Cash-flow news,market liquidity and liquidity risk

This article adopts Campbell's (1991) return decomposition model to decompose the unexpected stock return and unexpected excess stock return in the US stock market. The study also investigates the factors that cause the shock to stock return and excess stock return. We further examine the responses of stock market to cash-flow news, expected stock return news, expected excess stock return news and interest rate news. Last, we examine the reaction of market liquidity, liquidity risk and abnormal trading volume to cash-flow news, expected stock return news, expected excess stock return news and interest rate news. Our main findings are summarized as follows: first, cash-flow news is the main driver of stock return and excess stock return in stock market. Second, the dividend payout ratio is able to predict stock return and excess stock return. Third, under the model of stock return variance, unexpected market liquidity and unexpected liquidity risk are negatively related to expected stock return news, but not related to cash-flow news. Fourth, under the model of excess stock return variance, unexpected market liquidity and unexpected liquidity risk are negatively related to cash-flow news, expected excess stock return news, and interest rate news.     

Time-varying relationship of news sentiment,implied volatility and stock returns

I examine the relationship between aggregate news sentiment, S&P 500 index (SPX) returns, and changes in the implied volatility index (VIX). I find a significant negative contemporaneous relationship between changes in VIX and both news sentiment and stock returns. This relationship is asymmetric whereby changes in VIX are larger following negative news and/or stock market declines. Vector autoregression (VAR) analysis of the dynamics and cross-dependencies between variables reveals a strong positive relationship between previous and current period changes in implied volatility and stock returns, while current period and lagged news sentiment has a significant positive (negative) relationship with stock returns (changes in VIX). I develop a simple trading strategy whereby high (low) levels of implied volatility signal attractive opportunities to take short (long) positions in the underlying index, while extremely negative (positive) news sentiment signals opportunities to enter short (long) index positions. The investor fear gauge (VIX) appears to perform better than news sentiment measures in forecasting future returns.     

Stock market reactions to environmental disclosures: new evidence from China

We study how the stock market in China responds to announcements by an environmental risk index and find that China’s stock market penalizes firms associated with unfavourable environmental news if the information is provided directly to investors in a manner that is easily understood. We also find that the negative impact on stock prices fades after multiple disclosures of the same information.     

Daily market news sentiment and stock prices

In recent years there has been a tremendous growth in readily available news related to traded assets in international financial markets. This financial news is now available through real-time online sources such as Internet news and social media sources. The increase in the availability of financial news and investor’s ease of access to it has a potentially significant impact on market stock price movement as these news items are swiftly transformed into investors sentiment which in turn drives prices. In this study, we use the Thomson Reuters News Analytics (TRNA) data set to construct a series of daily sentiment scores for Dow Jones Industrial Average (DJIA) stock index constituents. We use these daily DJIA market sentiment scores to study the influence of financial news sentiment scores on the stock returns of these constituents using a multi-factor model. We augment the Fama–French three-factor model with the day’s sentiment score along with lagged scores to evaluate the additional effects of financial news sentiment on stock prices in the context of this model using Ordinary Least Square (OLS) and Quantile Regression (QR) to analyse the effect around the tail of the return distribution. We also conduct the analysis using the seven-day simple moving average (SMA) of the scores to account for news released on non-trading days. Our results suggest that even when market factors are taken into account, sentiment scores have a significant effect on Dow Jones constituent returns and that lagged daily sentiment scores are often significant, suggesting that information compounded in these scores is not immediately reflected in security prices and related return series. The results also indicate that the SMA measure does not have a significant effect on the returns. The analysis using Quantile Regression provides evidence that the news has more impact on left tail compared to the right tail of the returns.     

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.     

Evolving Weak-Form Informational Efficiency of Indian Stock Market

This empirical study deals with the changing weak-form informational efficiency of Indian stock market in recent years. The data cover daily information on BSE-100 index for a period of nine years from January 4, 1993 to December 31, 2001. In order to assess the evolving weak-form efficiency over time, entire data period is partitioned into 18 sub-periods and spectral shape tests are carried out separately in each sub-period. Based on empirical results it is seen that the extent of informational efficiency (weak form) of Indian stock market fluctuated substantially from sub-period to sub-period. The market was considerably inefficient during each sub-period till June 1996, achieved high level of efficiency during July 1996 to December 1999 and showed efficiency at relatively lower level thereafter except little aberration during 2000.

     

Foreign News and Spillovers in Emerging European Stock Markets

We analyze foreign news and spillovers in the emerging EU stock markets (the Czech Republic, Hungary, and Poland). We employ high‐frequency five‐minute intraday data on stock market index returns and four classes of EU and US macroeconomic announcements during 2004–07. We account for the difference of each announcement from its market expectation and we jointly model the volatility of the returns accounting for intraday movements and day‐of‐the‐week effects. Our findings show that intraday interactions on the new EU markets are strongly determined by mature stock markets as well as the macroeconomic news originating thereby. We show that strong contemporaneous links across markets are present even after controlling for macroeconomic announcements. Finally, in terms of specific announcements, we are able to show the exact sources of macro news spillovers from the developed foreign markets to the three new EU markets under research.     

