Abstract This study investigates the MAX effect regarding lottery mindset in the Chinese stock market. The MAX effect significantly affects stock returns through quintile portfolio and cross-sectional regression analyses. The most-overpriced stock groups, as categorized by mispricing index, show more support for the MAX effect. However, the idiosyncratic volatility (IVOL) effect continues regardless of consideration for the MAX effect, indicating that the MAX effect is not a source of the IVOL effect. Our results suggest that the MAX effect, which is highly relevant for overpriced stocks, might have information for determining stock price, and appears to be independent from information of the IVOL effect in the Chinese stock market.

In this paper, we analyze the economic value of predicting index returns as well as volatility. On the basis of fairly simple linear models, estimated recursively, we produce genuine out-of-sample forecasts for the return on the S&P 500 index and its volatility. Using monthly data from 1954 to 2001, we test the statistical significance of return and volatility predictability and examine the economic value of a number of alternative trading strategies. While we find strong evidence for market timing in both returns and volatility, the success of market timing and volatility timing varies considerably over the sample period. Further, it appears easier to forecast returns at times when volatility is high. For a mean-variance investor, this predictability is economically profitable, even if short sales are not allowed and transaction costs are quite large. The economic value of trading strategies that employ market timing in returns and volatility typically exceeds that of strategies that only employ timing in returns.

The study investigates the connection between international oil indices and Southeast Asian stock markets. The outcomes of both employed models, namely EGARCH and GARCH-jump, confirm the significant oil-stock linkage in Southeast Asian region. While the oil price fluctuations have positive effect on stock returns, the impacts of the implied crude oil volatility index (OVX) are negative, implying that the increase in level of future oil prices uncertainty leads to downward movement on stock markets. Additionally, the study further reports the existence of GARCH effects in Southeast Asian stock markets. The results from EGARCH models illustrate that the previously negative shocks seem to have greater effects on the current volatility of stock returns in analyzed countries than the positive shocks. Furthermore, the jump effects are found in most markets, as evidenced by the estimates for GARCH-jump models. Generally, the volatility driven by abnormal information positively affects the volatility of return while the jump behavior has negative impact on return in Southeast Asian markets. Providing greater understandings about new markets in Southeast Asian area, the research could be utilized in improving investment decisions and gaining the advantages of international portfolio diversification.

Purpose This study aims to identify calendar anomalies that can affect stock returns and asymmetric volatility. Thus, the objective of this study is twofold: on the one hand, it examines the impact of calendar anomalies on the returns of both conventional and Islamic indices in Indonesia, and on the other hand, it analyzes the impact of these anomalies on return volatility and whether this impact differs between the two indices. Design/methodology/approach The authors apply the GJR-generalized autoregressive conditional heteroskedasticity model to daily data of the Jakarta Composite Index (JCI) and the Jakarta Islamic Index for the period ranging from October 6, 2000 to March 4, 2022. Findings The authors provide evidence that the turn-of-the-month (TOM) effect is present in both conventional and Islamic indices, whereas the January effect is present only for the conventional index and the Monday effect is present only for the Islamic index. The month of Ramadan exhibits a positive effect for the Islamic index and a negative effect for the conventional index. Conversely, the crisis effect seems to be the same for the two indices. Overall, the results suggest that the impact of market anomalies on returns and volatility differs significantly between conventional and Islamic indices. Practical implications This study provides useful information for understanding the characteristics of the Indonesian stock market and can help investors to make their choice between Islamic and conventional equities. Given the presence of some calendar anomalies in the Indonesia stock market, investors could obtain abnormal returns by optimizing an investment strategy based on seasonal return patterns. Regarding the day-of-the-week effect, it is found that Friday’s mean returns are the highest among the weekdays for both indices which implies that investors in the Indonesian stock market should trade more on Fridays. Similarly, the TOM effect is significantly positive for both indices, suggesting that for investors are called to concentrate their transactions from the last day of the month to the fourth day of the following month. The January effect is positive and statistically significant only for the conventional index (JCI) which implies that it is more beneficial for investors to invest only in conventional assets. In contrast, it seems that it is more advantageous for investors to invest only in Islamic assets during Ramadan. In addition, the findings reveal that the two indices exhibit lower returns and higher volatility, which implies that it is recommended for investors to find other assets that can serve as a safe refuge during turbulent periods. Overall, the existence of these calendar anomalies implies that policymakers are called to implement the required measures to increase market efficiency. Originality/value The existing literature on calendar anomalies is abundant, but it is mostly focused on conventional stocks and has not been sufficiently extended to address the presence of these anomalies in Shariah-compliant stocks. To the best of the authors’ knowledge, no study to date has examined the presence of calendar anomalies and asymmetric volatility in both Islamic and conventional stock indices in Indonesia.

