Entropy-Based Financial Asset Pricing

Ormos, Mihály; Zibriczky, Dávid

doi:10.1371/journal.pone.0115742

Cited by 52 publications

(53 citation statements)

References 19 publications

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“…This finding is consistent with results of Dionisio et al ( , 2007 and Ormos and Zibriczky (2014) who showed the ability of entropy to measure risk in portfolio management.…”

Section: H X Tunindexsupporting

confidence: 92%

“…This finding is similar to that reported by Wagner and Low, with risk fades away with time. Then, we may conclude that entropy is sensitive to diversification corroborating thus the results of , Ormos and Zibriczky (2014).…”

Section: H X Tunindexsupporting

confidence: 85%

“…Consequently, applying entropy to finance is found to be very rewarding. Subscribing to these ends, Ormos and Zibriczky (2014) used entropy to measure financial risk. It was found that entropy explains portfolio and equity premiums in simple terms and at the same time it has a higher explanatory power than Beta of CAPM.…”

Section: Introductionmentioning

confidence: 99%

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2017

AJER

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Our main objective in this paper is to revisit Markowitz's (1952) mean-variance approach by applying Shannon Entropy as an alternative measure of financial risk. We studied 33 randomly selected stocks of the Tunis Stock Exchange, representing the daily values of the Tunindex over a period of 8 years. The obtained results indicate that entropy behaves in a similar way to standard deviation, as it decreases depending on the number of stocks held in a portfolio. Likewise, Sharpe single-index model was reinterpreted under entropy theory where total risk is divided into systematic and non-systematic risk. Then, standard measures like standard deviation or beta seem to be inadequate to assess risk and uncertainty. Consequently, entropy offers an ideal alternative to identify investment-related risk. Contribution/ OriginalityThe current paper proposes an alternative approach to portfolio selection. Entropy is one of the main concepts of information theory and market participants react to information when taking decisions. Entropy is believed to contribute to significantly improving the assessment of systematic and specific risks and thus assets pricing.

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“…This finding is consistent with results of Dionisio et al ( , 2007 and Ormos and Zibriczky (2014) who showed the ability of entropy to measure risk in portfolio management.…”

Section: H X Tunindexsupporting

confidence: 92%

Section: H X Tunindexsupporting

confidence: 85%

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2017

AJER

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“…Thus, it is possible to state that in a closed system, the entropy never decreases, only increases. Nevertheless, according to Ormos and Zibriczky [46], entropy is a mathematically defined quantity, usually used to characterize the probability of results in a system that is undergoing a process.…”

Section: Entropymentioning

confidence: 99%

“…Ormos and Zibriczky [46] affirm that the functions of entropy can be divided into two main types: discrete and continuous entropy functions. Let X be a discrete random variable, the possible outcomes of that variable are x 1 , x 2 , ..., x n and the corresponding possibilities are…”

Section: Entropymentioning

confidence: 99%

Entropic Data Envelopment Analysis: A Diversification Approach for Portfolio Optimization

Rotela

Rocha

Áquila

et al. 2017

Entropy

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Abstract:Recently, different methods have been proposed for portfolio optimization and decision making on investment issues. This article aims to present a new method for portfolio formation based on Data Envelopment Analysis (DEA) and Entropy function. This new portfolio optimization method applies DEA in association with a model resulting from the insertion of the Entropy function directly into the optimization procedure. First, the DEA model was applied to perform a pre-selection of the assets. Then, assets given as efficient were submitted to the proposed model, resulting from the insertion of the Entropy function into the simplified Sharpe's portfolio optimization model. As a result, an improved asset participation was provided in the portfolio. In the DEA model, several variables were evaluated and a low value of beta was achieved, guaranteeing greater robustness to the portfolio. Entropy function has provided not only greater diversity but also more feasible asset allocation. Additionally, the proposed method has obtained a better portfolio performance, measured by the Sharpe Ratio, in relation to the comparative methods.

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Diffusion in hierarchical systems: A simulation study in models of healthy and diseased muscle tissue

Hall

Clark

2016

Magnetic Resonance in Med

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PurposeTo investigate the sensitivity of diffusion‐MR signal to microstructural change in muscle tissue associated with pathology, and recommend optimal acquisition parameters.MethodsWe employ Monte‐Carlo simulation of diffusing spins in hierarchical tissue to generate synthetic diffusion‐weighted signal curves over a wide range of scan parameters. Curves are analyzed using entropy—a measure of curve complexity. Entropy change between a baseline and various microstructural scenarios is investigated. We find acquisitions that optimize entropy difference in each scenario.ResultsPermeability changes have a large effect on the diffusion‐weighted signal curve. Muscle fiber atrophy is also important, although differentiating between mechanisms is challenging. Several acquisitions over a range of diffusion times is optimal for imaging microstructural change in muscle tissue. Sensitivity to permeability is optimized for high gradient strengths, but sensitivity to other scenarios is optimal at other values.ConclusionsThe diffusion‐attenuated signal is sensitive to the microstructural changes, but the changes are subtle. Taking full advantage of the changes to the overall curve requires a set of acquisitions over a range of diffusion times. Permeability causes the largest changes, but even the very subtle changes associated with fiber radius distribution change the curves more than noise alone. Magn Reson Med 78:1187–1198, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

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Entropy-Based Financial Asset Pricing

Cited by 52 publications

References 19 publications

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Entropic Data Envelopment Analysis: A Diversification Approach for Portfolio Optimization

Diffusion in hierarchical systems: A simulation study in models of healthy and diseased muscle tissue

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