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Journal articles on the topic 'Trading volume'

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Author: Grafiati

Published: 4 June 2021

Last updated: 21 February 2023

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1

Bornholt, Graham, Paul Dou, and Mirela Malin. "Trading Volume and Momentum: The International Evidence." Multinational Finance Journal 19, no. 4 (December 1, 2015): 267–313. http://dx.doi.org/10.17578/19-4-2.

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2

Guasoni, Paolo, and Marko Weber. "DYNAMIC TRADING VOLUME." Mathematical Finance 27, no. 2 (June 19, 2015): 313–49. http://dx.doi.org/10.1111/mafi.12099.

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3

McSherry, Bernard, and Berry K. Wilson. "Deflation and Reflation: The Pre-WW I Impact on NYSE Trading Volumes and Seat Prices." Journal of Economics and Public Finance 2, no. 1 (March 29, 2016): 106. http://dx.doi.org/10.22158/jepf.v2n1p106.

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<em>The study analyzes a unique time period of sustained deflation from 1867 to 1896, followed by sustained reflation after 1896. We use these periods to test two hypotheses concerning the impact on NYSE trading volumes and seat prices. The first is the “liquidity-trading” hypothesis, which hypothesizes that liquidity trading, a component of total trading volume, is positively correlated with interest rates. The second is the price-volume relationship, which hypothesizes a positive relationship between stock prices returns and changes in trading volume. These hypotheses suggest that NYSE trading volume should fall (rise) with falling (rising) stock prices and interest rates. We find strong support for both hypotheses, and additionally show that the impact of stock market prices on trading volumes is highly asymmetrical. As well, the study argues and finds evidence that the high level of systematic risk found in the pricing of NYSE seats is another reflection of the price-volume relationship. Therefore, the study finds strong evidence of a link between deflation, reflation and market liquidity as reflected in trading volumes and the pricing of NYSE seats.</em>

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4

Mudalige, Priyantha, Petko S. Kalev, and Huu Nhan Duong. "Individual and institutional trading volume around firm-specific announcements." International Journal of Managerial Finance 12, no. 4 (August 1, 2016): 422–44. http://dx.doi.org/10.1108/ijmf-01-2016-0007.

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Purpose – The purpose of this paper is to investigate the immediate impact of firm-specific announcements on the trading volume of individual and institutional investors on the Australian Securities Exchange (ASX), during a period when the market becomes fragmented. Design/methodology/approach – This study uses intraday trading volume data in five-minute intervals prior to and after firm-specific announcements to measure individual and institutional abnormal volume. There are 70 such intervals per trading day and 254 trading days in the sample period. The first 10 minutes of trading (from 10.00 to 10.10 a.m.) is excluded to avoid the effect of opening auction and to ensure consistency in the “starting time” for all stocks. The volume transacted during five-minute intervals is aggregated and attributed to individual or institutional investors using Broker IDs. Findings – Institutional investors exhibit abnormal trading volume before and after announcements. However, individual investors indicate abnormal trading volume only after announcements. Consistent with outcomes expected from a dividend washing strategy, abnormal trading volume around dividend announcements is statistically insignificant. Both individual and institutional investors’ buy volumes are higher than sell volumes before and after scheduled and unscheduled announcements. Research limitations/implications – The study is Australian focused, but the results are applicable to other limit order book markets of similar design. Practical implications – The results add to the understanding of individual and institutional investors’ trading behaviour around firm-specific announcements in a securities market with continuous disclosure. Social implications – The results add to the understanding of individual and institutional investors’ trading behaviour around firm-specific announcements in a securities market with continuous disclosure. Originality/value – These results will help regulators to design markets that are less predatory on individual investors.

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5

de Beer, Johan. "The price and volume effect of initial single stock futures trading." Corporate Ownership and Control 7, no. 2 (2009): 367–86. http://dx.doi.org/10.22495/cocv7i2c3p4.

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The introduction of single stock futures to a market allows for a per company impact-assessment of futures trading activity. Thirty-eight South African companies were evaluated in terms of a possible price and volume effect due to the initial trading of their respective single stock futures contracts. An event study revealed that SSF trading had little impact on the underlying share prices while a normalised volume comparison pre to post SSF trading showed a general increase in spot market trading volumes.

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6

Pak, Dohyun, and Sun-Yong Choi. "Economic Policy Uncertainty and Sectoral Trading Volume in the U.S. Stock Market: Evidence from the COVID-19 Crisis." Complexity 2022 (April 25, 2022): 1–15. http://dx.doi.org/10.1155/2022/2248731.