FREQUENT NEWS AND PURE SIGNALS: THE CASE OF A PUBLICLY TRADED FOOTBALL CLUB

We use stock market data for Borussia Dortmund GmbH & Co. KGaA – one of the leading German football clubs – for an application of the news model. Owing to the specific characteristics of the news‐generating process, the case of a publicly traded sport club is a very appropriate candidate for testing this model. By applying a traditional as well as a reversed news model, we elaborate whether new information can explain subsequent changes in the stock price of Borussia Dortmund. We find that sport as well as corporate governance‐related variables are important drivers of the stock price.     

Mass media effects on trading activities: television broadcasting evidence from Japan

This study examines how information broadcasting through television (TV) media influences stock market activities. Consistent with the effect of TV information to attract investor attention, we find that increased information flow through TV is significantly associated with greater trading volume and larger price change. For information type, hard news from business-oriented programmes and earnings-related news strongly contributes to the attention effect, while the effect of soft news is weaker. Bid–ask spread widens for more TV information flows, suggesting that new information arrival in the market expands information asymmetry. Finally, the impact of TV is more influential for stocks with more individual shareholders than those with institutional shareholders.     

金融资产收益率波动预测——基于我国股票市场跳跃行为研究

金融资产收益率波动是资产定价和金融风险管理的核心部分,而跳跃是收益率波动中的重要组成部分。基于修正Z-检验,本文检测识别我国股市波动中跳跃行为,并且研究了跳跃的时序特征,统计结果表明,在市场大波动时期,和连续成份相比,跳跃对于波动率具有极其重要的贡献。建立包含跳跃的已实现波动率非齐次自回归模型,在波动模型中纳入滞后绝对日收益率和杠杆效应预测股指收益率波动。实证分析结果显示,对于短期的波动预测,包含跳跃和两种影响因素的波动模型表现最好,然而对于提前1月的长期预测,跳跃和连续波动成份分离模型预测明显优于其它模型,这些事实说明跳跃对股指波动率预测具有重要的影响,好坏消息对波动率非对称性具有短期显著影响,而对长期水平的波动率预测影响不显著。     

Exploring the interaction between stock price index and exchange rates: an asymmetric threshold approach

This article examines the impact of stock market news on the foreign exchange markets of USA, Canada and UK, employing an innovative extension of the asymmetric threshold model of Apergis and Miller (2006). Under this framework we can disentangle the reaction of foreign exchange market to bad or good news and small or large news of stock returns. Our comprehensive daily data-set spans the period from January 1990 to June 2014. Using a cointegration and error correction model, we document the existence of a causal relationship between stock market and foreign exchange markets. Most interestingly, our results derived from the asymmetric threshold model confirm that the relationship between stock and foreign exchange markets is sensitive to short-term good or bad news and short-term small or large news. Our findings entail significant implications for policymakers, governments, risk managers and international investors.     

Investment horizons,cash flow news,and the profitability of momentum and reversal strategies in the Chinese stock market

This study investigates the profitability of momentum and reversal strategies of different investment horizons in the Chinese stock market. The findings indicate that momentum strategies are profitable for investment horizons less than one week. For longer investment horizons, reversal strategies are profitable. This result is very different from the US market, where profitable momentum strategies yield to much longer investment horizons. We show that this is because investors in Chinese stock market generally overreact to the company cash flow news while investors in US market underreact to cash flow news.     

Assessing volatility transmission between Brent and stocks in the major global oil producers and consumers – the multiscale robust quantile regression

This paper investigates the volatility transmission effect between Brent oil futures and stock markets in the major global oil producing and consuming countries – the U.S., Russia, China and Saudi Arabia. In that process, we employ a mixture of novel and elaborate methodologies – wavelet signal decomposing procedure, GARCH model with complex distribution and recently developed robust quantile regression. Our results indicate that the effect is stronger in short-term horizon than in midterm and long-term in most cases. The magnitude is much stronger in turbulent times, whereas in tranquil times, this effect is very weak. We find that Russian RTS index endures the strongest volatility transmission effect from oil market. Surprisingly, Saudi stock market does not suffer heavy spillover effect even in the periods of increased market unrest. In the U.S. and China, the effect is much stronger from stocks to oil than vice-versa, and this particularly applies for the U.S. case.

