Factor Based Forecasts in Universal Portfolios via Dirichlet Weights
Published in Arxiv, 2023
We revisit the problem of online portfolio allocation first introduced by Cover. We propose factor weigh ing of the dirichlet sampling to construct universal portfolios that out-perform those using uniform dirichlet sampling. When the returns follow a factor model, we establish a lower bound on the average portfolio growth rate. We analytically establish that the wealth generated by the factor weighted dirichlet sampled portfolios dominate the wealth generated by the uniformly sampled Dirichlet portfolios. We corroborate our analytical results with empirical studies on equity markets that are known to be driven by factors.
Recommended citation: Parthasarathy, Purushottam, Avinash Bhardwaj, and Manjesh K. Hanawal. "Online Universal Dirichlet Factor Portfolios." arXiv preprint arXiv:2308.07763 (2023).
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