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The purpose of this paper is to investigate the impact of individual investor sentiment on the return process and conditional volatility of three main US market indices (Dow Jones Industrial Average, S&P500 and Nasdaq100). Individual investor sentiment is measured by aggregate money flows in and out of domestically oriented US mutual funds.

A generalised autoregressive conditional heteroscedasticity (GARCH)‐in‐mean specification is used, where our measure for individual sentiment enters the mean and conditional volatility equation.

For a sample period of six years (February 1998 until December 2004), we find that sentiment has a significant and asymmetric impact on volatility, increasing it more when sentiment is bearish. Using terminology of De Long et al., we find evidence for the “hold more” effect, which states that when noise traders hold more of the asset, they also see their returns increase, and the “create space” effect, which states that noise traders are rewarded for the additional risk they generate themselves.

In contrast to existing studies using explicit measures of market sentiment on low sampling frequencies, the use of daily mutual flow data offers a unique picture on investors' portfolio rebalancing and trading behavior. We propose an integrated framework that jointly tests for the effects of mutual fund flows on stock return and conditional volatility.

The purpose of this paper is to research and analyze the influence of institutional investors in the present securities market due to behavior alienation with “running after rising and falling” and “herd behavior”.

A DeLong, Shleifer, Summers, and Waldmann (DSSW) model with positive feedback trading is established first to show the trading process, and these securities prices are calculated considering the investors' emotion. Through numerical analysis, the influence of institutional investors on securities price fluctuation is simulated. Further, the analysis of institutional investors' incomes is processed based on this model.

Through these analyses, the following conclusions are drawn: it lies on the scale of positive feedback traders and their sensitivity to past market performances whether the institutional investors can stabilize the market, and it is not necessary for the institutional investors to benefit from manipulating the market due to the existence of noise trader risk, so the positive feedback traders may survive in the security market over the long term.

The DSSW model considering positive feedback trading, presented in the paper, is more effective in analyzing the relation among the behavior of institutional investors, securities pricing and securities price fluctuation. The paper proposes some advice for policy decisions, which is helpful for government and institutions to maintain the stability of securities markets.

The purpose of this paper is to provide market risk calculation for an equity-based trading portfolio. Instead of relying on the purely stochastic internal model method which banks currently apply in line with the Basel regulatory requirements, the author also propose including alternative price mechanisms from the financial literature in the regulatory framework.

For this purpose, a financial market model with heterogeneous agents is developed, capturing the realistic feature that parts of the investors do not follow the assumption of no arbitrage, but are motivated by behavioral heuristics instead.

Although both the standard stochastic and the behavioral model are restricted to a calibration including the last 250 trading days, the latter is able to capitalize possible turbulence on financial markets and likewise the well-known phenomenon of excess volatility – even if the last 250 days reflect a non-turbulent market.

Thus, including agent-based models in the regulatory framework could create better capital requirements with respect to their level and counter-cyclicality.

This in turn could reduce the extent to which bubbles arise, since market participants would have to anticipate comprehensively the costs of such bubbles bursting. Furthermore, a key ratio is deduced from the agent-based construction to lower the influence of speculative derivatives.

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.

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.

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.

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.

The purpose of this paper is to examine the impact of corporate governance, investor sentiment and financial liberalization on downside systematic risk and the interplay of socio-political turbulence on this relationship through static and dynamic panel estimation models.

The evidence is based on a sample of 230 publicly listed non-financial firms from Pakistan Stock Exchange (PSX) over the period 2008–2018. Furthermore, this study analyzes the data through Blundell and Bond (1998) technique in the full sample as well sub-samples (big and small firms).

The authors document that corporate governance mechanism reduces the downside risk, whereas investor sentiment and financial liberalization increase the investors’ exposure toward downside risk. Particularly, the results provide some new insights that the socio-political turbulence as a moderator weakens the impact of corporate governance and strengthens the effect of investor sentiment and financial liberalization on downside risk. Consistent with prior studies, the analysis of sub-samples reveals some statistical variations in large and small-size sampled firms. Theoretically, the findings mainly support agency theory, noise trader theory and the Keynesians hypothesis.

Stock market volatility has become a prime area of concern for investors, policymakers and regulators in emerging economies. Primarily, the existence of market volatility is attributed to weak governance, irrational behavior of market participants, the liberation of financial policies and sociopolitical turbulence. Therefore, the present study provides simultaneous empirical evidence to determine whether corporate governance, investor sentiment and financial liberalization hinder or spur downside risk in an emerging economy. Furthermore, the work relates to a small number of studies that examine the role of socio-political turbulence as a moderator on the relationship of corporate governance, investor sentiment and financial liberalization with downside systematic risk.

The main purpose of the research is to determine if the relationship between trading volume and price changes is connected to market effectiveness and to use the volume-price relationship to compare the efficiency levels of foreign markets. The degree of the relationship is determined in this study, and the efficiency levels of different countries' capital markets are compared.

In this study, 1,024 observations are used as a data set, which includes daily closing prices and trading volume in the stock market indices of 25 countries between the dates of 01.12.2016 and 31.12.2020. In the first step of the analysis, descriptive statistics of price and volume series are examined. The stationarity of the series is then controlled using the ADF unit root test. Simple linear regression models with the dependent variable of trading volume are generated for all stock market indices after each series has reached stationarity, and the ARCH heteroscedasticity test is used to determine whether these models contain the ARCH effect. Because all models have the ARCH effect, autoregressive models are chosen, and EGARCH models are conducted for all indices to see whether there is an asymmetry in the price-volume relationship.

