AN OVERVIEW OVER THE IMPACT OF NEURAL NETWORKS AND DEEP LEARNING IN FINANCIAL FORECASTING

Ene Cezar Catalin
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Ene Cezar Catalin: UNIVERSITY OF CRAIOVA, EUGENIU CARADA DOCTORAL SCHOOL OF ECONOMIC

Annals - Economy Series, 2024, vol. 1, 138-149

Abstract: In this paper we aim to analyze the influence of Deep Learning (DL) and Neural Networks (NN), on financial forecasting, as a result we have extensively examined research papers and real life implementations of these sophisticated computer models that forecast patterns and movements in financial markets, in the same way we will explore and compare the development of financial forecasting techniques starting from classical methods to the adoption of deep learning models, like Convolutional Neural Network (CNN), Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM). To be more specific, in this article we delve into the abilities of these models to capture non-linear patterns in financial data such as stock market asset prices. We highlight how they excel in some situations compared to classical methods used for predicting the future of a financial asset. We emphasize how these models outperform conventional techniques for projecting the future of a financial asset in some circumstances, we also address the challenges and barriers associated with using these models to forecasting of financial outcomes, some challenges include overfitting, the need for high quality data, and the requirement for large databases. By examining and combining insights from multiple sources, our goal is to present a global perspective on the current state of neural networks and deep learning used for predicting future changes in the financial sector, we aim to identify areas where these models demonstrate some kind of performance while also showing areas that necessitate more research and development.

Keywords: Financial Forecasting; Deep Learning; Neural Networks; Long Short Term Memory; Recurrent Neural Networks; Convolutional Neural Network (search for similar items in EconPapers)
Date: 2024
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