Universal Portfolios

Ordentlich, E.

doi:10.1002/9780470061602.eqf14017

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…One desired theoretical result for an on-line portfolio selection algorithm is universality [14,50]. An algorithm Alg is universal if the average (external) regret [54,5] over all sequences of price relative vectors for n periods asymptotically approaches zero, that is,…”

Section: Benchmarksmentioning

confidence: 99%

Moving average reversion strategy for on-line portfolio selection

Hoi

Sahoo

et al. 2015

Artificial Intelligence

123

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Please cite this article in press as: B. Li et al., Moving Average Reversion Strategy for On-Line Portfolio Selection, Artificial Intelligence (2015), http://dx. AbstractOn-line portfolio selection, a fundamental problem in computational finance, has attracted increasing interests from artificial intelligence and machine learning communities in recent years. Empirical evidence shows that stock's high and low prices are temporary and stock price relatives are likely to follow the mean reversion phenomenon. While existing mean reversion strategies are shown to achieve good empirical performance on many real datasets, they often make the single-period mean reversion assumption, which is not always satisfied, leading to poor performance in certain real datasets. To overcome this limitation, this article proposes a multiple-period mean reversion, or so-called "Moving Average Reversion" (MAR), and a new on-line portfolio selection strategy named "On-Line Moving AverageReversion" (OLMAR), which exploits MAR via efficient and scalable online machine learning techniques. From our empirical results on real markets, we found that OLMAR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results, especially on the datasets where existing mean reversion algorithms failed. In addition to its superior empirical performance, OLMAR also runs extremely fast, further supporting its practical applicability to a wide range of applications. Finally, we have made all the data sets and source codes of this work publicly available at our project website: http://OLPS.stevenhoi.org/.

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