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Krýcha, Josef. "Fraktální analýza ekonomických časových řad." Master's thesis, Vysoká škola ekonomická v Praze, 2009. http://www.nusl.cz/ntk/nusl-15558.
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This thesis focuses on fractal analysis of economic time series. Chapter One introduces fractal analysis as a method of exploring time series and gathers information about progress and current state of understanding in this field. Chapter Two focuses on design and development of computer software, which will calculate selected fractal indices. Chapter Three is experimental and shows the results and discussion of economic time series (popular stock market indexes and currency exchange rate) analysis that have been obtained from the software developed in Chapter Two.
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Ozdemir, Duygu. "Stock Market Liquidity Analysis: Evidence From The Istanbul Stock Exchange." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613789/index.pdf.
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The purpose of this thesis is to identify the factors playing a key role in the determination of the Turkish stock market liquidity in aggregate terms in a time series context and discuss the joint dynamics of the market-wide liquidity with its selected determinants and the trade volume. The main determinants tested are the level of return, the return volatility and the monetary stance of the Central Bank of the Republic of Turkey. The expected positive relationship between the liquidity and the return is confirmed, while the negative effect of the volatility on liquidity appears one-week later. The behavior of various liquidity variables are also examined around the macroeconomic data announcement dates, during the 2008 financial crisis, and after the tick size change in the Istanbul Stock Exchange (ISE). The time series dynamics between the trade volume, return, volatility and the liquidity are put forward within the Vector Autoregression analysis framework. The GARCH modeling of the return series, which is an input to the liquidity model estimations, is a byproduct of this thesis. It is observed that the return series exhibits volatility clustering, persistence, leverage effects and mean reversion. In addition, while the level of the ISE market return decreased, the volatility of the return increased during the 2008 crisis. Accordingly, EGARCH model assuming normally distributed error terms and allowing a shift in the variance during the crisis period is chosen as the best model.
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Cheng, Jiadi. "The Stock Connect Programs: A Study of their Impact on Chinese Stock Returns and Global Stock Markets Integration." Oberlin College Honors Theses / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=oberlin1589560178304999.
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Lindgren, Pierre. "Classifying Stock Market Tweets : Sentiment analysis applied to tweets published by Swedish stock market influencers." Thesis, Umeå universitet, Institutionen för datavetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-142511.
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Stock market enthusiasts have hooped around Twitter to exchange their opinions about companies and new investment cases. New qualitative information and questioning discussions are carried out on a daily basis. Non or less frequent Twitter users are at risk of missing out of good and relevant information. This thesis investigates if sentiment analysis can be used to classify tweets published by stock market influencers as positive or negative. Can this machine learning approach be used within an application to present relevant information? The two machine learning algorithms Naïve Bayes and Support Vector Machine are applied to historical tweets. Their performance of classifying historical tweets published by ten stock market influencers are evaluated. The results shows accuracies ranging from 68.6 - 75.6% for Naïve Bayes and 76.1 -78.9% for the Support Vector Machine.
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Lange, Joe. "An intraday analysis of stock market liquidity /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 1998. http://wwwlib.umi.com/cr/ucsd/fullcit?p9906485.
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Bahceci, Oktay, and Oscar Alsing. "Stock Market Prediction using Social Media Analysis." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166448.
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Stock Forecasting is commonly used in different forms everyday in order to predict stock prices. Sentiment Analysis (SA), Machine Learning (ML) and Data Mining (DM) are techniques that have recently become popular in analyzing public emotion in order to predict future stock prices. The algorithms need data in big sets to detect patterns, and the data has been collected through a live stream for the tweet data, together with web scraping for the stock data. This study examined how three organization's stocks correlate with the public opinion of them on the social networking platform, Twitter. Implementing various machine learning and classification models such as the Artificial Neural Network we successfully implemented a company-specific model capable of predicting stock price movement with 80% accuracy.
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Rubenking, Brian Harold. "Market forces and aircraft safety: a daily stock market return analysis." Thesis, Virginia Tech, 1988. http://hdl.handle.net/10919/45178.
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The relationship between market forces and product safety in the context of the commercial passenger air travel market was investigated. The analysis was based on a detailed review of the events surrounding three airline accidents, each resulting in a substantial number of fatalities, and the subsequent investigations.
The presence or absence of statistically significant market impacts of the accidents on aircraft manufacturers and airlines was determined using a combination of event analysis and the market model of modern finance theory. For a period following each accident, daily and cumulative abnormal stock market returns (i.e., returns not explained by pre-accident market trends) were calculated for the three domestic commercial aircraft manufacturers, the airline involved, and the major airline carrier industry.
The results indicated that market forces exist that provide an incentive for manufacturers and airlines to devote resources to product safety, even though it is not possible for consumers to rationally evaluate the level of safety being provided, due to the inherent complexity of the products. The calculated market impacts generally conformed to expectations, in terms of sign and significance, and varied depending on the primary cause of a particular accident. However, the results with respect to the individual airlines involved in each accident did not support the hypotheses in several cases, indicating that other market, regulatory, or judicial factors may have had an impact on the calculated abnormal stock market returns.
A description of the background theory, the methodology used, and the detailed results is included.Master of Arts
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Pan, Lijin. "Which Factors Explain Stock Returns on the Shanghai Stock Exchange Market? : A Panel Data Analysis of a Young Stock Market." Thesis, KTH, Industriell ekonomi och organisation (Avd.), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-98085.
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This paper studies factors that influence the stock return on the Shanghai Stock Exchange (SSE) market. To achieve this goal, a stock-fixed effects model is estimated using a panel data sample comprising 100 companies listed on the SSE market during the 72-month period from January 2002 to December 2007. I find that number of trades and book-to-market value in both up and down markets have a significant and positive impact on stock returns during the studied period, whereas stock returns were negatively affected by systematic risk in both up and down markets although less so in up markets. Price to earnings ratio did not show any significant effect on stock returns on the SSE. My overall results indicate that SSE did not satisfy the efficient market hypothesis 1 during the studied period from January 2002 to December 2007.
