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This study investigates domestic individual, institutional and foreign investors’ trading, to test Hong and Stein (1999)’s behavioral explanation that momentum profit is generated as some uninformed investors underreact to information on medium-term prices. Using Hvidkjaer (2006)’s methodology, we examine the respective investors’ trading tendencies reflected in their active price-setting orders. We analyze a special database compiling details on every transaction for the stocks listed on the KSE during 1996:12~2009:08. During 2001~2007, individual investors’ underreaction in trading large-size winner stocks contributes to positive momentum profits. They seem to induce weak negative profits to emerge in 1997~2000, too. Foreign investors underreact to small-size loser stocks, incurring positive momentum profits during 2001~2007. They engage in positive feedback trading, when they trade large-size winner stocks. This trading tendency does not seem to be based on information on firm fundamentals, as we find those winner stocks’ returns are not sustained. Institutional investors’ trading seems to be relatively in line with future returns, but evidences are not strong enough to support they are informed investors. Overall, the behavioral hypothesis on investors’ underreaction seems to explain medium-term momentum profits in Korea, but evidences differing across subsamples suggest possibility of other unknown influences.

Opinions have been divided on whether there is a rational explanation to the reason behind seasoned equity offerings (SEOs) or whether the explanation lies within the behavioural intricacies attributed to stock market participants. The paper aims to discuss these issues.

This study investigates the long-run performance of firms conducting SEOs on the Johannesburg Stock Exchange (JSE) over the period of 1998–2015, by examining the return performance and operating performance of firms, along with the impact of investor sentiment on these variables.

The results of this study are inconsistent with the existing literature, which argues that the long-run performance of issuing firms signalled an initial underreaction to SEOs buoyed by over-optimistic investors.

Instead, the long-run performance of issuing firms is adequately explained by the rational models centred on the risk-return framework, implying that investors are reacting swiftly to SEOs in an unbiased fashion.

Investor sentiment does not materially influence the long-run share performance or operating performance of issuing firms, casting doubt on the ability of the market timing theory to explain the long-run performance of SEOs. The authors thus find that SEO performance cannot be explained by behavioural-based reasoning, in contrast to some asset pricing studies on the JSE which indicate the role of sentiment in explaining returns.

This study aims to focus on the profitability of momentum trading in the Korean stock market. More specifically, it aims to conduct an examination of the relationship between momentum returns and idiosyncratic volatility (IVol) to determine whether momentum profits can be explained by IVol.

Portfolios are formed based on their past performance and examine the momentum, or contrarian returns, as the difference between winning and losing portfolios. To confirm that the momentum strategy provides excess returns, the relationship between momentum returns and IVol is studied. The Fama and French three‐factor model is also examined to see whether systematic risk affects momentum profits. Firm size, stock price, and turnover are controlled to determine robustness. Finally, a time‐series relationship between aggregate IVol and momentum profits is investigated.

The paper illustrates that excess returns are obtained from a momentum strategy, not a contrarian strategy, in the Korean stock market. Momentum returns are higher among high IVol stocks, especially high IVol winners. Examining the Fama and French three‐factor model, it is found that momentum returns cannot be explained by systematic risk. The findings are robust after controlling for factors such as firm size, book‐to‐market ratio, and turnover. The paper confirms the effect of IVol on momentum returns by illustrating that a time‐series relationship between momentum returns and aggregate IVol is positive.

This paper is among the first, to the authors' knowledge, to examine the relationship between momentum profits and IVol in the Korean stock market, one of the mature financial markets. The findings in this study can be applied to better understand the sources of gains from the momentum strategy in international stock markets.

The purpose of this paper is to consider two global real estate periods (2000‐2006 and 2007‐2008) that will probably be recorded in history as the most significant periods in terms of a surge and then an eventual downturn in the real estate prices and returns. The paper aims to offer investment strategies in the real estate sector and pinpoint the optimum momentum strategies that provide the maximum returns in the real estate investment trusts (REITs) markets of seven countries.

Within an iterative framework, a two‐step procedure was employed. The first step drew upon an established momentum approach. The second step, however, departed from it and employed an evolutionary (genetic) algorithm to optimize the investment strategies that could be pursued.

The findings suggest that momentum effects have been present during the 2000‐2008 periods. However, in contrast with prior studies, momentum portfolio returns are statistically insignificant during the boom years, but are highly significant during the downturn periods. These findings are attributed to the heterogeneous returns during these periods.

