Performance of moving average trading strategies over varying stock market conditions: the Finnish evidence

This article examines the profitability of dual moving average crossover (DMAC) trading strategies in the Finnish stock market over the period 1996 to 2012. It contributes to the existing technical analysis literature by comparing for the first time the performance of DMAC trading portfolios of individual stocks to the performance of index trading strategies based on trading on an index that consists of the same stocks. The results show that their relative performance varies over time, whereas previous studies have documented outperformance of index trading strategies over trading strategies of stock portfolios. Moreover, the great majority of 3020 DMAC strategies examined in this article outperform the corresponding buy-and-hold (B and H) strategy for both trading targets (i.e., OMX Helsinki 25 index and individual stocks included in the index) in out-of-sample tests. In addition, the decomposition of the full-sample-period performance into separate bull- and bear-period performance shows clearly that the outperformance of DMAC strategies over B and H strategy is mostly attributable to their better performance during bearish periods.     

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.     

The impact of trading floor closure on market efficiency: evidence from the Toronto Stock Exchange

On 23 April 1997, the Toronto Stock Exchange closed its trading floor, making it at that time the second-largest stock exchange in North America to choose a purely electronic trading environment for its equities. Exploiting this natural experiment, we find that the move to electronic trading resulted in a higher cost of immediacy (bid–ask spreads), increased information asymmetry and an overall deterioration of short-horizon return predictability from past order flows, reducing the efficiency of price discovery. Our results suggest that the human element plays an important role in order execution and complements automated electronic trading by improving the efficiency of incorporating new information into prices.     

Improving market timing of time series momentum in the Chinese stock market

ABSTRACT

This paper is the first study to present firm-level evidence that the time-series momentum (TSMOM) strategies with look-back-period k of 10 to 200 days outperform the buy-and-hold strategy (BH) on individual stocks in the Chinese stock market. We document that the optimal k* generating the best performance is different across assets and varies over time. We hence propose a model to predict the asset-specific and time-dependent k*, and examine the performance of the TSMOM strategies with the predicted k*. Our analysis shows that using the time-varying predicted k* substantially improves the predictability of the TSMOM strategies. Our new model and findings shed the light on trading strategy for both academia and applied investment practitioners.     

On the relationship between energy returns and trading volume: a multifractal analysis

Forecasting and modelling commodities price movements and the activity of energy markets are of real interest to investors and policymakers, especially during turbulent times. This study investigates the volume–returns relationship for two major energy markets (oil and gas) during the recent global financial crisis. Unlike previous studies, we examine this relationship by applying an original fractal approach to intraday data, which has the advantage of accounting for further non-normality, nonstationarity, and fat-tailedness properties. Our study provides two interesting findings. First, we find a significant multifractal relationship between returns and volume in both markets and across all timescales, suggesting nonlinearity in the cross-correlation between returns and volume and rejecting the efficiency assumption. Second, the measure of multifractality in this relationship shows that the magnitude of the fluctuations during bearish and bullish trends affects the volume–return relationship differently, and that the oil market exhibits higher volatility than does the gas market.     

Noise trading, transaction costs, and the relationship of stock returns and trading volume

This article constructs an economic model of a rational trader who operates in a market with transaction costs and noise trading. The level of trading affects the rational trader's marginal cost of transacting; as a result, trading volume (through its effect on marginal cost) is a source of risk. This engenders an equilibrium relationship between returns and volume. The model also provides a simple way to scrutinize this relationship empirically. Empirical evidence supports the implications of the model.     

Intraday patterns and trading strategies in the Spanish stock market

Different rating and investment companies have recently pointed out Spain’s brightening growth outlook, which has energized the Spanish stock market. By anticipating greater interest in the behaviour of the Spanish stock market, we show that the best trading strategy is that in which the investor enters long or short after the opening of the New York Stock Exchange (NYSE) till the end of the trading day at 17:30. This strategy should be complemented with that of entering long or short from the opening of the trading day till the closing price before the opening of the NYSE in no-coincidence phases.     

