Computerized stock screening rules for portfolio selection

Recent studies have uncovered several systematic patterns that increase the probability that individual investors can select stock portfolios with excess returns. This study tests the feasibility of using a commercially available computerized stock screening program for investors to take advantage of these patterns. The screening program searches the three major exchanges and selects stocks on both fundamental and technical indicators: low price-to-sales ratio, small firm size, accelerating stock prices above their 50 day moving average, high trading volume, and high earnings growth. Of the 18 models tested between 1994 and 1998, those that allow for selection between exchanges yield portfolio returns that significantly exceed the average market indices.     

The Empirical Relationship Between Earnings Information and Stock Returns

Abstract:

The objective of this study was to examine, using a vector autoregressive model, whether the difference in earnings growth rates caused different reaction speeds in stock prices. Monthly returns of stocks listed in the Taiwan stock market from May 2003 to April 2013 were used as empirical data in this study. The analytical results showed that the returns of portfolios with higher earnings growth rates significantly led those portfolios with lower earnings growth rates when size, trading volume, institutional ownership ratio, and revenue factors were controlled, respectively. This paper finds that the earnings growth rate is a significant determinant of the lead-lag patterns observed in monthly stock returns.     

Non-linear Dependence in Stock Returns: Does Trading Frequency Matter?

The UK has a quote-driven pure dealer market structure that is very different from order driven markets such as the NYSE and Japanese markets. This paper investigates non-linear dependence in stock returns for an exhaustive sample of UK stocks for a 21 year period. The results are analysed on the basis of trading frequency. It is found that non-linear dependence is highly significant in all cases for both individual stocks and stock portfolios formed on the basis of trading frequency. The non-linear dependence is primarily over a one day interval, although statistically significant non-linear dependence exists consistently even up to five trading days. Most of the non-linear dependence is in the form of ARCH-type conditional heteroskedasticity. However, statistically significant non-linearity in addition to an EGARCH(1,1) dependence also appears to be present. This additional non-linearity is greater for individual stocks than for portfolios and greater for smaller, less-liquid portfolios. Non-linear dependence does not appear to be caused by non-stationarity in underlying economic fundamentals or by non-linearity in the conditional mean. However, low dimensional chaos is not generally supported. The limited evidence on chaotic behaviour is stronger for portfolios with long price adjustment delays across component stocks. The main results are consistent with US studies on stock indices, suggesting that the process generating non-linear dependence is not dependent on market microstructure characteristics.     

The impact of Saturday trading on stock returns: Evidence from the Tokyo stock exchange January 1976 to January 1989

This paper examines the impact of TSE Saturday trading on daily TOPIX returns and TSE trading volume over the January 1976 to January 1989 period. Saturday trading is shown to have no significant impact on mean stock returns for the other days of the week. However, a significant shift in the pattern of Monday and Tuesday TOPIX returns is documented in the post-August 1986 period. This shift does not appear to be related to Saturday trading. TSE Saturday trading is found to have a significant impact on the variance of stock returns on surrounding days. In addition, trading volume is significantly lower on trading days surrounding Saturday trading. These findings are relevant to the timing of portfolio adjustment decisions.     

Glamour among value: P/E ratios and value investor attention

The trailing-four-quarter price–earnings (P/E) ratio is the most popular fundamental value proxy. This article is the first to examine the P/E ratio as the preeminent measure of value investor attention. Trailing-four-quarter P/E ratios predict significantly greater cross-sectional variation in stock returns than lagged P/E ratios or current price-to-book (P/B) ratios. P/E strategy returns are robust to variables that proxy for fundamental risk, variables mechanically related to P/E ratios, relative trading volume, and liquidity. The role of attention is evident in return patterns across long and short portfolios, day of the week, and time since formation. Stocks with low P/E ratios exhibit an increase in total trading volume driven by small trades, an improvement in liquidity, and lower idiosyncratic volatility. These patterns are consistent with the typical trading activity of individual investors, who have the strongest attention constraints.     

Trading volume,time-varying conditional volatility,and asymmetric volatility spillover in the Saudi stock market

Despite the well known importance of volatility–volume relationship, there is a paucity of research on this topic in emerging markets. We attempt to partially fill this gap by investigating volatility–volume relationship in the most important exchange market in the Middle East. We test the effect of trading volume on the persistence of the time-varying conditional volatility of returns in the Saudi stock market. Overall our results support the mixture of distribution hypothesis at the firm level. We also use two different proxies for information arrival, intra-day volatility, and overnight indicators. We find that these are good proxies for information and are important as contemporaneous volume in explaining conditional volatility. We also test for the volatility spillover direction between large- and small-cap portfolios. Our results show that the spillover effect is larger and statistically significant from large to small companies.     

