Technical analysis: An asset allocation perspective on the use of moving averages

In this paper, we analyze the usefulness of technical analysis, specifically the widely employed moving average trading rule from an asset allocation perspective. We show that, when stock returns are predictable, technical analysis adds value to commonly used allocation rules that invest fixed proportions of wealth in stocks. When uncertainty exists about predictability, which is likely in practice, the fixed allocation rules combined with technical analysis can outperform the prior-dependent optimal learning rule when the prior is not too informative. Moreover, the technical trading rules are robust to model specification, and they tend to substantially outperform the model-based optimal trading strategies when the model governing the stock price is uncertain.     

Time series momentum trading strategy and autocorrelation amplification

This paper investigates why general Moving Average (MA) trading rules are widely used by technical analysts and others. We assume general stationary processes for prices and we derive the autocorrelation function for an MA trading rule. Based on our results, we conjecture that autocorrelation amplification is one of the reasons why such trading rules are popular. Using simulated results, we show that the MA rule may be popular because it can identify price momentum and is a simple way of assessing and exploiting the price autocorrelation structure without necessarily knowing its precise structure. This paper then, provides empirical evidence of autocorrelation amplification using 15-year daily price data for 11 major international stock indices.     

Predictability of nonlinear trading rules in the U.S. stock market

Most of the existing technical trading rules are linear in nature. This paper investigates the predictability of nonlinear time series model based trading strategies in the U.S. stock market. The performance of the nonlinear trading rule is compared with that of the linear model based rules. It is found that the self-exciting threshold autoregressive (SETAR) model based trading rules perform slightly better than the AR rules for the Dow Jones and Standard and Poor 500, while the AR rules perform slightly better in the NASDAQ market. Both the SETAR and the AR rules outperform the VMA rules. The results are confirmed by bootstrap simulations.     

Are tightened trading rules always bad? Evidence from the Chinese index futures market

This paper investigates the impact of tightened trading rules on the market efficiency and price discovery function of the Chinese stock index futures in 2015. The market efficiency and the price discovery of Chinese stock index futures do not deteriorate after these rule changes. Using variance ratio and spectral shape tests, we find that the Chinese index futures market becomes even more efficient after the tightened rules came into effect. Furthermore, by employing Schwarz and Szakmary [J. Futures Markets, 1994, 14(2), 147–167] and Hasbrouck [J. Finance, 1995, 50(4), 1175–1199] price discovery measures, we find that the price discovery function, to some extent, becomes better. This finding is consistent with Stein [J. Finance, 2009, 64(4), 1517–1548], who documents that regulations on leverage can be helpful in a bad market state, and Zhu [Rev. Financ. Stud., 2014, 27(3), 747–789.], who finds that price discovery can be improved with reduced liquidity. It also suggests that the new rules may effectively regulate the manipulation behaviour of the Chinese stock index futures market during a bad market state, and then positively affect its market efficiency and price discovery function.     

Market Efficiency and the Returns to Simple Technical Trading Rules: New Evidence from U.S. Equity Market and Chinese Equity Markets

Numerous studies in the finance literature have investigated technical analysis to determine its validity as an investment tool. This study is an attempt to explore whether some forms of technical analysis can predict stock price movement and make excess profits based on certain trading rules in markets with different efficiency level. To avoid using arbitrarily selected 26 trading rules as did by Brock, Lakonishok and LeBaron (1992) and later by Bessembinder and Chan (1998), this paper examines predictive power and profitability of simple trading rules by expanding their universe of 26 rules to 412 rules. In order to find out the relationship between market efficiency and excess return by applying trading rules, we examine excess return over periods in U.S. markets and also compare the excess returns between U.S. market and Chinese markets. Our results found that there is no evidence at all supporting technical forecast power by these trading rules in U.S. equity index after 1975. During the 1990s break-even costs turned to be negative, –0.06%, even failing to beat a buy-holding strategyin U.S. equity market. In comparison, our results provide support for the technical strategies even in the presence of trading cost in Chinese stock markets.     

The intertemporal risk-Return relation,investor behavior,and technical trading profits: evidence from the G-7 countries

The intertemporal risk-return relation and investor behavior are both important pricing factors that jointly determine the expected market risk premium. Using the price adjustment process as a control variable, we find that the intertemporal risk-return relation is positive conditional on bad market news, but is non-positive conditional on good market news. This implies that good (bad) market news weakens (strengthens) the positive risk-return relation. The pattern in the distortion of the risk-return relation is consistent with short-term mispricing in which investors overvalue (undervalue) the stock market in reaction to good (bad) market news. We also show that ignoring the price adjustment process in the estimation of the risk-return relation leads to model misspecification and induces an upward (downward) bias in estimates of the relative risk aversion parameter conditional on good (bad) news. Our model of the asymmetric risk-return relation along with the price adjustment process is capable of generating the return dynamics that is attributable to technical trading profits. We suggest that the profitability of technical trading rules is not a violation of market efficiency, but a consequence of trading rules exploiting the asymmetric effect of price changes on the risk-return relation, along with the persistence property of price changes.     

