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How Boltzmann Entropy Improves Prediction with LSTM

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  • Grilli, Luca
  • Santoro, Domenico

Abstract

In this paper we want to demonstrate how it is possible to improve the forecast by using Boltzmann entropy like the classic financial indicators, throught neural networks. In particular, we show how it is possible to increase the scope of entropy by moving from cryptocurrencies to equities and how this type of architectures highlight the link between the indicators and the information that they are able to contain.

Suggested Citation

  • Grilli, Luca & Santoro, Domenico, 2020. "How Boltzmann Entropy Improves Prediction with LSTM," MPRA Paper 100578, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:100578
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    File URL: https://mpra.ub.uni-muenchen.de/100578/1/MPRA_paper_100578.pdf
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Neural Network; Price Forecasting; LSTM; Entropy;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • F17 - International Economics - - Trade - - - Trade Forecasting and Simulation
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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