Return Predictability of Turkish Stocks: An Empirical Investigation

Employing the portfolio method and cross-sectional regressions, this paper provides a comprehensive analysis of stock return predictability in Turkey from January 1997 to July 2011. In the risk-related predictors, we found predictive power for beta, total volatility, and idiosyncratic volatility. The "cheapness" variable, book-to-market ratio, is the most important return predictor for the stocks traded on the Istanbul Stock Exchange (now part of the Borsa Istanbul). Grouping the stocks as small and large according to the median value of the market capitalization of the stocks adds important insights to the analysis. Our results show the set of large stocks on the Istanbul Stock Exchange to be the least predictable set of stocks.     

基于不同分布假设下波动模型估计效果比较——以上海股票市场为例

本文基于不同分布假设,即正态分布、Student-t分布以及EGB2分布,使用2005年1月4日至2011年6月30日上证综指日收益率数据对GARCH模型和GJR GARCH模型估计效果进行实证比较。实证结果显示：（1）基于非对称EGB2分布的GJR GARCH模型更适合中国证券市场;（2）中国股票市场存在波动不对称性,且好消息引发的波动大于坏消息引发的波动,这可能与中国股票市场特有的市场结构和交易制度有关;（3）波动的不对称特性可能部分来自于对分布偏度特性考虑的欠缺,验证了合理的分布假设在波动行为分析过程中的重要性。     

Stock market liberalization and return volatility: Evidence from the emerging market of Sri Lanka

This study examines the impact of liberalization of the Sri Lankan stock market on return volatility. We specify GARCH and TGARCH models of volatility, and estimate them using 16 years of weekly returns for the period from 1985 to 2000. The results show that liberalization of the market to foreign investors significantly increased the return volatility in the Colombo Stock Exchange. Both conditional and unconditional volatility measures are the highest in the liberalization period. Negative return shocks lead to lower volatility suggesting that there is no leverage effect, and this appears to reflect the very low levels of leverage used by Sri Lankan companies.     

The impact of salient political and economic news on the trading activity

This paper investigates the impact of salient political and economic news on the intraday trading activity, namely, the stock return volatility, the stock price volatility, the number of shares traded, and the trading frequency. Using transactions data on 33 constituent stocks of the Hang Seng Index in the Stock Exchange of Hong Kong (SEHK), we find that political news has a distinct impact on market activity when compared with economic news. We argue that the observed phenomenon is related to the precision of signals associated with these two types of news and investors' perceptual biases.     

经营利润率、股东收益与股票价格的价值相关性研究

本文以沪、深两市A股上市公司为研究对象,在F-O模型(剩余收益定价模型)的基础上,进一步探讨了分解后的会计信息:经营利润率和股东收益与股价的价值相关性。研究结果表明,上市公司的经营利润率和股东收益对股价有显著的解释能力。本研究的贡献在于进一步拓展了市场定价模型,为证券投资提供具有实用价值的参考。     

Constructing a Multifactor Model for the Shanghai Stock Exchange

ABSTRACT

We examine the validity of five factor models for explaining the time-series and cross-sectional variations in stock returns in the Shanghai Stock Exchange. The factor models include four models proposed by previous literature. Moreover, we propose a four-factor model (comprising market, size, book-to-market, and sales-to-price factors) to explain variations of stock returns in the Shanghai Stock Exchange. The results show that the Shanghai stock market exhibits size, book-to-market, and sales-to-price effects. Both the adjusted coefficient of determination and regression model intercepts indicate that the proposed four-factor model explains variations of stock returns in the Shanghai Stock Exchange more effectively in comparison with other multifactor models.     

Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation

Linear and nonlinear Granger causality tests are used to examine the dynamic relation between daily Dow Jones stock returns and percentage changes in New York Stock Exchange trading volume. We find evidence of significant bidirectional nonlinear causality between returns and volume. We also examine whether the nonlinear causality from volume to returns can be explained by volume serving as a proxy for information flow in the stochastic process generating stock return variance as suggested by Clark's (1973) latent common-factor model. After controlling for volatility persistence in returns, we continue to find evidence of nonlinear causality from volume to returns.     

