Can long-run dynamic optimal strategies outperform fixed-mix portfolios? Evidence from multiple data sets
European Journal of Operational Research, ISSN: 0377-2217, Vol: 236, Issue: 1, Page: 160-176
2014
- 10Citations
- 31Captures
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Article Description
Using five alternative data sets and a range of specifications concerning the underlying linear predictability models, we study whether long-run dynamic optimizing portfolio strategies may actually outperform simpler benchmarks in out-of-sample tests. The dynamic portfolio problems are solved using a combination of dynamic programming and Monte Carlo methods. The benchmarks are represented by two typical fixed mix strategies: the celebrated equally-weighted portfolio and a myopic, Markowitz-style strategy that fails to account for any predictability in asset returns. Within a framework in which the investor maximizes expected HARA (constant relative risk aversion) utility in a frictionless market, our key finding is that there are enormous difference in optimal long-horizon (in-sample) weights between the mean–variance benchmark and the optimal dynamic weights. In out-of-sample comparisons, there is however no clear-cut, systematic, evidence that long-horizon dynamic strategies outperform naively diversified portfolios.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0377221714000514; http://dx.doi.org/10.1016/j.ejor.2014.01.030; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84900641862&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0377221714000514; https://api.elsevier.com/content/article/PII:S0377221714000514?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S0377221714000514?httpAccept=text/plain; https://dx.doi.org/10.1016/j.ejor.2014.01.030
Elsevier BV
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