Low Trading Volume Research Articles

A long-term analysis of research unbundling: implications for research provision and market quality

AbstractThis replication study assesses the long-term effects of MiFID II’s research unbundling rules on investment research provision and stock market quality. We extend existing studies by utilizing a post-event period exceeding five years, by regional differentiation within Europe, and by incorporating the new option to rebundle payments for order execution and research services for SMEs. In line with existing studies, we find a decrease in analyst coverage after MiFID II, particularly for large caps, while SMEs remain unaffected. These findings are consistent across different European regions but not for the United Kingdom. Market quality experiences a decline, with lower trading volume and increased volatility, offset by liquidity improvements for larger firms. After the introduction of rebundling, which has not been analyzed by existing studies, SME research coverage declines, suggesting that investment firms are not utilizing this option. Our findings offer insights for evidence-based policy-making as regulatory discussions on research unbundling persist in different jurisdictions.

What drives cryptocurrency pump and dump schemes: Coin versus market factors?

This paper investigates both coin-specific and market-based factors that drive cryptocurrency pump-and-dump schemes. It analyzes a data set comprising 1457 pump events that occurred from January 3, 2018, to January 2, 2022. Empirical findings, derived from binary cross-sectional regression models, reveal several characteristics that increase the likelihood of cryptocurrencies being pumped. These include lower market capitalization, lower trading volume, greater social media popularity, increased developer activity, and fewer exchanges trading them. Furthermore, the study employs count time-series models to examine market-based factors. The results indicate that periods of higher volatility or uncertainty are associated with an increase in pre-announced pump-and-dump activities. Additionally, the analysis shows that macroeconomic factors and specific time-related effects – such as Sundays, certain months, and the COVID-19 period – are significant in explaining the frequency of pump occurrences. Based on these findings, the article discusses several targeted recommendations.

Behavioral Bias of Equity Investors in Different Market Conditions and Events in Pakistan Stock Exchange (PSX)

The herding behavior of equity investors in the Pakistan Stock Exchange (PSX) is examined in this study. There are two ways to look at herding behavior: first, in different market conditions, like bull and bear, extreme and less extreme, high and low trading volume, and high and low volatility; second, in different events, like the month of Ramadan, the financial crisis of 2007–2008, and the Covid-19 pandemic. Financial herding behaviour is when equity investors follow the actions of other equity investors without making their own decisions because they lack information or they think others have more trustworthy information. Regression analysis using Newey West Consistent Estimators is performed on Pakistan Stock Exchange (PSX) daily stock market data ranging from January 2001 to June 2021. The study found that the herding behavior among equity investors is absent during extreme market conditions, less extreme market conditions, high trading volume, high market volatility, and low market volatility; however, it is present during low trading volume, the month of Ramadan, financial crisis (2007-2008) and during Covid-19 pandemic as well.

Research on the Amplification Effect of Trading Volume on Mispricing in the Chinese A-share Market

With the continuous growth of the economy, the residents' demand for preserving and appreciating family wealth has gradually become prominent. The stock market has become the primary choice for investors pursuing asset appreciation. Investors expect to achieve higher returns through scientific methods, and factor models, as well as mispricing, have become the focus of attention, aiming to obtain excess returns through market anomalies. However, with technological advances, the complexity of the market is increasing, making it increasingly difficult to find excess returns through pricing models. As trading volume is the most fundamental indicator in the market, containing many effective market insights and capturing investor disagreements, it is necessary to delve into the role of trading volume in the impact of mispricing on expected returns. This research selects all A-share stocks from 2000 to 2022, excluding the Sci-Tech Innovation Board, measuring mispricing through the anomaly in pricing models. Then, by independently double-sorting mispricing and trading volume into 5x5 portfolios, constructing market-weighted investment portfolios, and holding for one month, we observe the predictive ability of mispricing for future returns at different levels of trading volume. The results reveal that in stocks with high trading volume, the predictive ability of mispricing is stronger. Specifically, in stocks with high trading volume, the difference in future returns between the undervalued stock portfolio and the overvalued stock portfolio is greater than the difference in returns in low trading volume stocks. In other words, trading volume plays a moderating role in the impact of mispricing on expected returns, enhancing the predictive ability of mispricing on returns.