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Larsen, Fredrik. "Automatic stock market trading based on Technical Analysis." Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8707.
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The theory of technical analysis suggests that future stock price developement can be foretold by analyzing historical price fluctuations and identifying repetitive patterns. A computerized system, able to produce trade recommendations based on different aspects of this theory, has been implemented. The system utilizes trading agents, trained using machine learning techniques, capable of producing unified buy and sell signals. It has been evaluated using actual trade data from the Oslo Børs stock exchange over the period 1999-2006. Compared to the simple strategy of buying and holding, some of the agents have proven to yield good results, both during years with extremely good stock market returns, as well as during times of recession. In spite of the positive performance, anomalous results do exist and call for cautionous use of the system’s recommendations. Combining them with fundamental analysis appears to be a safe approach to achieve succesful stock market trading.
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Memari, Majid. "Predicting the Stock Market Using News Sentiment Analysis." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/theses/2442.
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ABSTRACT MAJID MEMARI, for the Masters of Science degree in Computer Science, presented on November 3rd, 2017 at Southern Illinois University, Carbondale, IL. Title: PREDICTING THE STOCK MARKET USING NEWS SENTIMENT ANALYSIS Major Professor: Dr. Norman Carver Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. GDELT is the largest, most comprehensive, and highest resolution open database ever created. It is a platform that monitors the world's news media from nearly every corner of every country in print, broadcast, and web formats, in over 100 languages, every moment of every day that stretches all the way back to January 1st, 1979, and updates daily [1]. Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable [2]. On the other hand, other studies show that it is predictable. The stock market prediction has been a long-time attractive topic and is extensively studied by researchers in different fields with numerous studies of the correlation between stock market fluctuations and different data sources derived from the historical data of world major stock indices or external information from social media and news [6]. The main objective of this research is to investigate the accuracy of predicting the unseen prices of the Dow Jones Industrial Average using information derived from GDELT database. Dow Jones Industrial Average (DJIA) is a stock market index, and one of several indices created by Wall Street Journal editor and Dow Jones & Company co-founder Charles Dow. This research is based on data sets of events from GDELT database and daily prices of the DJI from Yahoo Finance, all from March 2015 to October 2017. First, multiple different classification machine learning models are applied to the generated datasets and then also applied to multiple different Ensemble methods. In statistics and machine learning, Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Afterwards, performances are evaluated for each model using the optimized parameters. Finally, experimental results show that using Ensemble methods has a significant (positive) impact on improving the prediction accuracy. Keywords: Big Data, GDELT, Stock Market, Prediction, Dow Jones Index, Machine Learning, Ensemble Methods
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Zhang, Jun. "Organization & Analysis of Stock Option Market Data." Digital WPI, 2011. https://digitalcommons.wpi.edu/etd-theses/34.
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Option market data are quoted in terms of option prices and are fragmented into over 100 individual contract files per day for each symbol. Traders and quantitative analysts compare values of options in terms of implied volatilities. The current project refactors fragmented option price data into implied volatility files organized by stock symbols and expiration dates. Each resulting file comprises the temporal evolution of daily volatility smile curves for every day prior to expiration. Possible analysis enabled by the refactored data is demonstrated.
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Holmqvist, Carl. "Opinion analysis of microblogs for stock market prediction." Thesis, KTH, Teoretisk datalogi, TCS, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233197.
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This degree project investigates if a company’s stock price development can be predicted using the general opinion expressed in tweets about the company. The project starts off with the model from a previous project and then tries to improve the results using state-of-the-art neural network sentiment analysis and more tweet data. This project also attempts to perform hourly predictions along with daily predictions in order to investigate the method further. The results show a decrease in accuracy compared to the previous project. The results also indicate that the neural network sentiment analysis improves the accuracy of the stock price development when compared to the baseline model under comparable conditions.Detta examensarbete undersöker om ett företags aktievärdesutveckling kan förutspås genom att använda sig av den generella opinionen hos tweets skrivna om företaget. Examensarbetet utgår ifrån en model från ett tidigare projekt och försöker förbättra resultaten från denna genom att använda sig av dels state-of-the-art sentimentanalys med neurala nätverk, dels mer tweet data. Examensarbetet undersöker både prognoser timvis samt dygnsvis för att undersöka metoden djupare. Resultaten tyder på en minskad träffsäkerhet jämfört med det tidigare projektet. Resultaten indikerar också att sentimentanalys med neurala nätverk förbättrar träffsäkerheten hos aktievärdesprognosen jämfört med tidigare sentimentanalysmetod givet jämförbara förutsättningar.
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Wong, Michael C. S. "Technical analysis and market inefficiency a study of the Hong Kong stock market /." online access from ProQuest databases, 1997. http://libweb.cityu.edu.hk/cgi-bin/er/db/pqdiss.pl?9907800.
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Chen, Gang. "The Chinese stock market : an emperical analysis of market segmentation, inter-relationships and theoretical versus actual stock prices." Thesis, University of Aberdeen, 2011. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=165872.
