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The research examines the effect of the COVID-19 outbreak on the dynamic co-movement of the stock markets of China, Japan, and the USA using a VAR model and a DCC-GJR-GARCH model. Especially, this study focuses on during and preceding the COVID period. This study data period extends from 1 January 2016 to 31 April 2022. The results demonstrate that COVID-19’s effect increases stock market volatility. Meanwhile, the VAR model revealed that the USA’s exogeneity was greater during COVID-19. In addition, the pre-crisis Granger Causality between China–USA and Japan–China is substantially higher than during the crisis. The findings of DCC-GJR-GARCH indicate the presence of volatility clustering in each of the stock markets. Moreover, the results suggest that the time-varying correlations between China and the USA during the pre-COVID period are greater than during the COVID period. The study’s findings highlight that investors attempting to increase investment diversification opportunities worldwide should always consider dynamic co-movement in different periods to maximize returns and minimize risk.

Using a large set of daily US and Japanese stock returns, we test in detail the relevance of Student models, and of more general elliptical models, for describing the joint distribution of returns. We find that while Student copulas provide a good approximation for strongly correlated pairs of stocks, systematic discrepancies appear as the linear correlation between stocks decreases, that rule out all elliptical models. Intuitively, the failure of elliptical models can be traced to the inadequacy of the assumption of a single volatility mode for all stocks. We suggest several ideas of methodological interest to efficiently visualise and compare different copulas. We identify the rescaled difference with the Gaussian copula and the central value of the copula as strongly discriminating observables. We insist on the need to shun away from formal choices of copulas with no financial interpretation.

This paper reports an empirical analysis of the relationship between return autocorrelation, trading volume and volatility, following the seminal paper by Campbell, Grossman and Wang (1992) using data for A shares traded on the Shanghai and Shenzhen stock exchanges for the period 1992–2002. Campbell et al. argue that autocorrelation of returns will be negatively related to trading volume given that market makers will need to be rewarded with higher returns for accommodating noise traders. For our full sample we find remarkably consistent support for the CGW hypothesis and results — return autocorrelations are negatively but non-linearly related to lagged trading volume and less strongly to volatility. These results are quite robust with respect to different messures of volume and volatility. We argue that this is a striking result in view of the substantial differences between the US market in the 1960s, 1970s and 1980s and the Chinese market of the 1990s. The relationship proves to be unstable over short sub-periods although whether this is due to the relatively short sample we use or to the inherent instability of the Chinese market in its first decade of operation will not be clear until much longer data sets are available for Chinese stock prices.

This study empirically investigates the interaction between trading volume and cross-autocorrelations of stock returns in the Taiwan stock market. The result shows that returns on high trading volume portfolios lead returns on low trading volume portfolios when controlled for firm size, indicating that trading volume determines lead-lag cross-autocorrelations of stock returns. Overall, the empirical findings of this study demonstrate similar results for both monthly and daily returns, suggesting that nonsynchronrous trading is not the main reason for the lead-lag cross-autocorrelations presented in this study. Consequently, the empirical results presented here support the speed of adjustment hypothesis, and suggest that some market inefficiency exists in the Taiwan stock market. Additionally, compared with evidence of lead-lag cross-autocorrelations in the larger, less regulated US stock market, as examined by Chordia and Swaminathan (2000), Taiwan stock market displays less evidence of VARs and Dimson beta regressions. We conjecture that this weak evidence may result from the regulations limiting daily price movements in the Taiwan stock market. Although the price limits policy lowers risk and stabilizes stock prices, it also prevents stock prices and trading volume from instantaneously and fully reflecting new information.

This study investigates whether there is a "China-concept factor", a common variation of stock returns, for firms that are listed in Taiwan stock markets and have real investments in China. We employ a methodology similar to that used by Lamont et al. (2001) in examining whether there is a financial-constraints factor. Listed firms in Taiwan stock markets for the period 1990–2004 are used to form portfolios of firms based on observable characteristics related to their real investments in China. We find that firms investing heavily in China have stock returns moving together over time, which suggests that firms investing in China are subject to common shocks. Firms investing heavily in China are found to exhibit higher average stock returns. There exists a China-concept factor for firms listed in Taiwan stock market and have real investments in China.

In this paper, we apply several variants of the EGARCH model to examine the role of depreciation of the Indian rupee on India's stock market returns using daily data. Our findings suggest that volatility persistence has been high; depreciation of the rupee has increased volatility; and asymmetric volatility confirms that negative shocks generate more volatility than positive shocks. We also find that an appreciation of the Indian rupee over the 2002 to 2006 has generated more returns and less volatility.

