Journal articles: 'DJIA'

1

Borowski, Krzysztof. "Analysis of monthly rates of return in April on the example of selected world stock exchange indices." Equilibrium 11, no. 2 (June 30, 2016): 307. http://dx.doi.org/10.12775/equil.2016.014.

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The article presents a study of the effectiveness of 22 selected stock indices with the use of the rates of return in the month of April. The portfolio replicating the stock index was bought at the closing prices on the last session in March, and sold at the closing prices on the last session in April. The presence of market inefficiency is demonstrated in cases of the following indices: All-Ord, AMEX, BUX, CAC40, DAX, DJIA, DJTA, DJUA, EOE, FTSE100, SMI, SP500, but for the following indices: B-Share, Bovespa, Buenos, Hang-Seng, MEX-IPC, Nasdaq, Nikkei, Russel, TSE and WIG, the obtained monthly rates of return were statistically equal to zero. In the last part of the article, the correlation coefficients of rates of return for analyzed indices in month of April were surveyed.

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2

Clarke, Roger G., and Meir Statman. "The DJIA Crossed 652,230." Journal of Portfolio Management 26, no. 2 (January 31, 2000): 89–92. http://dx.doi.org/10.3905/jpm.2000.319741.

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Magiera, Frank T. "The DJIA Crossed 652,230." CFA Digest 30, no. 3 (August 2000): 99. http://dx.doi.org/10.2469/dig.v30.n3.746.

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Platt, Harlan D., Licheng Cai, and Marjorie A. Platt. "Is the DJIA Index Biased?" Journal of Index Investing 4, no. 4 (February 28, 2014): 43–52. http://dx.doi.org/10.3905/jii.2014.4.4.043.

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Biktimirov, Ernest N., and Yuanbin Xu. "Market reactions to changes in the Dow Jones industrial average index." International Journal of Managerial Finance 15, no. 5 (April 29, 2019): 792–812. http://dx.doi.org/10.1108/ijmf-10-2017-0226.

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Purpose The purpose of this paper is to examine changes in stock returns, liquidity, institutional ownership, analyst following and investor awareness for companies added to and deleted from the Dow Jones Industrial Average (DJIA) index. Previous studies report conflicting evidence regarding the market reactions to changes in the DJIA index membership. Design/methodology/approach This study uses the event-study methodology to calculate abnormal returns and trading volume around the announcement and effective days of DJIA index changes from 1929 to 2015. It also tests for significant changes in liquidity, institutional ownership, analyst following and investor awareness in the 1990–2015 period. Multivariate regressions are used to perform a simultaneous analysis of competing hypotheses. Findings This study resolves the mixed results of previous DJIA index papers by documenting different stock price and trading volume reactions over the 1929–2015 period. Focusing on the most recent period, 1990–2015, the study finds that stocks added to (deleted from) the index experience a significant permanent stock price gain (loss). The observed stock price reaction seems to be associated with changes in liquidity proxies thus lending support for the liquidity hypothesis. Research limitations/implications Limited data availability for the periods prior to 1990 prevents this study from identifying the exact reasons for different stock price and trading volume reactions across subperiods of the 1929–2015 period. Originality/value This study provides the most comprehensive examination of market reactions to changes in the DJIA index and resolves the mixed results of previous studies. A better understanding of market reactions around the DJIA index changes can help both individual and institutional investors with developing effective trading strategies and index managing companies with designing optimal announcement policies.

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Alawneh, Ateyah. "Investigation of Co-Integration between Standard and Poor Index and Dow Jones Index in the New York Financial Market." International Journal of Economics and Finance 10, no. 5 (April 18, 2018): 197. http://dx.doi.org/10.5539/ijef.v10n5p197.

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The study investigates the co-integration between (the S&P 500 index)and (Dow Jones index) the DJIA by busing the method Engle-granger co-integration Test. The study use annual data from 1990 to 2016.The study examines the stability of the index of S&P 500 and DJIA using the E-views program through a unit root test. The study found that the indicators are unstable, but they become stable when taking the first difference. This condition integrates (the S&P 500 index) and (the DJIA index) during the long-term co-integration test. The analysis shows that there is a negative co-integration between the two variables. It should be emphasized that the short-term dynamic analysis showed a positive co-integration between both indexes. The study concluded that there is an urgent need to take into account the long-term negative co-integration between (the S&P 500 index) and (the DJIA index) by investors in the New York market. Also, the study considers short-term positive integration between (the S&P 500 index) and (DJIA index), which turns into a negative relationship in the long term when taking into account the markets linked with the New York market as a major global market and other international financial markets when making any financial investment. The result of this study could help users of major international financial markets in investment diversification to reduce risk.

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7

Lin, Feng-Li. "Do DJIA Firms Reflect Stationary Debt Ratios?" Economies 8, no. 4 (September 28, 2020): 76. http://dx.doi.org/10.3390/economies8040076.

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To form optimum firm capital structure strategies to face unanticipated economic events, firm managers should understand the stability of a firm’s capital structure. The aim of this research was to study whether the debt ratio is stationary in listed firms on the Dow Jones Industrial Average (DJIA). Two vital capital structure concepts regarding pecking order and trade-off theory are fairly contradictory. Using opposing theoretical contexts, the Sequential Panel Selection Method apparently categorizes which and how many series are stationary processes in the panel. This method was used to test the mean reverting properties of the 25 companies listed on Dow Jones Industrial Average between 2001 and 2017 in this study, which is expected to fill the current gap in the literature. The overall results show that stationary debt ratios exist in 10 of the 25 studied firms, supporting the trade-off theory. Moreover, the 10 firms utilizing trade-off theory are affected by firm size, profitability, growth opportunity, and dividend payout ratio. These results provide vital information for firms to certify strategies to optimize capital structure.

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8

QUAH, T. "DJIA stock selection assisted by neural network." Expert Systems with Applications 35, no. 1-2 (July 2008): 50–58. http://dx.doi.org/10.1016/j.eswa.2007.06.039.

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Anagnoste, Sorin, and Petre Caraiani. "The Impact of Financial and Macroeconomic Shocks on the Entropy of Financial Markets." Entropy 21, no. 3 (March 23, 2019): 316. http://dx.doi.org/10.3390/e21030316.

