Forecasting financial time series with Boltzmann entropy through neural networks

Luca Grilli and Domenico Santoro ()
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Domenico Santoro: University of Bari Aldo Moro

Computational Management Science, 2022, vol. 19, issue 4, No 5, 665-681

Abstract: Abstract Neural networks have recently been established as state-of-the-art in forecasting financial time series. However, many studies show how one architecture, the Long-Short Term Memory, is the most widespread in financial sectors due to its high performance over time series. Considering some stocks traded in financial markets and a crypto ticker, this paper tries to study the effectiveness of the Boltzmann entropy as a financial indicator to improve forecasting, comparing it with financial analysts’ most commonly used indicators. The results show how Boltzmann’s entropy, born from an Agent-Based Model, is an efficient indicator that can also be applied to stocks and cryptocurrencies alone and in combination with some classic indicators. This critical fact allows obtaining good results in prediction ability using Network architecture that is not excessively complex.

Keywords: Neural networks; Price forecasting; LSTM; Boltzmann entropy; Financial markets; Cryptocurrency (search for similar items in EconPapers)
JEL-codes: C45 C63 C88 G17 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10287-022-00430-2

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