What are returns outside trading hours capturing for volatility of individual stocks?

This study is the first to harness the negative returns and squared returns outside trading hours, trading volume and leverage effects in an augmented heterogeneous autoregressive model for forecasting volatility of individual stocks. Besides significant leverage effects and trading volume impact, we find that an increase in the negative returns is associated with a decline in volatility, but an increase in the squared returns is associated with a rise in volatility. This new finding suggests that the negative returns and squared returns outside trading hours are capturing additional leverage effects and additional volatilities, respectively. Moreover, the relations display differences amongst various firm categories which arise from firm heterogeneity.     

Noise traders,firm-specific uncertainty and technical trading effectiveness

This is the first study to document evidence of technical trading effectiveness at firm level in the Chinese A-share market by investigating the relationship between excess profits of technical trading rules and firm-specific characteristics. Our results reveal that firms with higher excess profits from technical trading have more noise traders and higher institutional ownership and that those firms tend to be growth firms with lower liquidity and higher firm-specific uncertainty. Further analysis shows that the profitability of technical trading rules is unsustainable and the excess profits of the highest technical trading profit quintile portfolio disappear in the following year.     

On the Use of the Moving Average Trading Rule to Test for Weak Form Efficiency in Capital Markets

The examination for the possible existence of predictive power in the moving average trading rule has been used extensively to test the hypothesis of weak form market efficiency in capital markets. This work focuses mainly on the study of the variation of the moving average (MA) trading rule performance as a function of the length of the longer MA. Empirical analysis of daily data from NYSE and the Athens Stock Exchange reveal high variability of the performance of the MA trading rule as a function of the MA length and on some occasions the series of successive trading rule total returns is non‐stationary. These findings have direct implications in weak form market efficiency testing. Indeed, given this high variability of the performance of the MA trading rule, by just finding out that trading rules with some specific combinations of MA lengths can or cannot beat the market, as is the case in most of the published work thus far, is not enough evidence for or against the existence of weak form market efficiency. Results also show that on average in about three out of four cases trading rule signals are false, a fact that leaves a lot of space for improved trading rule performance if trading rule signals are combined with other information (e.g. filters, or volume of trade). Finally, some evidence of enhanced trading rule performance for the shorter MA lengths was found. This enhanced performance is partly attributed to the higher probability that a trading rule signal is not a whipsaw, as well as to the larger number of days out‐of‐the‐market which are associated with shorter MA lengths.     

Information Transmission Through Rumors in Stock Markets: A New Evidence

The authors provide new evidence of the influence of false rumors based on Taiwan's stock market. The results indicate significant patterns of abnormal returns and trading volumes surrounding the event day and that the rumors seem to be disseminated in the stock market before appearing in newspapers. The results also indicate asymmetry: Investors hearing a positive rumor about a stock may tend to buy the stock, prompting a price run-up until the rumor dies away, while negative rumors usually have greater and longer negative impacts on stock returns than positive rumors do. The presence of a daily price limit is negatively correlated to the size of abnormal returns and abnormal trading volumes on the event day, and the abnormal trading volumes are more sensitive to the price limit surrounding the event day. Finally, firm managers might receive rumor information earlier and then conduct stock trading before the rumor's announcement.     

Predictability of equity returns during a financial crisis

An occurrence of a market crash or a financial crisis has long been considered a cause of market inefficiency. An inefficient market commonly implies return predictability and the existence of profitable opportunities for traders and speculators. Technical analysis has been a popular tool to identify predictable patterns in asset prices. The usefulness of a large universe of technical trading rules popularized in the existing literature on technical analysis is tested when they are applied to a set of equity markets that are generally considered developed and efficient during the two most recent periods of major financial turmoil: the 1997 Asian financial crisis and the 2008 global financial crisis. Three major statistical deficiencies that existing studies on return predictability are commonly criticized for – data snooping bias, nonsynchronicity bias and transaction costs – have been incorporated in the analysis. Technical trading rules are largely unable to yield abnormal excess returns over the passive benchmark after data snooping bias, nonsynchronous pricing and transaction costs are accounted for. Chaotic price movements typical for a volatile market during a financial crisis are likely to have an adverse effect on the performance of active trend chasing trading strategies.     