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We propose here a method to analyze whether financial and macroeconomic shocks influence the entropy of financial networks. We derive a measure of entropy using the correlation matrix of the stock market components of the DOW Jones Industrial Average (DJIA) index. Using VAR models in different specifications, we show that shocks in production or the DJIA index lead to an increase in the entropy of the financial markets.

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10

Prashant, Joshi. "Volatility Interactions among India and US Stock Markets." Case Studies in Business and Management 1, no. 1 (June 19, 2014): 107. http://dx.doi.org/10.5296/csbm.v1i1.5830.

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The study examines the return and volatility spillover among BSE and DJIA of India and US Stock Markets respectively. It employed GARCH-BEKK model to examine the relationship. The period of study is from January 2, 2012 to April 4, 2014. We find evidences of bidirectional shock and volatility interactions among the stock markets. The results indicate that DJIA exercises more influence on BSE in terms of shocks and volatility transmission. The overall persistence of volatility is highest in US stock market.

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11

Iman, Shofal, Imron Mawardi, and Md Atiqur Rahman Sarker. "ANALYSIS OF INTERNATIONAL INDEX ON INDONESIAN SHARIA STOCK INDEX." Jurnal Ekonomi dan Bisnis Islam (Journal of Islamic Economics and Business) 6, no. 1 (June 30, 2020): 60. http://dx.doi.org/10.20473/jebis.v6i1.10961.

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This study aims to determine the influence of long-term and short-term global stock index on the Indonesian Islamic stock index. The approach used is a quantitative approach and uses the Error Correction Model (ECM) method. ECM is an analytical model that can be used in time series data to estimate the effect of independent variables on long-term and short-term use variables. The sample used was taken from secondary data, namely global stock index data consisting of the DJIA, N225 and HSI indices, and the Indonesian sharia stock index in the form of the ISSI index in the period of January 2013 to December 2017, so that 60 samples were obtained. The test results show that in the long run, the DJIA and HSI indices have a significant positive effect on the ISSI index, while the N225 index has a significant negative effect on the ISSI index. In the short term, only the DJIA index has a significant positive effect on the ISSI index.

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12

Tse, Yiuman, Paramita Bandyopadhyay, and Yang-Pin Shen. "Intraday Price Discovery in the DJIA Index Markets." Journal of Business Finance & Accounting 33, no. 9-10 (November 2006): 1572–85. http://dx.doi.org/10.1111/j.1468-5957.2006.00639.x.

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13

Urbański, Stanisław. "The Cost of Capital for Investment in the Warsaw Stock Exchange Indexes – Versus Djia." Folia Oeconomica Stetinensia 21, no. 1 (June 1, 2021): 122–43. http://dx.doi.org/10.2478/foli-2021-0009.

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Abstract Research background and purpose: The CAPM, Fama-French and modified Fama-French models were used to estimate the cost of the capital of the DJIA and selected Polish stock indexes were used. The estimated cost of capital was the cost of the portfolio of corporate investment projects estimated by market returns. Research methodology: The model tests were run on 276 monthly returns of stocks listed on the markets in the years 1995–2019. The bootstrap method to estimate the confidence interval of the cost of capital was used. Results: The highest and positive cost of capital median was found for the DJIA index, about 0.85% monthly, and for the WIG20 and WIGDIV indexes, about 0.25% monthly. The cost of capital median for the mWIG80, WIGBANK and WIGCHEMIA indexes were found to be negative. This was due to large errors in the estimated cost of capital. Novelty: Minor errors in the estimation of the cost of capital of index DJIA may result from a more rational policy for the implementation of investment projects by companies included in the index.

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14

Lopes, António M., and Jóse A. Tenreiro Machado. "Dynamical Analysis of the Dow Jones Index Using Dimensionality Reduction and Visualization." Entropy 23, no. 5 (May 13, 2021): 600. http://dx.doi.org/10.3390/e23050600.

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Time-series generated by complex systems (CS) are often characterized by phenomena such as chaoticity, fractality and memory effects, which pose difficulties in their analysis. The paper explores the dynamics of multidimensional data generated by a CS. The Dow Jones Industrial Average (DJIA) index is selected as a test-bed. The DJIA time-series is normalized and segmented into several time window vectors. These vectors are treated as objects that characterize the DJIA dynamical behavior. The objects are then compared by means of different distances to generate proper inputs to dimensionality reduction and information visualization algorithms. These computational techniques produce meaningful representations of the original dataset according to the (dis)similarities between the objects. The time is displayed as a parametric variable and the non-locality can be visualized by the corresponding evolution of points and the formation of clusters. The generated portraits reveal a complex nature, which is further analyzed in terms of the emerging patterns. The results show that the adoption of dimensionality reduction and visualization tools for processing complex data is a key modeling option with the current computational resources.

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15

Susilo, Didik, Sugeng Wahyudi, and Irene Rini Demi Pangestuti. "Factors Affecting the Indonesia Stock Exchange: A Multi-Index Approach." International Journal of Financial Research 11, no. 2 (March 16, 2020): 196. http://dx.doi.org/10.5430/ijfr.v11n2p196.

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This study examines the influence of world and regional capital market conditions on the Indonesian capital market (Indonesia Stock Exchange) condition. The DJIA (Dow Jones Industrial Average) index was used as a representative of the international capital market while the Hang Seng index and the Nikkei 225 index were used as a representative of regional capital market conditions. These two indices were chosen because the Japanese capital market was one of the most advanced capital markets in the world and the Hong Kong capital market, although not as big as Japan, still played an important role in the world. The data were obtained from Yahoo Finance during the period of 2014-2018. The dependent variable was the change in the JCI (Jakarta Composite Index), while the independent variables were changes in the index of DJIA, Nikkei 225 and Hang Seng index. Using daily data analyzed by the ARIMA method (1,1), it was found that there was a significant positive effect of DJIA with lag 1 and Hang Seng index on the JCI, but no significant effect was found from the Nikkei 225 index on the JCI.

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Wu, Maoguo, and Yanyuan Wang. "Risk Analysis of World Major Stock Index Before and After the 2008 Financial Crisis – Based on GARCH-VaR Approach." International Journal of Financial Research 9, no. 2 (February 5, 2018): 39. http://dx.doi.org/10.5430/ijfr.v9n2p39.