The Asymmetric Momentum Effect in the Chinese Class A Share Market Amid Market Swings

This paper investigates the asymmetric momentum effect over time periods following UP and DOWN market states in the Shanghai and Shenzhen Stock Exchanges of the Chinese Class A share market. We show that the post-UP-market momentum effect eclipses the post-DOWN-market momentum effect in unison in both market segments. Notably, the asymmetric pattern of the market-state-dependent momentum effect in the Shenzhen Stock Exchange is outpaced by that found in the Shanghai Stock Exchange. Furthermore, through decomposing momentum returns, we reveal that low liquidity, higher market return volatility, and weak under-reaction of share prices towards firm-specific news jointly contribute to the subdued asymmetry of market-state-dependent momentum returns in the Shenzhen Stock Exchange.     

Variations in Trading Volume,Return Volatility,and Trading Costs: Evidence on Recent Price Formation Models

Patterns in stock market trading volume, trading costs, and return volatility are examined using New York Stock Exchange data from 1988. Intraday test results indicate that, for actively traded firms trading volume, adverse selection costs, and return volatility are higher in the first half-hour of the day. This evidence is inconsistent with the Admati and Pfleiderer (1988) model which predicts that trading costs are low when volume and return volatility are high. Interday test results show that, for actively traded firms, trading volume is low and adverse selection costs are high on Monday, which is consistent with the predictions of the Foster and Viswanathan (1990) model.     

上证指数收益率波动的实证分析：基于ARCH族模型

股价指数的收益率序列具有时变波动性、厚尾特征、波动性群集等特点,传统的计量分析无法刻画这些特点。文章利用ARCH族模型,选取2003年1月20日～2013年12月12日上证指数每日收益率共2621个数据对其波动进行定量与定性的分析,结果显示,上证指数日收益率存在高阶的ARCH效应,杠杆效应,波动集聚性特征,条件方差对日收益率有很强的影响,其中EGARCH模型在反映股市波动性方面优于其他模型。     

The impact of derivatives hedging on the stock market: Evidence from Taiwan’s covered warrants market

We examine the impact of derivatives hedging on the spot market using accurate hedge ratios of covered warrants traded in the Taiwan Stock Exchange (TWSE). Results present significant positive abnormal returns and trading volumes before the announcement of a warrant’s issuance, and the effect is stronger when the hedging demand is larger. Moreover, a significantly positive relationship exists between stock return volatility and the price elasticity of hedging demand. Finally, we observe a significantly negative price effect upon the underlying stock after a call warrant has expired in-the-money due to the liquidation of hedging portfolios.     

Speculative dynamics and price behavior in the Shanghai Stock Exchange

This article examines the extent to which the trading behavior of heterogeneous investors manifests in stock price changes of asset portfolios which constitute the Shanghai Stock Exchange. There are three major findings that materialize. Firstly, reliable statistical evidence of a negative relation between the conditional first and second moments of the return distributions of stock prices lends support to the volatility feedback effect. Secondly, ‘feedback’, or momentum-type investors, are not present in this market as is often detected from the daily price changes of other industrialized markets. Finally, trade volume as a proxy for ‘information-driven’ trading suggests that such investors play a statistically significant role in stock price movements. Parameter estimates from this latter group of investors imply that a rise in stock prices from a high volume trading day is more likely than a rise resulting from a low volume trading day.     

ARE PRICE LIMITS EFFECTIVE? EVIDENCE FROM THE ISTANBUL STOCK EXCHANGE

There is considerable discussion about controlling volatility by imposing price limits on asset prices. We examine the effects of price limits on a stock market by testing the volatility spillover, delayed price discovery, and trading interference hypotheses in a leading emerging market, the Istanbul Stock Exchange, which has a unique market microstructure as related to price limits. Our results support the volatility spillover, delayed price discovery, and trading interference hypotheses. We also show price locks at limits provide significantly stronger evidence regarding the effects of price limits than limit moves only. Finally, price limits have a significant effect on the stock market, casting doubt on their effectiveness.     

The macroeconomic determinants of technology stock price volatility

Stock prices reflect the value of anticipated future profits of companies. Since business cycle conditions impact the future profitability of firms, expectations about the business cycle will affect the current value of firms. This paper uses daily and monthly data from July 1986 to December 2000 to investigate the macroeconomic determinants of US technology stock price conditional volatility. Technology share prices are measured using the Pacific Stock Exchange Technology 100 Index. One of the novel features of this paper is to incorporate a link between technology stock price movements and oil price movements. The empirical results indicate that the conditional volatilities of oil prices, the term premium, and the consumer price index each have a significant impact on the conditional volatility of technology stock prices. Conditional volatilities calculated using daily stock return data display more persistence than conditional volatilities calculated using monthly data. These results further our understanding of the interaction between oil prices and technology share prices and should be of use to investors, hedgers, managers, and policymakers.     