Profitable REITs momentum investment is not uniform across the countries considered. Taking long positions in the winners and short positions in the losers during the boom periods in the real estate market may not necessarily be an optimum strategy. Since the profitability of the investment strategy during the downturn is driven by short positions, the practical implementation of such strategy will be limited if short sales constraints are imposed.

The paper employs a unique and novel approach for the first time in the field of real estate investment. It introduces genetic algorithm into the momentum literature. This approach pinpoints the optimum profitability of the momentum/persistence strategies that could be pursued.

The purpose of the paper is to empirically test the overconfidence hypothesis at Bombay Stock Exchange (BSE).

The study applies bivariate vector autoregression to perform the impulse-response analysis and EGARCH models to understand whether there is self-attribution bias and overconfidence behavior among the investors.

The study shows the empirical evidence in support of overconfidence hypothesis. The results show that the overconfident investors overreact to private information and underreact to the public information. Based on EGARCH specifications, it is observed that self-attribution bias, conditioned by right forecasts, increases investors’ overconfidence and the trading volume. Finally, the analysis of the relation between return volatility and trading volume shows that the excessive trading of overconfident investors makes a contribution to the observed excessive volatility.

The study focused on self-attribution and overconfidence biases using monthly data. Further studies can be encouraged to test the proposed hypotheses on daily data and also other behavioral biases.

Insights from the study suggest that the investors should perform a post-analysis of each investment so that they become aware of past behavioral mistakes and stop continuing the same. This might help investors to minimize the negative impact of self-attribution and overconfidence on their expected utility.

To the best of the authors’ knowledge, this is the first study to examine the investors’ overconfidence behavior at market-level data in BSE, India.

This paper aims to examine the presence of momentum profit in the Indian stock market and seeks to explore the sources of momentum profit employing both risk based and behavioral models. R2, idiosyncratic volatility, and delay measures are employed in order to test behavioral models.

The paper follows Jegadeesh and Timan's methodology in constructing momentum portfolios.

The study finds strong presence of momentum profits in India during 1995‐2006. The risk based models such as CAPM and Fama‐French fail to account for the phenomenon. Idiosyncratic risk exhibits a positive relation with momentum, lending support to behavioural factors as source of momentum phenomenon.

In forming portfolios, selecting the stocks which have been winners in the last three and six months can help investors and fund mangers earn substantial profit.

The study employs behavioral variables to explain the momentum phenomenon. In the Indian context it is an unexplored area.

In this study, I investigate analysts’ ability to process public information for investors by examining price reactions to a sample of analysts’ recommendation revisions issued shortly after quarterly earnings announcements. I find that these recommendation revisions are used by investors to reassess the valuation implications of announced earnings. Confirmatory (contradictory) recommendation revisions that have the same (opposite) sign as prior earnings surprises can cause investors to revise their beliefs about the valuation implications of announced earnings upward (downward) and thus cause price reactions that are positively (negatively) associated with prior earnings surprises. In addition, I find that as the information complexity of earnings announcements gets higher, these recommendation revisions play a more important role in helping investors understand the valuation implications of announced earnings. Finally, I find that analysts’ ability to interpret the valuation implications of announced earnings for investors has remained at a similar level since the adoption of Regulation Fair Disclosure. Overall, this study provides additional evidence on how analysts help improve corporate information environment.

Momentum is an unresolved puzzle for the financial economists. The purpose of this paper is to dissect the sources of momentum profits and investigate the possible role played by the macro-economic variables in explaining them.

The data for 493 companies that form part of Bombay Stock Exchange 500 index in India is used for calculating 6-6 momentum profits. Profits from the strategy are regressed on Capital Asset Pricing Model (CAPM) and Fama-French (FF) model to see whether they can explain these profits. Guided by prior research, three methodologies are used to see the possible role played by macro-economic variables in explaining momentum payoffs.

The empirical results show that momentum profits are persistent in the intermediate horizon. CAPM and FF three-factor model fail to explain these returns. Price momentum seems to be explained in one of the model by lagged macro-economic variables which lend an economic foundation to the Carhart factor. The “Winner minus Loser” factor explains about 37 percent of abnormal returns on the winner portfolio that are missed by the FF model. The unexplained momentum profits seem to be an outcome of investors’ over-reaction to past information. Hence, the sources of price momentum profits seem to be partially behavioral and partially rational.

The failure of risk models in fully explaining the momentum profits may be good news for portfolio managers who are looking out for stock market arbitrage opportunities.

