Herding behaviour and the declining value relevance of accounting information: evidence from an emerging stock market

This article sheds light on the underlying mechanisms behind the changes in the value relevance of accounting information in the Karachi Stock Exchange (KSE) during the 1999–2010 period. We find that neither changes in earnings quality nor the earnings lack of timeliness hypothesis can explain the decline in the value relevance of accounting information in the KSE. Based on the stylized facts associated with the growth of the KSE and the broader economics literature, we argue that the reduction in the explanatory power of accounting information vis-à-vis stock returns was caused by herding behaviour. Empirical estimates from state-space model of herding behaviour confirm the existence of herding, and we find that the value relevance of accounting information is significantly lower in periods characterized by herding behaviour. This article is also amongst the first attempts to empirically demonstrate that an expansionary monetary policy and increases in foreign portfolio investment lead to increased levels of herding.     

Asymmetric effect of margin-trading activities on price crashes: evidence from Chinese stock market

Prior literature on the impact of margin-trading activity on stock price crashes is mixed and does not reach consensus. Using data from a Chinese margin-trading pilot programme initiated in 2010, this article employs both margin-buying and margin-covering activities to investigate the asymmetric impact on stock price crashes. We find that margin-buying activities are beneficial reducing the price crash prone, especially in bad times. In contrast, margin-covering activities amplify price crashes in both good times and bad times.     

EU Emission Allowances and the stock market: Evidence from the electricity industry

This paper constitutes - to our best knowledge - the first econometric analysis on stock market effects of the EU Emission Trading Scheme (EU ETS). Our results suggest that EU Emission Allowance (EUA) price developments matter to the stock performance of electricity firms: EUA price changes and stock returns of the most important European electricity corporations are shown to be positively related. This effect does not work asymmetrically, so that stock markets do not seem to react differently to EUA appreciations in comparison to depreciations. The carbon market effect is shown to be both time- and country-specific: It is particularly strong for the period of EUA market shock in early 2006, and differs with respect to the countries where the electricity corporations analysed are headquartered. Stock market reactions to EUA volatility could not be shown.     

Bond rating changes and stock returns: evidence from the Spanish stock market

This study analyzes the effect of corporate bond rating changes by international agencies on stock prices. This topic has not yet been analyzed for the Spanish stock market, despite the growing importance of ratings in Spanish financial markets. On an efficient market, rating changes will only have an effect if they contain some new information. The results from an event study indicate that rating actions cause significant negative abnormal returns in issuing firms around the date of the announcement. This evidence indicates an informational effect related to downgrades, which supports the hypothesis that credit rating agencies provide information that may reduce the asymmetric information problem between firms and investors. In the case of upgrades, our results are compatible with a redistribution of wealth between bondholders and owners or with the reputation hypothesis.      

Forecasting the realized volatility in the Chinese stock market: further evidence

In this study, the impact of noise and jump on the forecasting ability of volatility models with high-frequency data is investigated. A signed jump variation is added as an additional explanatory variable in the volatility equation according to the sign of return. These forecasting performances of models with jumps are compared with those without jumps. Being applied to the Chinese stock market, we find that the jump variation has a significant in-sample predictive power to volatility and the predictive power of the negative one is greater than the positive one. Furthermore, out-of-sample evidence based on the fresh model confidence set (MCS) test indicates that the incorporation of singed jumps in volatility models can significantly improve their forecasting ability. In particular, among the realized variance (RV)-based volatility models and generalized autoregressive conditional heteroscedasticity (GARCH) class models, the heterogeneous autoregressive model of realized volatility (HAR-RV) model with the jump test and a decomposed signed jump variation have better out-of-sample forecasting performance. Finally, the use of the decomposed signed jump variations in predictive regressions can improve the economic value of realized volatility forecasts.     

Economic policy uncertainty and the Chinese stock market volatility: Novel evidence

In this study, we investigate the impact of global economic policy uncertainty (GEPU) on Chinese stock market volatility. More importantly, for the first time, we explore the effects of directional GEPU based on the changing directions of GEPU and Chinese economic policy uncertainty (EPU). We make several noteworthy findings. First, the in-sample estimated results show that up and down GEPU can lead to substantially high stock market volatility for China. Second, the out-of-sample estimated results support the contention that the GEPU index is helpful for predicting volatility. Moreover, compared to GEPU alone, directional GEPU can provide more useful information that can increase the forecast accuracy. Third, we empirically find that directional GEPU is more effective in predicting Chinese stock market volatility when GEPU and EPU rise in the same month.     

