How Boltzmann Entropy Improves Prediction with LSTM

Luca Grilli and Domenico Santoro

MPRA Paper from University Library of Munich, Germany

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.

Keywords: Neural Network; Price Forecasting; LSTM; Entropy (search for similar items in EconPapers)
JEL-codes: C45 E37 F17 G17 (search for similar items in EconPapers)
Date: 2020-05-22
New Economics Papers: this item is included in nep-big, nep-cmp, nep-mac and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:100578

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