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We empirically analyze the impact of economic uncertainty due to the COVID-19 pandemic on the trading volume of each sector in the S&P 500 index. Wavelet coherence analysis is carried out using economic policy uncertainty data and the trading volume of each sector in the S&P 500 index from July 2004 to September 2020. Furthermore, we apply multifractal detrended fluctuation (MF-DFA) analysis to the trading volume series of all sectors. The wavelet coherence analysis shows that the COVID-19 pandemic has substantially influenced trading volume in all sectors. However, the impact of the pandemic is different from that during the global financial crisis in some sectors, such as information technology, consumer discretionary, and communication services. Because of the lockdown taken to suppress COVID-19, increased remote working and remote learning are the main reasons for these results. Additionally, according to the MF-DFA analysis, the trading volume of all the sectors has clear multifractal characteristics, and they are all nonpersistent. Specifically, trading volumes of the real estate and materials sector are highly correlated, whereas the trading volumes of industry and information technology sectors are comparatively less correlated.

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7

Mpofu, Raphael Tabani. "The relationship between trading volume and stock returns in the JSE securities exchange in South Africa." Corporate Ownership and Control 9, no. 4-2 (2012): 199–207. http://dx.doi.org/10.22495/cocv9i4c2art1.

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This study examines the relationship between trading volume and stock returns in the JSE Securities Exchange in South Africa. The study looked at the price and trading returns of the FTSE/JSE index from July 22, 1988 till June 11, 2012. The study revealed that stock returns are positively related to the contemporary change in trading volume. Further, it was found that past returns were not affected significantly by changes in trading volumes. The results present a significant relationship between trading volume and the absolute value of price changes. Autoregressive tests were used to explore whether return causes volume or volume causes return. The results suggest that volume is influenced by a lagged returns effect for the FTSE/JSE index. Therefore, return seems to contribute some information to investors when they make investment decisions.

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8

Kim, Taejin. "Trust and trading volume." Economics Letters 207 (October 2021): 110003. http://dx.doi.org/10.1016/j.econlet.2021.110003.

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9

Glaser, Markus, and Martin Weber. "Overconfidence and trading volume." Geneva Risk and Insurance Review 32, no. 1 (June 2007): 1–36. http://dx.doi.org/10.1007/s10713-007-0003-3.

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10

Dewi, Catur Kumala. "JKSE AND TRADING ACTIVITIES BEFORE AFTER COVID-19 OUTBREAK." Research Journal of Accounting and Business Management 4, no. 1 (June 6, 2020): 1. http://dx.doi.org/10.31293/rjabm.v4i1.4671.

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The Covid-19 outbreak gave a negative implication not only on the Indonesian stock exchange but throughout the world until WHO declared Covid-19 a pandemic. Stock market movements are formed by stock market participants, including issuers, brokers and investors. IHSG continues to fluctuate sharply and tends to decrease in the beginning of January 2020 to April 2020. Different things happen for trading volumes that have increased. Trading volume is a combination of sell and buy during the trading session. Before and after Covid-19, IHSG gives an illustration that trading volume is increasingly fluctuating sharply with an upward trend above trading volume before Covid-19. Pandemic provides an overview of IHSG volatility and trading volumes that experience sharp fluctuations with a downward trend. Therefore, reserves are better if diversification is carried out in real assets other than financial assets.

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11

Syamni, Ghazali, Aiyub ,, Juilimursyida Ganto, and Azhar ,. "PENJELASAN POLA VOLUME PERDAGANGAN TRADER DENGAN DATA TRANSAKSI ORDER SAHAM DI BURSA EFEK INDONESIA." Media Riset Akuntansi, Auditing dan Informasi 9, no. 2 (August 24, 2009): 21. http://dx.doi.org/10.25105/mraai.v9i2.726.

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<p class="Style1"><strong><em>The objectives of this research is to explain pattern of behavior of trading </em></strong><strong><em>volume intraday investor inform and investor uninformed, and analysis </em></strong><strong><em>contribution of the both investors in explaining pattern behavior of investors </em></strong><strong><em>trading volume in Indonesia Stock Exchange. Regression analysis result </em></strong><strong><em>indicates that investor or trader informed is more contributionly in explaining </em></strong><strong><em>trading volume pattern in all time intervals, but not all investors or traders </em></strong><strong><em>uninformed contributions in all time intervals. Only order informed is more </em></strong><strong><em>can explain trading volume pattern compared with order uninformed. </em></strong><strong><em>Regression result finds that order status match have to share is determine </em></strong><strong><em>trading volume pattern intraday. The role of more determined by INFBM and </em></strong><strong><em>INFSM compared with UNFBM and UNFSM. While order status amend, open </em></strong><strong><em>and withdraw is less have casting for determining trading volume pattern intraday. Some possibility of this development of researchs in the future, </em></strong><strong><em>between the are test the relation of behavior of investors at trading volumes by </em></strong><strong><em>dividing investor inform with block tradings. This division anticipated to give </em></strong><strong><em>different response at trading volume pattern. usage of stock transaction data </em></strong><strong><em>intraday before applying ofpre-opening in Indonesia Stock Exchange.</em></strong></p><p class="Style1"><strong><em>Keywords: trading volume, investor behavior,</em></strong></p>

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12

Kambeu, Edson. "Trading Volume as a Predictor of Market Movement." International Journal of Finance & Banking Studies (2147-4486) 8, no. 2 (July 20, 2019): 57–69. http://dx.doi.org/10.20525/ijfbs.v8i2.177.