This paper investigates the behavior of stock returns and volatility in 10 emerging markets and compares them with those of developed markets under different measures of frequency (daily, weekly, monthly and annual) over the period January 1, 2002 to December 31, 2006. The ratios of mean return to volatility for emerging markets are found to be higher than those of developed markets. Sample statistics for stock returns of all emerging and developed markets indicate that return distributions are not normal and return volatility shows clustering. In most cases, GARCH (1, 1) specification is adequate to describe the stock return volatility. The significant lag terms in the mean equation of GARCH specification depend on the frequency of the return data. The presence of leverage effect in volatility behavior is examined using the TAR-GARCH model and the evidence indicates that is not present across all markets under all measures of frequency. Its presence in different markets depends on the measure of frequency of stock return data.

The main purpose of this paper is to investigate the relation between oil price movements and stock returns in US transportation companies. We estimate oil price risk exposures of the US oil transport sector at the firm level as well as at the industry level over November 1999 to February 2008 period using the Fama–French–Carhart's (1997) four-factor asset pricing model augmented with oil price and interest rate factors. Overall, the results of our study suggest that oil price exposures of firms in the US transportation sector vary across firms and over time. The varying effects of oil shocks on stock returns may be attributed to several factors such as differences among firms' cost structure, financial policies, diversification activities, and hedging strategies.

The main purpose of this paper is to provide evidence on some of the standard models of accounting earnings and returns relations mainly collected through the literature. Standard models such as earnings level and earnings changes, among others, have been investigated in this study. Models that correspond better to the data drawn from the Athens Stock Exchange have been selected. Models I, II, V, VII and IX have statistically significant coefficients of explanatory variables. In addition, model II with the MSE (minimum value of squared residuals) loss function in ARIMAX (2,0,2) is prevalent. Models that include prior earnings in various forms using levels, changes in price and changes in earnings, change in price to beginning price, lagged parameters and differentiated price models have statistically significant explanatory power.

The main purpose of this paper is to examine the legal insider trading activities by directors of companies listed on the Hong Kong Exchange over the period 1993 to 1999. One characteristic of insider trading in Hong Kong is the high frequency of transactions and the large amounts of money involved. Inside purchases appear to signal and correct undervaluation and inside sales appear to signal and correct overvaluation. In contrast to research from Britain and the United States, insider sales are more informative than purchases. On average, insiders earn HK$91,297 per trade, while outsiders who mimic insiders' transactions earn minimal returns. Many firms suffer from infrequent trading and our results are consistent with directors engaging in inside transactions so as to help create a market for the shares. In additional tests, we find that the frequency of insider trading is a function of information asymmetry.

This paper examines the effect of income smoothing on information uncertainty, stock returns, and cost of equity. I show that income smoothing through both total accruals and discretionary accruals tends to reduce firms' information uncertainty, as measured by stock return volatility, analyst earnings forecast dispersion, and analyst earnings forecast error. Further, I provide evidence that stocks of income smoothing firms are priced with a premium. Controlling for earnings shocks and other firm characteristics, income smoothing firms have significantly higher abnormal returns around earnings announcement. In addition, I show that income smoothing reduces firms' implied cost of equity or expected returns. The result is more robust over short horizons up to two years.

Asymmetric autoregressive conditional heteroskedasticity (EGARCH) models and asymmetric stochastic volatility (ASV) models are applied to daily data of Peruvian stock and Forex markets for the period of 5 January 1998–30 December 2011. Following the approach developed in [Omori, Y, S Chib, N Shephard and J Nakajima (2007). Stochastic volatility with leverage: Fast likelihood inference. Journal of Econometrics, 140, 425–449], Bayesian estimation tools are used with Normal and $t$-Student errors in both models. The results suggest the significant presence of asymmetric effects in both markets. In the stock market, negative shocks generate higher volatility than positive shocks. In the Forex market, shocks related to episodes of depreciation create higher uncertainty in comparison with episodes of appreciation. Thus, the Central Reserve Bank faces relatively major difficulties in its intention of smoothing Forex volatility in times of depreciation. The model with the best fit in both markets is the ASV model with Normal errors. The stock market returns have greater periods of volatility; however, both markets react to shocks in the economy, as they display similar patterns and have a significant correlation for the sample period studied.