We show that the value-weighted idiosyncratic stock volatility and aggregate stock market volatility jointly exhibit strong predictive abilities for excess stock market returns, although they don't do so individually. While we uncover a positive risk-return relation in the stock market, as stipulated by the CAPM, the idiosyncratic volatility is negatively related to future stock returns. One potential explanation for the latter result is that the idiosyncratic volatility is a measure of divergence of opinion, which, as argued by Miller (1977), could lead a stock to be overvalued initially and to suffer capital losses subsequently. However, we find that the idiosyncratic volatility forecasts stock returns mainly because of its negative co-movements with the consumption-wealth ratio, which, as argued by some recent authors, is a proxy for the liquidity premium.

Purpose This paper's purpose was to examine the impact of geomagnetic activity (GMA) on the timing and valuation of earnings information disclosed by firms every quarter. Design/methodology/approach The authors start the analyses with a sample of 112,669 client firms from 1989 to 2018. To analyze the impact of GMA on the earnings response coefficient (ERC), the authors use the three-day cumulative abnormal returns and cumulative abnormal returns for the extended post-earnings announcement window [2, 75] as the dependent variables. The authors interact unexpected earnings (UE) with the C9 Index, an index commonly used to measure GMA and study how GMA affects the pricing of new public information. To examine the effect of GMA on the timing of disclosure of earnings news, the authors regress a variant of the GMA index on the propensity to disclose bad earnings news. Findings The authors find significantly lower earnings response coefficients during periods of high GMA. This effect is permanent and stock prices do not correctly incorporate the implications of earnings information over time. The authors also show that managerial behavior is affected by GMA as well and the managers are more (less) likely to release bad (good) news during periods of higher activity. Finally, the authors also find that in situations where stakeholders are likely to rely on modern technology that depends minimally on humans, the adverse impact of GMA on the pricing of earnings information is mitigated

This paper analyses the effects of financial statements on the efficiency of the Russian stock market. Specifically, we analyse the impact of financial reporting on stock prices of the firms listed on the Moscow Stock Exchange. By means of the widely used event study method, which dates back to Ball and Brown [1], we analyse how corporate news publication affects stock prices. Our research analyses 1000 samples, each consisting of 30 events, independent of the underlying stocks/firms and analyses the relation between the behaviour of the share prices and the release of the firms’ annual, quarterly, and unscheduled financial statements. We use the daily stock price data of 56 components of the Russia Trading System Index from the years 2014 to 2020 in order to analyse the relation between the behaviour of the shares’ prices and the releases of the firms’ annual, quarterly, and unscheduled financial statements. Using an ordinary least squares market model, we estimate the market parameters and especially the so-called normal returns, i.e. benchmark values. With this, we calculate the abnormal returns, i.e. the price changes caused by the events cf. [1; 2]. We perform several statistical tests for non-Gaussian distribution of these abnormal returns and find that there is a significantly non-Gaussian relationship between the publication of financial statements and the prices of the shares, which should not be the case in an efficient market [2]. Our results indicate that stock pr

Stock price crash risk, defined as an adverse event, is a pervasive phenomenon at the market level. This implies that theStock price crash risk, defined as an adverse event, is a pervasive phenomenon at the market level. This implies that the decline in stock prices is not limited to a specific stock but extends across the entire market. Stock price crashes result in significant losses for shareholders and investors, as well as a decline in the overall capital market. Hence, understanding the factors influencing this phenomenon is of critical importance. The present study aims to investigate the impact of industry operating cash flow volatility on future stock price crash risk, considering the roles of economic policy uncertainty and conditional conservatism in companies listed on the Tehran Stock Exchange. A sample of 136 companies was selected using a screening method over the period from 2012 to 2022. To analyze the data and test the hypotheses, regression analysis and panel data techniques were employed. The findings indicate that industry operating cash flow volatility has a positive and significant effect on future stock price crash risk. Furthermore, economic policy uncertainty amplifies the positive effect of industry operating cash flow volatility on stock price crash risk. Conversely, conditional conservatism in accounting mitigates the positive relationship between operating cash flow volatility and future stock price crash risk. IntroductionThe expansion of the ca