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A logistic regression model is has also become a popular model because of its ability to predict, classify and draw relationships between a dichotomous dependent variable and dependent variables. On the other hand, the R programming language has become a popular language for building and implementing predictive analytics models. In this paper, we apply a logistic regression model in the R environment in order to examine whether daily trading volume at the Botswana Stock Exchange influence daily stock market movement. Specifically, we use a logistic regression model to find the relationship between daily stock movement and the trading volumes experienced in the recent five previous trading days. Our results show that only the trading volume for the third previous day influence current stock market index movement. Overall, trading volumes of the past five days were found not have an impact on today’s stock market movement. The results can be used as a basis for building a predictive model that utilizes trading as a predictor of stock market movement.

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13

Awan, Adil, and Syed M. Amir Shah. "The Price and Volume Effect of Single-Stock Futures Trading on the Pakistani stock market." Lahore Journal of Business 2, no. 2 (March 1, 2014): 1–32. http://dx.doi.org/10.35536/ljb.2014.v2.i2.a1.

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The advent of single-stock futures (SSFs) provides an opportunity to investigate the company-wide impact of futures trading rather than the market-wide response captured through index futures contracts. This study analyzes the price and volume effect of SSFs on the underlying spot market based on a sample of 26 Pakistani firms. The dataset used includes one-year pre- and post-event data on closing prices and trading volumes. We conduct an event study in which the abnormal returns of individual companies and average abnormal returns reveal that futures trading has very little impact on the underlying spot returns. The cumulative abnormal returns show that statistically significant positive abnormal returns are experienced after SSF trading but with negative returns in the pre-event period. We compare pre- and post-event average normalized volumes using the t-test and dummy variable regression; the trend coefficients show a general decrease in trading volume. Consequently, there is an increase in returns and decrease in trading volume post-SSF trading in the Pakistani market.

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14

Chang, Yung-Ho, Chia-Ching Jong, and Sin-Chong Wang. "Size, trading volume, and the profitability of technical trading." International Journal of Managerial Finance 13, no. 4 (August 7, 2017): 475–94. http://dx.doi.org/10.1108/ijmf-09-2016-0179.

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Purpose The purpose of this paper is to evaluate the profitability of technical trading relative to buy-and-hold (BH) strategy at firm level, controlling for firm size and trading volume. Design/methodology/approach This paper applies variable-length moving averages (VMAs) thoroughly to each and every stock listed on Taiwan Stock Exchange (TWSE) and computes the excess returns of technical trading relative to BH strategy. The samples are further grouped by firm size and trading volume. Furthermore, possible data snooping bias is investigated by employing Hansen’s (2005) Superior Predictive Ability tests. Findings The result shows that VMAs outperform the BH strategy. The profitability of VMAs, remarkably, is positively associated with size and trading volume. After correcting for data snooping bias, VMAs with longer moving averages outperform VMAs with shorter moving averages. The evidence suggests that size and volume information is accountable for trend projection. Originality/value Unlike past studies simply applying technical trading rules to market indices, portfolios, or selected stocks, this paper evaluates the profitability of technical trading by applying VMAs comprehensively to each and every individual stock listed on TWSE controlling for the effect of firm size and trading volume, providing more practical insights for trading individual stocks.

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15

Abba Abdullahi, Saada, Reza Kouhy, and Zahid Muhammad. "Trading volume and return relationship in the crude oil futures markets." Studies in Economics and Finance 31, no. 4 (September 30, 2014): 426–38. http://dx.doi.org/10.1108/sef-08-2012-0092.

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Purpose – The purpose of this paper is to examine the relationship between trading volume and returns in the West Texas Intermediate (WTI) and Brent crude oil futures markets. In so doing, the paper addresses two important issues. First, whether there is a positive relationship between returns and trading volume in the crude oil futures markets. Second, whether information regarding trading volume contributes to forecasting the magnitude of return in the markets, an important issue because the ability of trading volume to predict returns imply market inefficiency. Design/methodology/approach – The paper used daily closing futures price and their corresponding trading volumes for WTI and Brent crude oil markets during the sample period January 2008 to May 2011. Both the log volume and the unexpected component of the detrended volume are used in the analysis in other to have robust alternative conclusion. The generalized method of moments (GMM) approach is used to examine the contemporaneous relationship between returns and trading volume while the Granger causality approach, impulse response and variance decomposition analysis are used to investigate the ability of trading volume to predict returns in the oil futures markets. Findings – The results reject the postulation of a positive relationship between trading volume and returns, suggesting that trading volume and returns are not driven by the same information flow which contradicts the mixture of distribution hypothesis in all markets. The results also show that neither trading volume nor returns have the power to predict the other and therefore contradicting the sequential arrival hypothesis and noise trader model in all markets. Finally, the findings support the weak form efficient market hypothesis in the crude oil futures markets. Originality/value – The findings has important implications to market regulators because daily price movement and trading volume do not respond to the same information flow and therefore the measures that control price volatility should not focused more on volume; otherwise they may not provide fruitful outcomes. Additionally, traders and investors who participate in oil futures should not base their decisions on past trading volume because it will lead to profit loss. The results also have implications for market efficiency as past information cannot assist speculators to forecast returns in all the oil markets. Finally, investors can benefit from portfolio diversification across the two markets.