Deeper: A shared liquidity decentralized exchange design for low trading volume tokens to enhance average liquidity

AbstractThis paper presents Deeper, a design for a decentralized exchange that enhances liquidity via reserve sharing. By doing this, it addresses the problem of shallow liquidity in low trading volume token pairs. Shallow liquidity impairs the functioning of on‐chain markets by creating room for unwanted phenomena such as high slippage and sandwich attacks. Deeper solves this by allowing liquidity providers of multiple trading pairs against a common token to share liquidity. This is achieved by creating a common reserve pool for the shared token that is accessible by each trading pair. Independent from the shared liquidity, providers are free to add liquidity to individual token pairs without any restriction. The trading between one token pair does not affect the price of other token pairs even though the reserve of the shared token changes. The proposed design is an extension of concentrated liquidity automated market maker DEXs that is simple enough to be implemented on smart contracts. This is demonstrated by providing a template for a hook‐based smart contract that adds our custom functionality to Uniswap V4. Experiments on historical prices show that for a batch consisting of eight trading pairs, Deeper enhances liquidity by over 2.6– . The enhancement in liquidity can be increased further by increasing the participating tokens in the shared pool. While providing shared liquidity, liquidity providers should be cautious of certain risks and pitfalls, which are described. Overall, Deeper enables the creation of fair markets for low trading volume token pairs.

Unleashing the wind: The role of carbon reduction revenue in Shanghai's distributed wind power investments

Distributed wind power has the potential to contribute significantly to China's carbon neutrality goals. However, the recent policy shift away from wind power subsidies necessitates a thorough examination of alternative revenue streams, such as carbon emission reduction benefits. In response to this need, our paper aims to assess the impact of carbon reduction revenue on the investment viability of distributed wind power projects. Employing the Monte Carlo method, we construct investment models for a case study based in Shanghai, incorporating variables like feed-in tariffs and carbon trading prices. Our analysis reveals that, although distributed wind power investments are generally feasible, optimal investment should be deferred until 2031 according to real options analysis. We further note that carbon reduction revenue can enhance the investment value and shorten the dynamic payback period of these projects; however, current low trading volumes and prices for carbon credits do not sufficiently compensate for the absence of subsidies.

The Launch of a Night Trading Session and Currency Futures Market Liquidity: Evidence from the Thailand Futures Exchange

The Thailand Futures Exchange launched USD Futures as the first currency futures contract on 5 June 2012. However, it has been available for night trading since 27 September 2021. This research aims to analyze the effect of adding a night trading session on USD Futures market liquidity and to make a liquidity comparison between day and night session trading. By adding a dummy variable into the vector autoregression model of order 5 to capture the effect of a night session introduction on market liquidity, the results show that market depth and breadth are even stronger after a longer trading session. In addition, the t-test results show the presence of lower tightness but stronger depth and breadth in day session trading than in night session trading, because of the availability of a large number of orders and the ability of the market to have smoother trading in day as opposed to night. Due to the positive effect of extended trading hours on market depth and breadth, TFEX should consider a longer night session in line with other global futures markets. Night traders should also be aware of liquidity risk due to low night session trading volume.

거래량이 복권특성 종목의 기대수익률에 미치는 영향

Purpose - The purpose of this study is to examine whether trading volume amplifies the extent to which lottery-type stocks are overpriced, and whether economic sentiment index explains time-variation in the magnitude of the volume amplification effect.
 Design/methodology/approach - We examine monthly returns on 5x5 monthly bivariate portfolios formed by lottery characteristics (measured by maximum daily return) and trading volume. In addition, we perform time-series regression tests to examine how the volume amplification effect changes in high and low economic sentiment periods, after controlling for Fama-French three factors.
 Findings - Our bivariate portfolio analysis shows that the overpricing of lottery-type stocks are mostly pronounced among high trading volume stocks. In contrast, for low trading volume stocks, overpricing of lottery-type stocks appears to vanish. Furthermore, the amplification effect of trading volume on overpricing of lottery-type stock is concentrated in high economic sentiment periods.
 Research implications or Originality - This study is the first attempt to examine whether trading volume drives lottery-type stocks’ overpricing in the Korean stock market. Furthermore, our analysis unveils the time-varying nature of volume amplification effect. The results suggest that trading volume might play a important hidden role in asset pricing, opening a new line of researches in the future.

Does corporate social responsibility play a moderating role in trading silence prior to bad news earnings announcements?

AbstractWe examine whether corporate social responsibility (CSR) plays a role in moderating trading silence prior to bad news earnings announcements. Unusually low trading volume before the public release of negative earnings information constrains price discovery. We find that unusually low trading volume prior to earnings announcements is less pronounced for firms with a high level of CSR activities. We also find that this effect is stronger before bad news earnings announcements than good news earnings announcements. These findings are robust to various alternative research design choices and to the endogeneity concern between CSR and trading activity. Taken together, our study demonstrates that CSR plays a moderating role in trading silence by improving firms’ pre‐disclosure business and information environments.

Effects of Information Overload on Financial Markets: How Much Is Too Much?