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In 2008, the U.S. subprime mortgage crisis overwhelmed the global financial system, which sparked drastic fluctuation of world stock index. Subsequently, the risk of investment in global stock markets has augmented considerably. Applying the VaR approach based on GARCH model, this paper attempts to thoroughly investigate the volatility of S&P 500, NASDAQ, DJIA, GDAXI and CSI 300. For the purpose of comparison, data are divided into 2 parts: before the 2008 financial crisis and after the 2008 financial crisis. Thus, the paper elaborates impacts of the 2008 financial crisis on global stock index. In addition, this paper puts forward policy implications of risk control in Chinese financial market. According to empirical results, before the 2008 financial crisis, S&P 500, NASDAQ and DJIA were relatively stable; GDAXI was slightly fluctuant while CSI 300 fluctuated dramatically. When confronting with the 2008 financial crisis, the volatility of three American stock indexes surged at once, even exceeding that of CSI 300. GDAXI, however, experienced a time lag in the increase of volatility. So far, S&P 500, NASDAQ, DJIA and GDAXI have gradually recovered. On the contrary, CSI 300 still undulates frequently and erratically.

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Soydemir, Gökçe A., and A. George Petrie. "Intraday information transmission between DJIA spot and futures markets." Applied Financial Economics 13, no. 11 (November 2003): 817–27. http://dx.doi.org/10.1080/0960310022000025460.

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18

Mahfudz, Muhammad Baharudin, and Nurhadi Nurhadi. "Pengaruh Indeks DJIA, Harga Minyak Dunia, Tingkat Inflasi, dan Nilai Tukar Rupiah terhadap ISSI." Al-Kharaj : Jurnal Ekonomi, Keuangan & Bisnis Syariah 3, no. 2 (April 24, 2021): 254–69. http://dx.doi.org/10.47467/alkharaj.v3i2.370.

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Indonesia is a country where the majority of the population is Muslim, so information about the Sharia Stock Index has an important role in understanding the dynamics of the capital market in Indonesia. This study aims to determine the simultaneous and partial influence of Dow Jones Industrial Average (DJIA), World Oil Price, Inflation Rate, and Rupiah Exchange Rate on Indonesia Sharia Stock Index (ISSI). This study uses secondary data in the form of a summary of monthly index reports obtained from financial websites. The population in this study is a time series data that amounts to 60 months and starts from January 2016 to December 2020 by determining samples using saturated samples. The data was analyzed using multiple linear regression analysis techniques. Hypothesis tested using F test and t test. The results showed that simultaneously (Test F) variable Dow Jones industrial Average (DJIA) (X1), World Oil Price (X2), Inflation Rate (X3), and Rupiah Exchange Rate (X4) had a significant effect on the Indonesia Sharia Stock Index (ISSI) on the Indonesia Stock Exchange (IDX). Partially (Test t) DJIA has a significant positive effect on ISSI. Rupiah exchange rate has a significant negative effect on ISSI. Meanwhile, The World Oil Price and Inflation Rate have an insignificant negative effect on ISSI.

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19

Sihombing,, Pardomuan, and Rizal ,. "PENGARUH INDEKS SAHAM GLOBAL DAN KONDISI MAKRO INDONESIA TERHADAP INDEKS HARGA SAHAM GABUNGAN BURSA EFEK INDONESIA." Media Ekonomi 22, no. 2 (August 4, 2014): 133. http://dx.doi.org/10.25105/me.v22i2.3171.

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The objective of this research is to examine the effect of global stock indices and marco economic condition of Indonesia to Jakarta Stock Exchange Composite Index (JCI). The global stock indices that had been analyzed in this research are Dow Jones Industrial Average (DJIA), Nikkei 225 (N225), Shanghai Stock Exchange Composite (SSE), Financial Times Stock Exchange 100 (FTSE 100), and Hang Seng Index (HSI). The macro economic indicator that had been analyzed in this research are exchange rate United States dollar to Indonesian rupiah, inflation and BI rate. This research was conducted using secondary data. Research periods are 10 years for 120 months since January 2008 until December 2012. This study was analyzed by using error correction model (ECM). By using this method, it can be analyzed the short and long term influence from the independent variables to the dependent variable with its analysis techniques to correct long term imbalances. The result shows that in short term, only DJIA, exchange rate and BI rate have significant effect on JCI. While in long term, DJIA, N225, SSE, HSI, and BI rate have significant effect on JCI. Adjusted R-square value of 0.444987 can illustrate that the dependent variable is explained by the independent variables for 44.499 percent, while the rest are influenced by the other variables.

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Smirnov, Valery, Denis Osipov, Vladimir Gurdzhiyan, Irina Soshko, Mikhail Alexandrov, and Vladimir Ivanov. "Analysis of the Russian finance connectivity." SHS Web of Conferences 106 (2021): 01014. http://dx.doi.org/10.1051/shsconf/202110601014.

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As a result of evaluation of growth rates of major commodity prices and Russian share quotes there is discovered dominating dynamics of shares of Rosneft and Urals oil futures. Assessment of dynamics of RTSI, IMOEX, S&P500, WTI futures, USD/RUB showed IMOEX breakdown from RTSI. RTSI remained with the WTI futures, while IMOEX joined S&P500 trend. As a result of neural network analysis of importance of global indices growth rates there is determined a condition of achievement of their maximum value – minimum growth rate of RTSI and maximum rate of FTSE100 growth. Cluster analysis of the global indices in terms of their growth rates revealed connectivity between RTSI, DJIA and US Dollar Index. Russian economy structure can’t ensure direct connectivity of RTSI and DJIA. RTSI is indirectly connected to DJIA via S&P500. The leading role in this connection belongs to US Dollar Index that largely determines the dynamics of USD/RUB and IMOEX. Cluster analysis in terms of major currencies exchange rates growth defined a USD and CNY currency basket that is acceptable for the Russian economy. Analysis of the Russian finance connectivity has sufficiently identified a basis and conditions of its existence in the context of the strengthening negative factors which bind and overburden the Russian economy with oil dependence.

