Sequential asset ranking in nonstationary time series
This work addresses portfolio optimization for investors by providing an incremental improvement in asset ranking algorithms for nonstationary markets.
The authors tackled the problem of ranking assets in nonstationary financial time series by developing the naive Bayes asset ranker, which computes posterior probabilities for asset rankings and adaptively reweights experts based on performance, resulting in outperformance of the S&P 500 index and a regress-then-rank baseline.
We create a ranking algorithm, the naive Bayes asset ranker. Our algorithm computes the posterior probability that individual assets will be ranked higher than other portfolio constituents. Unlike earlier algorithms, such as the weighted majority, our algorithm allows poor-performing experts to have increased weight when they start performing well. We outperform the long-only holding of the S&P 500 index and a regress-then-rank baseline.