Persistence of announcement effects on the intraday volatility of stock returns: Evidence from individual data

We analyze the persistence effects in the empirical relationship between announcement releases and return volatilities of four major companies of the French stock market using high frequency data over the period 1995–1999. Besides its institutional stability, this sample period avoids the econometric difficulties inherent to simultaneous news arrivals. Our approach contributes to the relevant literature in that we focus on individual stock volatilities rather than indices, we distinguish firm‐specific and macroeconomic announcements, and we endogenize both the durations of announcement effects and the response patterns of equity prices. We find that our individual volatilities are affected by a systematic market effect, calendar effects, announcements related to the firms’ macroeconomic environment and announcements related to the firms’ and their competitors’ strategic dealings and commercial outcomes. We find evidence that all volatility responses are gradual with persistence horizons ranging from one to three hours, revealing a significant degree of inefficiency of the French stock market over the period. This inefficiency can be viewed as a breeding ground for the implementation of more performant informational and trading systems that allowed markets to move towards more efficiency.     

Stock returns,trading volume,and volatility: The case of African stock markets

The presence of the African Stock Markets (ASMs) in the global frontier markets indices confirms their global portfolio diversification role. This study investigates the asymmetric and intertemporal causality among the stock returns, trading volume, and volatility of eight ASMs. Results based on the linear model reveal that return generally Granger cause trading volume. However, evidence from the quantile regression shows that lagged trading volume has a negative causal effect on returns at low quantiles and positive causal effects at high quantiles. This evidence is consistent with volume-return equilibrium models, disposition and overconfidence models, and information asymmetry models. The positive causal effects of volatility on volume support the dispersion of beliefs model. In contrast, intertemporal evidence of contemporaneous and lagged causal relationships from trading volume to volatility supports the mixture of distribution hypothesis, sequential information acquisition hypothesis, and dynamic efficient market hypothesis. Volume-return and return-volume causality dynamics are quantile-specific and therefore driven by market conditions. However, the volume-volatility causality is dependent on volatility regimes. The linear model results confirm how model misspecification can distort and even reverse empirical evidence relative to nonlinear models.     

Effect of auditing: Evidence from variability of stock returns and trading volume

Although the benefits of auditing are uncontroversial in developed markets,there is scant evidence about its effect in emerging economies.Auditing derives its value by increasing the credibility of financial statements,which in turn increases investors’reliance on them in developed markets.Financial statement information is common to all investors and therefore increased reliance on it should reduce divergence in investors’assessment of firm value.We examine the effect of interim auditing on inter-investor divergence with a large sample of listed Chinese firms and find that it decreases more for firms whose reports are audited compared to non-audited firms.This finding suggests that investors rely more on audited financial information.Results of this study are robust to variations in event window length and specification of empirical measures.     

Noise trading and stock market volatility

We investigate the relative effects of fundamental and noise trading on the formation of conditional volatility. We find significant positive (negative) effects of investor sentiments on stock returns (volatilities) for both individual and institutional investors. There are greater positive effects of rational sentiments on stock returns than irrational sentiments. Conversely, there are significant (insignificant) negative effects of irrational (rational) sentiments on volatility. Also, we find asymmetric (symmetric) spillover effects of irrational (rational) bullish and bearish sentiments on the stock market. Evidence in favor of irrational sentiments is consistent with the view that investor error is a significant determinant of stock volatilities.     

Weather and intraday patterns in stock returns and trading activity

We examine the relation between weather in New York City and intraday returns and trading patterns of NYSE stocks. While stock returns are found to be generally lower on cloudier days, cloud cover has a significant influence on stock returns only at the market open. There are significantly more seller-initiated trades when there is more cloud cover at the market open, which is consistent with the return results. Cloudy skies are associated with higher volatility and less market depth over the entire trading day. Finally, cloud cover is not significantly correlated with spread measures and turnover ratios. The findings overall suggest that weather has a significant influence on investors’ intraday trading behavior.     