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This thesis contributes to our knowledge of the behaviour of the Chinese stock market by offering an empirical investigation into issues such as market segmentation, inter‐relationships between Chinese stock markets and inter‐relationships with foreign stock markets. Basic questions which have been typically analysed for developed stock markets are considered in this thesis. These include an analysis of core concepts such as volatility; causal links with economic variables and the reasons why the theoretical stock price may be different from the actual stock price. Methodological methods include; cointegration, generalised autoregressive heteroscedastic modelling (GARCH), vector autoregressive framework modelling (VAR) and panel data analysis. Both daily and monthly observations are used over a time period from 1996 to 2006. The results indicate that there is a rich set of reasons why we may observe phenomena such as a discount on B shares and a relationship between A shares and B shares. The findings also suggest that China is not isolated from the rest of the world and that there is evidence of inter‐relationships with foreign stock markets and that Chinese stock market prices are close to their fundamental value. This is not generally the finding for developed stock markets. Overall, it appears that the methodological approaches usually associated with developed stock markets can serve us well as useful tools in creating a deeper understanding of the underlying fundamentals describing the Chinese stock market.
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Alshogeathri, Mofleh Ali Mofleh. "Macroeconomic determinants of the stock market movements: empirical evidence from the Saudi stock market." Diss., Kansas State University, 2011. http://hdl.handle.net/2097/11989.
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Doctor of PhilosophyDepartment of Economics
Lance J. Bachmeier
This dissertation investigates the long run and short run relationships between Saudi stock market returns and eight macroeconomic variables. We investigate the ability of these variables to predict the level and volatility of Saudi stock market returns. A wide range of Vector autoregression (VAR) and generalized autoregressive conditional heteroskedasticity (GARCH) models estimated and interpreted. A Johansen-Juselius cointegration test indicates a positive long run relationship between the Saudi stock price index and the M2 money supply, bank credit, and the price of oil, and a negative long run relationship with the M1 money supply, the short term interest rate, inflation, and the U.S. stock market. An estimated vector error correction model (VECM) suggests significant unidirectional short run causal relationships between Saudi stock market returns and the money supply and inflation. The VECM also finds a significant long run causal relationship among the macroeconomic variables in the system. The estimated speed of adjustment indicates that the Saudi stock market converges to the equilibrium within half a year. Granger causality tests show no causal relationship between Saudi stock market returns and the exchange rate. Impulse response function analysis shows no significant relationship between Saudi stock market returns and the macroeconomic variables. Forecast error variance decompositions suggest that 89% of the variation in Saudi stock market returns is attributable to its own shock, which implies that Saudi stock market returns are largely independent of the macroeconomic variables in the system. Finally, a GARCH-X model indicates a significant relationship between volatility of Saudi stock returns and short run movements of macroeconomic variables. Implications of this study include the following. (i) Prediction of stock market returns becomes more difficult as the volatility of the macroeconomic variables increases in the short run. (ii) Investors should look at the systematic risks revealed by these macroeconomic variables when structuring their portfolios and diversification strategies. (iii) Policymakers should seek to minimize macroeconomic fluctuations considering the effect of macroeconomic variables changes on the stock market when formulating economic policy.
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Reynolds, Paul Edward III. "A Sectoral Analysis of the 1929 Stock Market Crash." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1487.
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The stock market crash of 1929 stands today as the largest decline in market value in the history of the United States. Consequently, the event destroyed the wealth of thousands of American families and institutions. On October 28th and 29th, the United States stock market fell 11.3 percent and 12.4 percent respectively, marking the beginning of a down market that lasted over three years, the time period known today as the Great Depression. This paper empirically analyzes the effects felt by each individual industry sector in the crash of 1929, identifying gross and abnormal returns over three major days in the crash. I then compare my findings to previous literature and economic theories, analyzing which sector returns were expected and which were abnormal.
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Kihlström, Gustav, and Przybysz Patryk. "Technical analysis inspired machine learning for stock market data." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-186863.
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In this thesis we evaluate four different machine learning algorithms, namely Naive Bayes Classifier, Support Vector Machines, Extreme Learning Machine and Random Forest in the context of stock market investments. The aim is to provide additional information that can be beneficial when creating stock market models to be used in a machine learning setting. All four algorithms are trained on different configurations of data, based on concepts from technical analysis. The configurations contain closing prices, volatility and trading volume in different combinations. These variables are taken from past trading days, where the number of days from which data is to be collected ranges from 2 to 30. The resulting predictors attained from the various algorithms and configurations above reach accuracy rates between $50-54\%$. This thesis concludes that the effect of the different evaluated features vary depending on which algorithm is used as well as how many past trading days are included. Concluding, it is ascertained that the usage of volatility features should at least be considered when building a machine learning model in a stock market context.
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Alharaib, Mansour. "STOCK MARKET RETURNS AND VOLATILITY: MACROECONOMIC NEWS ANNOUNCEMENTS, INTERACTIONS, AND MARKET RISK ANALYSIS." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1583.
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This study examines how stock market returns and volatility responses to macroeconomic news announcements in US and Europe, and oil prices. Moreover, the market risk associated with these stock markets based on selected countries and regions is also analyzed here. In all chapters, the data is in a weekly time horizon and it covers 21 countries from different contents. In particular, Data covers three different time periods, i.e. full sample from 1/1/2000 to 12/31/2015, before the financial crisis, i.e. from 1/1/2000 to 9/27/2008 and after the financial crisis, i.e. from 10/11/2008 to 12/31/2015. Chapter 2 studies the impact of macroeconomic news announcements on stock markets in 21 countries using US and European countries macroeconomic news announcements. The first part investigates the impact of macroeconomic news announcements surprises in US and European Countries on stock markets returns in these countries. The second part analyzes the impact of macroeconomic news announcements in US and European Countries on stock markets volatility in these countries. Our results show that stock markets in selected countries react differently to macroeconomic news announcement in US and Europe. Chapter 3 study the interaction and volatility spillover between oil prices and stock markets returns and volatility in selected countries and regions. Oil prices are based on West Texas Intermediate (WTI). The analysis use VAR(1)-GARCH(1,1) model to capture the interdependence between stocks market and oil prices. The findings show that there is interdependence between stock markets and oil price changes in most selected countries and regions. Chapter 4 study the market risk in stock markets returns in selected countries and regions using IGARCH(1,1) and GARCH(1,1) to obtain the value at risk (VaR) and the expected shortfall (ES). The findings of chapter 4 show that market risk was high for most selected countries before the financial crisis and low after the financial crisis.