This study empirically examines how Green Credit Policy (GCP) issued by the State Bank of Vietnam in 2023 affects the stock price crash risk of high-polluting firms. The policy aims to promote sustainable development and green growth by channeling credit toward environmentally friendly projects and restricting financing for high-polluting activities. In the short term, high-polluting firms face increased compliance pressure and costs, compelling managers to enhance self-discipline and transparency to meet regulatory and stakeholder expectations. Treating the issuance of the policy as a quasi-natural experiment, we employ a Difference-in-Differences (DID) framework and find robust evidence that the policy significantly reduces stock price crash risk among high-polluting firms relative to low-polluting counterparts. This effect is mediated primarily through reduced information asymmetry and strengthened managerial self-discipline, as firms gradually release negative information rather than hoard it. The findings remain robust to endogeneity checks (i.e., instrumental variable (IV) analysis and propensity score matching (PSM)). Furthermore, heterogeneity analyses show that these risk-mitigating effects are more pronounced in non-state-owned enterprises, firms in the low-corruption region, and especially firms with stronger ESG disclosure commitment. Our study contributes novel evidence from Vietnam on how green financial reforms can mitigate tail risks in capital markets, highli

<div> <p>We study how the availability of alternative data affects the performance of active mutual funds, using the release of stock-specific alternative data that provide information on firms in the retail industry. We find that such releases significantly reduce funds’ stock-picking ability for covered stocks. The effect is stronger for funds relying on traditional expertise, such as industry or geographic specialization, and leads them to reallocate capital from covered to peer, uncovered stocks. Overall, our results suggest that alternative data can reshape the drivers of fund performance by reducing the value of traditional expertise.</p> </div>

As carbon policies expand under net zero goals, policy news in carbon markets can send fast signals for equity prices. Using daily data for more than 1,600 listed firms from July 2021 to December 2025, we build carbon policy shocks from carbon price moves on 12 national ETS release days and estimate their link to stock returns. Carbon price changes on release days have a much stronger link to stock returns than changes on other days. Channel tests suggest the response is tied to shifts in short-run risk conditions and changes in trading conditions around release days. The effect is stronger when the carbon market is more mature and more active, and it differs by ETS coverage status, policy type and firm carbon exposure. The findings show how transition risk is priced in China and inform ETS communication and market depth.

variability of the fund's performance. High historical volatility may indicate high future volatility, and therefore increased investment risk in a fund. Depending An investment fund is a way of investing money alongside other investors in order to benefit from the inherent advantages of working as part of a group such as reducing the risks of the investment by a significant percentage. These advantages include an ability to: hire professional investment managers, who may offer better returns and more adequate risk management; benefit from economies of scal Alpha represents the fund's return when the benchmark's return is 0. This shows the fund's performance relative to the benchmark and can demonstrate the value added by the fund manager. The higher the 'alpha' the better the manager. Alpha investment strategies tend to favour stock selection methods to achieve growth. Beta represents an estimate of how much the fund will move if its benchmark moves by 1 unit. This shows the fund's sensitivity to changes in the market. Beta investment strategies tend to favour asset allocation models to achieve outperformance. R-squared is a measure of the association between a fund and its benchmark. Values are between 0 and 1. Perfect correlation is indicated by 1, and 0 indicates no correlation. This measure is useful in determining if the fund manager is adding value in their investment choices or acting as a closet tracker mirroring the market and making little difference. For example, an index fund will have an R-squared with its benchmark index very close to 1, indicating close to perfect correlation (the index fund's fees and tracking error prevent the correlation from ever equalling 1). Standard deviation is a measure of volatility of the fund's performance over a period of time. The higher the figure the greater the variability of the fund's performance. High historical volatility may indicate high future volatility, and therefore increased investment risk in a fund.