Motivated by cognitive theories verifying that investors have limited capacity to process information, we study the effects of information overload on stock market dynamics. We construct an information overload index using textual analysis tools on daily data from The New York Times since 1885. We structure our empirical analysis around a discrete-time learning model, which links information overload with asset prices and trading volume when investors are attention constrained. We find that our index is associated with lower trading volume and predicts higher market returns for up to 18 months, even after controlling for standard predictors and other news-based measures. Information overload also affects the cross-section of stock returns: Investors require higher risk premia to hold small, high beta, high volatile, and unprofitable stocks. Such findings are consistent with theories emphasizing that information overload increases information and estimation risk and deteriorates investors' decision accuracy amid their limited attention.

Analyzing The Relationship Between Return and Trading Volume in Relation to Cross-Sectional Absolute Deviation (CSAD) In Order to Detect Herding Behavior in Indonesia Emerging Stock Market

Investor herding behavior is a primary source of speculative bubbles since it implies that investors make identical trading decisions, which can lead to stock prices deviating from their underlying worth. The goal of this study is to detect herding behavior in the Indonesian stock market between 2016 and 2021. The relationship between return and trading volume, known as Cross Sectional Absolute Deviation, is used to assess herding behavior (CSAD). Time-series regression and quantile regression analysis will be employed as data analytic techniques in this study to investigate herding behavior under various market scenarios. Herding behavior is evident in the Indonesian stock market with low trading volume, high market return, and low market return in quantile 0,95. Herd behavior has both beneficial and harmful consequences during certain investing seasons. The best method to reduce the impact is to strengthen the investor’s trading strategy and trading platform

Disruption and stock markets: Evidence from Hong Kong

We examine the impact of disruption on stock markets using the 2019 Hong Kong protests for identification. We find that greater protest intensity corresponds to higher bid–ask spreads, lower trading volume, and greater return volatility for dual-listed Chinese firms’ Hong Kong (H) shares but not their home (A) shares. We also document negative abnormal returns only for these firms’ H-shares around major protest events, which shortly after exhibit reversal. Next, we validate our main findings by documenting similar results using Hong Kong-listed firms only. Overall, we provide new evidence highlighting the impact of protest-induced disruption on financial markets.

Does herding behavior exist in China's carbon markets?

This paper introduces a modified cross-sectional absolute deviation approach to measure the existence of herding behavior and herding asymmetry, and uses the runs test to measure the herding intensity in China's carbon markets. It is the first study to examine the existence of herding behavior in China's carbon markets. The empirical results show that no herding behavior exists in China's overall carbon market. As for herding asymmetry, no herding behavior exists in both up and down markets and in the markets with high and low trading volumes, as well as in the markets with high volatility. Among China's eight carbon markets, herding behavior exists in Beijing, Shanghai, Chongqing, and Fujian, in which Chongqing has the highest herding intensity, while Guangdong, Hubei, Shenzhen, and Tianjin have no herding behavior. The herding behavior is prone to the occurrence when carbon price continuously rises or falls with high volatility. When the carbon market is highly volatile with a large transaction volume, the herding intensity is high. Herding intensity is positively correlated with market volatility. Finally, we propose policy implications to mitigate herding behavior.

The stock market infrastructure development in formation of the investment bank business model in Ukraine

Introduction. Modern trends in the world economy development cause an objective process of increasing stock market role and stimulate forming of investment bank business model in the domestic financial markets.
 Aim and tasks. The aim is to determine the role of the stock market infrastructure development in formation of the investment bank business model in Ukraine. It is necessary to perform the following tasks to achieve this goal: to determinate direction of Ukrainian financial market development and to analyze dynamic of the structure of loan and investment bank portfolio in last five years which allow to evaluate forming of the investment bank business model in Ukraine; to estimate the stock market infrastructure development through its participants.
 Results. The analysis of the loan and investment bank portfolio structure has shown rising of banks’ interest to the operations in the stock market and forming of the investment bank business model in Ukraine. But the structure of the investment portfolio of Ukrainian banks is dominated by domestic government bonds. The main reasons of such investment structure: the low total trading volume in the stock market; the absence of other financial instruments in the stock market; the low level of stock market development through the need to form stock market infrastructure. Studying the stock market infrastructure essence has substantiated the using of institutional approach to identify the level of its development. To the infrastructure participants in the stock market it is carried out organization of trade in financial instruments, clearing institutions, depository system, information, analytical and rating agencies. The infrastructural participants of the stock market in Ukraine has been characterized and analyzed.
 Conclusions. The Ukrainian stock market infrastructure is actively developing in the direction of creating conditions that brings it as close as possible to European and world standards. Such situation will make foundation of the investment bank business model forming. But conducted research has shown that it is necessary, on the one side, to stimulate the increase in stock trading volume and, on the other side, to make the stock market more accessible not only to investors, but to individuals too. The development of information technologies as an integral part of the stock market infrastructure in Ukraine has been worked out.

