A trend factor: Any economic gains from using information over investment horizons?

Han, Yufeng; Zhou, Guofu; Zhu, Yingzi

doi:10.1016/j.jfineco.2016.01.029

Cited by 157 publications

(99 citation statements)

References 54 publications

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“…Han et al (2016) provide further evidence by constructing a trend factor that performs better than the three isolating patterns that differ in horizon, with an average return of 1.93% versus 1.81% on the long run and 0.31% versus 1.02% on the short run. And it is reasonable to assume that in terms of return patterns, the return in the long run shall outperform that in the short run.…”

Section: Abbreviationmentioning

confidence: 97%

“…On the other hand, focusing on the short investment horizon while ignoring the relatively long ones is prone to generate biased risk loadings, which are dependent largely on time interval and systematic risk (e.g., Levhari & Levy, 1977). Besides, the stock price dynamics reflect the heterogeneity in investors, which is embodied in three stock price patterns (Han, Zhou, & Zhu, 2016) that are investment horizon dependent: the short-term (at daily, weekly, and monthly level) reversals (Jegadeesh, 1990;Lehmann, 1990;Lo & MacKinlay, 1990), the medium-term (6-to 12-month level) momentum (Jegadeesh & Titman, 1993), and the long-term (3-to 5year level) reversal effects (De Bondt & Thaler, 1985). They focus on how investors and traders in financial markets differ in investment time horizons (for instance, intraday investors, day investors, market makers, short-term traders, and long-term traders) and how stock price reflects the aggregation of term-varying activities.…”

Section: Introductionmentioning

confidence: 99%

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Common idiosyncratic volatility and returns: From an investment horizon perspective

Yin

Shu

2018

Int. J Fin Econ

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“Idiosyncratic risk–return puzzle” is conflicting and confusing. It becomes more complex by the introduction of the common idiosyncratic volatility (CIV). We shed new light on the issue from the perspective of heterogeneity of investors with different investment horizons. We study the “CIV puzzle” with Chinese A‐Share market evidence and further contribute by employing the wavelet multiresolution analysis framework to decompose the overall CIV into timescales that refer to risks in different terms. We apply these timescales in Fama–Macbeth regressions to investigate their pricing effects. The results suggest that the “CIV puzzle” is an investment horizon specification problem. The relationship between returns and common idiosyncratic risk is negative in the short run, positive in the intermediate run, and then negative again in the longer run. We also contribute to the international empirical evidence with an in‐depth analysis of the Chinese stock market over the period 1999–2016. The results are robust across different specifications of the CIV. Our findings have important implications for portfolio and risk management.

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Section: Abbreviationmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Common idiosyncratic volatility and returns: From an investment horizon perspective

Yin

Shu

2018

Int. J Fin Econ

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“…They provide a trend factor cross-sectionally that outperforms substantially the momentum factor, as well as the Fama-French three factors (Market, SMB, and HML). The trend factor of Han et al (2016b), which is obtained by regressing MAs with daily price lags on the monthly stock returns, captures all the three stock price trends (i.e., short-, intermediate-, and long-terms). Let P t,d denotes the closing price on the last trading day d of month t. The MA based on daily prices (noted as MAD) and the correspond predictor (noted as MADP) of the last trading day d with lag length K (days) at each month t are defined as:…”

Section: B Predictive Regressionsmentioning

confidence: 99%

“…Based on the equilibrium model,Han et al (2016b) construct a trend factor cross-sectionally and find that the trend factor performs better than the momentum factor. © 2017 International Review of Finance Ltd. 2017…”

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confidence: 99%

Further Mining the Predictability of Moving Averages: Evidence from the US Stock Market

2017

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Most studies on the predictability of moving average (MA) technical analysis use the discrete (buy/sell) trading recommendations. However, it is possibly incomplete or unreliable to explore the predictability of MA by only employing its generated trading signals. To further explore the forecastability of MA, we study its measurable impact on the stock market returns by using a conventional predictive regression framework. Our empirical study on the US stock market with respect to more detailed price information finds, (i) that the proposed predictor, MADP (MA based on daily prices) shows significant predictability in‐ and out‐of‐sample, and significantly outperforms the historical average (HA) benchmark as well as the MA based on monthly prices, (ii) that the predictability of MADP centers on the short‐term lags (within the most recent 10 days) and disappears when lags are beyond 20 days, and (iii) that the economic evaluation of the portfolios based on trading strategies confirms the superior performance of MADP with short‐term lags against the benchmark even though considering transaction costs.

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“…In addition, Huang and Zhou (2013) use the moving average indicator to predict the return on the US stock market while Goh et al (2012) apply the same idea to government bond yields and risk premia. Motivated in part by the predictive power of the moving average indicator, Han et al (2016) and Jiang (2013) construct a trend factor with considerable cross-sectional explanatory power and substantial historical performance. In a similar vein, Glabadanidis (2014Glabadanidis ( , 2015aGlabadanidis ( , 2015b investigates and documents the performance of the simple moving average strategy with various US and international portfolios as well as individual US stocks.…”

Section: Introductionmentioning

confidence: 99%

Timing the Market with a Combination of Moving Averages

Glabadanidis

2016

Int Rev Finance

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A combination of simple moving average trading strategies with several window lengths delivers a greater average return and skewness as well as a lower variance and kurtosis compared with buying and holding the underlying asset using daily returns of value‐weighted US decile portfolios sorted by market size, book‐to‐market, momentum, and standard deviation as well as more than 1000 individual US stocks. The combination moving average (CMA) strategy generates risk‐adjusted returns of 2% to 16% per year before transaction costs. The performance of the CMA strategy is driven largely by the volatility of stock returns and resembles the payoffs of an at‐the‐money protective put on the underlying buy‐and‐hold return. Conditional factor models with macroeconomic variables, especially the market dividend yield, short‐term interest rates, and market conditions, can explain some of the abnormal returns. Standard market timing tests reveal ample evidence regarding the timing ability of the CMA strategy.

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A trend factor: Any economic gains from using information over investment horizons?

Cited by 157 publications

References 54 publications

Common idiosyncratic volatility and returns: From an investment horizon perspective

Common idiosyncratic volatility and returns: From an investment horizon perspective

Further Mining the Predictability of Moving Averages: Evidence from the US Stock Market

Timing the Market with a Combination of Moving Averages

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