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Ruano, Francisco Nunes Moutinho Salgado. "Impact of the quantitative easing programs on North American equity market." Master's thesis, Instituto Superior de Economia e Gestão, 2014. http://hdl.handle.net/10400.5/7039.
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Mestrado em Economia Monetária e FinanceiraThe aim of this research is to assess how the unconventional monetary policy instruments used by the Federal Reserve impacted on the North American Stock Market from the period between January 2009 and September 2012. We present the economic theory concerning the transmission mechanism of the monetary policy to the Economy, the channels through which this transmission becomes effective and, in particular, the functioning of the stock price transmission channel. We also present the economic theory on how unconventional monetary policy instruments, the Quantitative Easing programs, impact on assets and particularly on the stock prices. In the spirit of the Arbitrage Pricing Theory (APT) we develop a GARCH model in order to assess which macroeconomic, financial and conventional and unconventional monetary variables impacted on the evolution of the North-American Stock market in the period referred above. We observe that almost all of the variables chosen in this study tend to impact on the equity prices in the long run, but they have no impact in a period of financial distress such as the one between January 2009 and September 2012. We also found no evidence that the Quantitative Easing programs launched by the Federal Reserve after January 2009 had a permanent and direct impact on the recovery of the North American Markets until September 2012.
O presente trabalho tem como objectivo avaliar se a política monetária não convencional, levada a cabo pela Reserva Federal Norte-Americana (FED) entre Janeiro de 2009 e Setembro de 2012, teve impacto na recuperação do Mercado Accionista dos Estados Unidos da América no referido período. Em primeiro lugar, começamos por apresentar a teoria económica referente à transmissão da política monetária para os restantes agregados macroeconómicos, os canais através dos quais essa transmissão se processa e, em particular, através do canal do mercado accionista. Apresentamos, também, a teoria relativa ao modo como os programas de Quantitative Easing afectam os diversos activos financeiros e, em especial, a evolução do mercado accionista. Em seguida, e no espírito da Arbitrage Pricing Theory (APT), desenvolvemos um modelo GARCH que nos permite avaliar quais as variáveis macroeconómicas, financeiras e de política monetária convencional e não convencional, que influenciaram a evolução do mercado accionista norte-americano no período supra referido. Verificamos que a quase totalidade das variáveis consideradas têm um impacto estatisticamente significativo no mercado accionista quando consideramos períodos temporais longos, mas aparentam não ter impacto em períodos de instabilidade financeira, como os vividos entre Janeiro de 2009 e Setembro de 2012. De referir, também, que não encontramos evidência empírica de que os programas de Quantitative Easing, lançados pela FED após Janeiro de 2009, tivessem tido um impacto directo e permanente na recuperação do mercado accionista norte-americano.
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Nieuwland, Frederik Gertruda Maria Carolus. "Speculative markets dynamics an econometric analysis of stock market and foreign exchange market dynamics /." Proefschrift, Maastricht : Maastricht : Universitaire Pers Maastricht ; University Library, Maastricht University [Host], 1993. http://arno.unimaas.nl/show.cgi?fid=6219.
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Hallberg, Martin, and Marcus Ryhage. "Effects of Monetary Policy on Stock Market Liquidity : Empirical Analysis on the Swedish Market." Thesis, Umeå universitet, Nationalekonomi, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160987.
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After the 2008-2009 crisis, many studies have been done to assess the stock market liquidity and what influences this. The monetary policy of a central bank can have a broad impact of a country's economy and is believed to also affect the stock market. In addition, the goal of the central bank differs for every country which may affect the transmission effects seen in previous studies. This study takes a closer look at how monetary policy affect stock market liquidity fora small open economy, Sweden. The method of use includes vector autoregression, Granger tests and impulse response functions. The results show that we cannot distinguish a clear effect on stock market liquidity. However, findings show that there is a disturbance in stock market liquidity after a change in monetary policy. Granger tests also suggest an interesting dual causality. These results are consistent when testing both with monthly and daily data.
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Hsu, Tien-shin, and 許恬忻. "Study of Stock Market Analysis Television Programs and Viewer Behavior." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/72369675575863657128.
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碩士國立政治大學
傳播學院碩士在職專班
95
This analysis will 1) examine the current format for Stock Market Analysis Television Programs, the program content, the program hosts types, and the host speaking styles. 2) Examine program viewer behavior, based on viewer ratings, audience profile, and the role that these programs play in the daily lives of its viewers. 3) Ascertain the key elements of these programs and provide recommendations based on conclusions reached through cumulative analysis of such television programs and the behavior of their viewers. Based on text analysis, in-depth interviews, and second-hand data, viewers appear to have clear utilitarian motivations for watching these television programs, they are active listeners, and their primary objective is to profit on their stock market investments. Program design should take into consideration the following elements: 1) Viewer Demographics: even proportion of male and female viewers, higher age-bracket, highly educated, living primarily in Northern Taiwan, high household incomes, strong demand for new information, split on whether such programs provide educational value. 2) The Nature of the Domestic Stock Market: information changes quickly, factors affecting the rise and fall of stock prices are many, professional market insight and analysis is needed. Stock Market Analysis Television Programs, which are quite different from other television programs, fall into one of two main types: Investment Consulting Company-produced Analysis Programs, Real-time Market Data and Analysis Programs, with very little variation between competing programs in each category. The former generally relies on a string of new program hosts in an attempt to maintain viewer interest. There are currently only four of the latter type of program currently running with only two of them showing even mild success. This report will recommend that producers of such programs should offer more in-depth content so as to better attract and maintain viewers. Competing programs should also distinguish themselves from one another by offering different types of data and analysis content, and different levels of educational content in their programs.
