Tianzi’s First Post

/Tianzi’s First Post

Tianzi’s First Post

By | 2017-08-18T16:55:00+00:00 July 10th, 2012|Trend Following, Research Insights|1 Comment
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(Last Updated On: August 18, 2017)

Hello readers, we have a new contributing author. Tianzi is her name and reading cool academic papers is here game. Here is her first post. She’ll be posting updates and analysis throughout the summer. If you have any special requests, please email them to us at support *@* turnkeyanalyst.com

A New Anomaly: The Cross-Sectional Profitability of Technical Analysis

  • Yufeng Han, Ke Yang, Guofu Zhou
  • A version of the paper  can be found here.


“In this paper, we document that an application of the moving averages (a popular form of technical analysis) to portfolios sorted by volatility generates investment timing portfolios that outperform the buy-and hold strategy greatly, with returns that have negative or little risk exposures on the market factor and the Fama-French SML and HML factors. As a result, the abnormal returns, relative to the CAPM and the Fama and French (1993) three-factor models, are high, and higher than those from the momentum strategy for high decile portfolios. The abnormal returns remain high even after accounting for transaction costs. While the moving average is a trend-following strategy as the momentum, its performance has little correlation with the momentum, and behaves differently over business cycles, default and liquidity risks.”

Data Sources:

The data on price and daily returns comes from NYSE, AMEX, and NASDAQ . Other relevant data used in the study comes from CRSP. The period tested is 1973-2009.


Technical analysis uses past data to predict future market movements. Many studies find  strong evidence that technical analysis works. Moving averages have been shown to forecast the market. In practice, all major brokerage firms publish technical commentary on the market and many of the advisory services are based on technical analysis.

So what’s special about this paper?

Unlike existing studies that apply technical analysis to either market indices or individual stocks, this paper apply it to volatility decile portfolios, i.e., portfolios of stocks that are sorted by their standard deviation of daily returns.
The investment timing strategy uses the moving average as the timing signal. For a given portfolio, the MA investment timing strategy is to buy, or continue to hold, the portfolio when yesterday’s price is above its 10-day MA price, and to invest the money into the risk-free asset otherwise.

Mathematically, the returns on the MA timing strategy are


To test the profitability of the moving average portfolios, the author uses MAP to test how MA timing strategy outperforms the buy-and-hold strategy. MAP is defined as


The authors find that the MAP returns, the CAPM risk-adjusted or abnormal returns, and the Fama-French model risk-adjusted returns are positive and increasing with volatility decile. The same qualitative results hold for both equal-weighted and value-weighted size decile portfolios from the Nasdaq, NYSE, and NYSE/Amex. Considering alternative lag lengths, the abnormal returns appear more short-term with decreasing magnitude over the lag lengths. The abnormal returns remain high even after accounting for transaction costs. While the moving average is a trend-following strategy as the momentum, they respond quite differently to default and liquidity risks and capture different aspects of the market because their return correlation is low, and the MAPs generate economically and statistically significant abnormal returns in both expansion and recession periods.

Investment Strategy:

  1. Sort stocks on volatility by decile.
  2. Calculate the average price of the past L days.
  3. Long the decile portfolio j if the last closing price is above the MA price and go short the decile portfolio j otherwise.
  4. Repeat/rebalance.
  5. Make money.


MAPs are more sensitive to recessions than UMD, insensitive to the default spread, and sensitive to liquidity risks.

Given the size of the abnormal returns and the wide use of technical analysis, explaining the moving average anomaly with new asset pricing models will be important and desirable.

By forming the portfolio by choosing stocks, we can diversify unsystematic risk. By using MAPs, we can also diversify systematic risk, because from the regressions of the MAPs on the market excess return, MAPs have negative market betas and relatively large risk-adjusted abnormal returns than buy-and-hold strategy.

  • The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).
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About the Author:

Prior to joining the Alpha Architect team, Ms. Yao was a Research Assistant to Dr. Gray. She studied quantitative models and summarized over 200 academic articles on psychology and behavioral finance. Her prior experience includes work as a financial analyst at United Asset Growth (China) LLC, and as a business development intern for Shanghai Pudong Development Bank. Tian earned a Masters in Finance at Drexel University. She earned her Bachelors degree in Finance at Nanjing Normal University, China.