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16

Go, You-How, and Wee-Yeap Lau. "Does Trading Volume explain the Information Flow of Crude Palm Oil Futures Returns?" Review of Finance and Banking 12, no. 2 (December 31, 2020): 115–36. http://dx.doi.org/10.24818/rfb.20.12.02.02.

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This study examines the role of trading volume in the crude palm oil (CPO)futures market as a proxy for information áow from the perspective of the mixture-of-distributions hypothesis (MDH). Using the data from January 2000 to April 2017, a sym-metric GARCH model has been estimated, in which the residuals follow alternatively thenormal Student-t and generalised error distribution. An alternative augmented model thatconsists of trading volume as an exogenous variable is estimated with the same error dis-tributions. Our results suggest several conclusions: First, the trading volume could not actas a true proxy for information áow. This indicates that volume of futures trading containsrelatively less price-sensitive information. Secondly, the inclusion of trading volume into theconditional variance equation with Student-t distributed errors is important for modellingpurposes when the returns are leptokurtic and positively skewed. Hence, it can be concludedthat the use of return and trading volume will enhance the current information set usedby practitioners and analysts in pricing the CPO futures contract when there exists a highdegree of leptokurtosis in the returns. This is the Örst study that validates the MDH in thecontext of the CPO futures market

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17

Kabir, M. R. "Share price and trading volume behaviour around trading suspensions." Maandblad Voor Accountancy en Bedrijfseconomie 66, no. 1/2 (January 1, 1992): 49–56. http://dx.doi.org/10.5117/mab.66.13982.

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18

Qadan, Mahmoud, and David Y. Aharon. "The length of the trading day and trading volume." Eurasian Business Review 9, no. 2 (January 29, 2019): 137–56. http://dx.doi.org/10.1007/s40821-019-00119-8.

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19

Gehde-Trapp, Monika, Philipp Schuster, and Marliese Uhrig-Homburg. "The Term Structure of Bond Liquidity." Journal of Financial and Quantitative Analysis 53, no. 5 (September 6, 2018): 2161–97. http://dx.doi.org/10.1017/s0022109018000364.

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We analyze the impact of market frictions on the trading volume and liquidity premia of finite-maturity assets when investors differ in their trading needs. Our equilibrium model generates a clientele effect (frequently trading investors hold only short-term assets) and predicts i) a hump-shaped relation between trading volume and maturity, ii) lower trading volumes of older compared with younger assets, iii) an increasing liquidity term structure from ask prices, iv) a decreasing or U-shaped liquidity term structure from bid prices, and v) spillovers of liquidity from short-term to long-term maturities. Empirical tests for U.S. corporate bonds support our theoretical predictions.

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20

Halling, Michael, Pamela C. Moulton, and Marios Panayides. "Volume Dynamics and Multimarket Trading." Journal of Financial and Quantitative Analysis 48, no. 2 (March 1, 2013): 489–518. http://dx.doi.org/10.1017/s0022109013000094.

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AbstractThe trading of shares of the same firm in multiple markets has become common over the last 30 years, but there is little empirical evidence on the extent to which investors actively exploit multimarket environments. We introduce a volume-based measure of multimarket trading to address this question. Analyzing a large set of cross-listed firms, we find higher multimarket trading among markets with similar designs and strong enforcement of insider trading laws and for firms with higher institutional ownership. These findings are important for firms evaluating the benefits of cross listing and for markets competing for order flow.

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21

Chong, Terence Tai Leung, Yueer Wu, and Jue Su. "The Unusual Trading Volume and Earnings Surprises in China’s Market." Journal of Risk and Financial Management 13, no. 10 (October 16, 2020): 244. http://dx.doi.org/10.3390/jrfm13100244.

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This study examines the empirical relationship between unusual trading volume and earnings surprises in China’s A-share market. We provide evidence that an unusually low trading volume can signify negative information about firm fundamentals. Moreover, unusual trading volumes could predict abnormal returns close to the earnings announcement date. The degree of, and changes in, divergence of opinion could explain this result. Our study provides an insight into China’s market, where short sales are strictly forbidden. We report a strong relationship that is quite different from that described in most studies on the United States market.

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22

Adjei, Frederick, and Mavis Adjei. "Trading Volume and Cryptocurrency Returns." Journal of Finance and Accounting 10, no. 1 (August 16, 2022): 23–27. http://dx.doi.org/10.12691/jfa-10-1-4.

