On entropy, financial markets and minority games

Zapart, Christopher

doi:10.1016/j.physa.2008.11.047

Cited by 16 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While simple in its conception, MG has been used in many fields like econophysics [85][86][87], multi-agent resource allocation [88,89], emergence of cooperation [90], and heterogeneity [91,92]. The Minority Game (MG) is intuitively applicable to many domains, such as traffic (the driver that chooses the 'less-travelled' road experiences less congestion), packet traffic in networks, ecologies of foraging animals, etc.…”

Section: Minority Gamementioning

confidence: 99%

Clonal plasticity: an autonomic mechanism for multi-agent systems to self-diversify

Nallur

Clarke

2017

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

Diversity has long been used as a design tactic in computer systems to achieve various properties. Multi-agent systems, in particular, have utilized diversity to achieve aggregate properties such as efficiency of resource allocations, and fairness in these allocations. However, diversity has usually been introduced manually by the system designer. This paper proposes a decentralized technique, clonal plasticity, that makes homogeneous agents self-diversify, in an autonomic way. We show that clonal plasticity is competitive with manual diversification, at achieving efficient resource allocations and fairness.

show abstract

Section: Minority Gamementioning

confidence: 99%

Clonal plasticity: an autonomic mechanism for multi-agent systems to self-diversify

Nallur

Clarke

2017

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

show abstract

“…Szilard (1929) was the first to be interested in the physics-information relation. More recently, authors such as Peng (2005), Chen (2004), Van Nieuwerburgh and Veldkamp (2004), Dadpay et al (2007), Clarke (2007), Gajdos et al (2008), Sbuelz and Trojani (2008), and Zapart (2009) have introduced the entropy statistics in relation to the information theory.…”

Section: Entropy Statisticsmentioning

confidence: 99%

Information costs in financial markets: evidence from the Tunisian stock market

Chakroun

Hamdouni

2010

The Journal of Risk Finance

View full text Add to dashboard Cite

Purpose -The purpose of this paper is to discuss a widespread idea in the financial literature: information in financial markets is free. Indeed, whenever an investor wants to intervene to purchase and/or to sell, he/she faces the need to access the information, which he/she judges to ensure an optimal decision. Design/methodology/approach -The paper uses the entropy statistics in order to estimate the information cost of the assets of the Tunisian stock market over the period extending from 2002 to 2005. Findings -The obtained results show that the information costs follow a Brownian motion. This finding lends empirical support to the theoretical position that has always been adopted in the relevant literature: in finance, as in economy, the majority of the series follow a Brownian motion. Practical implications -The proposed methodology offers investors the opportunity to estimate the information cost by taking into account the quotation probability, a simple approach that can be used not only by fund managers, but also by financial market investors. Originality/value -The paper uses entropy as a relatively new tool applied in financial theory. It offers a new understanding of information cost. The paper will be of interest for financial market investors and academics.

show abstract

“…In the analysis of time series generated by complex systems, entropy measures such as permutation entropy [3], and approximate and sample entropy [4] are widely used. Tools from information theory and based on entropy are common in neuroscience [5][6][7], genomics [8][9][10], physiology [11], economics [12,13], climatology [14][15][16], geoscience [17,18].…”

Section: Introductionmentioning

confidence: 99%

Estimating the variance of Shannon entropy

Ricci,

Perinelli,

Castelluzzo

2021

Preprint

View full text Add to dashboard Cite

The statistical analysis of data stemming from dynamical systems, including, but not limited to, time series, routinely relies on the estimation of information theoretical quantities, most notably Shannon entropy. To this purpose, possibly the most widespread tool is provided by the so-called plug-in estimator, whose statistical properties in terms of bias and variance were investigated since the first decade after the publication of Shannon's seminal works. In the case of an underlying multinomial distribution, while the bias can be evaluated by knowing support and dataset size, variance is far more elusive. The aim of the present work is to investigate, in the multinomial case, the statistical properties of an estimator of a parameter that describes the variance of the plugin estimator of Shannon entropy. We then exactly determine the probability distributions that maximize that parameter. The results presented here allow to set upper limits to the uncertainty of entropy assessments under the hypothesis of memoryless underlying stochastic processes.

show abstract

On entropy, financial markets and minority games

Cited by 16 publications

References 14 publications

Clonal plasticity: an autonomic mechanism for multi-agent systems to self-diversify

Clonal plasticity: an autonomic mechanism for multi-agent systems to self-diversify

Information costs in financial markets: evidence from the Tunisian stock market

Estimating the variance of Shannon entropy

Contact Info

Product

Resources

About