Trading asymmetric trend and volatility by leverage trend GARCH in Taiwan stock index

This article develops a leverage trend Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model by incorporating asymmetric trend of returns of the exponential autoregressive and asymmetric volatility of GARCH models to study the asymmetric effects. Using in-sample daily data of Taiex over the period 4 January 1980 to 25 August 1997 and postsample daily data over the period 26 August 1997 to 10 September 2007, the evidence reveals that a curvaceous risk–return relationship and both asymmetric volatility and asymmetric trend of returns are significant in Taiex. The episode of asymmetric trend of returns is that the positive information creates a higher return trend than the negative information of the same amount, while similarly to most studies, the evidence of asymmetric volatility appears that the negative information makes a higher volatility than the positive information of the same size. Most remarkably, we evidence that the volatility asymmetry effect is a conservative trading factor and the return trend asymmetry effect is an active trading factor. In comparison of post-sample performance using rolling-window technique, the leverage trend GARCH model indeed outperforms the other three models with single asymmetry adjusted or without asymmetry adjusted, while the asymmetry nonadjusted model performs the worst. It implies that the return trend asymmetry (active trading) and the volatility asymmetry effects (conservative trading) tend to compensate, but not offset each other.     

Momentum and reversals in Taiwan index futures returns during periods of extreme trading imbalance

The study analyzes the relation between a trading imbalance metric that captures data observable by investors, and future momentum and reversals in Taiwan index futures returns. Standard regression analyses do not show any significant dynamic relations between daily index futures returns and the trading imbalance, regardless of whether the trading imbalance metric is lagged, contemporaneous, or leads the index futures return. However, when the analyses are focused on periods with extreme trading imbalances I find that the daily index futures returns exhibit significant reversals following periods of extreme (low) trading imbalances and low returns. I also find some evidence of residual momentum in consecutive daily index futures returns following periods of extreme (high) trading imbalances and high returns. Trading simulation, directional accuracy, and market timing tests show these effects to be economically significant, even after accounting for transaction costs.     

Profiteering from the Dot‐Com Bubble,Subprime Crisis and Asian Financial Crisis

The paper explores the characteristics associated with the formation of bubbles that occurred in the Hong Kong stock market in 1997 and 2007, as well as the 2000 dot‐com bubble of Nasdaq. It examines the profitability of technical analysis (TA) strategies generating buy and sell signals, with and without our proposed trading rules. The empirical results show that, by applying long and short strategies during the bubble formation and a short strategy after the bubble burst, it not only produces returns that are significantly greater than buy‐and‐hold strategies, but also produces greater wealth compared with TA strategies without trading rules. We conclude that these bubble detection signals help investors generate greater wealth from applying appropriate long and short moving average (MA) strategies.     

IS TECHNICAL ANALYSIS USEFUL FOR STOCK TRADERS IN CHINA? EVIDENCE FROM THE SZSE COMPONENT A‐SHARE INDEX

Abstract.  This paper uses daily stock prices and the trading volume of 39 constituent companies in the SZSE Component A-Share Index on the Shenzhen Stock Exchange to examine the usefulness of technical analysis. It finds very weak evidence to support the view that traders and/or technical analysts can learn more about the future pattern of returns by actively using volume in conjunction with returns than those who only watch price movements.     

