How Boltzmann Entropy Improves Prediction with LSTM

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.

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Year of publication:	2020-05-22
Authors:	Grilli, Luca ; Santoro, Domenico

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Type of publication:	Book / Working Paper
Language:	English
Notes:	Grilli, Luca and Santoro, Domenico (2020): How Boltzmann Entropy Improves Prediction with LSTM.
Classification:	C45 - Neural Networks and Related Topics ; E37 - Forecasting and Simulation ; F17 - Trade Forecasting and Simulation ; G17 - Financial Forecasting
Source:	BASE

Persistent link: https://ebvufind01.dmz1.zbw.eu/10015212224