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Hsin, Huang Shih, and 黃世欣. "The Impacts of Stock Repurchase Programs on Taiwan Stock Market." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/14187992031192041763.
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碩士淡江大學
財務金融學系
89
This thesis mainly discusses the impacts of stock repurchase programs on stock market in Taiwan. So the purpose of this study is twofold: 1. We want to evaluate the price effects of a series of legal events leading to the passage of Stock Repurchase Act on August, 2000. 2. After the Act passed, we also examine the announcement effect of companies that buy back their own common stock in the open market. The major findings of this paper are summarized as follows: 1.Events leading to the passage of Stock Repurchase Act do have influence on industries listed in the Taiwan Stock Exchange. Among eleven related events, we find four events have significant influence on stock price, especially event 1 and event 9. 2.Abnormal Returns last for three days after company’s repurchase announcements and at the fourth trading day, the significant and positive abnormal returns vanish. 3.Compared with announcement effects of other industries, electronic industry does not have significant price reaction while financial and traditional industry do have reaction on stock price. 4.As for the announced industries listed in the OTC, we find the announcement effects of traditional industry is more significant than financial and electronic industry. And this effect seems to have reacted prior to stock repurchase announcement.
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Zhong, Xiang-Ting, and 鍾享庭. "Stock Volatility Analysis on the China Stock Market." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/11632902834929091221.
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碩士南華大學
財務管理研究所
90
Abstract This study examines the chaos and volatility of China daily stock indexes, including Shanghai and Shenzhen A and B Stock Markets. Three techniques i.e. BDS Test, Rescale Range Analysis, Correlation Dimension Analysis are applied to detect chaotic behavior in this research. We use these techniques to analyze four categories of China listed stocks for the period form January 1, 1997 to September 30, 2001. The BDS statistics of four return series imply the rejection of the hypothesis of independent, identical distribution (I.I.D.).The Correlation Dimensions are not convergent that tell us there are not obviously evidence chaotic behavior be presented in the four stock indexes return . Four H values for Rescale Range Analysis are quite greater than 0.5, it indicates that the time series is probably persistent (long memory). The results of this approach might provide us with a better understanding of China stock market. Although the linear dependence is excluded from the returns series by fitting ARMA model, the phenomena of non-linearity is present. Therefore, GARCH models are employed to capture the non-linearity embedded in the return series. We find significant existence of volatility from the results of GARCH models. In order to examine the relation between risk and expected return, in addition, GARCH-M models are chosen and show no significant evidence on the coefficient of conditional volatility in Chinese B stock market. Keywords：BDS Test, Rescale Range Analysis, Correlation Dimension Analysis, GARCH model
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Brás, Luís Pedro Airosa Carvalho. "Market Graph Analysis to the Portuguese Stock Market." Dissertação, 2017. https://repositorio-aberto.up.pt/handle/10216/109364.
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Brás, Luís Pedro Airosa Carvalho. "Market Graph Analysis to the Portuguese Stock Market." Master's thesis, 2017. https://repositorio-aberto.up.pt/handle/10216/109364.
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Cunha, João da. "Fundamental Analysis on Iberian Stock Market." Dissertação, 2016. https://repositorio-aberto.up.pt/handle/10216/86177.
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Huang, Ying-Hsiu, and 黃英修. "Analysis of International Stock Market Portfolios." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/99582138204195835069.
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Siregar, Bakti, and 錫誠嘉. "Statistical Analysis of Indonesia Stock Market." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/06857037012516554624.
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碩士國立中山大學
應用數學系研究所
104
Liberalization and economic integration become topics of discussion and research in recent years. Indonesia is one of the countries that actively participates in the achievement of liberalization and economic integration, especially in the ASEAN region. Indonesia stock market has a high degree of volatility which can be used to produce high investment returns, which is one of the reasons to attract foreign investors to enter Indonesia stockmarket. Volatility plays an important role for market participants to control and reduce their market risk of financial assets In this study we establish the volatility models for the stocks listed in the Indonesia stock market index LQ45. The models we considered include the Autoregressive Conditional Heteroskedasticity (ARCH) proposed by Engle (1982), Generalized Autoregrassive Conditional Heteroskedasticity (GARCH) by Bollerslev (1986), the Stochastic Volatility Model (SVM) by Jacquier, Polson and Rossi (1994), and Autoregressive Moving Average (ARMA) by Box, Jankins, and Reinsel (1994). We use the daily log returns to establish the models and select the best one via Akaike information criterion (AIC).Moreover, we use it to predict the future volatility. In the end, we also apply machine learning application such as the K-means method to figure out how itsmovement of the clusters volatility in Indonesia stocks.
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Cunha, João da. "Fundamental Analysis on Iberian Stock Market." Master's thesis, 2016. https://repositorio-aberto.up.pt/handle/10216/86177.
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Chan, Li-Lin, and 陳麗玲. "Cross-sectional analysis of stock return in Taiwan stock market." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/44213087829560649537.
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Jou, Chian Guan, and 錢冠州. "Individual Stock''s Technical Analysis on the Taipei Stock Market." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/cq6p56.
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碩士銘傳大學
經濟學研究所
92
To obtain the essential value and the relative price trajectory is the purpose of stock analysis. We expect getting a better investment effect after making an investment decision. Microeconomic and macroeconomic environment can affect the stock price. There are a lot of causes can affect the prosperity change. For example, economic factors, politics, industrial change and operating achievements of a company. Most of them are hard to quantify and analyze. But, if we research it by the way of stock demand factor, we can proceed the analytic work easily. However, stock price is a result of equilibrium when supply equal to demand in the stock market. Therefore, if we can find the total power of affecting stock price from the stock demand factor, we can forecast the rise or fall of stock price.