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23

Rathnam, John P. "Price Momentum and Trading Volume." CFA Digest 31, no. 2 (May 2001): 18–19. http://dx.doi.org/10.2469/dig.v31.n2.858.

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24

Pagano, Marco. "Trading Volume and Asset Liquidity." Quarterly Journal of Economics 104, no. 2 (May 1989): 255. http://dx.doi.org/10.2307/2937847.

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25

Bamber, Linda Smith, Orie E. Barron, and Thomas L. Stober. "Differential Interpretations and Trading Volume." Journal of Financial and Quantitative Analysis 34, no. 3 (September 1999): 369. http://dx.doi.org/10.2307/2676264.

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26

Lee, Charles M. C., and Bhaskaran Swaminathan. "Price Momentum and Trading Volume." Journal of Finance 55, no. 5 (October 2000): 2017–69. http://dx.doi.org/10.1111/0022-1082.00280.

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27

Brown, Jeffrey H., Douglas K. Crocker, and Stephen R. Foerster. "Trading Volume and Stock Investments." Financial Analysts Journal 65, no. 2 (March 2009): 67–84. http://dx.doi.org/10.2469/faj.v65.n2.4.

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28

Garcia-Feijoo, Luis. "Investor Overconfidence and Trading Volume." CFA Digest 37, no. 2 (May 2007): 54–55. http://dx.doi.org/10.2469/dig.v37.n2.4601.

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29

Banks, Doyle W. "INFORMATION UNCERTAINTY AND TRADING VOLUME." Financial Review 20, no. 1 (February 1985): 83–94. http://dx.doi.org/10.1111/j.1540-6288.1985.tb00166.x.

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30

KARPOFF, JONATHAN M. "A Theory of Trading Volume." Journal of Finance 41, no. 5 (December 1986): 1069–87. http://dx.doi.org/10.1111/j.1540-6261.1986.tb02531.x.

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31

Yin, Yugang, and Yahui Liu. "Information of Unusual Trading Volume." Emerging Markets Finance and Trade 54, no. 11 (July 6, 2018): 2409–32. http://dx.doi.org/10.1080/1540496x.2017.1399355.

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32

Malinova, Katya, and Andreas Park. "Trading Volume in Dealer Markets." Journal of Financial and Quantitative Analysis 45, no. 6 (September 21, 2010): 1447–84. http://dx.doi.org/10.1017/s002210901000061x.

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AbstractWe develop a financial market trading model in the tradition of Glosten and Milgrom (1985) that allows us to incorporate nontrivial volume. We observe that in this model price volatility is positively related to the trading volume and to the absolute value of the net order flow (i.e., the order imbalance). Moreover, higher volume leads to higher order imbalances. These findings are consistent with well-established empirical findings. Our model further predicts that higher trader participation and systematic improvements in the quality of traders’ information lead to higher volume, larger order imbalances, lower market depth, shorter duration, and higher price volatility.

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33

Statman, Meir, Steven Thorley, and Keith Vorkink. "Investor Overconfidence and Trading Volume." Review of Financial Studies 19, no. 4 (2006): 1531–65. http://dx.doi.org/10.1093/rfs/hhj032.

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34

Anderson, Anne-Marie, and Edward A. Dyl. "MARKET STRUCTURE AND TRADING VOLUME." Journal of Financial Research 28, no. 1 (March 2005): 115–31. http://dx.doi.org/10.1111/j.1475-6803.2005.00117.x.

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35

Dupont, Dominique. "Extracting Information from Trading Volume." Finance and Economics Discussion Series 1997, no. 20 (1997): 1–38. http://dx.doi.org/10.17016/feds.1997.20.

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36

Yoo, Shiyong. "Volatility and Trading Volumes of Trader Types in KOSPI200 Index, Futures, and Options Markets." Journal of Derivatives and Quantitative Studies 22, no. 1 (February 28, 2014): 91–115. http://dx.doi.org/10.1108/jdqs-01-2014-b0005.

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In this study, we explore the empirical relationship between trading volume and volatility among KOSPI200 index stock market, futures and options markets. In particular, in explaining the volatility of each market, the trading in other markets, as well as the trading volume of other markets, also served as explanatory variables. In other words, cross-market effects of trading volume by investor types are analyzed. The empirical results show that there exist the cross-market effects of the relationship between trading volume and volatility in deeply integrated financial markets such as KOSPI200 index stock, futures and options markets. That is, the volatility of one market is explained by the trading volume of trader types in other financial markets. And, overall options trading increases the volatility of each market, while the overall futures trading volume of foreign investors reduce the volatility of each market. Trading volume of Individual investors does not reduce the volatilities of KOSPI200 index and futures markets. That is, trading volume of Individual investors in stock, futures, and options markets increase the volatilities of stock and futures. This implies that foreign investors are informed traders, whereas individual investors are liquidity traders.

