A Comparative Study of Deep Learning Techniques for Financial Indices Prediction

Ketsetsis, Argyrios P.; Giannoutakis, Konstantinos M.; Spanos, Georgios; Samaras, Nikolaos; Hristu-Varsakelis, Dimitrios; Thomas, Dimitrios; Tzovaras, Dimitrios

doi:10.1007/978-3-030-79150-6_24

Cited by 1 publication

(1 citation statement)

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“…Through this type of architecture, we want to demonstrate that the entropy indicator calculated in this way has a predictive capacity at least equal to the indicators most used in technical analysis and, in addition to these, how the predictive ability of the features varies overall. With Google Colab and given the simplicity of the data, we have set the structure of the network with only 1 input layer with several neurons from 7 to 9 (according to the general theory that the number of neurons in the input layer is equal to the number of features plus a bias), 1 output layer with 1 neuron only and no hidden layer, based on the work of Ketsetsis et al (2021). The remaining hyperparameters, which control the learning process, have been tuned using the state-of-the-art values in the literature and are shown in Table 1.…”

Section: Setting Up the Machinementioning

confidence: 99%

Forecasting financial time series with Boltzmann entropy through neural networks

Grilli

Santoro

2022

Comput Manag Sci

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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.

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Section: Setting Up the Machinementioning

confidence: 99%