股市波动、金融政策和宏观经济关系研究——基于因子VAR模型

通过因子分析从诸宏观经济变量中提取了金融政策因子和宏观经济状态因子,建立了基于VAR的股价波动、金融政策和宏观经济三变量回归模型。研究表明：金融政策影响股价的表现,而宏观经济状态对股价、股价对金融政策和宏观经济状态的影响均不显著;基于标准差的VAR（5）模型相对于基于收益率的VAR（3）模型能更好地刻画股市波动与金融政策、宏观经济三者之间的关系。     

On the efficacy of stop-loss rules in the presence of overnight gaps

A stop-loss rule is a risk management tool whereby the investor predefines some condition that, upon being triggered by market dynamics, implies the liquidation of her outstanding position. Such a tool is widely used by practitioners in financial markets with the hope of improving their investment performance by cutting losses and consolidating gains. We analyze in this work the performance of four popular implementations of stop-loss rules applied to asset prices whose returns are modeled with consideration of overnight gaps, that is, jumps from the closing price of one day to the opening price of the next trading day. In addition, our models include acute momentary price drops (flash crashes), which are often believed to erode the performance gains that might be derived from stop-loss rules. For this analysis we consider different models of asset returns: random walk, autoregressive and regime-switching models. In addition, we test the performance of the considered stop-loss rules in a non-parametric, data-driven framework based on the stationary bootstrap. As a general conclusion we find that, even when including overnight gaps and flash crashes in our price models, in rising markets stop-loss rules improve the expected risk-adjusted return according to most metrics, while improving absolute expected return in falling markets. Furthermore, we find that in general the simple fixed percentage stop-loss rule may be, in risk-adjusted terms, the most powerful among the popular rules that this work considers.     

数量规则还是利率规则？——我国转型时期量价混合型货币规则的理论基础

本文对我国货币政策转型时期兼顾数量和价格的货币政策调控实践进行了深入的分析。在货币数量论和货币效用模型的基础上,从理论上阐明了货币数量规则与利率价格规则的等价关系,并构建了符合中国货币政策实践的数量与价格混合型货币政策规则。这对于更好地理解我国货币政策转型时期的量价混合型货币政策操作具有重要的理论和现实意义。相关推论表明,正是由于数量和价格混合型货币规则,在利率低于均衡水平的情形下,中国的货币增速并未引发恶性通胀;修正的物价稳定泰勒原理表明,利率调整幅度小于通胀变化仍能够实现物价稳定。对中国的经验分析支持了理论和推论结果。在利率市场化基本完成和流动性格局逆转的当下,货币政策价格调控方式转型的必要性和迫切性日益上升,转型的条件日趋成熟。     

Using Rule Induction for Expert System Development: The Case of Asset Writedowns

There was a significant increase in the frequency and magnitude of asset writedowns by US firms during the 1980s. Auditors, financial analysts and regulators have shown considerable interest in evaluating the writedown phenomenon. This paper reports on the use of inductive learning to discover knowledge in financial data structures and describes the development and testing of a prototype expert system, WDXPERT, which evaluates asset writedowns. Real-world data relating to writedown and non-writedown firms are used for rule induction. A set of training examples comprising 42 writedown and 25 non-writedown firms are used to generate the rules using IXL, a machine-learning program. A separate holdout sample containing 43 writedown and 25 non-writedown firms are used to validate the expert system that incorporates these rules. A second validation procedure is performed by comparing the performance of the expert system with a conventional discriminant analysis model and a logit model using the same data sets. The results indicate that the expert system, WDXPERT, is a useful classification tool to group firms into writedown and non-writedown classes.     

An analysis of trading strategies in eleven European stock markets

Abstract

In recent years, the validity of the weak form efficient market hypothesis (EMH) has been called into question as several studies have uncovered evidence that technical trading rules have predictive ability with respect to both developed and emerging stock market indices. This study analyses the forecasting power of 2 of the most popular trading rules using index data for a selection of 11 European stock markets over the January 1991 to December 2000 period. The findings indicate that the emerging markets included in this paper are informationally inefficient; these markets displayed some degree of predictability in their share returns, although the developed markets did not. Furthermore, the results point to large differences in the performance of the rules examined; while small size filters consistently outperformed the buy-and-hold strategy in the emerging markets examined even after the consideration of transaction costs, the performance of the moving average rules was erratic and varied dramatically from market to market.     

对完善我国股票市场价格稳定机制的探讨

价格波动性是股票市场与生俱来的特性,也是股票市场存在并正常运行的前提条件。但如果股价波动过大,价格变化不连续,就必然加大投资风险,影响股票市场的规范和稳定发展。因此,必须建立和完善股票市场价格稳定机制。     

The predictability of security returns with simple technical trading rules

Technical traders base their analysis on the premise that the patterns in market prices are assumed to recur in the future, and thus, these patterns can be used for predictive purposes. This paper uses the daily Dow Jones Industrial Average Index from 1897 to 1988 to examine the linear and nonlinear predictability of stock market returns with simple technical trading rules. The nonlinear specification of returns are modelled by single layer feedforward networks. The results indicate strong evidence of nonlinear predictability in the stock market returns by using the past buy and sell signals of the moving average rules.     