Structural changes in volatility and stock market development: Evidence for Spain

In this paper we review the factors that lead to changes in stock market volatility and use alternative methodologies of endogenous breakpoint detection in order to analyze whether the volatility of the Spanish stock market has changed significantly over the period 1941–2001. This period corresponds to years of profound development of both the financial and the productive sides of the economy in this country. The analysis of the Spanish stock market suggests that volatility has behaved in a different manner over the period 1941–2001: After three decades of low volatility, a structural break in volatility is detected in 1972, coinciding with the opening of the Spanish economy. From 1972 to 2001, the years of more intense financial development, the stock market presents a higher level of volatility and lower persistence. This effect is partly attributable to the increased growth of trading volume brought about by the economic development process.     

Abnormal trading volume,news and market efficiency: Evidence from the Jamaica Stock Exchange

This paper investigates market efficiency of the Jamaica Stock Exchange (JSE). Together, weak and semi-strong form efficiency claim that historical and newly released public information do not predict future stock price movement. We test both forms of market efficiency by analyzing stock price behavior during times of abnormal trading volume and around the release dates of earnings information. Abnormal trading volume may be driven by liquidity demand or reflect new or private information flow to the market. Using JSE data over the period 2000 to 2021, we find price dynamics consistent with price pressure as firms experience negative abnormal returns on the day of abnormal trading activity but offsetting positive abnormal stock returns on the following day. Further findings show post earnings announcement drift on the JSE. Taken as a whole, the evidence suggests violations of market efficiency and has implications for capital allocation in this emerging market.     

Information content of options trading volume for future volatility: Evidence from the Taiwan options market

This study follows the approach of Ni et al. [Ni, S.X., Pan, J., Poteshman, A.M., 2008. Volatility information trading in the option market. Journal of Finance 63, 1059–1091] – based upon the vega-weighted net demand for volatility – to determine whether volatility information exists within the Taiwan options market. Our empirical results show that foreign institutional investors possess the strongest and most direct volatility information, which is realized by the delta-neutral options/futures trades. In addition, a few individual investors (less than 1% of individuals’ trades) might be informed and realize their volatility information using the strangle strategy. Surprisingly, we find no evidence to support the predictive ability of the volatility demand from straddle trades, despite the widespread acknowledgement that such trades are sensitive to volatility.     

Extreme volumes and expected stock returns: Evidence from China's stock market

We examine the relation between extreme trading volumes and expected returns for individual stocks traded on the Shanghai Stock Exchange and the Shenzhen Stock Exchange over the July 1994–December 2000 interval. Contrasted with the evidence obtained from the US data [J. Finance 56 (2001) 877], our results show that stocks experiencing extremely high (low) volumes are associated with low (high) subsequent returns. Moreover, this extreme volume–return relation significantly co-varies with security characteristics like past stock performance, firm size, and book-to-market values. In particular, stocks with extreme volumes are related to poorer performance if they are past winners, large firms, and glamour stocks than if they are past losers, small firms, and value stocks, respectively. These results are robust to both daily and weekly samples as well as stock exchange sub-samples. Although the liquidity premium hypothesis of Amihud and Mendelson [J. Financ. Econ. 17 (1986) 223] provides a partial explanation for the extreme volume–return relation, our results fit better the behavioral hypothesis of Baker and Stein [J. Financ. Mark. 7 (2004) 271].     

The bivariate GARCH approach to investigating the relation between stock returns,trading volume,and return volatility

We use a bivariate GJR-GARCH model to investigate simultaneously the contemporaneous and causal relations between trading volume and stock returns and the causal relation between trading volume and return volatility in a one-step estimation procedure, which leads to the more efficient estimates and is more consistent with finance theory. We apply our approach to ten Asian stock markets: Hong Kong, Japan, Korea, Singapore, Taiwan, China, Indonesia, Malaysia, the Philippines, and Thailand. Our major findings are as follows. First, the contemporaneous relation between stock returns and trading volume and the causal relation from stock returns and trading volume are significant and robust across all sample stock markets. Second, there is a positive bi-directional causality between stock returns and trading volume in Taiwan and China and that between trading volume and return volatility in Japan, Korea, Singapore, and Taiwan. Third, there exists a positive contemporaneous relation between trading volume and return volatility in Hong Kong, Korea, Singapore, China, Indonesia, and Thailand, but a negative one in Japan and Taiwan. Fourth, we find a significant asymmetric effect on return and volume volatilities in all sample countries and in Korea and Thailand, respectively.     

