  Recent studies have shown that online portfolio selection strategies that
exploit the mean reversion property can achieve excess return from equity
markets. This paper empirically investigates the performance of
state-of-the-art mean reversion strategies on real market data. The aims of the
study are twofold. The first is to find out why the mean reversion strategies
perform extremely well on well-known benchmark datasets, and the second is to
test whether or not the mean reversion strategies work well on recent market
data. The mean reversion strategies used in this study are the passive
aggressive mean reversion (PAMR) strategy, the on-line moving average reversion
(OLMAR) strategy, and the transaction cost optimization (TCO) strategies. To
test the strategies, we use the historical prices of the stocks that constitute
S\&P 500 index over the period from 2000 to 2017 as well as well-known
benchmark datasets. Our findings are that the well-known benchmark datasets
favor mean reversion strategies, and mean reversion strategies may fail even in
favorable market conditions, especially when there exist explicit or implicit
transaction costs.