The effect of the liberalization of the Chinese stock market on returns

We examine the short-term effects of the liberalization of the Chinese stock market on returns. We find a positive and significant abnormal return associated with the announcement of the liberalization of the Shanghai Stock Exchange. Exploiting features of the reform, we are able to compare stocks directly and indirectly affected by the liberalization. We find that all stock prices reflect this announcement premium equally, suggesting that the premium does not reflect an increase in expected liquidity. We further find that observed liquidity, as measured by volume and price impact, did not increase following the liberalization. We conclude that the observed premium reflects a diversification benefit for Chinese investors.     

Information asymmetry,cluster trading,and market efficiency: Evidence from the Chinese stock market

This study investigates the effect of asymmetry information and illiquidity related to cluster trading on information integration efficiency in the Chinese stock market. The results show that information asymmetry and illiquidity related to cluster trading both negatively affect market efficiency in the Chinese stock market. While the effect of information asymmetry on market efficiency dominates in the informational period, the effect of illiquidity related to cluster trading dominates in other periods, when trading is less concentrated. Noise trading has a positive effect on market efficiency by greatly reducing the illiquidity related to cluster trading; however, its effect on information asymmetry is not significant. Our results provide insight into investors’ trading strategies.     

The impact of market trading mechanism on A-H share price premium

After the Shanghai-Hong Kong Stock Connect policy was launched, markets become integrated, while A-H premium rises. This phenomenon is contrary to the existing market segmentation hypotheses. We provide a supplementary explanation for the A-H share price premium based on stock market trading mechanism. As margin trading and securities lending mechanism in Chinese mainland stock market is still immature and incomplete, limited arbitrage and asymmetric margin buying power amplifies the A-H premium even after markets are integrated. Our findings complement the financial microstructure researches on how trading mechanism may influence the dynamic price discovery process, especially those related to issues of short constraints and levered investing.     

Nonlinear predictability of stock market returns: Evidence from nonparametric and threshold models

Recent empirical evidence suggests that stock market returns are predictable from a variety of financial and macroeconomic variables. However, with two exceptions this predictability is based upon a linear functional form. This paper extends this research by considering whether a nonlinear relationship exists between stock market returns and these conditioning variables, and whether this nonlinearity can be exploited for forecast improvements. General nonlinearities are examined using a nonparametric regression technique, which suggest possible threshold behaviour. This leads to estimation of a smooth-transition threshold type model, with the results indicating an improved in-sample performance and marginally superior out-of-sample forecast results.     

股票市场系统动力学分析：以上海股票市场为例

本文突破了传统经济理论研究的线性框架,视股票为一非线性系统,运用分形、混沌等复杂性理论对上海股票市场的系统动力学特征进行实证研究,得出了上海股票市场系统的分形特征、复杂性程度、系统演化类型及稳定性,最后,探讨了这些结论对股票市场的理论与实践意义。     

Moments of cross-sectional stock market returns and the German business cycle

Based on monthly data covering the period from 1987 to 2021, we analyse whether cross-sectional moments of stock market returns may provide information about the future position of the German business cycle. We apply in-sample forecasting regressions with and without leading indicators as control variables, pseudo-out-of-sample exercises, autoregressive distributed lag models, and impulse-response functions estimated by local projections. We find in-sample predictive power of the first and third cross-section moments for the future growth of industrial production, even if one controls for well-established leading indicators for the German business cycle. Out-of-sample tests show that these variables reduce the relative mean squared error compared with benchmark models. We do not find a long-run relation between the moment series and industrial production. The dynamic response of industrial production to a shock on the cross-section moments is in line with the other results.     

Macroeconomic shocks and stock market returns: the case of Korea

This study examines the effects of macroeconomic shocks on key macro variables, including stock market returns in Korea, using the structural vector autoregression (SVAR) model. We suggest a three-variable SVAR model incorporating inflation, output growth and stock returns. We adopt a nonzero z-ratio restriction for the long-run identifying assumption to allow for economically meaningful relationships among variables. While our results support the negative (positive) relation of demand (supply) shocks to stock returns, we also find that demand shocks influence stock market variance more significantly than supply shocks do. The sub-period analysis finds that global market fluctuations during the global financial crisis have relatively little effect on Korean stock market performance. We also examine a generalized five-variable model that includes the foreign exchange rate and interest rate, confirming the results from the three-variable case.     

