Comparing classic time series models and the LSTM recurrent neural network: An application to S&P 500 stocks

Comparativa de los models clásicos de series temporales con la red neuronal recurrente LSTM: Una aplicación a las acciones del S&P 500

Javier Oliver Muncharaz
Additional contact information
Javier Oliver Muncharaz: UPV - Universitat Politècnica de València = Universitad Politecnica de Valencia = Polytechnic University of Valencia

Post-Print from HAL

Abstract: In the financial literature, there is great interest in the prediction of stock prices. Stock prediction is necessary for the creation of different investment strategies, both speculative and hedging ones. The application of neural networks has involved a change in the creation of predictive models. In this paper, we analyze the capacity of recurrent neural networks, in particular the long short-term recurrent neural network (LSTM) as opposed to classic time series models such as the Exponential Smooth Time Series (ETS) and the Arima model (ARIMA). These models have been estimated for 284 stocks from the S&P 500 stock market index, comparing the MAE obtained from their predictions. The results obtained confirm a significant reduction in prediction errors when LSTM is applied. These results are consistent with other similar studies applied to stocks included in other stock market indices, as well as other financial assets such as exchange rates.

Keywords: Recurrent Neural Network; Long short-term neural network; S&P 500; Arima; Redes neuronales recurrentes (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ets
Note: View the original document on HAL open archive server: https://hal.science/hal-03149342
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Published in Finance, Markets and Valuation, 2020, 6 (2), pp.137-148. ⟨10.46503/ZVBS2781⟩

Downloads: (external link)
https://hal.science/hal-03149342/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03149342

DOI: 10.46503/ZVBS2781

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().