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37

Huang, Han-Ching, and Jung-Tzu Chang. "The effect of enforcement intensity on illegal insider trading volume: the case of Taiwan." Investment Management and Financial Innovations 13, no. 2 (July 4, 2016): 141–48. http://dx.doi.org/10.21511/imfi.13(2-1).2016.02.

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In this paper, the authors examine the illegal insider trading volume and cumulative abnormal return by the relative variables of the amendment, the change of the securities price, the number of defendants, the penalty and the fine for insider who committed a crime, and the quality of concealed important information. Illegal insider trading is prohibited by the article 157-1 of Securities and Exchange Act in Taiwan. It has been amended three times to provide a sound and rigorous law and completely protect investors. The authors examine the illegal insider trading volume after the amendment to explore whether the Securities and Exchange Act is efficient enough to lower illegal insider trading. The authors find that the change of the securities price and the quality of concealed important information are the critical factors which affect the illegal insider trading volume and cumulative abnormal returns. Nevertheless, the relative variables of the amendment do not show significant effects

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38

Jeong, Seung Hwan, Hee Soo Lee, Hyun Nam, and Kyong Joo Oh. "Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market." Sustainability 13, no. 3 (January 20, 2021): 1011. http://dx.doi.org/10.3390/su13031011.

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Research on stock market prediction has been actively conducted over time. Pertaining to investment, stock prices and trading volume are important indicators. While extensive research on stocks has focused on predicting stock prices, not much focus has been applied to predicting trading volume. The extensive trading volume by large institutions, such as pension funds, has a great impact on the market liquidity. To reduce the impact on the stock market, it is essential for large institutions to correctly predict the intraday trading volume using the volume weighted average price (VWAP) method. In this study, we predict the intraday trading volume using various methods to properly conduct VWAP trading. With the trading volume data of the Korean stock price index 200 (KOSPI 200) futures index from December 2006 to September 2020, we predicted the trading volume using dynamic time warping (DTW) and a genetic algorithm (GA). The empirical results show that the model using the simple average of the trading volume during the optimal period constructed by GA achieved the best performance. As a result of this study, we expect that large institutions will perform more appropriate VWAP trading in a sustainable manner, leading the stock market to be revitalized by enhanced liquidity. In this sense, the model proposed in this paper would contribute to creating efficient stock markets and help to achieve sustainable economic growth.

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39

McGowan, Jr., Carl B., and Junaina Muhammad. "The Relationship Between Price And Volume For The Russian Trading System." International Business & Economics Research Journal (IBER) 11, no. 9 (August 23, 2012): 963. http://dx.doi.org/10.19030/iber.v11i9.7251.

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The objective of this study is to analyze the relationship between the Russian Trading System Index and trading volume for the Russian Trading System Index. We use daily closing price and trading volume for the data for the RTS Index from September 4, 1995 to November 8, 2011. We find a positive statistically significant relationship between the natural logarithm of price volume changes and changes in the RTS Index and for the natural logarithm of price volume changes relative to a five-day average of price volume changes; thus the impact of trading volume is persistent.

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Yeap, Xiu Wei, and Hooi Hooi Lean. "Trading Activities and the Volatility of Return on Malaysian Crude Palm Oil Futures." Journal of Risk and Financial Management 15, no. 1 (January 13, 2022): 34. http://dx.doi.org/10.3390/jrfm15010034.

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Trading activities represent the flow of market information to the investors. This paper examines the effect of trading activities, i.e., trading volume and open interest, on the volatility of return for Malaysian Crude Palm Oil Futures. The GARCH model is applied by adding the expected and unexpected elements of trading activities (trading volume and open interest) as the independent variables. The results show that there is a negative contemporaneous relationship between the expected volume and volatility, but that a positive relationship exists between unexpected volume and volatility. On the contrary, the expected and unexpected open interest mitigate the volatility. Therefore, both trading volume and open interest should be considered together when information flows into the market.

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Augustin, Patrick, Menachem Brenner, and Marti G. Subrahmanyam. "Informed Options Trading Prior to Takeover Announcements: Insider Trading?" Management Science 65, no. 12 (December 2019): 5697–720. http://dx.doi.org/10.1287/mnsc.2018.3122.

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We quantify the pervasiveness of informed trading activity in target companies’ equity options before the announcements of 1,859 U.S. takeovers between 1996 and 2012. About 25% of all takeovers have positive abnormal volumes, which are greater for short-dated, out-of-the-money calls, consistent with bullish directional trading before the announcement. Over half of this abnormal activity is unlikely due to speculation, news and rumors, trading by corporate insiders, leakage in the stock market, deal predictability, or beneficial ownership filings by activist investors. We also examine the characteristics of option trades litigated by the Securities and Exchange Commission (SEC) for alleged illegal insider trading. Although the characteristics of such trades closely resemble the patterns of abnormal option volume in the U.S. takeover sample, we find that the SEC litigates only about 8% of all deals in it. This paper was accepted by Lauren Cohen, finance.