The study concludes that the stock market in the United States is the most effective, since it has the strongest relationship between trading volume and price changes. However, because of the financial distress caused by the COVID-19 pandemic, the relationship between price and trading volume is lower in Eurozone countries. The price-volume relationship could not be observed in some shallow markets. Furthermore, whereas the majority of countries have a negative relationship between price changes and transaction volume, China, the United Arab Emirates and Qatar have a positive relationship. When prices rise in these countries, investors buy with the sense of hope provided by the optimistic atmosphere, and when prices fall, they sell with the fear of losing money.

The study's most significant limitation is that it is difficult to ascertain a definitive conclusion about the subject under investigation. In reality, if the same research is done using data from different countries and time periods, the results are quite likely to vary.

As a result of the study, investors can decide which market to enter by comparing and analyzing the price-volume relationship of several markets. According to the study's findings, investors are advised to examine the price-volume relationship in a market before beginning to trade in that market. In this way, investors can understand the market's efficiency and whether it is overpriced.

The relationship between price movements and trade volume gives crucial information about a capital market's internal structure. Some concerns can be answered by assessing this relationship, such as whether the market has a speculative pricing problem, how information flows to the market, and whether investment decisions are rational and homogenous. Empirical studies on modeling this relationship, on the other hand, have not reached a definite outcome. The main reason for this is that the price-to-volume relationship fluctuates depending on the market structure. The purpose of this study is to fill a gap in the literature by presenting the reasons why this critical issue in the literature cannot be answered, as well as empirical findings.

The significance and originality of this research are that it examines the price-volume relationship to evaluate the efficiency levels of various markets. This relationship is being investigated in a number of multinational studies. These researches, on the other hand, were conducted to see if there is a relationship between trading volume and market volatility, and if so, how that interaction is formed. The size of the price and volume relationship is emphasized in this study, unlike previous studies in the literature.

The closed‐end fund puzzle is one of the most famous unsolved issues in financial economics and as such, over time, it has raised the interest of many authors also in the real estate field. The aim of this paper is both to determine whether the effect of leverage on net asset value (NAV) discount is biased by an accounting effect as well as to investigate the determinants of NAV discount by means of the “rational” approach.

The hypotheses are tested by using both the traditional formula as well as a new, unlevered one to calculate the NAV discount. A best subset analysis is carried out to ascertain the better set of determinants.

The main result of the analysis is that the influence of leverage on the NAV discount is biased by an accounting effect while other factors are highly significant.

This paper is a starting point for additional research on some of the identified factors as well as on similar samples for which a wider set of data is available.

The homogeneity of the Italian real estate investment funds sample, which is not biased by any fiscal effect, and the use of an unlevered formula to calculate NAV discount are important factors when trying to understand the determinants of NAV discount.

The authors’ goal is to provide an overview and historical context for the various alternatives to the efficient market hypothesis (EMH) that have emerged over time. The authors found eight current alternatives that have emerged to address the EMH's flaws. Each of the proposed alternatives improves some of the assumptions made by the EMH, such as investor homogeneity, the immediate incorporation of information into asset values and the inadequacy of rationality to explain asset prices.

To come up with the list of studies relevant to this review article, the authors used three databases, namely Scopus, Web of Science and Google Scholar. The first two were mostly used to get peer-reviewed articles while Google Scholar was used to extract articles that are still work in progress. The following words were used as the search queries; “efficient market hypothesis” and “alternatives to the efficient market hypothesis”.

The alternatives to the EMH presented in this article demonstrate that market efficiency is a dynamic concept that can be best understood with a multidisciplinary approach. To better comprehend how financial markets work, it is crucial to draw on concepts, theories and ideas from a variety of disciplines, including physics, economics, anthropology, sociology and others.

The authors comprehensively summarise the current state of the behavioural finance literature on alternatives to the EMH.

The purpose is of this paper is to investigate whether the tracking ability of exchange traded funds (ETFs) is lower in highly volatile periods, and to shed more light on the factors behind the tracking error.

The authors apply the Error Correction Model that incorporates a short‐run adjustment mechanism on domestic US ETFs that follow industrial indices.

It was found that tracking errors attained pronounced levels during 2008 compared to 2006 and 2007, mainly in ETFs from the real estate and banking and finance sectors. In addition, tracking error is positively correlated with the daily volatility of the ETF, while trading volume has a limited effect on reducing tracking errors.

The paper sheds more light on the relationship between securities and their fundamentals, and contributes to the literature on the information transmission mechanism for dually‐listed securities.

The paper uses the co‐integration tests and the error correction model (ECM) to test the long‐run relationship between returns on domestic exchange trade funds (ETFs) and the returns on the underlying indices. In particular, the ECM is used for ETFs for the first time.

This paper aims to capture tail dependence between sentiment index and Shanghai composite index (SCI) by proposing a sentiment index based on text mining.

Online text mining and the Copula model were used in this study.

First, the paper finds herding effect in the expression of investors’ sentiment from online text data, and the usage occurrence frequency of most vocabulary is less correlative with SCI. Second, given these two features, the paper uses weighted divide-and-conquer algorithm to construct a sentiment index. Finally, because of multivariate non-Gaussian joint distribution between them, the paper uses the Copula model to detect their tail dependences, and finds that both upper and lower tail dependences could have a significant influence between positive sentiment and SCI, with a higher probability on the upper one. Additionally, only the upper tail dependence exhibits the significant influence between negative sentiment and SCI.

This paper proposes a framework of constructing investment sentiment index with the weighted conquer-and-divide algorithm, and characterizes tail dependence between sentiment index and SCI. The implication can measure the environment of investment market of China and provide an empirical ground for bandwagon effect and bargain shopper effect.