Market Liquidity and Its Dimensions: Linking the Liquidity Dimensions to Sentiment Analysis through Microblogging Data

Market liquidity has an immediate impact on the execution of transactions in financial markets. Informed counterparty risk is often priced into market liquidity. This study investigates whether microblogging data, as a non-financial information tool, is priced along with market liquidity dimensions. The analysis is based on the Australian Securities Exchange (ASX), and from the results, we conclude that microblogging content in pessimistic periods has a higher impact on liquidity and its dimensions. On a daily basis, pessimistic investor sentiments lead to higher trading costs, illiquidity, a larger price dispersion and a lower trading volume.

Investment behavior during COVID pandemic: An evidence from emerging Asian Markets

This study investigates the investor behavior in Asian emerging markets during the COVID pandemic period and assesses the asymmetric patterns and the influence of global and regional return dynamics on local investor behavior. It uses daily firm-level data from January 2001 to December 2020 for nine Asian emerging countries: China, India, Indonesia, Korea, Malaysia, Pakistan, Philippines, Taiwan and Thailand to estimate the impact of extreme price movements, market liquidity and US and Chinese market returns on local return dispersions using a non-linear specification. The findings indicate that rational price behavior prevailed in all markets over the COVID period. Though herding manifested itself occasionally during the low trading volume days in the pandemic period; overall, herd formation was not observed conditioned on domestic or non-domestic factors. Infact, our comparison of pre-COVID and COVID periods shows a significant shift in investment behavior away from herding and in favor of market efficiency for emerging markets of China, India and Korea. The findings on prevalence of rational price behavior are unique and reflective of improved informational environment in Asian emerging markets.

PERFORMANCES COMMERCIALES ET HETEROGENEITE DES PAYS DANS L’UEMOA: UNE ANALYSE EMPIRIQUE A PARTIR DE L’ESTIMATEUR GMM

This paper aims to analyse whether the differences in trade performance between the countries of the West African Economic and Monetary Union (WAEMU) do not compromise their convergence. The empirical analysis is carried out using the GMM estimator and covers all eight WAEMU countries over the period 2011-2016. The results confirm the existence of conditional convergence with a slow speed of convergence. The results also show that a low volume of intra-WAEMU trade compromises their convergence.

Wealth Effects of Dividend Announcements on Bondholders: The Case of Taiwan Bond Market

Although bonds play an important role in U.S. capital markets, this financial instrument is less common in the emerging markets. Taiwan is a typical case. In fact, both bond issuances and bond transactions in Taiwan have been declining in the past years. Consistent with the previous studies, this research documents that wealth transfer effects exist between bondholders and stockholders. We hypothesize that this wealth transfer discourages investors from investing in bond markets because companies in Taiwan seem to care less about the interest of bondholders. Using the event study methodology, we examine the price change of bonds and stocks in Taiwan capital market around cash dividend announcements. We find that there are significant abnormal returns before cash dividend announcements from 30 days to 60 days and that there is insignificant price change of bonds during the three-day period around the announcement. Possible explanations of the results include low bond trading volumes, insider trading before announcements, and mixing signaling and wealth transfer effects. Although this study cannot prove that the results are directly related to management holdings, we tend to believe that insider trading somehow matters.

Bond intraday momentum

In this paper, we explore bond intraday momentum. By utilizing high-frequency data from the Chinese Treasury bond futures market, we find that the first fifteen-minute return is a good predictor of the last fifteen-minute return. The predictive power of the first fifteen-minute return is stronger on trading days with low trading volume, high trading volatility, positive first fifteen-minute return, and no news release ahead of the trading period. The economic significance of this intraday momentum is also demonstrated by measuring the profits earned through market timing and assessing the difference of certainty equivalent return. Also, this intraday momentum is robust to different bond futures and different predictor construction methods.

Features of the ripple effect in the US regional housing markets: a viewpoint of nonsynchronous trading

ABSTRACT The present study used a new perspective that integrates the commonly observed market characteristics to analyse why the ripple effect is formed. This study proposed a model to infer that in a regional housing market with a relatively low trading probability, low trading volume, high price volatility, and low price efficiency coexist and cause the market to lag behind other regions in information response, forming a ripple effect. The empirical tests used data cover four regions of the United States (Northeast, Midwest, South, and West) between January 1999 and May 2020, and showed that the housing market in the South had the greatest liquidity and the lowest housing price volatility, however, housing prices were more informative in the South region. The empirical results also revealed the characteristics of the ripple effect in the US housing market. Changes in house prices occur earlier in the South, and information is transmitted from the South to the Northeast.