Trading volume and stock returns: A meta-analysis

Bajzík, Josef

doi:10.1016/j.irfa.2021.101923

Cited by 18 publications

(13 citation statements)

References 134 publications

(172 reference statements)

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“…Contrary to the results of the abovementioned studies, a low return induces overconfidence behavior in BIST. According to Bajzik ( 2021 ), the impact of trading volume on stock returns in emerging stock markets has the opposite sign to that on returns in developed markets. This may be a possible reason for the contradictory results of our analysis compared to those of the abovementioned studies.…”

Section: Empirical Results and Discussionmentioning

confidence: 99%

“…However, many studies have found evidence that overconfident investors frequently monitor their portfolios and generally very often review their investment strategies daily (Abreu and Mendes 2012 ; Strahilevitz et al 2015 ; Tourani-Rad and Kirkby 2005 ). Moreover, Bajzik ( 2021 ) pointed out that the use of monthly data or VAR models makes the effect of trading volume on returns substantially more negative, thus suggesting caution. Therefore, we used daily data to observe overconfidence, following the relevant literature that suggests that daily activities are related to overconfidence.…”

Section: Data and Empirical Methodologymentioning

confidence: 99%

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Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach

2023

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Overconfidence behavior, one form of positive illusion, has drawn considerable attention throughout history because it is viewed as the main reason for many crises. Investors’ overconfidence, which can be observed as overtrading following positive returns, may lead to inefficiencies in stock markets. To the best of our knowledge, this is the first study to examine the presence of investor overconfidence by employing an artificial intelligence technique and a nonlinear approach to impulse responses to analyze the impact of different return regimes on the overconfidence attitude. We examine whether investors in an emerging stock market (Borsa Istanbul) exhibit overconfidence behavior using a feed-forward, neural network, nonlinear Granger causality test and nonlinear impulse-response functions based on local projections. These are the first applications in the relevant literature due to the novelty of these models in forecasting high-dimensional, multivariate time series. The results obtained from distinguishing between the different market regimes to analyze the responses of trading volume to return shocks contradict those in the literature, which is the key contribution of the study. The empirical findings imply that overconfidence behavior exhibits asymmetries in different return regimes and is persistent during the 20-day forecasting horizon. Overconfidence is more persistent in the low- than in the high-return regime. In the negative interest-rate period, a high-return regime induces overconfidence behavior, whereas in the positive interest-rate period, a low-return regime induces overconfidence behavior. Based on the empirical findings, investors should be aware that portfolio gains may result in losses depending on aggressive and excessive trading strategies, particularly in low-return regimes.

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Section: Empirical Results and Discussionmentioning

confidence: 99%

Section: Data and Empirical Methodologymentioning

confidence: 99%

Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach

2023

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“…About 60% of collected estimates is based on the same variable transformation -credit growth and the borrower-based index or dummy (see Table 2). One prevailing variable transformation allows us to provide an empirical summary of the effect of changes to the borrower-based limits on bank lending which provides a significant benefit compared to a unitless standardized effect approach (Doucouliagos and Laroche, 2003;Ahmadov, 2014;Valickova et al, 2015;Bajzik, 2021). 3 Our findings will let us draw more convincing conclusions not only on the true direction of the analyzed effect but also on the determinants of the observable heterogenenity in the estimates.…”

Section: Collection Process and Formation Of The Datasetmentioning

confidence: 90%

Borrower-Based Macroprudential Measures and Credit Growth: How Biased is the Existing Literature?

Malovaná

Hodula

Gric

et al. 2022

Preprint

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The ever-so-increasing use of borrower-based measures such as loan-to-value, debt-to-income, and debt service-to-income limits has created a demand to better understand the transmission of the policy and its effectiveness. In this paper, we collect more than 700 estimates from 34 studies on the effect of borrower-based measures on bank loan provision. A birds-eye view of our dataset points to a significant fragmentation of the literature in terms of the estimated coefficients. On average, the introduction or tightening of borrower-based measures reduces annual credit growth by 1.6 pp. Using battery of empirical tests, we verify the presence of a strong publication bias, especially against positive and not statistically significant estimates. The bias-corrected coefficient is about half the size of the uncorrected mean of the collected estimates, but remains safely negative. Further, we explore the context in which researchers obtain such estimates and we find that differences in the literature are best explained by model specification, estimation method, and the underlying data characteristics.

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“…The recent study on NYSE done by Yoni (2013) found that there is a positive contemporaneous relationship between return and trading volume, which contradicts Huang and Heian(2010) and supports Granger and Morgenstern (1963) and Gallant and Rossi (1990). The fundamentalists' studies such as Huana, Capretz and Ho (2021), Malinowska (2010), Wu, Liu, Zou and Weng(2022) and Bajzik (2020) empirically evidence that the stock price adjustment is due to the information released. That is, the price response to public new information is gradual and accompanied by increases in trading activities.…”

Section: Introductionmentioning

confidence: 90%

Homo-Hetero Pairing Regression Model: An Econometric Predictive Model of Homo Paired Data

Bundala¹

2022

IJFR

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The study aimed to examine the technical and fundamental hypotheses in NYSE, NASDAQ and S&P 500 stock exchange markets. The main determinants (variables) that were examined were stock trading volumes, closing stock prices and stock information available in the stock exchange market. The 240 days, 197 days and 253 days data of closing stock prices and trading volumes at NYSE, S&P500 and NASDAQ stock exchange markets were systematically collected from June 2021 to June 2022. The data was analysed by using the Homo-Hetero Pairing (HHP) Regression Model. This model was developed to detect the linear and non-linear behaviour of data. The study evidenced that both the technical and fundamental hypotheses in NYSE, S&P500 and NASDAQ stock exchange markets are defined by the inverse and S-curved models in two distinctive pairing classes called the positive-positive pairing (PPP) class and the negative-positive pairing (NPP) class. The study concluded that the optimal prediction of the stock price or return is achieved by the fundamentalists in the stock exchange markets. The study recommends that stock investors should priorities the use of the fundamental hypothesis to make their portfolio investment decision. Moreover, the study recommends the application of the HHP regression model in financial markets, economics, psychology, sociology, and medicine studies. In addition, the HHP regression model is recommended for the prediction of water waves in the investigation of hydrodynamic and erosion-accretion processes

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Trading volume and stock returns: A meta-analysis

Cited by 18 publications

References 134 publications

Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach

Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach

Borrower-Based Macroprudential Measures and Credit Growth: How Biased is the Existing Literature?

Homo-Hetero Pairing Regression Model: An Econometric Predictive Model of Homo Paired Data

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