Pandemic-induced fear and stock market returns: Evidence from China

We construct a pandemic-induced fear (PIF) index to measure fear of the COVID-19 pandemic using Internet search volumes of the Chinese local search engine and empirically investigate the impact of fear of the pandemic on Chinese stock market returns. A reduced-bias estimation approach for multivariate regression is employed to address the issue of small-sample bias. We find that the PIF index has a negative and significant impact on cumulative stock market returns. The impact of PIF is persistent, which can be explained by mispricing from investors' excessive pessimism. We further reveal that the PIF index directly predicts stock market returns through noise trading. Investors' Internet search behaviors enhance the fear of the pandemic, and pandemic-induced fear determines future stock market returns, rather than the number of cases and deaths caused by the COVID-19 pandemic.     

Trading performance and market efficiency: Evidence from algorithmic trading

In India, National Stock Exchange directly identifies algorithmic trading participation. Algorithmic traders possess intraday market timing skills. Results are not motivated by extreme short-term signals or transitory price trading. Magnitude of market timing performance in cross-sectional group of traders shows that they earn profit across all the cases, and maximize while providing liquidity. Volume-weighted-average-price decomposition analysis reports algorithmic traders earn profits through intraday market timing performance for five-minute and one-minute intervals, and it is higher compared to short-term market timing performance across all trader groups. Order imbalance and price delay regressions show that algorithmic trading significantly improves price efficiency.     

Structural transmissions among investor attention,stock market volatility and trading volumes

We employ data-based approaches to identify the transmissions of structural shocks among investor attention measured by Google search queries, realised volatilities and trading volumes in the United States, the United Kingdom and the German stock market. The two identification approaches adopted for the structural vector autoregressive analysis are based on independent component analysis and the informational content of disproportional variance changes. Our results show robust evidence that investors' attention affects both volatilities and trading volumes contemporaneously, whereas the latter two variables lack immediate impacts on investors' attention. Some movements in investors' attention can be traced back to market sentiment.     

Determinants of idiosyncratic volatility: Evidence from the Indian stock market

This paper investigates whether firm-specific characteristics explain idiosyncratic volatility in the stocks of non-financial firms traded in the Indian stock market. It employs the linear time series five-factor model, augmented with a liquidity factor and the conditional EGARCH model, to extract yearly idiosyncratic volatility. We estimate a panel data regression to quantify the relationship between firm-specific characteristics and the volatility of individual securities. The results show that idiosyncratic volatility is significant in emerging markets such as India, and that cross-sectional return variations of firms are associated with firm-specific characteristics such as firm size, book-to-market ratio, momentum, liquidity, cash flow-to-price ratio, and returns on assets. We find that the idiosyncratic risk documented in this study is associated with smaller size of company, higher liquidity, low momentum, high book-to-market ratio, and low cash flow-to-price ratio. The findings suggest need to develop alternative tools to make investment decisions in emerging markets.     

Long-term adjusted volatility: Powerful capability in forecasting stock market returns

We develop the long-term adjusted volatility (LV_ADJ) by removing the interference information of short-term volatility from the simple long-term volatility and examine the role of LV_ADJ in the predictability of stock market returns. Using a sample from January 2000 to December 2019 and considering 19 popular predictors, LV_ADJ positively predicts the next-month returns of S&P 500 and the univariate model with LV_ADJ has the best forecasting performance with adjusted in-sample r-squared of 3.825%, out-of-sample r-squared of 3.356%, return gains of 5.976%, CER gains of 4.708 and Sharpe ratio gains of 0.394. The predictive information of LV_ADJ is independent of that obtained from the 19 popular predictors. Furthermore, we find that LV_ADJ also has predictive power for long-term (3–12 months) stock returns, and can forecast returns of industry portfolios and characteristic portfolios.     

