How Boltzmann Entropy Improves Prediction with LSTM

Grilli, Luca and Santoro, Domenico (2020): How Boltzmann Entropy Improves Prediction with LSTM.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Neural Network; Price Forecasting; LSTM; Entropy
• Subjects: C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C45 - Neural Networks and Related Topics; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications; F - International Economics > F1 - Trade > F17 - Trade Forecasting and Simulation; G - Financial Economics > G1 - General Financial Markets > G17 - Financial Forecasting and Simulation

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