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Sochi, Maria, and Steve Swidler. "A Test of Market Efficiency When Short Selling Is Prohibited: A Case of the Dhaka Stock Exchange." Journal of Risk and Financial Management 11, no. 4 (October 2, 2018): 59. http://dx.doi.org/10.3390/jrfm11040059.

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A ban on short selling exists on several exchanges, especially in emerging markets. In most cases, short selling has always been prohibited, thus making it difficult to examine the ban’s effect on price discovery. In this paper, we consider data from the Dhaka Stock Exchange (DSE) to test for a short selling ban on market efficiency. The analysis examines runs in daily stock returns and then forms a distribution of return clusters according to their duration. Using Monte Carlo simulation, we find that runs of longer duration appear more frequently in the DSE data than we would expect in efficient markets. We compare these results to stocks in the Dow Jones Industrial Average (DJIA). We find that the same runs tests accord with market efficiency for liquid and easily shorted DJIA stocks.

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Sankaran, Harikumar, Jayashree Harikumar, and Violeta Diaz. "Overreaction around DJIA Milestone Events: Evidence from an Intraday Analysis." GLOBAL BUSINESS & FINANCE REVIEW 19, no. 2 (December 31, 2014): 1–18. http://dx.doi.org/10.17549/gbfr.2014.19.2.01.

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Tang, Qi, Ruchen Shi, Tongmei Fan, Yidan Ma, and Jingyan Huang. "Prediction of Financial Time Series Based on LSTM Using Wavelet Transform and Singular Spectrum Analysis." Mathematical Problems in Engineering 2021 (June 8, 2021): 1–13. http://dx.doi.org/10.1155/2021/9942410.

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In order to further overcome the difficulties of the existing models in dealing with the nonstationary and nonlinear characteristics of high-frequency financial time series data, especially their weak generalization ability, this paper proposes an ensemble method based on data denoising methods, including the wavelet transform (WT) and singular spectrum analysis (SSA), and long-term short-term memory neural network (LSTM) to build a data prediction model. The financial time series is decomposed and reconstructed by WT and SSA to denoise. Under the condition of denoising, the smooth sequence with effective information is reconstructed. The smoothing sequence is introduced into LSTM and the predicted value is obtained. With the Dow Jones industrial average index (DJIA) as the research object, the closing price of the DJIA every five minutes is divided into short term (1 hour), medium term (3 hours), and long term (6 hours), respectively. Based on root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and absolute percentage error standard deviation (SDAPE), the experimental results show that in the short term, medium term, and long term, data denoising can greatly improve the stability of the prediction and can effectively improve the generalization ability of LSTM prediction model. As WT and SSA can extract useful information from the original sequence and avoid overfitting, the hybrid model can better grasp the sequence pattern of the closing price of the DJIA.

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Caldeira, João F., Rangan Gupta, and Hudson S. Torrent. "Forecasting U.S. Aggregate Stock Market Excess Return: Do Functional Data Analysis Add Economic Value?" Mathematics 8, no. 11 (November 16, 2020): 2042. http://dx.doi.org/10.3390/math8112042.

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This paper analyzes the forecast performance of historical S&P500 and Dow Jones Industrial Average (DJIA) excess returns while using nonparametric functional data analysis (NP-FDA). The empirical results show that the NP-FDA forecasting strategy outperforms not only the the prevailing-mean model, but also the traditional univariate predictive regressions with standard predictors used in the literature and, most cases, also combination approaches that use all predictors jointly. In addition, our results clearly have important implications for investors, from an asset allocation perspective, a mean-variance investor realizes substantial economic gains. Indeed, our results show that NP-FDA is the only one individual model that can overcome the historical average forecasts for excess returns in statistically and economically significant manners for both S&P500 and DJIA during the entire period, NBER recession, and expansions periods.

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Handayani, Wiwik, and Safitri Oktavia. "Effect of Rupiah Exchange Rate, GDP Growth, and Dow Jones Index on Composite Stock Price Index in Indonesia Stock Exchange." Journal of Accounting and Strategic Finance 1, no. 01 (December 30, 2018): 23–32. http://dx.doi.org/10.33005/jasf.v1i01.24.

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A capital market is a meeting place for stock sellers and buyers with the aim of getting maximum profits. To get these benefits, investors need information about the stock price index. Factors that influence the Stock Price Index are important information for investors. The composite stock price index (CSPI) is one of the main indicators that reflects the performance of the capital market whether it is experiencing an increase or is experiencing a decline. These factors include the rupiah exchange rate, GDP growth, and the Dow Jones index. This study aims to prove and analyze the effect of the rupiah exchange rate, GDP growth, and the Dow Jones index Average (DJIA) on the composite stock price index on the Indonesia stock exchange for the period 2012-2015. The population and sample of this study are forty-eight CSPI data from the Indonesia Stock Exchange. Data is collected by means of documentation and then analyzed. The data analysis technique used in this study is multiple linear regression analysis techniques. Based on the results of the analysis it is known that the rupiah exchange rate has no effect on the Composite Stock Price Index (CSPI). While GDP growth and the Dow Jones index Average (DJIA) have affected the Composite Stock Price Index (CSPI). For further research, it is considered necessary to review other factors that can influence the movement of the stock price index, for example, the company's fundamental factors such as profit, loss, financial ratios, and others. Keywords: Exchange Rate, GDP Growth, The Dow Jones (DJIA), Composite Stock Price Index (CSPI).

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Biage, Milton, and Pierre Joseph Nelcide. "Effects of asset frequency components on value-at-risk in emerging and developed markets." Brazilian Review of Econometrics 40, no. 1 (August 17, 2020): 145. http://dx.doi.org/10.12660/bre.v40n12020.77437.

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Value-at-Risk was estimated using the technique of wavelet decomposition with goal to analyze the frequency components' impacts on variances of daily stock returns, and on forecasts. Daily returns of twenty-one shares of the Ibovespa and daily returns of twenty-two shares of the DJIA were used. The model was applied to the reconstructed returns to model and establish the prediction of conditional variance, applying the rolling window technique. The Value-at-Risk was then estimated, and the results showed that the DJIA shares showed more efficient market behavior than those of Ibovespa. The differences in behavior induces to affirm that VaRs, used in the analysis of financial assets from different markets with different governance premises, should be estimated by series of returns reconstructed by aggregations of components of different frequencies. A set of back-testing was applied to confront the estimated , which demonstrated that the estimation of models are consistent.

