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Exploring the dynamics of financial markets: from stock prices to strategy returns

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  • Borland, Lisa

Abstract

Exploring the dynamics of financial time-series is an exciting and interesting challenge because of the many truly complex interactions that underly the price formation process. In this contribution we describe some of the anomalous statistical features of such time-series and review models of the price dynamics both across time and across the universe of stocks. In particular we discuss a non-Gaussian statistical feedback process of stock returns which we have developed over the past years with the particular application of option pricing. We then discuss a cooperative model for the correlations of stock dynamics which has its roots in the field of synergetics, where numerical simulations and comparisons with real data are presented. Finally we present summarized results of an empirical analysis probing the dynamics of actual trading strategy return streams.

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  • Borland, Lisa, 2016. "Exploring the dynamics of financial markets: from stock prices to strategy returns," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 59-74.
    • Handle: RePEc:eee:chsofr:v:88:y:2016:i:c:p:59-74
    DOI: 10.1016/j.chaos.2016.03.014
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    1. Parameswaran Gopikrishnan & Vasiliki Plerou & Luis A. Nunes Amaral & Martin Meyer & H. Eugene Stanley, 1999. "Scaling of the distribution of fluctuations of financial market indices," Papers cond-mat/9905305, arXiv.org.
    2. Lisa Borland, 2002. "A theory of non-Gaussian option pricing," Quantitative Finance, Taylor & Francis Journals, vol. 2(6), pages 415-431.
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    Cited by:

    1. Lahmiri, Salim & Bekiros, Stelios, 2017. "Disturbances and complexity in volatility time series," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 38-42.
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    3. Duarte Queirós, Sílvio M. & Anteneodo, Celia, 2016. "Complexity in quantitative finance and economics," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 1-2.
    4. Luca Grilli & Domenico Santoro, 2022. "Forecasting financial time series with Boltzmann entropy through neural networks," Computational Management Science, Springer, vol. 19(4), pages 665-681, October.

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