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This paper scrutinizes the relationship between idiosyncratic risks and stock returns at different quantiles, especially the extremely low and high ones, to explore the applicability of the Prospect Theory’s rationale in Vietnam’s stock market.

The Prospect Theory demonstrates that investors’ attitudes towards risks can change from risk-seeking in the loss domain to risk-averse in the gain domain. This can be observed by the negative (positive) connection between idiosyncratic risks and returns for the losing (winning) stocks. To explore if the aforesaid patterns occur in Vietnam’s stock market, this paper employs the quantile regression method which is suitable for inspecting the relationship at the high and low tails of the stock returns.

The estimation results acknowledge the changes in attitudes towards risks as mentioned by the Prospect Theory.

The negative relationship between idiosyncratic risks and stock returns confirms investors’ risk-seeking behavior in the loss domain, which is in line with the prediction of the Prospect Theory. This behavior may cause worse investment performance as the losing stocks in investors’ portfolios remain overvalued, leading to subsequent negative returns. Therefore, investors should establish and follow their investment disciplines to protect themselves from larger losses.

Existing research found little evidence for the Prospect Theory’s rationale in Vietnam’s stock market, which can stem from the usage of the conditional-mean regression methods. Different from the prior studies, this paper is the first to apply the quantile regression method and provide new evidence supporting the Prospect Theory’s rationale in Vietnam’s stock market.

Prior literature has shown that, theoretically, holding-period returns of a leveraged exchange-traded fund (LETF) are generally negatively affected by the volatility of the underlying benchmark’s daily returns, particularly for long holding periods. However, recent empirical studies simulate LETFs’ returns using historical benchmark returns and report results that are not entirely consistent with the theoretical predictions, leading to the possibility that the distribution of real-world returns may have certain characteristics that influence the outcomes. In this paper, the authors examine how asymmetric volatility affects LETFs’ performance and provide detailed explanations for the behavior of the performance of LETFs under different market conditions.

The authors conduct simulation analyses on a +3x LETF and a −3x LETF based on historical S&P 500 stock index returns, with asymmetric volatility incorporated into the model.

By incorporating the asymmetric volatility effect, the simulation results suggest that, contrary to the theoretical predictions, higher volatility does not always lead to more negative impact on LETFs’ performance. Rather, the performance depends on the market conditions under which high volatility occurs. The findings therefore help reconcile prior theoretical predictions with reported empirical findings.

The analysis adds to the literature by incorporating the asymmetric volatility effect of stock returns in studying LETFs’ performance. The authors also provide detailed explanations for the behavior of LETFs’ returns and compounding effect under different market conditions, thus providing contexts to prior empirical results.

Equity research in experimental psychology reveals investors' overreactions to bad news events. This study of asymmetric price structures in equity markets investigates whether such behavior predicts stock returns in an emerging market of India.

The research decomposes Bombay Stock Exchange (BSE) Sensex returns into Extremely Positive Returns (EPR) and Extremely Negative Returns (ENR) based on extreme values at first and then tests their lead–lag relations.

The empirical finding is consistent with the existing evidence of asymmetric news effects on stock returns in India. In precise, ENR robustly predicts one-month-ahead EPR for the sample period from January 1991 to March 2020. This predictive power persists even in the presence of popular valuation ratios and business cycle variables.

The paper explains the rationale of extreme value modeling in price forecasting. Investors can find additional utility gains from market cycle information while predicting extreme returns in Indian stock market.

The paper is unique to understand business cycle effects in extreme return reversals in emerging markets.

Investigation of the anomalies associated with crashes and jackpots in the Chinese stock market.

We propose a logit model to predict the events of crashes and jackpots in the Chinese stock market. The model introduces a new variable of the price-to-sales ratio and takes into account the market states, Up and Down.

The anomalies associated with crashes and jackpots are not related to variations in economic conditions, but are associated with limits to arbitrage. High-liquidity stocks have strong mispricing effects. The institutions’ speculative trading will push liquid stock prices further away from their fundamentals but avoid buying illiquid stocks with a higher probability of price crashes and jackpots.

We propose a logit model to predict the extreme events of both crash and jackpot in the Chinese stock market. Our model effectively disentangles from CRASHP and JACKP. Compared with the traditional model, it substantially enhances in-sample and out-sample predictions. Based on the predictions of the extreme events, we find two strong and robust pricing effects associated with ex ante CRASHP and JACKP in the Chinese stock market.

This study aims to examine the effects of firms’ accounting disclosure policies on stock price synchronicity and stock crash risk, using a sample including 13 emerging markets. Furthermore, this research investigates how these relationships are affected by country-level investor protection and firm-level governance rankings.