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Chang, Kook Hyun, and Byung Jo Yoon. "Spot Trading Volume Volatility, Futures Trading Volume Volatility, and the Volatility of Korean Stock Market." Journal of Derivatives and Quantitative Studies 19, no. 2 (May 31, 2011): 149–73. http://dx.doi.org/10.1108/jdqs-02-2011-b0002.

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This paper tries to investigate whether the information contained in trading volume volatilities of spot and futures may be statistically useful in explaining the volatility of korean stock market. This paper uses both the component-jump model and the bivariate GJR-GARCH type BEKK model to estimate the trading volume volatilities of spot and futures from 1/2/2001 to 9/30/2010. By using the component-jump model, the volume volatility is decomposed into a permanent component and a transitory component. According to this study, the relative importance of permanent component to the transitory component contained in both trading volume volatilities of spot and futures has been more significant in explaining the volatility of the korean stock markets.

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Hadady, Hartaty, and Rachman Dano Mustafa. "Investor Herding Behavior in Infrastructure Companies on the IDX: Data Panel Approach." Society 10, no. 2 (December 30, 2022): 375–89. http://dx.doi.org/10.33019/society.v10i2.483.

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This research aims to examine the behavior of herding investors due to the information on interest rates and trading volume. By using daily infrastructure company data on the IDX, it is found that interest rates have a negative effect, while volume has a positive effect on herding behavior. The results show that herding behavior decreases when information on interest rates is entered, while herding behavior increases when there is a trend in trading volume. These results indicate that information announced and scheduled will reduce the behavior of herding investors, such as information about interest rates. On the other hand, investor herding behavior tends to increase when information is random, such as trends in stock trading volumes.

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Burhop, Carsten, and Sergey Gelman. "Trading costs and trading quantity at the Berlin Stock Exchange, 1892–1913." Zeitschrift für Unternehmensgeschichte 67, no. 1 (March 19, 2022): 21–42. http://dx.doi.org/10.1515/zug-2022-0014.

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Abstract We estimate two components of stock market liquidity, namely trading costs and trading volume, for the Berlin Stock Exchange, 1892–1913. Our three trading cost indicators are highly correlated with each other. To estimate trading volume, we use turnover tax revenues and turnover data from the Berliner Kassenverein. We use information on stock exchange turnover taxes collected in Berlin and we have compiled monthly data. We show that the three indicators of trading volume are highly correlated.

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Jung, Woosung, and Mhin Kang. "The short-term mean reversion of stock price and the change in trading volume." Journal of Derivatives and Quantitative Studies: 선물연구 29, no. 3 (June 18, 2021): 190–214. http://dx.doi.org/10.1108/jdqs-01-2021-0003.

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This study aims to analyze the effect of change in trading volume on the short-term mean reversion of the stock price in the Korean stock market. Through the variance ratio test, this paper finds that the market shows the mean reversion pattern after 2000, but not before. This study also confirms that the mean reversion property is significantly reduced if the effect of change in trading volume is excluded from the return of a stock with a significant contemporaneous correlation between return and change in trading volume in the post-2000 market. The results appear in both the Korea Composite Stock Price Index and Korea Securities Dealers Automated Quotation. This phenomenon stems from the significance of the return response to change in trading volume per se and not the sign of the response. Additionally, the findings imply that the trading volume has a term structure because of the mean reversion of the trading volume and the return also has a partial term structure because of the contemporaneous correlation between return and change in trading volume. This conclusion suggests that considering the short-term impact of change in trading volume enables a more efficient observation of the market and avoidance of asset misallocation.

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Mauring, Eeva. "Informational Cycles in Search Markets." American Economic Journal: Microeconomics 12, no. 4 (November 1, 2020): 170–92. http://dx.doi.org/10.1257/mic.20180129.

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I show that market participants’ equilibrium beliefs can create fluctuations in the volume of trading, even in a stationary environment. I study a sequential search model where buyers face an unknown distribution of offers. Each buyer learns about the distribution by observing whether a randomly chosen buyer traded yesterday. A cyclical equilibrium exists where the informational content of observing a trade fluctuates, which leads to fluctuations in the volume of trading. The cyclical equilibrium is more efficient than steady-state equilibria. The efficiency result holds also if buyers get a signal about past transaction prices or past trading volumes. (JEL D82, D83)

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Chiao, Chaoshin, and Ko-I. Lin. "The Informative Content of the Net-Buy Information of Institutional Investors: Evidence from the Taiwan Stock Market." Review of Pacific Basin Financial Markets and Policies 07, no. 02 (June 2004): 259–88. http://dx.doi.org/10.1142/s0219091504000123.

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This paper studies the informative content of the NB (net-buy) information of institutional investors, including foreign investors (FIs), security investment trust companies (SITCs), and security dealers (SDs), in the Taiwan stock market. First, with/without considering prevailing market frictions, the investment strategies based on the NB trading volume and dollar trading volume of SITCs, outperform the market and those strategies based on those of FIs and SDs. Second, on average, institutional investors trade mostly large and growth stocks. Third, evidence supports negative leading roles of the aggregate/disaggregate NB dollar trading volumes of FIs over those of SITCs.