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Hseu, Mei-Maun, Huimin Chung, and Erh-Yin Sun. "Price Discovery across the Stock Index Futures and the ETF Markets: Intra-Day Evidence from the S&P 500, Nasdaq-100 and DJIA Indices." Review of Pacific Basin Financial Markets and Policies 10, no. 02 (June 2007): 215–36. http://dx.doi.org/10.1142/s0219091507001045.

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This paper investigates the intra-day price dynamics of the S&P 500, Nasdaq-100 and DJIA indices for the periods both before and after the Nasdaq market crash which occurred between March 2000 and March 2001. We explore the relative price efficiencies of the three indices in the spot, futures, E-mini futures and ETF markets, and find that a cointegrating relationship existed between the three indices during the period after the crash. This would seem to imply that in the aftermath of the crash, the three indices shared common macroeconomic fundamentals. We find that where there is some disturbance in the equilibrium relationship between the indices, the market which adjusts to retain equilibrium is the Nasdaq-100 market. In the long run, the S&P 500 index leads the other index contracts, a finding which is consistent with the trading cost hypothesis. Nevertheless, the Nasdaq-100 index retains short-run price leadership over both the S&P 500 and DJIA indices.

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Nuraeni, Risky, and Jihad Lukis Panjawa. "Analisis pengaruh indeks saham asing terhadap indeks harga saham gabungan dengan pendekatan Error Correction Model." Journal of Economics Research and Policy Studies 1, no. 1 (April 29, 2021): 25–39. http://dx.doi.org/10.53088/jerps.v1i1.37.

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The Composite Stock Price Index (IHSG) is a composite index of many shares listed on the stock exchange and their movements show conditions that occur in the capital market. JCI is confident of macroeconomic factors and foreign exchange indexes. The purpose of this study was to analyze the effect of the Dow Jones Index, the Straits Time Index, the Hang Seng Index, the Nikkei 225 Index, and the FTSE 100 Index on the composite price index. The research method used is the Error Correction Model (ECM). In the short term, the DJIA and FTSE 100 variables have a positive effect on the JCI, the STI and Hang Seng variables have no significant on the JCI, while the Nikkei 225 has a negative effect on the JCI. In the long term, the DJIA and STI variables have a positive effect on the IHSG, the JSI and FTSE 100 variables have no effect on the IHSG, while the Nikkei 225 variable has a negative effect on the JCI.

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Mikhail, Ossama, and Josiah Baker. "Further Evidence on the Presence of Non-Linearity in the DJIA." International Advances in Economic Research 12, no. 4 (September 7, 2006): 552. http://dx.doi.org/10.1007/s11294-006-9048-9.

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Sihombing, Pardomuan, and Rizal ,. "PENGARUH INDEKS SAHAM GLOBAL DAN KONDISI MAKRO INDONESIA TERHADAP INDEKS HARGA SAHAM GABUNGAN BURSA EFEK INDONESIA." Media Ekonomi 22, no. 2 (July 10, 2018): 135. http://dx.doi.org/10.25105/me.v22i2.2966.

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The objective of this research is to examine the effect of global stock indices and marcoeconomic condition of Indonesia to Jakarta Stock Exchange Composite Index (JCI). The global stock indices that had been analyzed in this research are Dow Jones Industrial Average (DJIA), Nikkei 225 (N225), Shanghai Stock Exchange Composite (SSE), Financial Times Stock Exchange 100 (FTSE 100), and Hang Seng Index (HSI). The macro economic indicator that had been analyzed in this research are exchange rate United States dollar to Indonesian rupiah, inflation and BI rate. This research was conducted using secondary data. Research periods are 10 years for 120 months since January 2008 until December 2012. This study was analyzed by using error correction model (ECM). By using this method, it can be analyzed the short and long term influence from the independent variables to the dependent variable with its analysis techniques to correct long term imbalances. The result shows that in short term, only DJIA, exchange rate and BI rate have significant effect on JCI. While in long term, DJIA, N225, SSE, HSI, and BI rate have significant effect on JCI. Adjusted Rsquare value of 0.444987 can illustrate that the dependent variable is explained by the independent variables for 44.499 percent, while the rest are influenced by the other variables.

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SMIRNOV, Valerii V. "The content analysis of Russian finance." Finance and Credit 27, no. 3 (March 30, 2021): 585–610. http://dx.doi.org/10.24891/fc.27.3.585.

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Subject. The article focuses on the Russian finance. Objectives. I determine the basics and conditions needed for the Russian finance. Methods. The study is based on the systems approach and the method of statistical, neural network and cluster analysis. Results. Having evaluated growth rates of prices for basic commodities and quotations of the Russian stocks, I determined what underlies the Russian finance as the prevailing trend in Rosneft’s stocks and Urals oil futures. Observing the movement of RTSI, IMOEX, S&P500, WTI oil future, USD/RUB rate, I discovered the gap between IMOEX and RTSI. RTSI remains with the WTI oil futures trend, while IMOEX joined the trend in S&P500. Having analyzed the importance of growth rates of global indices, I understood what is required for their maximum, i.e. the lowest growth rates of RTSI and the highest FTSE100. Considering the global indices and their growth rates, the Russian finance will be viable if RTSI indices are associated with DJIA and US Dollar Index. Structurally, the Russian economy cannot ensure the direct association of RTSI and DJIA. RTSI gets associated with DJIA through S&P500. US Dollar Index is a leading components in this correlation, as it determined the dynamics of USD/RUB and IMOEX. As for the trend in the rate of principal currencies, the basket with USD and CNY seems to be acceptable for the financial regulator. Conclusions and Relevance. The content analysis reveals the threatening intensification of adverse factors that make the Russian economy dependent on oil production, and outlines what can be done to eliminate them. The findings constitute new knowledge and advance the competence of the financial market regulator to make administrative decisions concerning the allocation, reallocation of the public product value and a part of national wealth so as to maintain the Russian finance in terms of form and substance.

