MA trading rules, herding behaviors, and stock market overreaction

Ni, Yensen; Liao, Yi-Ching; Huang, Paoyu

doi:10.1016/j.iref.2015.04.009

Cited by 45 publications

(32 citation statements)

References 48 publications

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“…Neely et al (2014) and Ilomäki (2018) find, using monthly data, that MA rules are beneficial for investors, and Marshall et al (2017) draws the same conclusion using daily data on US small stocks. In addition, Ni et al (2015) report that a combination of two MA rules (or the so-called dead cross emerges) is useful for investors. However, Hudson et al (2017) and Yamamoto (2012) conclude that MA rules are totally useless in high-frequency trading.…”

Section: Literature Reviewmentioning

confidence: 99%

Long Run Returns Predictability and Volatility with Moving Averages

Chang

Ilomäki

Laurila

et al. 2018

Risks

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This paper examines how the size of the rolling window, and the frequency used in moving average (MA) trading strategies, affects financial performance when risk is measured. We use the MA rule for market timing, that is, for when to buy stocks and when to shift to the risk-free rate. The important issue regarding the predictability of returns is assessed. It is found that performance improves, on average, when the rolling window is expanded and the data frequency is low. However, when the size of the rolling window reaches three years, the frequency loses its significance and all frequencies considered produce similar financial performance. Therefore, the results support stock returns predictability in the long run. The procedure takes account of the issues of variable persistence as we use only returns in the analysis. Therefore, we use the performance of MA rules as an instrument for testing returns predictability in financial stock markets.

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Section: Literature Reviewmentioning

confidence: 99%

Long Run Returns Predictability and Volatility with Moving Averages

Chang

Ilomäki

Laurila

et al. 2018

Risks

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“…Thus, there is not only restricted on financial events, but rather the investment activity in the capital market. Many previous research also found the investor overconfidence and stocks' price overreaction on investment activity in capital market (Daniel et al 1998;Ma et al 2005;Boussaidi 2013;Ni et al 2015). But the existing research not fully explained what and how the private information signals affected on the overconfidence behavior.…”

Section: Introductionmentioning

confidence: 94%

Research of the Stock Price Overreaction and Investor Overconfidence Issues

Raharja

Suhaeli

Mranani

2017

Business, Management and Education

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This research examines the existing of investor overconfidence in the capital market and the phenomena of stock prices reversal in the future due to the existing of this behavior. It has a different approach to test the existing of investor overconfidence by introducing firm’s growth as the information which has triggered many investors to behave overconfidently. By using multiple regression analysis, the results of this research confirmed our conducted hypothesis, investor tends to behave overconfident to firms which have higher growth. It proofed by the positive relation between firms’ growth and trading volume. Afterward, this research also found that higher growth firms tend to have declining on its performance in the future. The negative relation between firms’ growth and longterm performance means that the stock’s price reversal caused by the existing of investor overconfidence.

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“…The empirical literature on predictability with MA rules is extensive. For example, Neely et al [43], Ni et al [44], and Marshall et al [45] found that investors benefit from the use of MA rules, whereas Hudson et al [46] claim that MA rules are useless in intra-day trading. The main inspiration for this paper comes from the findings in Chang et al [47] that the DJIA index returns are predictable in the long run according to MA rules, which provides empirical support for the analytical result in Reference [42].…”

Section: Literature Reviewmentioning

confidence: 99%

Moving Average Market Timing in European Energy Markets: Production Versus Emissions

et al. 2018

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This paper searches for stochastic trends and returns predictability in key energy asset markets in Europe over the last decade. The financial assets include Intercontinental Exchange Futures Europe (ICE-ECX) carbon emission allowances (the main driver of interest), European Energy Exchange (EEX) Coal ARA futures and ICE Brent oil futures (reflecting the two largest energy sources in Europe), Stoxx600 Europe Oil and Gas Index (the main energy stock index in Europe), EEX Power Futures (representing electricity), and Stoxx600 Europe Renewable Energy index (representing the sunrise energy industry). This paper finds that the Moving Average (MA) technique beats random timing for carbon emission allowances, coal, and renewable energy. In these asset markets, there seems to be significant returns predictability of stochastic trends in prices. The results are mixed for Brent oil, and there are no predictable trends for the Oil and Gas index. Stochastic trends are also missing in the electricity market as there is an ARFIMA-FIGARCH process in the day-ahead power prices. The empirical results are interesting for several reasons. We identified the data generating process in EU electricity prices as fractionally integrated (0.5), with a fractionally integrated Generalized AutoRegressive Conditional Heteroscedasticity (GARCH) process in the residual. This is a novel finding. The order of integration of order 0.5 implies that the process is not stationary but less non-stationary than the non-stationary I(1) process, and that the process has long memory. This is probably because electricity cannot be stored. Returns predictability with MA rules requires stochastic trends in price series, indicating that the asset prices should obey the I(1) process, that is, to facilitate long run returns predictability. However, all the other price series tested in the paper are I(1)-processes, so that their returns series are stationary. The empirical results are important because they give a simple answer to the following question: When are MA rules useful? The answer is that, if significant stochastic trends develop in prices, long run returns are predictable, and market timing performs better than does random timing.

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MA trading rules, herding behaviors, and stock market overreaction

Cited by 45 publications

References 48 publications

Long Run Returns Predictability and Volatility with Moving Averages

Long Run Returns Predictability and Volatility with Moving Averages

Research of the Stock Price Overreaction and Investor Overconfidence Issues

Moving Average Market Timing in European Energy Markets: Production Versus Emissions

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