Market Timing and Moving Averages: An Empirical Analysis of by P. Glabadanidis

By P. Glabadanidis

There is a triumphing view between researchers and practitioners that irregular risk-adjusted returns are an anomaly of monetary industry inefficiency. This outlook is deceptive, considering that such returns in basic terms make clear the imperfect types common to degree and benchmark funding functionality. particularly, utilizing static asset pricing types to pass judgement on the functionality of a dynamic funding method results in fallacious inferences while predicting marketplace indicators.

Market Timing and relocating Averages investigates the functionality of relocating commonplace cost signs as a tactical asset allocation method. Glabadanidis offers a purpose for reading and checking out the marketplace timing and predictive energy of any indicator in accordance with earlier common costs and buying and selling quantity. He argues that yes buying and selling thoughts are top applied as a dynamic asset allocation with no promoting brief, in flip attaining the impact of a less than excellent at-the-money protecting positioned choice. This paintings comprises an empirical research of the functionality of assorted types of buying and selling concepts in response to easy relocating averages.

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13 portfolios. 14 1960 1980 2000 1960 1980 2000 1960 1980 2000 HiTec Other 1960 1980 2000 Cumulative returns of BH versus MA strategy: Industry portfolios. few instances of a false-negative signal where we switch into the risk-free asset while the underlying risky asset has a positive excess return in the following period. Nevertheless, the signal is right about two out of every three times and in those instances the scatter plot resembles the payoff of an at-the-money put option combined with a long position in the underlying risky asset.

The sample period covers January 4, 1960 until December 31, 2013 with daily value-weighted portfolio returns. The length of the MA window is 20 days. Newey and West (1987) standard errors with five lags are used in reporting statistical significance of a two-sided null hypothesis at the 1%, 5%, and 10% level given by a ∗∗∗ , a ∗∗ , and a ∗ , respectively. 257 R¯ 2 Investment Performance 19 performance of a stock index. More recently, Neely, Rapach, Tu and Zhou (2010) provide evidence in favor of the usefulness of technical analysis in forecasting the stock market risk premium.

Holding the signal success rate constant, risky assets with more volatile returns will experience a higher average outperformance and this is evidenced in all of the previous tables. In order to develop this idea further, I introduce some additional notation. Suppose that the price of the underlying risky asset is S0 which follows a geometric Brownian motion process. 9) Investment Performance 29 where (r ± σ2 )T d1,2 = , √ σ T 2 and N (d ) is the cumulative distribution of a standard Gaussian random variable.

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