Detailed Study of a Moving Average Trading Rule

Quantitative Finance, 18:9, 1599-1617 (2018)

32 Pages Posted: 30 Jun 2019

See all articles by Fernando Ferreira

Fernando Ferreira

Universidade de Sao Paulo

A. Christian Silva

idatafactory

Ju-Yi Yen

University of Cincinnati

Date Written: October 7, 2017

Abstract

We present a detailed study of the performance of a trading rule that uses moving average of past returns to predict future returns on stock indexes. Our main goal is to link performance and the stochastic process of the traded asset. Our study reports short, medium and long term effects by looking at the Sharpe ratio (SR). We calculate the Sharpe ratio of our trading rule as a function of the probability distribution function of the underlying traded asset and compare it with data. We show that if the performance is mainly due to presence of autocorrelation in the returns of the traded assets, the SR as a function of the portfolio formation period (look-back) is very different from performance due to the drift (average return). The SR shows that for look-back periods of a few months the investor is more likely to tap into autocorrelation. However, for look-back larger than few months, the drift of the asset becomes progressively more important. Finally, our empirical work reports a new long-term effect, namely oscillation of the SR and propose a non-stationary model to account for such oscillations.

Keywords: Momentum, Trend Following, Regimes, Cycles, Sharpe Ratio, Information Ratio, Trading Strategies, Non-stationary, Quantitative

JEL Classification: C00, C10, C22, C50, G00

Suggested Citation

Ferreira, Fernando and Silva, Christian and Yen, Ju-Yi, Detailed Study of a Moving Average Trading Rule (October 7, 2017). Quantitative Finance, 18:9, 1599-1617 (2018), Available at SSRN: https://ssrn.com/abstract=3412110

Fernando Ferreira

Universidade de Sao Paulo ( email )

Universidade de Sao Paulo
Sao Paulo, Sao Paulo 03828-000
Brazil

Christian Silva (Contact Author)

idatafactory ( email )

Houston, TX 77030
United States

HOME PAGE: http://www.idatafactory.com

Ju-Yi Yen

University of Cincinnati ( email )

Cincinnati, OH 45221-0389
United States

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