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Gürsakal, Necmi, Fırat Melih Yilmaz, and Erginbay Uğurlu:. "Finding opportunity windows in time series data using the sliding window technique: The case of stock exchanges." Econometrics 24, no. 3 (2020): 1–19. http://dx.doi.org/10.15611/eada.2020.3.01.

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Data have shapes, and human intelligence and perception have to classify the forms of data to understand and interpret them. This article uses a sliding window technique and the main aim is to answer two questions. Is there an opportunity window in time series of stock exchange index? The second question is how to find a way to use the opportunity window if there is one. The authors defined the term opportunity window as a window that is generated in the sliding window technique and can be used for forecasting. In analysis, the study determined the different frequencies and explained how to evaluate opportunity windows embedded using time series data for the S&P 500, the DJIA, and the Russell 2000 indices. As a result, for the S&P 500 the last days of the patterns 0111, 1100, 0011; for the DJIA the last days of the patterns 0101, 1001, 0011; and finally for the Russell 2000, the last days of the patterns 0100, 1001, 1100 are opportunity windows for prediction

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Tetik, Metin, and Ercan Ozen. "Overreaction Hypothesis and Reaction of Borsa Istanbul to Dow-Jones." Business and Economic Research 6, no. 2 (December 21, 2016): 412. http://dx.doi.org/10.5296/ber.v6i2.10353.

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The aim of this study is to investigate whether or not there are any over-reactions to the positive and negative events of the Borsa Istanbul 100 index (BIST-100) in relation to the Dow Jones Industrial index (DJIA). The daily stock indexes between January 2010 and June 2016 are used in this research. The research finding showed that BIST-100 reacts in the same way as DJIA up to 3.31% and the reaction decreases and was lost between 30 and 60 days against the positive changes. In case of adverse events the BIST 100 shows abnormal decline in protecting the efficient market hypothesis is valid for 30 days after the event, however, the decline is reversed until the 60’th days when all losses are compensated. This terminates the validity of the efficient market hypothesis. This study shows that the BIST 100 index does not comply with the efficient market hypothesis and demonstrate the validity of the overreaction hypothesis. Study results include stock investments, whose findings will have an impact on portfolio management decisions of investors.

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Martín Cervantes, Pedro Antonio, Salvador Cruz Rambaud, and María del Carmen Valls Martínez. "An Application of the SRA Copulas Approach to Price-Volume Research." Mathematics 8, no. 11 (October 26, 2020): 1864. http://dx.doi.org/10.3390/math8111864.

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The objective of this study was to apply the Sadegh, Ragno, and AghaKouchak (SRA) approach to the field of quantitative finance by analyzing, for the first time, the relationship between price and trading volume of the securities using four stock market indices: DJIA, FOOTSIE100, NIKKEI225, and IBEX35. This procedure is a completely new methodology in finance that consists of the application of a Bayesian framework and the development of a hybrid evolution algorithm of the Markov Chain Monte Carlo (MCMC) method to analyze a large number (26) of parametric copulas. With respect to the DJIA, the Joe’s copula is the one that most efficiently models its succinct dependence structures. One of the copulas included in the SRA approach, the Tawn’s copula, is jointly adjusted to the FOOTSIE100, NIKKEI225, and IBEX 35 indices to analyze the asymmetric relationship between price and trading volume. This adjustment can be considered almost perfect for the NIKKEI225, and a relatively different characterization for the IBEX35 seems to indicate the existence of endogenous patterns in the price and volume.

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Darrat, Ali F., Shafiqur Rahman, and Maosen Zhong. "Intraday trading volume and return volatility of the DJIA stocks: A note." Journal of Banking & Finance 27, no. 10 (October 2003): 2035–43. http://dx.doi.org/10.1016/s0378-4266(02)00321-7.

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Tse, Yiuman. "Price discovery and volatility spillovers in the DJIA index and futures markets." Journal of Futures Markets 19, no. 8 (December 1999): 911–30. http://dx.doi.org/10.1002/(sici)1096-9934(199912)19:8<911::aid-fut4>3.0.co;2-q.

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Emmert-Streib, Frank, and Matthias Dehmer. "Identifying critical financial networks of the DJIA: Toward a network-based index." Complexity 16, no. 1 (September 2010): 24–33. http://dx.doi.org/10.1002/cplx.20315.

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Machado, José A. Tenreiro. "Fractal and Entropy Analysis of the Dow Jones Index Using Multidimensional Scaling." Entropy 22, no. 10 (October 8, 2020): 1138. http://dx.doi.org/10.3390/e22101138.

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Financial time series have a fractal nature that poses challenges for their dynamical characterization. The Dow Jones Industrial Average (DJIA) is one of the most influential financial indices, and due to its importance, it is adopted as a test bed for this study. The paper explores an alternative strategy to the standard time analysis, by joining the multidimensional scaling (MDS) computational tool and the concepts of distance, entropy, fractal dimension, and fractional calculus. First, several distances are considered to measure the similarities between objects under study and to yield proper input information to the MDS. Then, the MDS constructs a representation based on the similarity of the objects, where time can be viewed as a parametric variable. The resulting plots show a complex structure that is further analyzed with the Shannon entropy and fractal dimension. In a final step, a deeper and more detailed assessment is achieved by associating the concepts of fractional calculus and entropy. Indeed, the fractional-order entropy highlights the results obtained by the other tools, namely that the DJIA fractal nature is visible at different time scales with a fractional order memory that permeates the time series.

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Artha, Algia, and R. A. Sista Paramita. "Pengaruh Makroekonomi dan Indeks Global terhadap Indeks Harga Saham Gabungan Selama Pandemi COVID-19 di Indonesia." Jurnal Ilmu Manajemen 9, no. 2 (June 30, 2021): 681. http://dx.doi.org/10.26740/jim.v9n2.p681-697.