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Wei, Ching-Lin, and 魏慶林. "A Dynamic Analysis of Money market and Stock Market Bubbles." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/36338306818838882068.
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碩士國立高雄大學
應用經濟學系碩士班
98
Since the financial market posses the feature of the self-fulfilling prophecy, the growth and collapse of stock market bubbles reflects the amplification and diminishing of the beliefs of bubbles. The financial instability hypothesis proposed by Minsky (1992) suggested that the fluctuation in the economy may be resulted from the instability of financial market and such instability could be triggered without exogenous disturbances. As the money market dominates the increases and decreases in stock market funds and the impact of credit amplification on the future expectation of the economy, the money market may be capable of dominating the expectation of bubbles in stock market if the economy system is characterized sufficiently by the financial instability hypothesis. Due to that the rational expectation hypothesis is unable to illustrate endogenous fluctuations in the economy and the reoccurrence of bubbles after complete collapse, the goal of this thesis is to examine whether the belief of repeated crash and arise on bubbles is dominated by the money market following the structure of the financial instability hypothesis. Therefore, this thesis derives cointegration vectors which represent existing intrinsic bubbles and market fundamentals. These vectors can be utilized to filter out the market participant’s belief about bubbles. By using Probit model, the influence of monetary variables on the prior belief of bubbles can be depicted. These vectors and Probit model can be estimated by combining Bayesian econometric framework and Markov Regime-switching approach. The empirical result can display the dynamic process of beliefs of repeat crash and arise on bubbles and show how money market does play a crucial role to dominate these beliefs.
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Lee, Wen-Yi, and 李玟怡. "Technical Analysis of the Australian Stock Market." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/21093385091604460516.
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碩士銘傳大學
國際企業學系碩士在職專班
99
Australia’s stable financial market environment so that investors have confidence in the good prospects for the Australian stock Markets,investment analysis tools for investors to choose how to apply investment targets, in the stock market can get Optional excess return, as long-term investors should be aware of holding, and in recent years for the domestic and foreign scholars Moving average method of the empirical phenomenon of different results obtained in the past domestic and international research literature,Less technical indicators for the Australian market research, this study used the current market investors Technical analysis tool for the study for the Australian market index, so in this study are as follows: 1、 the moving average method of simulation, in compliance with the best investment performance. 2、to explore whether the Australian stock market efficient market hypothesi. 3 to explore the basis of technical analysis - moving average method, as trading decision-making "technical indicators trading Operation "remuneration and" buy and hold "returns compared to the availability of significant excess return.
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Wu, Dian-Geng, and 吳典庚. "GRG-based Analysis of Taiwan Stock Market." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/06038464691210515874.
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碩士國立臺灣科技大學
電機工程系
89
In Grey System, the Grey Relational Grade (GRG) can describe the relationship between the main factor and others. For three targets included hot stock, leading factors and technical analysis, we could find out the characteristics of stock market and construct the simple and efficient investment models. The hot stock and leading factor analysis are fetched by GRG. The technical analysis is composed of three steps: fetching, degree defining and risk control. In fetching, GRG is used to decide the proper technical indices. In degree defining, fuzzy membership function is used. In risk control, there are two methods to filter high risk and keep profit. Finally, Taiwan stock market is given to demonstrate the performance and the validity of our models.
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"Principal factor analysis of stock market sentiment." 2007. http://library.cuhk.edu.hk/record=b5893308.
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Duan, Xin.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.
Includes bibliographical references (leaves 36-43).
Abstracts in English and Chinese.
Abstract --- p.i-ii
Acknowledgement --- p.iii
Table of Contents --- p.iv
Chapter Chapter 1 --- Introduction --- p.1
Chapter Chapter 2 --- Related Literature --- p.6
Chapter 2.1 --- Exchange Market Pressure Index --- p.6
Chapter 2.2 --- The Sentiment Index --- p.11
Chapter Chapter 3 --- Stock market sentiment --- p.16
Chapter 3.1 --- Data --- p.16
Chapter 3.2 --- Methodology --- p.23
Chapter 3.3 --- Estimated Results --- p.25
Chapter Chapter 4 --- Application to the Hong Kong stock market --- p.28
Chapter 4.1 --- Threshold Model Estimation --- p.28
Chapter 4.2 --- Trading Strategy --- p.30
Chapter Chapter 5 --- Conclusion --- p.32
Appendix: Principle Component --- p.34
References --- p.36
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Hu, Yu-Luen, and 胡毓倫. "Partial correlation analysis of Taiwan stock market." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/q396c9.
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碩士國立東華大學
物理學系
105
In this thesis, we select 100 stocks in the Taiwan stock market from 2012 to 2016 to study the correlation structure in the Taiwan stock market. We calculate 1.) cross-correlations between price returns of every pair of stocks, 2.) partial correlation between price returns of every pair of stocks given TAIEX, and 3.) partial correlation between price returns of every pair of stocks given all other stocks. All time lags used are 30 minutes. The components of the first eigenvector of the correlation matrices in the Taiwan stock market have usually the same sign. It indicates every pair of stocks has positive correlation in this eigenmode. To leverage this phenomenon, we propose a new index defined as the weighted average of stocks in the Taiwan stock market using components of the first eigenvector as weights. Original TAIEX and this new index show the same trend at most time, but there are exceptions. For example, during February of 2014, due to lots of anti-correlation between price returns of different stocks in this period, return fluctuation of the Taiwan stock market is quite large and no unified trend can be identified. By comparing coefficients of (2) and original cross-correlation, we find TAIEX always increase cross-correlations between any two stocks. On the other hand, the coefficients of (3) are always very small, which means most correlations between two stocks in the Taiwan stock market coming from various kinds of collective motions of stocks, and the effects usually show positive influence. It’s an expected result that there is apparent decrease of the first eigenvalue of (2) compared to the original first eigenvalue, while in the case of (3), the first five eigenvalue are almost zero, indicating the resonance between stocks has been removed apparently. We also construct the minimum spanning trees by three kinds of correlations to depict the correlation structure of the Taiwan stock market. The minimum spanning trees constructed by using the coefficients of (2) reveal better results on stock classification. The minimum spanning trees constructed by using the coefficients of (3) are more decentralized due to distance increasing between stocks. Thus we create additionally the correlation-based networks to complement the information loss of the minimum spanning tree. We find in such networks constructed by using the coefficients of (2), there are strong connections in the same industry. These two complementary graph representations can be used together to probe the structure of the stock market in more details.
