Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index

Katarzyna Kryńska and Robert Ślepaczuk
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Katarzyna Kryńska: University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group

No 2022-25, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: This thesis investigates the use of various architectures of the LSTM model in algorithmic investment strategies. LSTM models are used to generate buy/sell signals, with previous levels of Bitcoin price and the S&P 500 Index value as inputs. Four approaches are tested: two are regression problems (price level prediction) and the other two are classification problems (prediction of price direction). All approaches are applied to daily, hourly, and 15-minute data and are using a walk-forward optimization procedure. The out-of-sample period for the S&P 500 Index is from February 6, 2014 to November 27, 2020, and for Bitcoin it is from January 15, 2014 to December 1, 2020. We discover that classification techniques beat regression methods on average, but we cannot determine if intra-day models outperform inter-day models. We come to the conclusion that the ensembling of models does not always have a positive impact on performance. Finally, a sensitivity analysis is performed to determine how changes in the main hyperparameters of the LSTM model affect strategy performance.

Keywords: machine learning; deep learning; recurrent neural networks; LSTM; algorithmic trading; ensemble investment strategy; intra-day trading; S&P 500 Index; Bitcoin (search for similar items in EconPapers)
JEL-codes: C14 C4 C45 C53 C58 G13 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2022
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-pay
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:war:wpaper:2022-25

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