This paper examines a sample of 167 publicly listed enterprises covered by eight regional pilot emissions trading markets in China from 2013 to 2023. Our empirical findings indicate that the carbon price returns negatively affect the stock returns of enterprises covered by the regional markets, with the Shenzhen and Guangdong regions suffering a more pronounced effect. Furthermore, high-carbon-intensity enterprises are more susceptible to this negative impact than their low-carbon-intensity counterparts. The robustness of the negative relationship is evident even after the national emissions trading market opened on July 16, 2021. This study provides insightful guidance for policymakers to regulate emissions trading markets.

This paper provides evidence regarding the relationship between asset returns and (expected) inflation in the U.S. market. Evidence indicates that inflation has a negative effect on stocks, REIT and bonds. However, its effect on housing and gold assets is positive. Evidence concludes both housing and gold tend to show a positive correlation with inflation. This study finds that inflation causes equity market volatility due to investors’ fears about the possibility of interest rate hikes by the Fed, which further aggravates the price of stocks and REIT, but helps to improve bond prices due to a flight-to-quality effect.

We utilize the multifractal detrended cross-correlation analysis (MF-DCCA) to investigate the cross-correlations between the US economic policy uncertainty (EPU) and US stock markets in the framework of Fractal Market Hypothesis (FMH). The data contain daily closing values of EPU, and the returns of Dow Jones Industrial Average Index (DJI), S&P 500 index (GSPC) and NASDAQ Composite Index (IXIC). Our empirical results show that changes in EPU and fluctuations in the US stock markets interact in a nonlinear way. Furthermore, there exists significant multifractality in the cross-correlations between EPU and stock markets. The cross-correlations exhibit dynamics and are affected by major international events. We capture the underlying mechanisms such as multifractality and nonlinear relation that dominate EPU-US stock markets nexus by means of FMH. The findings add a new dimension to the existing literature, and are important for market participants to adjust investment decisions.

This paper studies the effect of advertising on stock returns both in the short and in the long run. We find that a greater amount of advertising is associated with a larger stock return in the advertising year but a smaller stock return in the year subsequent to the advertising year, even after we control for other price predictors, such as size, book-to-market, and momentum. We conjecture that advertising affects stock returns by attracting investors’ attention to the firm’s stock. Stock price increases in the advertising year due to the attracted attention, but decreases in the subsequent year as the attracted attention wears out over time. We test this investor attention hypothesis and document consistent findings. We find that advertising increases a firm’s visibility among investors in the advertising year. We further find that the negative effect of advertising on the long-run reversal in stock returns is more pronounced if a firm attracts greater investor attention in the advertising year, or if investors face a larger cost of short selling the firm’s stock. It is also more pronounced for small firms, value firms, and firms with poor ex-ante stock or operating performance. Finally, we find that the effect of advertising on future stock returns is stronger when advertising increases compared to the case when advertising decreases.

We find evidence that markets anticipate the potential loss of firm value in the event of the CEO falling sick and eventually dying of COVID-19 in a sample of almost 3000 listed firms from across 137 regions in 10 European countries. First, we use soccer games as “super-spreader” events. The instrumented number of infected cases per capita in the region where company headquarters are located predicts a significant drop in stock returns during March and April 2020 for firms managed by CEOs with a higher probability of dying from COVID-19. Second, we show that the stock price of these firms increases significantly the day on which positive news on the development of COVID-19 vaccines are released in the market.

This paper predicts the two most common stock market exits — mergers and drops — using logit models based on firm-level variables and analyzes the returns of stocks that have high exit probabilities. Such analysis is important for investors given that frequent exits are partly responsible for the large US listing gap (Doidge et al., 2017). High merger probability stocks have positive 3-factor alphas and lower-than-average volatility. Firms with high drop probabilities have anomalously negative 3-, 4-, and 5-factor alphas between $-1.8$% and $-4$% per month. Results are robust to controlling for the effects of skewness, volatility, and turnover on returns.

This paper analyzes the impact of the recent acquisition of Motorola by Google on the subsequent performance of stock returns using an event study methodology. We obtain empirical results by a two-stage regression, by which the impact of market and industry effects can be controlled for. Our findings suggest that the Motorola takeover led to negative and significant excess returns to Google, but positive and highly significant excess returns to Motorola. Additionally, while the event led to significantly positive excess returns to direct competitors, it did not have a strong impact on indirect competitors, suggesting that the importance of the event was restricted to related industries.

We show analytically that animal spirit excess profits for uninformed investors fall (increase) when the risk-free rate rises (falls). In the theoretical analysis, we examine the expected returns of risk-averse, short-lived investors. In addition, we find empirically that the local risk-free rates explain 14% of the changes in the animal spirit excess profits in the global stock markets for the last 29 years when the animal spirits is characterized as a product of the trend-chasing rule.