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The COVID-19 pandemic has affected many sectors, one of which is the capital market. The Coronavirus has claimed lives and can shake the order of life of a country. From an economic point of view, almost all countries experience a recession, a reduction in economic activity, increased unemployment, and a decline in people's purchasing power. This research examines the effect of the BI interest rate, exchange rate, inflation, SSEC index, KLSE index, SET index, and DJIA index on the Composite Stock Price Index. The research population is daily data during the COVID-19 pandemic in Indonesia from March 2020 to November 2020. The sampling technique uses purposive sampling. The number of samples is 111 data. The data analysis method uses multiple linear regression with IBM SPSS 25 software tools. The results show that the rupiah exchange rate against the US dollar has a negative effect and the Kuala Lumpur Stock Exchange has a positive effect on the Composite Stock Price Index, while the BI interest rate, inflation, SSEC index, the SET index and the DJIA index have no impact on the Composite Stock Price Index. However, all independent variables simultaneously affect the Composite Stock Price Index.

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Prayoga, Nugraha Ibnu, and Khairunnisa Khairunnisa. "Pengaruh Inflasi, Bi Rate, Kurs Rupiah Dan Djia Terhadap IHSG Tahun 2014-2017." SAR (Soedirman Accounting Review) : Journal of Accounting and Business 4, no. 1 (July 26, 2019): 40. http://dx.doi.org/10.20884/1.sar.2019.4.1.1364.

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Penelitian ini bertujuan untuk mengetahui bagaimana pengaruh Inflasi, BI Rate, Kurs Rupiah dan Dow Jones Industrial Average terhadap Indeks Harga Saham Gabungan yang terdaftar di BEI pada tahun 2014-2017. Populasi dalam penelitian ini adalah data panel Inflasi, BI Rate, Kurs Rupiah dan Dow Jones Industrial Average pada tahun 2014-2017. Metode analisis data dalam penelitian ini adalah regresi linear berganda dengan menggunakan Software SPSS 25. Berdasarkan hasil pengolahan data menunjukkan bahwa, Kurs Rupiah memiliki pengaruh yang positif dan signifikan dan Dow Jones Industrial Average memiliki pengaruh yang positif dan signifikan. Sedangkan Inflasi dan BI Rate tidak berpengaruh terhadap IHSG.

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Zumbach, Gilles, and Christopher Finger. "A Historical Perspective on Market Risks Using the DJIA Index Over One Century." Wilmott Journal 2, no. 4 (August 23, 2010): 193–206. http://dx.doi.org/10.1002/wilj.36.

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Song, Xiaohua, Dongxiao Niu, and Yulin Zhang. "The Chaotic Attractor Analysis of DJIA Based on Manifold Embedding and Laplacian Eigenmaps." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/8087178.

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By using the techniques of Manifold Embedding and Laplacian Eigenmaps, a novel strategy has been proposed in this paper to detect the chaos of Dow Jones Industrial Average. Firstly, the chaotic attractor of financial time series is assumed to lie on a low-dimensional manifold that is embedded into a high-dimensional Euclidean space. Then, an improved phase space reconstruction method and a nonlinear dimensionality reduction method are introduced to help reveal the structure of the chaotic attractor. Next, the empirical study on the financial time series of Dow Jones Industrial Average shows that there exists an attractor which lies on a manifold constructed by the time sequence of Moving average convergence divergence; finally, Determinism Test, Poincaré section, and translation analysis are used as test approaches to prove both whether it is a chaos and how it works.

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TUNCAY, ÇAĞLAR. "STOCK MECHANICS: A GENERAL THEORY AND METHOD OF ENERGY CONSERVATION WITH APPLICATIONS ON DJIA." International Journal of Modern Physics C 17, no. 11 (November 2006): 1679–90. http://dx.doi.org/10.1142/s0129183106009138.

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A new method, based on the original theory of conservation of sum of kinetic and potential energy defined for prices is proposed and applied on the Dow Jones Industrials Average (DJIA). The general trends averaged over months or years gave a roughly conserved total energy, with three different potential energies, i.e., positive definite quadratic, negative definite quadratic and linear potential energy for exponential rises (and falls), sinusoidal oscillations and parabolic trajectories, respectively. Corresponding expressions for force (impact) are also given.

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Mollick, Andre. "VIX and the variance of Dow Jones industrial average stocks." Managerial Finance 41, no. 3 (March 9, 2015): 226–43. http://dx.doi.org/10.1108/mf-07-2013-0197.

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Purpose – The purpose of this paper is to examine what happens to the variance of individual stocks forming the Dow Jones Industrial Average (DJIA) allowing for aggregate uncertainty measured by VIX, the “fear gauge index” of US options contracts. In examining each individual stock belonging to DJIA in 2011, the authors reconsider aggregate market uncertainty (VIX) as the mixing variable. In contrast to studies on the effects of VIX on the aggregate equity market, the data set used in this paper allow a further look at the proposition that market aggregate uncertainty should have varying impact on individual stock variance. Design/methodology/approach – GARCH-M models estimate individual stock returns belonging to the DJIA in 2011 on its lags and on the ARCH-M term in the mean equation linking stock returns to the variance equation. The longest time span has 5,738 observations for most stocks under daily frequency from January 3, 1990 to December 30, 2011. The authors use one lag for the VIX2 term to address simultaneity problems in the variance equation. In order to allow for interactions between volatility and business cycles, the authors include a dummy variable for the three recessions identified by the NBER over the period. Findings – Adding the “fear gauge” VIX index and a dummy variable for recessions to the variance equation in GARCH-M models, the VIX coefficient always increases variance and the recession dummy has mixed effects. Overall, VIX acts as expected as mixing variable. Supporting the mixture of distribution hypothesis, the impact of VIX is always positive (1.039 on market variance) and GARCH effects vanish completely for the index and almost as much for 24 stocks. Research limitations/implications – In theory, the effects of VIX on stock variance should be positive and statistically significant, together with reductions of GARCH persistence. The authors find this to be the case for the aggregate stock market and for 24 out of its 29 DJIA stocks. The authors leave for further work extensions to estimating the variance equation for companies very exposed to idiosyncratic changes, such as oil price fluctuations or stock buybacks. The implication of this research for the academic or financial community relies on the estimation of VIX effects on individual stock variance, controlling for business cycles. Originality/value – Due to its benchmark in equities, stocks in the Dow Jones Industrials make it a very interesting case study. This paper reconsiders the aggregate uncertainty hypothesis for two main reasons. First, the financial press and traders keep a very close track on the daily evolution of VIX. Second, recent research emphasizes the formal predictive power of VIX in US stock markets. For the variance equation, existing works report positive values for the VIX-coefficient on the S&P 500 index but they have not examined individual stocks as the authors do in this paper.

