Cross‐asset Style Momentum

Kim, Daehwan

doi:10.1111/j.2041-6156.2012.01084.x

Cited by 12 publications

(6 citation statements)

References 41 publications

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“…where m is a simple moving average, and N is the look-back period used to calculate the moving average. This same set of rules was used in [3,45,61,50,54], and other prior studies 3 .…”

Section: Trading Rulementioning

confidence: 99%

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Detailed study of a moving average trading rule

Ferreira

Silva²,

Yen

2018

Quantitative Finance

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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. * ferfff@usp.br † csilva@idatafactory.com ‡ ju-yi.yen@uc.edu 1 Cross-sectional momentum is not the focus of this article, but it has been studied extensively if compared to time-series momentum or trend-following. Since the work of [41], momentum has been extended to different asset classes, portfolios, and other markets abroad. Momentum has been reported in international equity markets by [22,28,62,65]; in industries by [50,60]; in indexes by [8,2]; and in commodities by [24,61]. Single risky asset momentum is analyzed in [21,5,39,7,44,1,52,66] to cite a few. Recently, momentum has also been studied linking its performance to business cycles and regimes by [16,46,33,35,6]. arXiv:1907.00212v1 [q-fin.ST]

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“…where m is a simple moving average, and N is the look-back period used to calculate the moving average. This same set of rules was used in [3,45,61,50,54], and other prior studies 3 .…”

Section: Trading Rulementioning

confidence: 99%

“…In contrast to prior studies [45,50,54,61], we do not look only at the return of the trading rule. We use the variance (8) to calculate the Sharpe ratio (SR), defined here by the ratio between the average return and the standard deviation (square root of the variance).…”

Section: Limits and Interpretationsmentioning

confidence: 99%

Detailed study of a moving average trading rule

Ferreira

Silva²,

Yen

2018

Quantitative Finance

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“…where m is a simple moving average, and N is the look-back period used to calculate the moving average. This same set of rules was used in [4,35,45,38,41] and other prior studies. 1 In order to implement the algorithm (Eq.…”

Section: Modelmentioning

confidence: 99%

“…In contrast to prior studies [35,38,41,45] we do not look only at the return of the strategy . We use the variance (Eq.…”

Section: Limits and Interpretationsmentioning

confidence: 99%

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Information Ratio Analysis of Momentum Strategies

2014

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In the past 20 years, momentum or trend following strategies have become an established part of the investor toolbox. We introduce a new way of analyzing momentum strategies by looking at the information ratio (IR, average return divided by standard deviation). We calculate the theoretical IR of a momentum strategy, and show that if momentum is mainly due to the positive autocorrelation in returns, IR as a function of the portfolio formation period (look-back) is very different from momentum due to the drift (average return). The IR shows that for look-back periods of a few months, the investor is more likely to tap into autocorrelation. However, for look-back periods closer to 1 year, the investor is more likely to tap into the drift. We compare the historical data to the theoretical IR by constructing stationary periods. The empirical study finds that there are periods/regimes where the autocorrelation is more important than the drift in explaining the IR (particularly pre-1975) and others where the drift is more important (mostly after 1975). We conclude our study by applying our momentum strategy to 100 plus years of the Dow-Jones Industrial Average. We report damped oscillations on the IR for look-back periods of several years and model such oscilations as a reversal to the mean growth rate.

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Investment Styles in General

Glas

2022

Advanced Studies in Diginomics and Digitalization

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Cross‐asset Style Momentum

Cited by 12 publications

References 41 publications

Detailed study of a moving average trading rule

Detailed study of a moving average trading rule

Information Ratio Analysis of Momentum Strategies

Investment Styles in General

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