Volatility Spillover Effects in Interregional Equity Markets: Empirical Evidence from Brazil and Turkey

We investigate volatility spillovers between two stock markets: Turkey and Brazil. Using a misspecification-robust causality-in-variance test, we find evidence supporting volatility spillovers from the São Paulo Stock Exchange to the Istanbul Stock Exchange. Moreover, the results imply that financial crises may change the nature of volatility spillovers between the two markets by adding an additional channel of volatility transmission from Turkey to Brazil.     

The Cross-Section of Stock Returns on The Shanghai Stock Exchange

This study explores the cross-sectional stock return behavior on the A-share market of the Shanghai Stock Exchange (SSE), which is segmented from world's other equity markets. We estimate the effects of beta, firm size, book-to-market equity ratio and a variable unique to the Chinese stock markets, the proportion of firm's floating (tradable) equity over total equity on SSE stocks over the period 1993–2002. We find that smaller firms and value stocks perform better. Systematic risk is negatively significant in down markets. The proportion of floating equity has no direct effect on stock returns. JEL Classification: G14, G15     

The impact of COVID-19 on the stock market crash risk in China

This study investigates the impact of the COVID-19 pandemic on the stock market crash risk in China. For this purpose, we first estimated the conditional skewness of the return distribution from a GARCH with skewness (GARCH-S) model as the proxy for the equity market crash risk of the Shanghai Stock Exchange. We then constructed a fear index for COVID-19 using data from the Baidu Index. Based on the findings, conditional skewness reacts negatively to daily growth in total confirmed cases, indicating that the pandemic increases stock market crash risk. Moreover, the fear sentiment exacerbates such risk, especially with regard to the impact of COVID-19. In other words, when the fear sentiment is high, the stock market crash risk is more strongly affected by the pandemic. Our evidence is robust for the number of daily deaths and global cases.     

Tehran stock exchange prediction using sentiment analysis of online textual opinions

We investigate the impact of social media data in predicting the Tehran Stock Exchange variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from Sahamyab.com/stocktwits for about 3 months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon‐based and learning‐based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market domain, we built a particular sentiment lexicon for this domain. After designing and calculating daily sentiment indices using the sentiment of the comments, we examine their impact on the baseline models that only use historical market data and propose new predictor models using multi‐regression analysis. In addition to the sentiments, we also examine the comments volume and the users' reliabilities. We conclude that the predictability of various stocks in the Tehran Stock Exchange is different depending on their attributes. Moreover, we indicate that only comments volume could be useful for predicting the closing price, and both the volume and the sentiment of the comments could be useful for predicting the daily return. We demonstrate that users' trust coefficients have different behaviours toward the three stocks.     

A State‐Price Volatility Index for China's Stock Market

This study derives a volatility index for China's stock market with similar properties to the Chicago Board Options Exchange Volatility Index (the ‘VIX’). A long‐term benchmark of historic volatility expectations is here presented for China from 1996 to 2011, called the ‘China‐ State‐Price Volatility (SPV)’. Construction of this index involves the use of SPV methodology, using implied volatility calculated from options on the Hang Seng China Enterprise Index (HSCEI). Historic open–high–low–close volatility on the Shanghai Composite Index (SHCI) is also used to extend the benchmark prior to the availability of HSCEI options data. The China‐SPV successfully forecasts realised volatility for the Shanghai Stock Exchange. It also serves as a ‘fear gauge’ in that it monitors daily movements of the SHCI in the same way that the VIX monitors the S&P 500 index (Whaley, 2009). The China‐SPV evidences an increasing relation with the US market in terms of the dynamic correlation of levels and changes with the VIX since 2004.     

Post-acquisition performance in the short and long run. Evidence from the Copenhagen Stock Exchange 1993–1997

The paper investigates the short-run price adjustment around acquisition announcements and the long-run upward bias of cross-sectional average buy-and-hold returns. The geometric Brownian motion model is applied to decompose the cross-sectional average long-run returns into transformed mean and volatility components. The decomposition improves the interpretation of security performance. The methodology is demonstrated on the security performance of bidding firms listed on the Copenhagen Stock Exchange. The most surprising finding is that the long-run abnormal return after three years is not significantly different from zero. This implies that the bidding firms do not under-perform relative to the market. This result stands in contrast to findings in other studies and it may reflect that earlier studies do not adjust correctly for the volatility component. These current findings indicate that the market efficiency hypothesis is intact in the long run. It is only in the very short run, a few days around acquisition announcements, that the market makes a significant adjustment to uphold the efficiency hypothesis.