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Chutka, Jan, and Filip Rebetak. "Analysis of trading volume and its use in prediction future price movements in the process of maximizing trading earnings." SHS Web of Conferences 92 (2021): 02010. http://dx.doi.org/10.1051/shsconf/20219202010.

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Research background:When we start looking for tools that could give a trader a certain trading advantage, we will certainly come across the problem of analysing the trading volume. This is an advanced type of analysis where the primary price chart of the underlying asset is not analysed, but traders focus on the volume of trades that have been executed at certain price levels. Although it may seem like an innovative method, this type of analysis has been used for several decades. In our article, we elaborated the theoretical basis of the analysis of trading volume as a tool for predicting the movement of prices of financial instruments.Purpose of the article:The aim of our article is to explore the possibilities, methods and procedures of analysis of trading volumes and the possibilities of their use in maximizing earnings from trading of financial instruments.Methods:We used formal methods such as analysis and synthesis of theoretical findings and others.Findings & Value added:Based on the study of the analysis and synthesis of theoretical data, we identified and described the possibilities of using the analysis of trading volume in the process of predicting the price movements of financial instruments. We consider the aim of the article to be fulfilled and we believe that it will be a valuable contribution in the field of research on this issue.

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Shrestha, Shivaram. "Contemporaneous Relationship between Trading Volume and Stock Returns Volatility : Evidence from Nepalese Stock Market." PYC Nepal Journal of Management 10, no. 1 (August 31, 2017): 40–63. http://dx.doi.org/10.3126/pycnjm.v10i1.36067.

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This paper examines the contemporaneous relation between trading volume and stock returns volatility for Nepalese stock market using monthly data for the period 2005 mid-July to 2017 mid-April. The study uses ordinary least square method and analyzes whether rising price leads to higher volume or vice versa. The study also investigates the association between trading volume and stock returns volatility based on monthly data of NEPSE index and examines the effects of trading volume on stock returns volatility using GARCH (1, 1) model. The study finds positive contemporaneous relationship between trading volume and stock return volatility. The study result indicates that the relationship between trading volume and return volatility is asymmetric. The findings strongly support the hypothesis that higher trading volume is associated with an increase in stock return volatility, but offers little support to the sequential arrival hypothesis and the mixture of distribution hypothesis. Finally, the findings support the weak-form efficient market hypothesis in Nepalese stock market.

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Setiawan, Adhe Raka, and Bandi Bandi. "REAKSI PASAR TERHADAP PERUBAHAN DIVIDEN DENGAN INDIKATOR ABNORMAL RETURN DAN TRADING VOLUME ACTIVITY." Jurnal Economia 11, no. 2 (October 1, 2015): 200. http://dx.doi.org/10.21831/economia.v11i2.8291.

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Abstrak: Reaksi Pasar Terhadap Perubahan Dividen dengan Indikator Abnormal Return dan Trading Volume Activity. Penelitian ini bertujuan untuk mengetahui reaksi pasar terhadap perubahan dividen, yaitu dividen tetap, dividen naik, dividen turun, dividen inisiasi, dan dividen omisi dengan indikator abnormal return dan trading volume activity pada perusahaan yang terdaftar di Bursa Efek Indonesia pada sektor properti, real estate, dan konstruksi bangunan periode 1998-2015. Penelitian ini menggunakan desain event study, di mana dilakukan pengamatan 5 hari sebelum dan 5 hari sesudah peristiwa. Analisis data yang digunakan dalam penelitian ini adalah Uji Paired Sample t-test. Hasil penelitian menunjukkan bahwa hanya dividen tetap dan dividen inisiasi dengan indikator trading volume activity terjadi reaksi pasar secara signifikan. Hasil penelitian ini juga menunjukkan bahwa untuk melihat reaksi pasar lebih baik menggunakan indikator trading volume activity dari pada abnormal return.Kata kunci: dividen, abnormal return, trading volume activity.Abstract: Market Reaction to Dividend Change with Abnormal Return and Trading Volume Activity as Indicators. The aim of this study is to find the influence of dividend change on market reaction, which are fixed dividend, rise dividend, fall dividend, initiation dividend, and omission dividend with abnormal return and trading volume activity as indicators at the companies listed in Indonesian Stock Exchange in property, real estate, and building construction sectors in 1998-2015. This study employs event study, in which it is observed within 5 days before and 5 days after the event date. Paired Sample t-test is utilized to analyze the data. The results show that fixed dividend and initiation dividend using average trading volume activity have significant effect on market reaction. Furthermore, it also suggests that to comprehend market reaction, trading volume activity is better indicator rather than abnormal return.Keywords: dividend, abnormal return, trading volume activity.

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