Return predictability in emerging equity market sectors

This article investigates the predictive power of technical trading rules in the emerging equity market sector portfolios and finds that trading strategies based on technical indicators significantly outperform the buy-and-hold benchmark. Combination of data snooping bias, data measurement errors in the form of non-synchronicity bias and fluctuations in currency exchange rates is unable to explain the observed outperformance. The introduction of transaction costs tempers the results but technical analysis still possesses significant predictive power for a number of sectors. The performance of technical analysis in the emerging equity market sectors does not conform to historical trends observed in the developed equity markets as well as in the emerging equity markets when broadly diversified portfolios are considered, where predictive power of technical trading rules has been shown to decline over time.     

Tobin tax and trading volume tightening: a reassessment

This article extends the previous literature on the Tobin tax. We find that very roughly, a doubling in transaction costs would reduce trading volume by 25% to 40% in the Forex. Most importantly, this article is the first contribution to specify the trading volume of the Forex through different (low and high volatility) regimes. Our results show evidence of nonlinear patterns for trading volumes and transaction costs on the Forex. Thus, the Tobin tax would not have a monotonic impact on trading activity across market conditions. The change in elasticity between low and high volatility regimes would be slight but significantly different. We may suggest that the high-variance regime might be the fundamentalist regime and the low-variance regime might be the chartist regime. It is a first step towards understanding which categories of agents would react to the introduction of a tax. Our results seem consistent with Tobin’s underlying thinking; since a tax would penalize chartists more than fundamentalists, it could reduce exchange rate volatility.     

Variability of realized stock returns and trading volume

Using monthly data for 2005–2014 time period, this article documents the relationship between lagged stock returns and trading volume. We show that the dispersion of stock returns in a market portfolio positively affects future trading volume. We also show that extreme negative returns lead to high future trading volume while extreme positive returns have little effect on future trading. Dividing our sample into several sub-samples based on the Standard Industrial Classification (SIC) divisions leads to similar results for most of the SIC divisions.     

Imagery and Financial Judgment

Many studies have indicated that a buy-and-hold investment strategy is superior to a trading strategy. This is thought to be true because trading incurs transaction costs that lower net returns compared to a buy-and-hold strategy. We propose a behavioral finance argument to illustrate that merely switching between positive expected return assets can lead to a long-run negative expected return, even when transaction costs are ignored. This counterintuitive result may obtain because of Parrondo's Paradox. We provide a stylized theoretical example that demonstrates how a trader can lose money by trading between assets with positive long-run expected returns. We also present simulation results to support our example. Thus, long-run negative results from trading may not be due entirely to transaction costs. A trading strategy may prove inferior to buy-and-hold for agents simply because of their singular trading patterns, as we outline in the paper.     

What Is Common Among Return Anomalies? Evidence from Insider Trading

For a broad set of anomalies, we establish a common pattern of underreaction to information contained in preceding insider trading activity. Our main analysis focuses on the anomalies' short legs, which generate persistent negative abnormal returns. For stocks in the short legs, future returns are systematically related to the information signal contained in preceding insider trading activity, indicating underreaction. For insider trading information, we consider the possibility of net buying, net selling, and no trading (or silence). The underreaction effect is economically significant, with the most negative signal accounting for an average of 71% of short-leg returns. This underreaction effect survives numerous robustness checks and remains important after accounting for investor sentiment, information environment, and limits to arbitrage.     

Are Gulf stock markets efficient? Evidence from new multiple variance ratio tests

The purpose of this article is to test for the Random Walk Hypothesis (RWH) for seven stock markets in Gulf Cooperation Council (GCC) countries, and to determine the effect of the correction for thin trading. Three new multiple variance ratio tests are applied to both observed returns and returns corrected for thin trading. It is found overall that the RWH does not hold for the GCC stock markets at both daily and weekly frequencies. This evidence is particularly strong when daily returns are used, where the RWH is soundly rejected for both observed and corrected returns.     