股票市场发展与经济增长——从流动性的视角

基于前人的重要结论,从流动性的角度,将流动性分为成交量、换手率两个方面。通过向量自回归（VAR）建立模型进行进一步的研究,得出股票交易成交量与经济增长的长期均衡关系,并通过VECM模型与Granger因果关系检验对所建立的模型进行进一步验证。研究结果显示,股票市场的流动性与经济增长存在显著的关系。进一步研究换手率对股票流动性的影响可得出的结论是,股票市场的流动性与经济增长存在长期的均衡关系,并且流动性中,成交额与经济增长存在正向关系,而换手率与经济增长存在负向关系,两者都是通过影响股票市场的总市值来进一步影响经济增长的。     

The evolution of stock market efficiency in the US: a non-Bayesian time-varying model approach

A non-Bayesian time-varying model is developed by introducing the concept of the degree of market efficiency that varies over time. This model may be seen as a reflection of the idea that continuous technological progress alters the trading environment over time. With new methodologies and a new measure of the degree of market efficiency, we examine whether the US stock market evolves over time. In particular, a time-varying autoregressive (TV-AR) model is employed. Our main findings are: (i) the US stock market has evolved over time and the degree of market efficiency has cyclical fluctuations with a considerably long periodicity, from 30 to 40 years; and (ii) the US stock market has been efficient with the exception of four times in our sample period: during the long recession of 1873–1879; the recession of 1902–1904; the New Deal era; and the recession of 1957–1958 and soon after it. It is then shown that our results are partly consistent with the view of behavioural finance.     

我国股票市场存在的主要问题及对策分析

股票市场作为中国证券市场的重要组成部分,在促进我国经济发展方面发挥重要作用。但是,中国股市在不断完善和发展的同时,还存在诸多问题,需要及时解决。因此,将揭示股票市场存在内幕交易、退市规则的执行力度不足及"圈钱"行为未得到有效遏制等问题,并提出相应的对策建议。     

How does leverage in stock market influence the margin of stock index futures?

In this article, we propose a margin-setting model under the assumption of extreme stock price changes. Specifically, extreme stock price changes are caused by the positive feedback effect of leverage and market impact. By introducing these factors into the futures price changes through a cost-of-carry model for setting the margin of stock index futures, we find that leverage and market impact in stock market are positively correlated with the margin.     

Dynamic probability of informed trading and price movements: evidence from the CSI300 index futures market

This article first uses dynamic probability of informed trading (DPIN) for measuring the probability of informed trading in the CSI300 index futures market and proves its validity for predicting future price movements. Instead of using the original Lee–Ready algorithm, this study uses bulk volume classification (BVC) for classifying volume. BVC could effectively improve the predictive power of DPIN for future price movements. The relationship between DPIN and returns indicates that informed buying raises the futures price while informed selling moves the futures price downward. DPIN could effectively capture price information in the index future markets in China.     

The relation between asset growth and the cross-section of stock returns: Evidence from the Chinese stock market

This study examines the effect of firm investment on stock returns by using data on the Chinese stock market. We find that stocks with higher investment experience lower future returns and there is an obvious investment effect in the Chinese stock market. The investment effect is stronger for firms that have higher cash flows, lower debt or for state-owned firms. We further explore the relation between investment and returns over the 3 years around portfolio formation. The results show that the high investment firms earn higher returns than low investment firms before portfolio formation; however the high investment firms earn lower returns than low investment firms after portfolio formation, such evidence is supportive of investor's overreaction explanation. Additionally, the stock returns don't necessarily decrease after investment, and the stock returns don't significantly positively correlate with firm profitability or book-to-market, so the result don't support risk-based explanation. Overall, both our portfolio sort and two-stage cross-sectional regression analysis show that behavioral finance theories are better than risk-based theories in explaining the investment anomaly. Evidence from the Chinese stock market provides a useful perspective to understand the debate on the investment anomaly.