Economic policy uncertainty and the Chinese stock market volatility: new evidence

This study investigates the impacts of the economic policy uncertainty (EPU) indexes of China and the G7 countries on Chinese stock market volatility and further constructs a new diffusion index based on these indexes using principal component analysis (PCA) to achieve enhanced predictive ability. The in-sample results indicate that the EPU indexes of China and some of the G7 countries show a significantly negative impact on future volatility. Moreover, our constructed diffusion index also has a significantly negative impact. Furthermore, the out-of-sample results show that this diffusion index exhibits a significantly higher forecast accuracy than the EPU itself and combination forecasts. Finally, various robustness checks are consistent with our main conclusions. Overall, we construct a new and useful indicator that can substantially increase forecast accuracy with respect to the Chinese stock market.     

Herding in Chinese stock markets: a nonparametric approach

The paper reports new evidence of herding in the Chinese A-type and B-type markets by employing nonparametric kernel regression. We find statistically significant evidence of herding in A-type market under both extreme high and low market returns. Herding in B-type market, which predominantly consists of foreign investors, indicates only weak evidence of herding. We do not find any statistically significant evidence of herding in the pre-2001 sample of B-type market, when only foreign investors could do the trading. Lack of knowledge and experience of local investors may be attributed to the presence of herd behaviour in the Chinese markets.     

Transaction tax and stock market behavior: evidence from an emerging market

This study examines the impact of a stamp tax rate increase on market behavior, using data from two stock exchanges in China. We find that when the tax rate increases from 0.3 to 0.5% (which implies that the transaction cost increases by about 1/3) trading volume decreases by 1/3. This implies an elasticity of turnover with respect to a stamp tax of −50% and an elasticity of turnover with respect to transaction cost of −100%. The markets’ volatility significantly increases after the increase in the tax rate. Furthermore, the change in the volatility structure indicates that the markets become less efficient in the sense that shocks are less quickly assimilated in the markets.

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An empirical investigation of herding in the U.S. stock market

This paper proposes a new empirical testing method for detecting herding in stock markets. The traditional regression approach is extended to a vector autoregressive framework, in which the predictive power of squared index returns for the cross-sectional dispersion of equity returns is tested using a Granger causality test. Macroeconomic news announcements and the aggregate number of firm-level news items are treated as conditioning variables, while the average sentiment of firm-level news is treated as jointly determined. The testing algorithm allows the change points in the causal relationships between the cross-sectional dispersion of returns and squared index returns to be determined endogenously rather than being chosen arbitrarily a priori. Evidence of herding is detected in the constituent stocks of the Dow Jones Industrial Average at the onset of the subprime mortgage crisis, during the European debt and the U.S. debt-ceiling crises and the Chinese stock market crash of 2015. These results contrast with those obtained from the traditional methods where little evidence of herding is found in the US stock market.     

The effects of the Paris climate agreement on stock markets: evidence from the German stock market

We investigate the effects of the 2016 Paris Climate Agreement on the German stock market by considering the impact of 20 announcements pertaining to the Agreement on 17 industries. The event study methodology is used for this purpose, together with several robustness tests, such as the nonparametric rank test and non-parametric conditional distribution approach. The change in systematic risk following the announcements is captured by using various risk models. In general, we find that the Paris Climate Agreement is achieving its objectives in the short run. Our results show that the announcements affected polluting industries in terms of risk and return. Furthermore, we observe two distinct diamond risk structures when (1) Conference of the Parties (COP) 21 took place, and (2) the Agreement came into force.     

Public reaction to stock market volatility: evidence from the ATUS

How does the public react to changes in the stock market? We know from the existing body of research that sentiment can predict future stock-market movements. However, do market movements affect sentiment? This article addresses these questions by testing whether market movements precede changes in the emotional well-being of the general public. Using Granger causality analysis, we compare how market movements affect public well-being during periods of increased (2010) and decreased (2012) volatility. The results show that 30-day-lagged returns are associated positively and significantly with the public’s emotional well-being, and that this effect is stronger during periods of increased volatility. The results also show that this effect may persist for up to 120 days.     

Technological races and stock market value: evidence from the pharmaceutical industry

We provide estimates of the abnormal stock market returns associated with pharmaceutical firms’ announcements of technological and regulatory successes in drug development. On the basis of these estimates, we find empirical support for two key features of technological race models. First, we observe that leaders in the innovation race record higher abnormal returns than do latecomers when they announce a success. Hence, firms should indeed be racing to complete the innovation process before their rivals. Second, pharmaceutical firms are adversely affected by the technological and regulatory success of their rivals, implying that interfirm spillovers in drug development are not sufficient to offset technological rivalry. Additional results are also produced regarding the impact of competition on R&D racing and the extent of therapeutic competition.     