This paper fulfills an identified need to study the sources behind price momentum profits in Indian context.

This study aims to develop two sentiment indices sourced from news stories and corporate disclosures of the firms in the National Stock Exchange NIFTY 50 Index by extracting sentiment polarity. Subsequently, the two indices would be compared for the predictive accuracy of the stock market and stock returns during the post-digitization period 2011–2018. Based on the findings this paper suggests various options for financial strategy.

The news- and disclosure-based sentiment indices are developed using sentiment polarity extracted from qualitative content from news and corporate disclosures, respectively, using qualitative analysis tool “N-Vivo.” The indices developed are compared for stock market predictability using quantitative regression techniques. Thus, the study is conducted using both qualitative data and tools and quantitative techniques.

This study shows that the investor is more magnetized to news than towards corporate disclosures though disclosures contain both qualitative as well as quantitative information on the fundamentals of a firm. This study is extended to sectoral indices, and the results show that specific sectoral news impacts sectoral indices intensely over market news. It is found that the market discounts information in disclosures prior to its release. As disclosures in quarterly statements are delayed information input, firms can use voluntary disclosures to reduce the communication gap with investors by using the internet. Managers would do so only when the stock price is undervalued and tend to ignore the market and the shareholder in other cases. Otherwise, disclosure sentiment attracts only long horizon traders.

Finance managers need to improve disclosure dependence on investors by innovative disclosure methodologies irrespective of the ruling market price. In this context, future studies on investor sentiment would be interesting as they need to capture man–machine interactions reflected in market sentiment showing the interplay of human biases with machine-driven decisions. The findings would be useful in developing the financial strategy for protecting firm value.

This study is unique in providing a comparative analysis of sentiment extracted from news and corporate disclosures for explaining the stock market direction and stock returns and contributes to the behavioral finance literature.

A composite sentiment index (CSI) from quantitative proxy sentiment indicators is likely to be a lag sentiment measure as it reflects only the information absorbed in the market. Information theories and behavioral finance research suggest that market prices may not adjust to all the available information at a point in time. This study hypothesizes that the sentiment from the unincorporated information may provide possible market leads. Thus, this paper aims to discuss a method to identify the un-incorporated qualitative Sentiment from information unadjusted in the market price to test whether sentiment polarity from the information can impact stock returns. Factoring market sentiment extracted from unincorporated information (residual sentiment or sentiment backlog) in CSI is an essential step for developing an integrated sentiment index to explain deviation in asset prices from their intrinsic value. Identifying the unincorporated Sentiment also helps in text analytics to distinguish between current and future market sentiment.

Initially, this study collects the news from various textual sources and runs the NVivo tool to compute the corpus data’s sentiment polarity. Subsequently, using the predictability horizon technique, this paper mines the unincorporated component of the news’s sentiment polarity. This study regresses three months’ sentiment polarity (the current period and its lags for two months) on the NIFTY50 index of the National Stock Exchange of India. If the three-month lags are significant, it indicates that news sentiment from the three months is unabsorbed and is likely to impact the future NIFTY50 index. The sentiment is also conditionally tested for firm size, volatility and specific industry sector-dependence. This paper discusses the implications of the results.

Based on information theories and empirical findings, the paper demonstrates that it is possible to identify unincorporated information and extract the sentiment polarity to predict future market direction. The sentiment polarity variables are significant for the current period and two-month lags. The magnitude of the sentiment polarity coefficient has decreased from the current period to lag one and lag two. This study finds that the unabsorbed component or backlog of news consisted of mainly negative market news or unconfirmed news of the previous period, as illustrated in Tables 1 and 2 and Figure 2. The findings on unadjusted news effects vary with firm size, volatility and sectoral indices as depicted in Figures 3, 4, 5 and 6.

The related literature on sentiment index describes top-down/ bottom-up models using quantitative proxy sentiment indicators and natural language processing (NLP)/machine learning approaches to compute the sentiment from qualitative information to explain variance in market returns. NLP approaches use current period sentiment to understand market trends ignoring the unadjusted sentiment carried from the previous period. The underlying assumption here is that the market adjusts to all available information instantly, which is proved false in various empirical studies backed by information theories. The paper discusses a novel approach to identify and extract sentiment from unincorporated information, which is a critical sentiment measure for developing a holistic sentiment index, both in text analytics and in top-down quantitative models. Practitioners may use the methodology in the algorithmic trading models and conduct stock market research.