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Hsu, Shieh-Chieh, and 徐仕杰. "Application of Clustering Analysis on Stock Market." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/32505014052656089360.
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碩士靜宜大學
財務與計算數學系
102
We use clustering analysis to investigate the possible trends of share prices fluctuation for financial holding company in the Taiwanese stock market. Euclidean distance and recently proposed distance correlation are used as similarity measure for clustering analysis. Lowess smoothing methods are used to smooth the fluctuation as well as visualization of the trends for share prices. Our clustering strategy is applied to financial industry to observe whether there exists subsequent linked effect to stocks within same cluster.
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Lan, Yi-Chang, and 蘭宜昌. "Analysis of Stock Market Data Using SVM." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/99632165717725249524.
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碩士淡江大學
資訊工程學系碩士在職專班
94
Stock market is a popular financial tool. There are many factors that affect stock prices. Computer technology has been constantly improving, and there is a growing interest in applying more sophisticated mathematical tools in studying the stock market. Support Vector Machine (SVM) based on structural risk minimization theory is a modern algorithm of learning machine. Lots of scholars apply SVM to different kinds of problems due to many attractive features and promising empirical performance. In this paper, we try to use SVM as an analytical tool and apply it for analyzing Taiwan stock market. Return series constructed from The Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) time series and cutting by embedded dimension are regarded as the original data. Support vector machine is applied to construct analytical model for the stock index fluctuation simulation. Results reveal that the fluctuation of TAIEX is random walk in general. In amount of training history data, it shows that long training data period is not strikingly helpful to predict the trend of the stock index, but using medium-term or short-term training data is good for catching the future stock index''s tendency.
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Chin, Chao-Fan, and 秦少凡. "Multifractal Detrended Fluctuation Analysis Taiwan stock market." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/96123940314291340694.
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碩士國立東華大學
物理學系
103
We use the method of Multifractal detrended fluctuation analysis and multifractal detrended cross-correlation analysis to analyze four stocks in Taiwan stock market in 2014 and the Taiwan stock exchange from August, 2013 to August, 2015. We observe multifractality of Taiwan stock market and compute the multifractal spectrum f(α) of each stock and cross-correlations between them
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Wang, Fang-Ping, and 汪方平. "Performance analysis of technical analysis on Taiwan stock market." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/aavewf.
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碩士國立交通大學
管理學院財務金融學程
107
According to the Efficient-market hypothesis, the stock price will fully reflect all available information of the asset, when new information is generated, the stock price will change immediately, the future direction of the stock price is independent of its past historical price and will not be overvalued or undervalued. Therefore, investors cannot rely on any known information (such as technical analysis) to obtain long-term excess returns. However, many domestic financial TV programs, stock investment teaching websites and retail investors still use technical analysis as investment reference, among which the most widely known three technical indicators include KD, MACD and SMA. This study used the transaction data of all listed stocks in Taiwan from January 1, 2008 to December 18, 2018 as the research object, using common KD, MACD and SMA trading strategies for back testing, and compare with buy and hold long-term benchmark strategy. The results show that the KD is better than the buy and hold strategy only when buys at K<10 and sells at K>=90, and performance could be improved after adding the golden and death cross-condition. The winning rate and expected return rate of the MACD and SMA strategies are not as good as the buy and hold long-term holding strategy.
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Yu, Jung-Hsien, and 游榮賢. "An analysis of Stock Repurchase Announcement effectson Taiwan’s Financial Stock Market." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/96231922198016348846.
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碩士國立雲林科技大學
財務金融系
103
Abstract This study examines the treasury stock announcement effects on short-term stock returns in Taiwan finance industry stock. The sampling period is from January 1st 2006 to December 31th 2014 with a total of twenty- one companies involved. Using the mar-ket model of event study method, we find some positive / negative relationships existing among abnormal returns,capitalization, market value, price to book worthy and board of directors’ holdings. In addition, abnormal returns are also related to overall business conditions, foreigners’ holding percentages, repurchasing purposes/frequencies, and implementation degrees. It appears stock repurchase announcement effects are purely revelations of firm characteristics, which is beneficial to potential investors.
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Hsu, Hsiu-Fen, and 徐秀芬. "Idiosyncratic Risk and Stock Returns: Empirical Analysis of Taiwan Stock Market." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/59418460371486022574.
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碩士亞洲大學
國際企業學系碩士在職專班
103
ABSTRACT The previous studies on momentum and reversal strategy mostly adopt rate of return on stocks to construct portfolios. By following the residual strategy proposed by Blitz, Huij, Lansdorp and Verbeek (2013), this paper tries to explore whether their strategy works in emerging market like Taiwan. Target of studies is residual strategy of return on stocks; Study period of this paper is from January 1992 to December 2013. By following model of Blitz, Huij, Lansdorp and Verbeek (2013)， Fama - French factors (1993) ，and Charhart factors(1997)， We show that residual stock returns have predictive power for future returns above and beyond that of total stock returns. Empirical results show that the performance of residual reversal strategy is four times as large as that of traditional reversal strategy, a result consistent with Blitz et. al. (2013). However, the residual reversal portfolio is found to underperform the smallest residual quintile portfolio, of which the return is twice as large as that of the residual reversal portfolio. Keywords: residual， reversal strategy，three-factor model
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LIN, WEN-MEI, and 林美雯. "The Impact of Material Information Disclosure on Stock Market: An Analysis of the Taiwan Stock Market." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/93234268031808066088.