Causal and Dynamic Relationships among Stock Returns,Return Volatility and Trading Volume: Evidence from Emerging markets in South-East Asia

This paper examines the causal and dynamic relationships among stock returns, return volatility and trading volume for five emerging markets in South-East Asia—Indonesia, Malaysia, Philippines, Singapore and Thailand. We find strong evidence of asymmetry in the relationship between the stock returns and trading volume; returns are important in predicting their future dynamics as well as those of the trading volume, but trading volume has a very limited impact on the future dynamics of stock returns. However, the trading volume of some markets seems to contain information that is useful in predicting future dynamics of return volatility.     

How does trading volume affect financial return distributions?

We assess investors' reaction to new information arrivals in financial markets by examining the relationships between trading volume and the higher moments of returns in 18 international equity and currency markets. Our volume-volatility results support extant information theories and further contribute new evidence of cross market relations between volume and volatility. We also find that the direct impact of volume on the level of negative skewness is less significant for more diversified regional portfolios. Furthermore, the negative interaction between volume and kurtosis can be explained by the differences of opinion in financial markets. We observe stronger interdependence among higher moments in reaction to significant events, but the strength is dampened by trading volume. This result is consistent with trading volume being a source of heteroskedasticity in asset returns.     

State‐Level Business Cycles and Local Return Predictability

This study examines whether local stock returns vary with local business cycles in a predictable manner. We find that U.S. state portfolios earn higher future returns when state‐level unemployment rates are higher and housing collateral ratios are lower. During the 1978 to 2009 period, geography‐based trading strategies earn annualized risk‐adjusted returns of 5%. This abnormal performance reflects time‐varying systematic risks and local‐trading induced mispricing. Consistent with the mispricing explanation, the evidence of predictability is stronger among firms with low visibility and high local ownership. Nonlocal domestic and foreign investors arbitrage away the predictable patterns in local returns in 1 year.     

ROBUSTNESS OF THE HEADQUARTERS‐CITY EFFECT ON STOCK RETURNS

Recent studies report that U.S. firms headquartered near each other experience positive comovement in their stock returns, a finding suggestive of local biases in equity trading activity. We investigate the robustness of these findings and find that including additional pricing factors in models for monthly stock returns materially reduces the magnitude of the headquarters‐city effect in stock returns. Additionally, we find that an implicit null hypothesis of zero local return comovement is inappropriate as there is positive comovement between a stock's return and returns on portfolios of stocks from nonheadquarters cities, on average. Nevertheless, results benchmarked against estimates based on resampling methods indicate a significant and robust headquarters‐city effect in stock returns.     

成交量和回报率对信息扰动的市场反应研究——以中国A股市场为例

用BVAR和BMAR模型及脉冲—响应分析方法来考察中国股票市场上成交量和回报率对信息扰动的动态反应情况得出,公共扰动和永久性扰动是回报率的主要决定因素,而非公共扰动和暂时性扰动则是成交量的主要决定因素。但是公共扰动和永久性扰动对成交量有明显影响;非公共扰动和暂时性扰动对回报率有明显影响。     

A transactions data analysis of nonsynchronous trading

Weekly returns of stock portfolios exhibit substantial autocorrelation.Analytical studies suggest that nonsynchronous trading is capableof explaining from 5% to 65% of the autocorrelation. The varyingimportance of nonsynchronous trading in these studies arisesprimarily from differing assumptions regarding nontrading periodsof stocks. We simulate the effects of nonsynchronous tradingby sampling stock returns from a return generating process usingtransactions data to obtain the precise time of each stock'slast trade. We find that simulated weekly portfolio returnsexhibit autocorrelations that are roughly 25% that of theirobserved (CRSP) weekly returns.     

Trading volume and short-horizon contrarian profits: Evidence from the Malaysian market

We provide evidence on short-term predictability of stock returns on the Malaysian stock market. We examine the relation between return predictability and the level of trading activity. This is particularly relevant in emerging stock markets, where thin trading is more pervasive. We find that the returns from a contrarian portfolio strategy are positively related to the level of trading activity in the securities. Specifically, the contrarian profits on actively and frequently traded securities are significantly higher than that generated from the low trading activity securities. We find that the differential behavior of high- and low-volume securities is not subsumed by the size effect, although for the small firms, the volume–predictability relation is most pronounced. We also suggest that the price patterns may be related to the institutional arrangement in the Malaysian stock market.     