     

股票价格服从不对称跳跃-扩散过程的期权定价模型

经典期权定价公式B—S模型假设股票价格服从连续的几何布朗运动,然而,经验研究表明股票价格常常发生跳跃式的变化,这主要是由于股市上常出现一些重大的事件导致的。通过对期权市场出现的“隐含波动率微笑”现象进行观察和研究,可以发现引起股票价格上升或者下跌的跳跃式变化往往是不对称的。为简单起见,在假设跳跃幅度服从均匀分布假设前提下,初步建立起股票价格服从不对称跳跃-扩散过程期权定价模型。     

Long Range Dependence in the Indian Stock Market: Evidence of Fractional Integration,Non-Linearities and Breaks

This paper deals with the analysis of the Indian stock market prices using long range dependence techniques. In particular, we employ a variety of fractionally integrated models, which are very general in the sense that it allows us to incorporate structural breaks and non-linear structures. Our results indicate that the series corresponding to the NSE index is nonstationary and highly persistent, with an order of integration close to or above 1. The volatility, measured in terms of the squared returns indicates that the series is long memory, with an order of integration in the interval (0, 0.5). The results finally support the existence of a mean shift in the data at about January 2008, with the order of integration being around 1. Thus the Efficient Market Hypothesis (EMH) may be satisfied in the Indian stock market once a break is taken into account. However, the existence of short run dynamics suggests a degree of predictability in its behaviour.

     

Sign and phase asymmetry: News, economic activity and the stock market

In this paper, we present a general model of the joint data generating process underlying economic activity and stock market returns allowing for complex nonlinear feedbacks and interdependencies between the conditional means and conditional volatilities of the variables. We propose statistics that capture the long and short run responses of the system to the arrival of news, conditioning on the sign and time of arrival of the news. The model is applied to US data. We find that there are significant differences between the short and long run responses of economic activity and stock returns to the arrival of news. Moreover, for certain classifications of news, the respective responses of economic activity and stock returns vary according to the nature of the news and the phase of the business cycle at which the news arrives.     

Inflation news and stock returns: market direction and flow-through ability

Previous studies of the short-run response of daily stock prices to announcements of macroeconomic news could be biased when responses in different scenarios cancel each other out. In our analysis of inflation news in the Spanish stock market, we consider market direction arguments and implement our study based on the sector of activity to control for the ‘flow-through’ ability of the firms in each industry. In general, our results are quite consistent with the ‘market direction’ and the ‘flow-through’ hypotheses. Unanticipated inflation news implies abnormal returns depending on the direction of the news, the state of the economy and the flow-through ability of the sector. The impact of positive surprises affects the abnormal returns of many more sectors than does the impact of negative surprises, especially in the low states of economy. These significant effects are mainly observed in industries that are characterised by low flow-through ability.     

An entropy-based analysis of the relationship between the DOW JONES Index and the TRNA Sentiment series

This article features an analysis of the relationship between the DOW JONES Industrial Average (DJIA) Index and a sentiment news series using daily data obtained from the Thomson Reuters News Analytics (TRNA) provided by SIRCA (The Securities Industry Research Centre of the Asia Pacific). The recent growth in the availability of on-line financial news sources, such as internet news and social media sources provides instantaneous access to financial news. Various commercial agencies have started developing their own filtered financial news feeds which are used by investors and traders to support their algorithmic trading strategies. TRNA is one such data set. In this study, we use the TRNA data set to construct a series of daily sentiment scores for DJIA stock index component companies. We use these daily DJIA market sentiment scores to study the relationship between financial news sentiment scores and the stock prices of these companies using entropy measures. The entropy and mutual information (MI) statistics permit an analysis of the amount of information within the sentiment series, its relationship to the DJIA and an indication of how the relationship changes over time.     

Are Oil Price News Headlines Statistically and Economically Significant for Investors?

While much has been written about the effects of oil price on stock returns, surprisingly nothing is known about the effect of oil price news on stock returns. This article is a response to this research gap. For a large number of stocks on the New York Stock Exchange, the authors find that while oil price news does predict market returns it only predicts returns of some sectors and not all. They find that sorting stocks based on oil price news generates a significant return differential in the cross-section, which holds consistently across a range of models allowing for the well-known risk factors. Their findings suggest that information contained in oil price news affects stock returns.     

The impact of news,oil prices,and global market developments on Russian financial markets1

This paper analyses the impact of news, oil prices, and international financial market developments on daily returns on Russian bond and stock markets. First, regarding returns, energy news affects returns, while news from the war in Chechnya is not significant. Market volatility does not appear to be sensitive to either type of news. Second, a significant effect of the growth in oil prices on Russian stock returns is detected. Third, the international influence on Russian financial markets depends upon the degree of financial liberalization. The higher the degree of financial liberalization, the stronger is the impact of US stock returns on Russian financial markets. In addition, banking reform and interest rate liberalization efforts seem to dictate the globalization of Russian stock markets, while it is the progress in liberalizing securities markets and non‐bank financial institutions that matters more for the globalization of Russian bond markets.