Timing the US Stock Market Using Moving Averages and Momentum Rules: An Extensive Study
Abstract
In this thesis we investigate the performance of moving average and momentum
strategies by simulating returns, both in-sample and out-of-sample, while simultaneously
taking into account important market frictions. We do so for two stock indices
and four stock portfolios, at daily and monthly frequency, in the period from 1928 to
2015. This is carried out in order to examine if the active strategies outperform the passive
benchmark on a risk-adjusted basis, and to see if the trading rules pro table when
tested in-sample also are pro table out-of-sample. In addition, and for the rst time, we
examine the relevance of data frequencies in out-of-sample testing. A stationary block
bootstrap methodology is adopted in order to evaluate the statistical signi cance of
the risk-adjusted performance, measured by the Sharpe ratio. We nd that in-sample
pro table trading rules perform poorly when tested out-of-sample. However, we are
able to nd statistically signi cant outperformance when trading in small-cap stocks;
yet, the outperformance disappeared in recent past. Moreover, we investigate how the
performance depends on the split point between the in- and out-of-sample period and
the length of the in-sample period. We nd that the performance of an out-of-sample
test highly depends on the choice of split point as well as in-sample period length. Consequently,
the out-of-sample testing procedure is not a complete remedy for the \data
mining bias". Finally, we are not able to nd conclusive evidence suggesting any bene t
of trading more frequently.
Key words: technical analysis, market timing, moving averages, time-series momentum,
out-of-sample simulations, trading frequency
Description
Master's thesis Business Administration BE501 - University of Agder 2017