Are moving average trading rules profitable? Evidence from the European stock markets

This article examines the profitability of several simple technical trading rules for 16 European stock markets over the 1990 to 2006 period. Our results indicate that increasing moving average rules indeed have predictive power being able to discern recurring price patterns for profitable trading, even after accounting for the effects of data snooping bias. To assess the profitability of different technical trading rules and strategies, we adopt the White's (2000) Reality Check (RC) test that quantifies the data snooping bias and adjusts for its effects. Our empirical results also support the hypothesis that technical trading rules can outperform the buy and hold strategy after accounting for transaction costs.     

Rock around the clock: An agent-based model of low- and high-frequency trading

We build an agent-based model to study how the interplay between low- and high-frequency trading affects asset price dynamics. Our main goal is to investigate whether high-frequency trading exacerbates market volatility and generates flash crashes. In the model, low-frequency agents adopt trading rules based on chronological time and can switch between fundamentalist and chartist strategies. By contrast, high-frequency traders activation is event-driven and depends on price fluctuations. High-frequency traders use directional strategies to exploit market information produced by low-frequency traders. Monte-Carlo simulations reveal that the model replicates the main stylized facts of financial markets. Furthermore, we find that the presence of high-frequency traders increases market volatility and plays a fundamental role in the generation of flash crashes. The emergence of flash crashes is explained by two salient characteristics of high-frequency traders, i.e., their ability to i. generate high bid-ask spreads and ii. synchronize on the sell side of the limit order book. Finally, we find that higher rates of order cancellation by high-frequency traders increase the incidence of flash crashes but reduce their duration.     

Evidence of Stock Returns and Abnormal Trading Volume: A Threshold Quantile Regression Approach

This paper presents a capital asset pricing model‐based threshold quantile regression model with a generalized autoregressive conditional heteroscedastic specification to examine relations between excess stock returns and “abnormal trading volume”. We employ an adaptive Bayesian Markov chain Monte Carlo method with asymmetric Laplace distribution to study six daily Dow Jones Industrial stocks. The proposed model captures asymmetric risk through market beta and volume coefficients, which change discretely between regimes. Moreover, they are driven by market information and various quantile levels. This study finds that abnormal volume has significantly negative effects on excess stock returns under low quantile levels; however, there are significantly positive effects under high quantile levels. The evidence indicates that each market beta varies with different quantile levels, capturing different states of market conditions.     

THE DYNAMIC RELATION BETWEEN RETURNS,TRADING VOLUME,AND VOLATILITY: LESSONS FROM SPILLOVERS BETWEEN ASIA AND THE UNITED STATES

This paper investigates the dynamic relationship between index returns, return volatility, and trading volume for eight Asian markets and the US. We find cross‐border spillovers in returns to be non‐existent, spillovers in absolute returns between Asia and the US to be strong in both directions, and spillovers in volatility to run from Asia to the US. Trading volume, especially on the Asian markets, depends on shocks in domestic and foreign returns as well as on volatility, especially those shocks originating in the US. However, only weak evidence is found for trading volume influencing other variables. In the light of the theoretical models, these results suggest sequential information arrivals, with investors being overconfident and applying positive feedback strategy. Furthermore, new information causes price volatility to rise due to differences in its interpretation among traders, but the subsequent market reaction takes the form of adjustment in price level, not volatility. Lastly, the intensity of cross‐border spillovers seems to have increased following the 1997 crisis, which we interpret as evidence of increased noisiness in prices and diversity in opinions about news originating abroad. Our findings might also help to understand the nature of financial crises, to predict their further developments and consequences.     

Methodical Madness: Technical Analysis and the Irrationality of Exchange-rate Forecasts

Substantial empirical research documents that exchange-rate forecasts are not formed rationally. This paper identifies a common technical trading signal, the head-and-shoulders pattern, as a potential source of departures from rationality in exchange-rate forecasts. Forecasts based on this pattern are evaluated for daily dollar exchange rates over 1973 to 1994, using two criteria for rationality: profitability and efficiency. Resulting profits, replicable in real-time, are tested for statistical significance using a bootstrap technique. We find that the rule is profitable, but not efficient, since it is dominated by simpler trading rules.