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داغر, محمود محمد, and عباس كريم صدام. "قياس وتحليل العلاقة بين تقلبات مؤشرات اسواق المال الأمريكية وتقلبات أسعار النفط الخام." Journal of Economics and Administrative Sciences 24, no. 104 (October 22, 2018): 210. http://dx.doi.org/10.33095/jeas.v24i104.81.

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المستخلص تمارس تقلبات الاسواق المالية والسوق النفطية دوراً كبيراً في التأثير على النشاط الاقتصادي الكلي، فضلا عن الترابط الجوهري بين السوقين وحساسية أحدهما الشديدة للتغيرات الحاصلة في الاخر والتي تسبب انتقال تلك التقلبات بسرعة كبيرة الى القطاعات الاقتصادية الاخرى نتيجة ارتباط تلك القطاعات بأسواق المال واعتمادها الكبير على السوق النفطية، وقد سعت الدراسة لتحليل علاقة تقلبات مؤشرات السوق الأمريكي الرئيسة متمثلة بمؤشر DJIA, S&P500، وذلك لحجم وشمولية مؤشري السوق المالي واختلاف احجامهما، اذ انهما يلخصان أداء السوق الأمريكي الذي يعد الاقتصاد الأكبر عالميا، فضلا عن اختلافهما في الية الاحتساب، ومؤشرات السوق النفطي متمثلة بالخامات المرجعية الرئيسة الثلاثة، غرب تكساس، برنت وخام دبي، اعتمادا على البيانات الشهرية للمدة 1/1990-12/2016، وقد تبين ان البيانات متكاملة عند الدرجة الأولى لتقرر اعتماد منهجية جوهانسون جسليوس لتكشف تكامل المتغيرات، سببية كرانجر، فضلا عن نموذج متجه تصحيح الخطأ - VECM لإجراء القياس الاقتصادي. بيّنت نتائج القياس تأثير تقلبات القطاع الحقيقي على تقلبات السوق النفطية في الاجل الطويل من خلال معنوية معامل تصحيح الخطأ بمؤشر DJIA على مؤشرات غرب تكساس، برنت وخام دبي، فيما اقتصر تأثير تقلبات مؤشرات السوق النفطي على الاجل القصير فقط وعدم ممارسته تأثيرا طويل الاجل على تقلبات القطاع الحقيقي، كما كشفت نتائج نموذج متجه تصحيح الخطأ عن وجود تأثير متبادل لتقلبات مؤشر S&P500 ومؤشرات السوق النفطي في الاجل الطويل، بينما لم يظهر تأثيرا متبادلا لتقلبات قصيرة الاجل بل اقتصر على اتجاهه من السوق الحقيقي الى النفطي.

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Earl, John H., and David S. North. "The Patients' Bill of Rights and post-retirement benefits: The effect on the DJIA." Pensions: An International Journal 7, no. 3 (April 2002): 211–27. http://dx.doi.org/10.1057/palgrave.pm.5940197.

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Boudt, Kris, and Mikael Petitjean. "Intraday liquidity dynamics and news releases around price jumps: Evidence from the DJIA stocks." Journal of Financial Markets 17 (January 2014): 121–49. http://dx.doi.org/10.1016/j.finmar.2013.05.004.

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Ulu, Yasemin. "Volatility Distribution of the DJSTOXXE50 Index." Applied Economics and Finance 7, no. 6 (October 28, 2020): 101. http://dx.doi.org/10.11114/aef.v7i6.5065.

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In this paper using data from 1995-2005 on 5-minute intraday returns, we construct a model free estimate of the daily realized volatility for the DJSTOXXE50 index. We compute the unconditional volatility distribution of the DJSTOXXE50 index by a nonparametric kernel estimation method. Our results indicate that the unconditional volatility distribution of the DJSTOXXE50 returns are leptokurtic and highly skewed to the right. The logarithmic standard deviations seem to be approximately Gaussian. Our results are inline with previous research for individual DJIA equity return volatility and for Japanese index, Nikkei 225

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Lu, Tsung-Hsun, and Yung-Ming Shiu. "Can 1-day candlestick patterns be profitable on the 30 component stocks of the DJIA?" Applied Economics 48, no. 35 (January 22, 2016): 3345–54. http://dx.doi.org/10.1080/00036846.2015.1137553.

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Dewanti, Dhea, Suparti Suparti, and Alan Prahutama. "PEMODELAN INDEKS HARGA SAHAM GABUNGAN (IHSG) DAN JAKARTA ISLAMIC INDEX (JII) MENGGUNAKAN REGRESI BIRESPON SPLINE TRUNCATED BERBASIS GUI R." Jurnal Statistika Universitas Muhammadiyah Semarang 8, no. 2 (November 30, 2020): 134. http://dx.doi.org/10.26714/jsunimus.8.2.2020.134-143.

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The capital market is one of the economic drivers and representations for assessing the condition of companies in a country. Indonesia Stock Exchange (IDX) as one of the institutions in the capital market has 24 types of indexes that can be used as main indicators that reflect the performance of capital market, two of them are the Composite Stock Price Index (CSPI) and the Jakarta Islamic Index (JII). CSPI and JII movements are influenced by several factors, both from domestic and from foreign, such as inflation and the Dow Jones Industrial Average (DJIA). Modeling of CSPI and JII in this study was carried out using biresponses spline truncated nonparametric regression methods using Graphical User Interface (GUI) R with the intention of facilitating the analysis process. This method is used because there is a correlation between CSPI and JII and there is no specific relationship pattern between the response variable (CSPI and JII) and the predictor variable (inflation and DJIA). The best biresponses spline truncated model is determined by the order, number and location of the knots seen based on minimum GCV criteria. By using monthly data from January 2016 to December 2019, the best biresponses spline truncated model is obtained when the model for CSPI is in order 2 and the model for JII is in order 3 with 2 knots for each predictor variable. This model has a coefficient of determination of 85,54437% and MAPE of 2,65595% so that it has a very good ability in forecasting.

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Journal articles on the topic 'DJIA'

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