Stock Market Timing with Entropy

Efremidze, Levan; Stanley, Darrol J.; Kinsman, Michael D.

doi:10.3905/jwm.2015.18.3.057

Cited by 9 publications

(6 citation statements)

References 24 publications

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“…First and foremost, the market’s overall productivity has declined due to the Coronavirus, and bitcoin’s efficacy has declined across a wide range of industries, leading its founder to dub the cryptocurrency a "refuge resource" because of this. In addition, [ 23 ] Dubai’s record returns are closely linked to the model’s entropy, as seen by these findings. Entropy theory and Markowitz portfolio modelling go hand in hand in this groundbreaking demonstration of how expected return and risk go hand in hand.…”

Section: Literature Reviewmentioning

confidence: 81%

Entropy-based financial asset pricing: Evidence from Pakistan

et al. 2022

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Entropy is an alternative measure to calculate the risk, simplify the portfolios and equity risk premium. It has higher explanatory power than capital asset price model (CAPM) beta. The comparison of Entropy and CAPM beta provide in depth analysis about the explanatory power of the model that in turn help investor to make right investment decisions that minimizes risk. In this context, this study aims to compare Shannon and Rennyi Entropies with the CAPM beta for measuring the risk. Ordinary Least square approach has been utilized using a dataset of 67 enterprises registered in Pakistan Stock exchange. The comparative analysis of CAPM beta and entropy has been carried out with the R2 parameters. The result indicates that entropy has more explanatory power as compare to CAPM beta’s explanatory power, and this turns out to be the best option to evaluate the risk performances. The result implies that an investor should make the best investment decision by choosing an enterprise that provide with good returns at minimum risk based on entropy technique.

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Section: Literature Reviewmentioning

confidence: 81%

Entropy-based financial asset pricing: Evidence from Pakistan

et al. 2022

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“…16 Entropy approaches taken from information theory are of great interest for the evaluation of the degree of irregularity and complexity of physical, physiological, social, and econometric systems. 16 They have been successfully and widely used in many applications ranging from biomedical engineering and neuroscience to mechanical engineering, 17 financial data analysis, 18,19 climatology, 20 earth sciences, 21 cellular automata, 22,23 and others. 24,25 Shannon entropy (ShEn), conditional entropy (CE) and information storage (IS) are the most common concepts used in the context of analysis of physiological signals.…”

Section: Introductionmentioning

confidence: 99%

Entropy Analysis of Univariate Biomedical Signals: Review and Comparison of Methods

Azami

Faes

Escudero

et al. 2022

Contemporary Mathematics and Its Applications: Monographs, Expositions and Lecture Notes

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Nonlinear techniques have found an increasing interest in the dynamical analysis of various kinds of systems. Among these techniques, entropy-based metrics have emerged as practical alternatives to classical techniques due to their wide applicability in different scenarios, specially to short and noisy processes. Issued from information theory, entropy approaches are of great interest to evaluate the degree of irregularity and complexity of physical, physiological, social, and econometric systems. Based on Shannon entropy and conditional entropy (CE), various techniques have been proposed; among them, approximate entropy, sample entropy, fuzzy entropy, distribution entropy, permutation entropy, and dispersion entropy are probably the most well-known. After a presentation of the basic information-theoretic functionals, these measures are detailed, together with recent proposals inspired by nearest neighbors and parametric approaches. Moreover, the role of dimension, data length, and parameters in using these measures

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“…There are several statistical algorithms of entropy (B.G. Sharma, Bisen, R. Sharma, & M. Sharma, 2010;Thuraisingham & Gottwal, 2006;Richman & Moorman, 2000), but in this paper we follow the method of sample entropy used in Efremidze et al (2015), as it was effective in predicting movements in stock market index. Section 3 has more details of sample entropy algorithm implementation.…”

Section: Entropy As Measure Of Randomness and Capital Market Theorymentioning

confidence: 99%

“…The values of the parameters for sample entropy calculation are presented in Table 1. We followed the same algorithm of SaEn and parameter selection rational as in Efremidze et al (2015). Sample entropy statistic SaEn (m,r,N) is calculated with equation 1, where N is a length of time series used, m is a length of subseries (with consecutive elements) from N, r is called a tolerance threshold and is a multiple of standard deviation of N time series, D is the number of times any subseries from N with the length m are similar to other subseries with the same length (any two subseries of the length m are considered to be similar if all absolute differences between the corresponding elements of the two subseries are less than or equal to the tolerance threshold r), C is the number of times any subseries with the length m+1 are similar to other subseries with the length m+1.…”

Section: The Entropy Metric Employed In This Studymentioning

confidence: 99%

“…Recent financial markets research has focused on applying markers of complex systems to different segments of financial markets. Entropy is a statistical measure of randomness in a complex system and it shows reasonable effectiveness in timing a stock market index (Efremidze, Stanley, & Kinsman, 2015) and style rotation strategies (Efremidze, DeLillio, & Stanley, 2014). Entropy based methods have proven to be a powerful tool in physics, astronomy, biology, and medicine (Pincus, 2008).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Entropy Risk Factor Model of Exchange Rate Prediction

Stanley¹,

Efremidze²,

Rossouw³

2017

IJFR

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We investigate the predictability of an exchange rate with entropy risk factor model, as there is growing evidence that financial markets behave as complex systems. The model is tested on the data of South African Rand (ZAR) exchange rate for the period of 2004-2015. We calculate sample entropy based on the daily data of the exchange rate and conduct empirical implementation of several market timing rules based on these entropy signals. The dynamic investment portfolio based on entropy signals produces better risk adjusted performance than a buy and hold strategy. The returns are estimated on the portfolio values in U.S. dollars. The results raise the potential attractiveness of complex systems analyses, especially the methods of entropy, for foreign exchange market research and applications.

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Stock Market Timing with Entropy

Cited by 9 publications

References 24 publications

Entropy-based financial asset pricing: Evidence from Pakistan

Entropy-based financial asset pricing: Evidence from Pakistan

Entropy Analysis of Univariate Biomedical Signals: Review and Comparison of Methods

Entropy Risk Factor Model of Exchange Rate Prediction

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