This paper uses accounting disclosure measures constructed based on survey questions by Credit Lyonnais Securities Asia (2001, CLSA). The accounting disclosure measure is used to explain the two dependent variables, stock price synchronicity and stock crash risk. The stock price synchronicity measure is defined as the logistic transformation of R2 following Hutton et al. (2009) and Jin and Myers (2006). R2 is taken from the estimation of an extended market model. The stock crash risk variable is measured as the frequency difference between extremely negative and positive stock return residues following Jin and Myers (2006). These stock return residues are taken from the estimation of an extended market model. Because the CLSA firm-level disclosure data are from 2000, this paper matches other data taken from the same year, for consistency. The final sample includes 204 observations in 13 emerging countries.

This paper finds that firms’ stocks are less synchronized with the entire market and have less crash risk if firms have superior accounting disclosure policies. These results suggest that the cost to collect firm-specific information may be decreased for investors if firms are more transparent. Thus, these firms’ stocks have more firm-specific information content. These results also suggest that management is less likely to hide some negative information and release such negative information suddenly in the future if firms have higher levels of accounting disclosure. Thus, these firms’ stocks are less likely to crash. In addition, the influences of firms’ accounting disclosure policies on stock price synchronicity and crash risk are more significant for firms with superior country-level investor protection and firm-level governance rankings. These results imply that external investors place more value on accounting disclosure by well-governed firms because firms with superior governance standards are less likely to intentionally disclose misleading information. Thus, these firms’ stocks can incorporate more firm-specific information and have less crash risk.

The current study is the first to show that the effects of accounting disclosure on stock price synchronicity and crash risk are more pronounced for firms with superior country-level investor protection and firm-level governance standards. Thus, this research extends the literature by providing a comprehensive picture of the influences of accounting disclosure on stock markets. In addition, the existing literature (Chen et al., 2006; Durnev et al., 2004) shows that firms with lower stock price synchronicity are associated with higher investment efficiency because managers invest based on the information in stock prices. Obviously, higher stock crash risk is highly related to higher bankruptcy risk for firms. Thus, examining the effects of accounting disclosure on stock price synchronicity and stock crash risk is of obvious importance to policy makers.

This study aims to examine investors’ herd behaviour for various calendar events and size-based stock portfolios in Pakistan. The authors consider three calendar effects, crisis (COVID-19 and financial crisis 2018–19), announcement of political news and popular calendar anomalies (month-of-the-year and day-of-the-week), and investigate the impact of stock size on calendar effect in terms of investors’ herd behaviour.

The study uses non-linear specification to capture herd behaviour using firm-level daily data for 496 stocks listed on Pakistan Stock Exchange over the period 2001–2020.

The results indicate herd formation during periods of COVID-19, financial crisis, political news announcements and January (month-of-the-year). The authors also observe significant herding for the biggest and smallest size stocks over complete period. However, the authors find more pronounced herding in big stocks during January as compared to the more noticeable herding in small stocks over complete period. The findings suggest that herding in small stocks is not the main cause of January herding and hint on the prevalence of significant institutional herding during January.

The stock prices destabilize because of the mimicking behaviour during crisis periods, days of political announcements and month of January. Implementation of insider trading laws and transparent information environment can help in reducing these effects and increasing market efficiency.

The authors consider the recent COVID period in our analysis. In addition, we provide new evidence on the possible impact of stock size on calendar effect in terms of herd behaviour, which, to the best of the authors’ knowledge, has not yet been documented in literature.

We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

In this paper, we show that there is a negative premium for MAX stocks in the Korean stock market. However, there is no evidence that the MAX effect overwhelms the effects of idiosyncratic risk. When we control for idiosyncratic risk, the negative relationship between extreme returns and future returns is less robust. Rather, the cross-effect of the extreme returns and the idiosyncratic risk factors explains the negative premium. Furthermore, our results are not fully explained by the exposure to the market timing and economic state. Overall, both the extreme return and idiosyncratic risk effects appear to coexist in the Korean stock market, but they are not independently.

This study investigates the paradox of lotteries in financial markets, challenging traditional utility models predicated on rational behavior amid uncertainty. It explores why investors are drawn to lotteries despite the potential trade-off between risk-adjusted returns and sporadically substantial gains.

Employing a multifaceted approach, the study first scrutinizes diverse theories elucidating the perplexing behavior of lottery investors. Subsequently, it assesses the premium attached to lottery stock shares in the Brazilian financial market using distinct methodologies, thereby offering a comprehensive analysis of this phenomenon. Finally, the study estimates the risk premium associated with the lottery stocks applying an extended Fama–French multifactor model and searching for evidence of overlap with other risk-based anomalies.

This research unveils theories underpinning seemingly irrational investor behavior vis-à-vis lotteries, revealing the motivations propelling investors to willingly exchange risk-adjusted returns for the allure of substantial but infrequent gains. Empirical evidence delineates the extent of the premium paid for lottery stocks in the Brazilian market.

The study’s novelty lies in its amalgamation of theoretical exploration, empirical analysis and the application of the Fama–French factor model to gauge the risk premium associated with lottery-related behavior. Furthermore, its investigation of lottery stocks within the Brazilian market introduces a distinctive dimension, elucidating market dynamics and investor behaviors unique to the region.