Stochastic portfolio theory optimization and the origin of rule-based investing

Oderda, Gianluca

doi:10.1080/14697688.2015.1012840

Cited by 13 publications

(12 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on simulations of the market model, we show that the investor is able to control their risk-reward profile by tuning various tolerance parameters. Moreover, the optimal portfolios we find appear to outperform the static optimization of Oderda (2015) for all values of the penalty parameters in our simulation setting.…”

Section: Discussionmentioning

confidence: 71%

“…given by (4.11), as well as the optimal solution derived in Oderda (2015) which we will refer to as the maximal drift portfolio (MDP).…”

Section: Methodsmentioning

confidence: 99%

“…An application of SPT in the context of active management is found in Oderda (2015), where the author seeks to explain the superior risk-adjusted performance of rule-based, non capweighted investment strategies relative to the market portfolio. The dynamics of relative wealth are described using results from SPT and a static optimization is applied to solve for the investor's optimal portfolio.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The impetus for our work is Oderda (2015), where the author investigates optimizing relative returns using stochastic portfolio theory (SPT), but only partially solves the problem via static optimization. Here, we significantly improve on those results by using optimal stochastic control techniques.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Outperformance and Tracking: Dynamic Asset Allocation for Active and Passive Portfolio Management

Al-Aradi

Jaimungal

2018

Applied Mathematical Finance

View full text Add to dashboard Cite

Portfolio management problems are often divided into two types: active and passive, where the objective is to outperform and track a preselected benchmark, respectively. Here, we formulate and solve a dynamic asset allocation problem that combines these two objectives in a unified framework.We look to maximize the expected growth rate differential between the wealth of the investor's portfolio and that of a performance benchmark while penalizing risk-weighted deviations from a given tracking portfolio. Using stochastic control techniques, we provide explicit closed-form expressions for the optimal allocation and we show how the optimal strategy can be related to the growth optimal portfolio. The admissible benchmarks encompass the class of functionally generated portfolios (FGPs), which include the market portfolio, as the only requirement is that they depend only on the prevailing asset values. Finally, some numerical experiments are presented to illustrate the risk-reward profile of the optimal allocation.

show abstract

Section: Discussionmentioning

confidence: 71%

“…given by (4.11), as well as the optimal solution derived in Oderda (2015) which we will refer to as the maximal drift portfolio (MDP).…”

Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Outperformance and Tracking: Dynamic Asset Allocation for Active and Passive Portfolio Management

Al-Aradi

Jaimungal

2018

Applied Mathematical Finance

View full text Add to dashboard Cite

show abstract

“…Works on optimal active portfolio management with relative goals (i.e. attempting to outperform a given benchmark) can be found in Browne (1999a), Browne (1999b) and Browne (2000), Pham (2003) and, more recently, Oderda (2015).…”

Section: Introductionmentioning

confidence: 99%

Active and Passive Portfolio Management with Latent Factors

Al-Aradi

Jaimungal

2019

SSRN Journal

View full text Add to dashboard Cite

We address a portfolio selection problem that combines active (outperformance) and passive (tracking) objectives using techniques from convex analysis. We assume a general semimartingale market model where the assets' growth rate processes are driven by a latent factor. Using techniques from convex analysis we obtain a closed-form solution for the optimal portfolio and provide a theorem establishing its uniqueness. The motivation for incorporating latent factors is to achieve improved growth rate estimation, an otherwise notoriously difficult task. To this end, we focus on a model where growth rates are driven by an unobservable Markov chain. The solution in this case requires a filtering step to obtain posterior probabilities for the state of the Markov chain from asset price information, which are subsequently used to find the optimal allocation. We show the optimal strategy is the posterior average of the optimal strategies the investor would have held in each state assuming the Markov chain remains in that state. Finally, we implement a number of historical backtests to demonstrate the performance of the optimal portfolio.

show abstract

MAD risk parity portfolios

Ararat,

Cesarone,

Pınar

et al. 2024

Ann Oper Res

View full text Add to dashboard Cite

In this paper, we investigate the features and the performance of the risk parity (RP) portfolios using the mean absolute deviation (MAD) as a risk measure. The RP model is a recent strategy for asset allocation that aims at equally sharing the global portfolio risk among all the assets of an investment universe. We discuss here some existing and new results about the properties of MAD that are useful for the RP approach. We propose several formulations for finding MAD-RP portfolios computationally, and compare them in terms of accuracy and efficiency. Furthermore, we provide extensive empirical analysis based on three real-world datasets, showing that the performances of the RP approaches generally tend to place both in terms of risk and profitability between those obtained from the minimum risk and the Equally Weighted strategies.

show abstract

Stochastic portfolio theory optimization and the origin of rule-based investing

Abstract: To cite this article: Gianluca Oderda (2015) Stochastic portfolio theory optimization and the origin of rule-based investing, Quantitative Finance, 15:8, 1259-1266,

Cited by 13 publications

References 15 publications

Outperformance and Tracking: Dynamic Asset Allocation for Active and Passive Portfolio Management

Outperformance and Tracking: Dynamic Asset Allocation for Active and Passive Portfolio Management

Active and Passive Portfolio Management with Latent Factors

MAD risk parity portfolios

Contact Info

Product

Resources

About