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碩士東吳大學
會計學系
104
To reduce information asymmetry and ensure that public disclosure of connected information will be promptly achieved following the occurrence of an event having a material effect on the shareholder equity or the price of securities of any listed company, The TWSE specially adopts "Taiwan Stock Exchange Corporation Procedures for Verification and Disclosure of Material Information of Companies with Listed Securities" hereinafter referred to as, "The Procedures," in order to ensure that the information is accurate and accessible to the general public. According to the market efficiency hypothesis, investors behavior on the interpretation of that is reflected in stock price movements, this is one of the motivation on the thesis. By analyzing the correlation between the disclosure of material information and stock reactions, this purpose of this study is to offer the securities authorities and investors useful references. The study period is 2015, the empirical analysis sample is from TWSE's material information database, 874 TWSE listed companies are viewed as the research object, and a total of 28,916 samples. Trade volume and stock price return are used as the proxy variables of stock behavior, at the same time, the multiple regression model is implement to all industries. According to the empirical results, trade volume reacts more information contents than stock return on many different types of disclosures as defined by the Material information of TWSE listed companies. Meanwhile, the empirical results show as the following. (1)The reaction for trade volume, these material information disclosures related to finance, stockholders’ equity, investment, legal matters, change in representative members or directors, dividend distributions and shareholders’ meetings have certain information contents. (2)The reaction for stock returns, these material information disclosures related to finance, personnel changes, production and operation have more specific information contents.
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Liu, Tsair-Neng, and 劉財能. "A Study of Market Model on Taiwan Stock Market--Cointegration analysis." Thesis, 1997. http://ndltd.ncl.edu.tw/handle/31050437932552326597.
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碩士淡江大學
財務金融學系
85
The rate of return and risk are widely investigated in the field of the investment analysis. Their relationship can be described by a capital assets pricing model(CAPM). Beta coefficient, which is a parameter of CAPM, is frequently adopted as a measure of risk and assumed to be stable in the literature. But many empirical evidence indicated that beta coefficient is not stable. Therefore, we use cointegration analysis to part target stock two portfolios. Then we can see what is the difference between these two portfolios on the empirical results of CAPM. Base on the data of TAIEX and the last day transaction per week''sand per month''s ending price, we conclude that:1.In monthly data, we find out that there does not have significiantly differdnce between these two portfolios on R- squrae.2.Neither monthly data nor weekly data, all these portfolios do not support CAPM theory in Taiwan stock market.
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Liu, Feng. "Market microstructure, technical analysis, and stock price movements." Thesis, 1995. http://spectrum.library.concordia.ca/3634/1/NN10872.pdf.
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Chiou, Chi-Ruei, and 邱啟睿. "Stock Market Reaction on Share Repurchase---Fundamental Analysis." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/85984331252715183586.
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碩士元智大學
會計學系
96
This study examines firms’ financial condition when they announce repurchase their share outstanding by fundamental analysis. I try to find an indicator that can distinguish accounting equality of firms and measure the reliability of information conveyed by repurchase programs. When announcing repurchase, manager frequently indicates that they are doing so in response to mispricing, or their stock price is on undervaluation. Several theoretical papers have investigated the notion that repurchases are a potential signaling to the market. However, this study finds that the announcement return of repurchase firms with poor fundamentals is higher than that of repurchase firms with good fundamental. To be explained by this point, repurchase firms with poor fundamentals are more likely to be undervalued. Accordingly, investors who have difficulty to distinguish the quality of the firm could value the stock by using the signaling of the repurchase program. In addition, my result might be influenced by firm size. Smaller firms communicate to investors for information that could be regarded as a factor of information asymmetry, and smaller firms might have poor fundamental. These results will be consistent with the signaling hypothesis in explaining the motive of the share repurchase.
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Chen, Shu-Fang, and 陳淑芳. "On the Wave Analysis in Taiwan Stock Market." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/48606445720780073395.
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碩士臺灣大學
財務金融學研究所
96
In The Wave Principle, R. N. Elliott (1938) proposed a framework towards the stock market analysis. However, Elliott wave principle has a serious problem: the wave counting is too ambiguous and subjective. In order to solve this drawback, Bill Williams (1995) integrated Fibonacci numbers, Elliott wave principle, Fractal geometry, and Chaos theory to develop the Profitunity approach. This thesis used a modification of the Profitunity approach to examine Taiwan stock market. We found that this modified trading rule obtained significantly positive weekly returns. Therefore, Taiwan stock market seems to conform to the four underlying theories and their main implication that stock market is a product of a nonlinear dynamical process.
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NENG, CHEN FENG, and 陳豐能. "An Analysis of the Stock Market of China." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/70727294775928741307.
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碩士亞洲大學
國際企業學系碩士班
96
ABSTRACT The growth of a country’s stock market is a reflection of its economic development. Since China began its economic reform in the late 1970s, it keep a high speed economic growth. However, from 1991 to 2006, the composite index of Shanghai Stock Exchange lost half of its value while it enjoyed lower interest rates year after year. This article attempts discuss this observations by virtue of amounts of data. The reason that the deviation between the evolution of Chinese stock market and its economic development depends on the stock market structure, macro-control policies, rent-seeking, strong government intervention, and inconsistent market information.
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Chen, Shu-Fang. "On the Wave Analysis in Taiwan Stock Market." 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-1501200810171700.
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