电子商务运营中的羊群效应主要特征研究

随着电子商务企业的快速发展,羊群效应现象逐步显现。文章运用数理统计的特征和指标,从某电子商务网站对最佳商户的评定结果入手,定量分析和描述电子商务运营中的羊群效应现象,并以一致性特征、非泊松分布特征、突增性特征、同质连续性特征四个指标对此进行分析。其中一致性、同质连续性呈现出了羊群效应成员参与的群体特征;非泊松分布、突增性呈现出了羊群效应产生形成的过程特征。研究结果表明,会员级别、性别、消费额度、拥有粉丝数、撰写点评数、发表主题贴数均不会对商户的星级打分造成显著影响;以天、周、月为周期的前来点评的次数不服从Poisson分布;在观察期的20%时间点位前后,出现了人数突增情况;同质连续性占比和相近连续性占比两个指标均十分明显。     

Co-movement of the Chinese and U.S. aggregate stock returns

This article studies the co-movement of the levels, as well as of the volatilities, of the Chinese and U.S. aggregate stock returns in 1995–2014, focusing on the impact of the liberalization of Chinese stock market from 2005. The volatilities of the two returns appear to have started to co-move in 2006. To understand the co-movement, we use the GARCH BEKK method. The result suggests that before 2006, the evolution of the U.S. returns had a tendency to affect the Chinese returns in level and volatility. However, after 2006, the two returns affected each other in a more complex way.     

The stock market effect of air pollution: evidence from China

In financial studies, environmental stimuli such as sunshine, temperature, and daylight are often used as proxies for people’s collective mood swings to test their effects on the stock market. China has experienced serious air pollution problems in recent years, and Chinese public awareness of air pollution has soared. In this paper, we use China as a natural experiment to investigate the effect on stock returns of depressed moods induced by air pollution. Daily air-pollution data from 2005 to 2014 are analysed and the results obtained from the empirical research show that a contemporaneous negative and a two-day lagged positive relationship exists between air pollution levels and stock returns over this time period. The relationship is mediated by the influence of air pollution on investment decisions. The results also indicate that the effect is weakened for companies that protect air quality, but no stronger effect is detected for polluting companies. The findings imply that air pollution is a behavioural factor with some connection to stock returns in China.     

Identifying the Transition from Efficient-Market to Herding Behavior: Using a Method from Econophysics

We test whether “detrended fluctuation analysis” (DFA)—an econophysics method—identifies the transition from efficient-market trading to herding behavior and the rise of the NASDAQ dot.com stock market bubble. DFA divides a time series into “segments” of varying lengths and then tests whether power-law distributions exist within the segments. A power-law distribution of stock-price changes within a segment indicates herding behavior and the start of the dot.com bubble. The clarity of the transition indication depends on both segment lengths and segment starting dates. Our findings show that DFA can be used to identify the beginning of stock-market bubbles but not the beginning of crashes.     

Price discovery and the effects of fragmentation on market quality: evidence from Cypriot cross-listed stocks

Using a novel high-frequency data set, we examine the contribution of Greek trading to the price discovery process of a pair of Cypriot blue-chip, cross-listed stocks during overlapping trading hours. Additionally, we investigate the effects of market fragmentation on the home market’s quality, as measured by microstructure-based liquidity measures. Contrary to earlier studies from other markets, our findings show that foreign stock exchanges can act as the leading contributors to price discovery and can concentrate the majority of trading activity and produce the lowest transaction costs. Our results also show that market fragmentation can lead to negative effects on market liquidity.     

A bubble identification mechanism: Evidence from the Chinese stock market

This paper provides a bubble date-stamping mechanism using the agent-based computational finance method. The key steps of the bubble date-stamping mechanism are the construction of the simulated financial market, the computation of the characteristic indexes, and the thresholds of the Price Band in the simulated financial market. The present study adopts the mechanism to identify the bubbles of sample stocks in the Chinese stock market from April 2003 to December 2019. The findings show that the bubbles are primarily distributed in 2006–2008, 2009–2012 and 2014–2018, respectively. Furthermore, we analyse the bubble strength and the price fluctuation during the above three periods. In addition to the bubble date-stamping mechanism, the paper also studies the factors that drive the bubbles in the Chinese stock market from both macro and micro perspectives.