  The online portfolio selection (OLPS) problem differs from classical
portfolio model problems, as it involves making sequential investment
decisions. Many OLPS strategies described in the literature capture market
movement based on various beliefs and are shown to be profitable. In this
paper, we propose a robust optimization (RO)-based strategy that takes
transaction costs into account. Moreover, unlike existing studies that
calibrate model parameters from benchmark data sets, we develop a novel
adaptive scheme that decides the parameters sequentially. With a wide range of
parameters as input, our scheme captures market uptrend and protects against
market downtrend while controlling trading frequency to avoid excessive
transaction costs. We numerically demonstrate the advantages of our adaptive
scheme against several benchmarks under various settings. Our adaptive scheme
may also be useful in general sequential decision-making problems. Finally, we
compare the performance of our strategy with that of existing OLPS strategies
using both benchmark and newly collected data sets. Our strategy outperforms
these existing OLPS strategies in terms of cumulative returns and competitive
Sharpe ratios across diversified data sets, demonstrating its
adaptability-driven superiority.