Why Do Price Limits Exist in Stock Markets? A Manipulation‐Based Explanation

Numerous stock market regulators around the world impose daily price limits on individual stock price movements. We derive a simple model that shows that price limits may deter stock market manipulators. Based on our model's implications, we predict that regulators impose price limit rules for markets where the likelihood of manipulation is high. We present empirical evidence consistent with this hypothesis. Our study is the first to formally propose a manipulation‐based rationale for the existence of price limits in stock markets.     

Bonus schemes and trading activity

Little is known about how different bonus schemes affect traders' propensity to trade and which bonus schemes improve traders' performance. We study the effects of linear versus threshold bonus schemes on traders' behavior. Traders buy and sell shares in an experimental stock market on the basis of fundamental and technical information (past share price evolution, realized earnings, analysts' earnings forecasts, and evolution of the market index). We find that linear and threshold bonus schemes have different effects on trading behavior: traders make more transactions but of a smaller size under the threshold than under the linear bonus scheme. Furthermore, transaction frequency significantly decreases when bonus thresholds are reached but only after building in a safety margin. Under the threshold scheme, the traders' performance is lower (even when there are no transaction costs) than under the linear bonus scheme as a consequence of poorer market timing. This is especially the case when earning money by trading is relatively difficult (i.e., under low profitability conditions). Nevertheless, under low profitability conditions, traders seem to collect more information about the relationships between share price and market returns, earnings, and earnings forecasts, put more effort into understanding those relationships, and thus eventually learn to perform better.     

The Meta Taylor Rule

We characterize U.S. monetary policy within a generalized Taylor rule framework that accommodates uncertainties about the duration of policy regimes and the specification of the rule, in addition to the standard parameter and stochastic uncertainties inherent in traditional Taylor rule analysis. Our approach involves estimation and inference based on Taylor rules obtained through standard linear regression methods, but combined using Bayesian model averaging techniques. Employing data that were available in real time, the estimated version of the “meta” Taylor rule provides a flexible but compelling characterization of monetary policy in the United States over the last 40 years.     

基于财务数据的农业股票投资价值研究

在农业板块股票市场中,采用灰色关联度法找出与股票价格相关联的财务指标,并分别建立了对数线性模型与截面数据回归模型检验了每股净资产对股票价格的基础作用。研究结果表明：股票价格与业绩关联性显著,公司财务因素对于股价有着极强的解释能力,每股净资产是股票价格的价值基础。     

On the Existence of Visual Technical Patterns in the UK Stock Market

The aim in this paper is to replicate and extend the analysis of visual technical patterns by Lo et al. (2000) using data on the UK market. A non‐parametric smoother is used to model a nonlinear trend in stock price series. Technical patterns, such as the 'head‐and‐shoulders' pattern, that are characterised by a sequence of turning points are identified in the smoothed data. Statistical tests are used to determine whether returns conditioned on the technical patterns are different from random returns and, in an extension to the analysis of Lo et al. (2000), whether they can outperform a market benchmark return. For the stocks in the FTSE 100 and FTSE 250 indices over the period 1986 to 2001, we find that technical patterns occur with different frequencies to each other and in different relativities to the frequencies found in the US market. Our extended statistical testing indicates that UK stock returns are less influenced by technical patterns than was the case for US stock returns.     

中国化泰勒规则的构建与规则利率的估算——基于考虑金融周期信息的潜在产出与自然利率的再估算

我国货币政策正在向价格型调控方式转型,有必要探索符合我国实际的泰勒规则以确定政策利率目标水平,而如何科学合理地估算潜在产出和自然利率是关键。本文对构建多目标下的中国化泰勒规则时应考虑的因素作了深入探讨,并将金融周期信息纳入我国潜在产出的测算,同时采用基于潜在产出的方法对自然利率进行估算,进而分不同情形尝试估算我国的规则利率水平。结果表明,考虑金融周期信息后的潜在产出在金融扩张期将低于传统方法的估计结果,可为政策制定者有效应对经济运行的过热现象或泡沫化状态提供参考;无论是基于何种潜在产出和自然利率的测算组合,将国内外利差、房价涨幅偏离经济增长程度纳入中国化泰勒规则的构建均是合理的;不同情形下估算的规则利率走势,自2004年以来与货币市场利率走势均较为相近,且2013年后随着利率市场化改革的快速推进,差距呈明显缩小态势,但2017年下半年至2018年期间,受国际宏观形势和国内因素影响,估算的规则利率均快速上扬,与货币市场利率走低形成反差。     

OVERREACTION AND REVERSE ANTICIPATION: TWO RELATED PUZZLES?

In this article we investigate stock price behavior before and after surprise events. We form four base portfolios and sixteen control portfolios, taking into consideration the event direction, the magnitude of event-day surprises, the potentially confounding effects due to calendar regularities in stock returns, and the ex-post outlier month of October 1987. Using capital market data from 1964 to 1989, we find a pre-event stock price behavior pattern that we call the reverse anticipation puzzle. We also confirm the existence of the overreaction pattern. The pre- and post-event stock price behavior patterns are found to be similar.