Information,trading and stock returns: Lessons from dually-listed securities

This paper compares the intra-day patterns on the NYSE and AMEX of volatility, trading volume and bid-ask spreads for European and Japanese dually-listed stocks with American stocks of comparable average trading volume and volatility. It is shown that the intra-day patterns for these stocks are remarkably similar even though public information flows differ markedly across these stocks during the trading day. In the early morning, all stocks have higher volatility than later in the day, but this phenomenon is most pronounced for Japanese stocks and affects American stocks the least. We argue that these patterns are consistent with markets reacting to the overnight accumulation of public information but are inconsistent with the view that early morning volatility can be attributed to monopolistic specialist behavior.     

Revisiting the empirical linkages between stock returns and trading volume

This paper investigates whether the empirical linkages between stock returns and trading volume differ over the fluctuations of stock markets, i.e., whether the return–volume relation is asymmetric in bull and bear stock markets. Using monthly data for the S&P 500 price index and trading volume from 1973M2 to 2008M10, strong evidence of asymmetry in contemporaneous correlation is found. As for a dynamic (causal) relation, it is found that the stock return is capable of predicting trading volume in both bear and bull markets. However, the evidence for trade volume predicting returns is weaker.     

Idiosyncratic volatility,fundamentals, and institutional herding: Evidence from the Japanese stock market

Using Japanese data from 1975 to 2003, we show that both institutional herding and firm earnings are positively related to idiosyncratic volatility. We reject the hypothesis that institutional investors herd toward stocks with high idiosyncratic volatility and systematic risk. Our results suggest that a behavior story may explain the negative premium earned by high idiosyncratic volatility stocks found by Ang et al. [Ang, Andrew, Hodrick, Robert J., Yuhang Xing, Xiaoyan Zhang, 2004. The cross-section of volatility and expected returns, Forthcoming Journal of Finance]. We also find that the dispersions of change in institutional ownership and return-on-asset move together with the market aggregate idiosyncratic volatility over time. Our results suggest that investor behavior and stock fundamentals may both help explain the time-series pattern of market aggregate idiosyncratic volatility.     

The asymmetric relationship between returns and implied volatility: Evidence from global stock markets

We investigate the asymmetric relationship between returns and implied volatility for 20 developed and emerging international markets. In particular we examine how the sign and size of return innovations affect the expectations of daily changes in volatility. Our empirical findings indicate that the conditional contemporaneous return-volatility relationship varies not only based on the sign of the expected returns but also upon their magnitude, according to recent results from the behavioral finance literature. We find evidence of an asymmetric and reverse return-volatility relationship in many advanced, Asian, Latin-American, European and South African markets. We show that the US market displays the highest reaction to price falls, Asian markets present the lowest sensitivity to volatility expectations, while the Euro area is characterized by a homogeneous response both in terms of direction and impact. These results may be safely attributed to cultural and societal characteristics. An extensive quantile regression analysis demonstrates that the detected asymmetric pattern varies particularly across the extreme distribution tails i.e., in the highest/lowest quantile ranges. Indeed, the classical feedback and leverage hypotheses appear not plausible, whilst behavioral theories emerge as the new paradigm in real-world applications.     

Empirical relationship between macroeconomic volatility and stock returns: Evidence from Latin American markets

Emerging market stock returns have been characterized as having higher volatility than returns in the more developed markets. But previous studies give little attention to the fundamentals driving the reported levels of volatility. This paper investigates whether dynamics in key macroeconomic indicators like exchange rates, interest rates, industrial production and money supply in four Latin American countries significantly explain market returns. The MSCI world index and the U.S. 3-month T-bill yield are also included to proxy the effects of global variables. Using a six-variable vector autoregressive (VAR) model, the study finds that the global factors are consistently significant in explaining returns in all the markets. The country variables are found to impact the markets at varying significance and magnitudes. These findings may have important implications for decision-making by investors and national policymakers.