”, which have a lot of good in them. But this is actually satire. Sintong himself realized that "there is no blessing in stealing". Sintong is well aware that book piracy is a crime that is prohibited by law. But until now, the law that was at the beginning of the book has only been a decoration because no one has ever been prosecuted for pirating a book. Sintong himself is a writer. He wrote his thoughts in the mass media. He knows very well that pirated books are stealing the writer's fortune. The writer does not get a penny of money from the results of his hard work writing. The ones who get a lot of material benefits from pirated books are the producers. He simply reproduced what was already there, without ever paying for writers, cover designs, editors, lay outs, distributors and others. Just simply copy what is already there, then sell it for less than half the price of the original book. Of course that was enough profit.</p>
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Chatterjee, Devlina, and Chiranjit Mukhopadhyay. "Execution Times of Small Limit Orders: A Simpler Modeling Approach." Vikalpa: The Journal for Decision Makers 38, no. 1 (2013): 49–64. http://dx.doi.org/10.1177/0256090920130105.
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In an electronic stock market, an equity trader can submit two kinds of orders: a market order or a limit order. In a market order, the trade occurs at the best available price on the opposite side of the book. In a limit order, on the other hand, the trader specifies a price (lower limit in case of sell orders and higher limit in case of buy orders) beyond which they are not willing to transact. Limit orders supply liquidity to the market and aid in price discovery since they indicate the prices that traders are willing to pay at any point of time. One of the risks that a trader placing a limit order faces is the risk of delayed execution or non-execution. If the execution is delayed, then the trader also faces a “picking-off” risk, in the event of the arrival of new information. With these issues in the background, a trader placing a limit order at a certain price, given various economic variables such as recent price movements as well as characteristics of the company in question, is interested in the probability of execution of the order as a function of subsequent elapsed time. For example, if she places a small sell order at 0.5 percent above the last traded price for a given stock, what is the probability that the order will be executed in the next t minutes? With this motivation, this paper considers execution times of small limit orders in an electronic exchange, specifically the National Stock Exchange (NSE) of India. Order execution times have been studied in several other works, where they are modeled by reconstructing the history of the order book using high-frequency data. Here, for the first time, the much simpler approach of small hypothetical orders placed at certain prices at certain points of time has been used. Given that an order has been placed at a certain price, subsequent price movements determine the lower and upper bounds of the time to execution based on when (and if) the order price is first reached and when it is first crossed. Survival analysis with interval censoring is used to model the execution probability of an order as a function of time. Several Accelerated Failure Time models are built with historical trades and order book data for 50 stocks over 63 trading days. Additionally, choice of distributions, relative importance of covariates, and model reduction are discussed; and results qualitatively consistent with studies that did not use hypothetical orders are obtained. Interestingly, for the data, the differences between the above-mentioned bounds are not very large. Directly using them without interval censoring gives survival curves that bracket the correct curve obtained with interval censoring. The paper concludes that this approach, though data- and computation-wise much less intensive than traditional approaches, nevertheless yields useful insights on execution probabilities of small limit orders in electronic exchanges.