  On-line portfolio selection has attracted increasing interests in machine
learning and AI communities recently. Empirical evidences show that stock's
high and low prices are temporary and stock price relatives are likely to
follow the mean reversion phenomenon. While the existing mean reversion
strategies are shown to achieve good empirical performance on many real
datasets, they often make the single-period mean reversion assumption, which is
not always satisfied in some real datasets, leading to poor performance when
the assumption does not hold. To overcome the limitation, this article proposes
a multiple-period mean reversion, or so-called Moving Average Reversion (MAR),
and a new on-line portfolio selection strategy named "On-Line Moving Average
Reversion" (OLMAR), which exploits MAR by applying powerful online learning
techniques. From our empirical results, we found that OLMAR can overcome the
drawback of existing mean reversion algorithms and achieve significantly better
results, especially on the datasets where the existing mean reversion
algorithms failed. In addition to superior trading performance, OLMAR also runs
extremely fast, further supporting its practical applicability to a wide range
of applications.