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.     

Air pollution and mediation effects in stock market,longitudinal evidence from China

By obtaining a novel proprietary city-level panel dataset of stock returns and trading volume in China, this article investigates the effect of air pollution on the stock market while avoiding the possible confounding factors reported in previous similar studies. The analysis finds that there are significant negative effects generated by air pollution on stock returns and trading volume, and these effects manifest themselves on successive days. Air pollution affects stock returns on the same day, which then mediates the effect of air pollution on trading volume on the next day. Similar negative effects are also found in several different pollutants. Our results validate the negative relationship of air pollution on the stock market in literature with longitudinal evidence, and illustrate the steps of the air pollution effect in the stock market.     

CHANGES IN THE STOCK PRICE REACTION OF SMALL FIRMS TO COMMON INFORMATION

We hypothesize that changes in the technological and regulatory environment result in a more rapid response to marketwide information by small firms. We find that the correlations between small-firm returns and lagged large-firm returns decline over time, which suggests an increase in the efficiency of capital markets. Similar lead-lag patterns are found in the returns of portfolios sorted by dollar trading volume. The price response of low-volume stocks improves over time in much the same way as that of small-capitalization stocks.     

THE EMPIRICAL RELATIONSHIP BETWEEN TRADING VOLUME,RETURNS AND VOLATILITY

This paper presents an empirical analysis of the relationship between trading volume, returns and volatility in the Australian stock market. The initial analysis centres upon the volume-price change relationship. The relationship between trading volume and returns, irrespective of the direction of the price change, is significant across three alternative measures of daily trading volume for the aggregate market. This finding also provides basic support for a positive relationship between trading volume and volatility. Furthermore, evidence is found supporting the hypothesis that the volume-price change slope for negative returns is smaller than the slope for non-negative returns, thereby supporting an asymmetric relationship which is hypothesised to exist because of differential costs of taking long and short positions. Analysis at the individual stock level shows weaker support for the relationship. A second related hypothesis is tested in which the formation of returns is conditional upon information arrival which similarly affects trading volume. The hypothesis is tested by using the US overnight return to proxy for expected “news” and trading volume to proxy for news arrival during the day. The results show a reduction in the significance and magnitude of persistence in volatility and hence are consistent with explaining non-normality in returns (and ARCH effects) through the rate of arrival of information. The findings in this paper help explain how returns are generated and have implications for inferring return behaviour from trading volume data.     

Empirical Identification of Non-Informational Trades Using Trading Volume Data

This paper empirically identifies non-informational and informational trades using stock returns and trading volume data of the U.S., Japanese, and U.K. stock markets and five individual firms. We achieve the identification by imposing a restriction from theoretical considerations. Our results show that trading volume is mainly driven by non-informational trades, while stock price movements are primarily driven by informational trades. We also find that, around the 1987 stock market crash, trading volumes due to non-informational trades increased dramatically, while the decline in stock market prices was due mainly to informational trades. Increases in volatilities both in returns and in trading volumes during and after the crash are mainly due to non-informational trades. Regarding the trading volume-serial correlation in the stock returns relationship, we find evidence that is consistent with theoretical predictions that non-informational components can account for high trading volume accompanied by a low serial correlation of stock returns.     

The Joint Cross Section of Stocks and Options

Stocks with large increases in call (put) implied volatilities over the previous month tend to have high (low) future returns. Sorting stocks ranked into decile portfolios by past call implied volatilities produces spreads in average returns of approximately 1% per month, and the return differences persist up to six months. The cross section of stock returns also predicts option implied volatilities, with stocks with high past returns tending to have call and put option contracts that exhibit increases in implied volatility over the next month, but with decreasing realized volatility. These predictability patterns are consistent with rational models of informed trading.     

The price adjustment and lead-lag relations between stock returns: microstructure evidence from the Taiwan stock market

This paper investigates the price adjustment and lead-lag relations between returns on five size-based portfolios in the Taiwan stock market. It finds evidence that the price adjustment of small-stock portfolios is not slower than that of large-stock portfolios. Additionally, limited evidence supports a positive leading role of large-stock portfolio returns over small-stock portfolio returns. These two findings are substantially different from the results of previous research on developed markets.