The performance of time series forecasting based on classical and machine learning methods for S&P 500 index

Maudud Uzzal and Robert Ślepaczuk
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Maudud Uzzal: University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group

No 2023-05, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: Based on one step ahead forecasts, this study compares the forecasting abilities of the traditional technique (ARIMA) with recurrent neural network (LSTM). In order to check the possible use of these forecasts in different asset management methods, these forecasts are afterwards included into trading signals of investment strategies. As a benchmark method, the Random Walk model producing naive forecasts has been utilized. This research examines daily data from the S&P 500 index for 20 years, from 2000 to 2020, and it includes information on some significant market turbulence. The methods were tested in terms of robustness to changes in parameters and hyperparameters and evaluated based on various error metrics (MAE, MAPE, RMSE MSE). The results show that ARIMA outperforms LSTM in terms of one step ahead forecasts. Finally, LSTM model with a variety of hyperparameters - including a number of epochs, a loss function, an optimizer, activation functions, a number of units, a batch size, and a learning rate - was tested in order to check its robustness.

Keywords: deep learning; recurrent neural networks; ARIMA; algorithmic investment strategies; trading systems; LSTM; walk-forward process; optimization (search for similar items in EconPapers)
JEL-codes: C14 C4 C45 C53 C58 G13 (search for similar items in EconPapers)
Pages: 36 pages
Date: 2023
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ets and nep-for
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https://www.wne.uw.edu.pl/download_file/2554/0 First version, 2023 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2023-05

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