The efficiency of various types of input layers of LSTM model in investment strategies on S&P500 index

Thi Thu Giang Nguyen and Robert Ślepaczuk
Additional contact information
Thi Thu Giang Nguyen: University of Warsaw, Faculty of Economic Sciences, Quantitative Finance Research Group

No 2022-29, Working Papers from Faculty of Economic Sciences, University of Warsaw

Abstract: The study compares the use of various Long Short-Term Memory (LSTM) variants to conventional technical indicators for trading the S&P 500 index between 2011 and 2022. Two methods were used to test each strategy: a fixed training data set from 2001–2010 and a rolling train–test window. Due to the input sensitivity of LSTM models, we concentrated on data processing and hyperparameter tuning to find the best model. Instead of using the traditional MSE function, we used the Mean Absolute Directional Loss (MADL) function based on recent research to enhance model performance. The models were assessed using the Information Ratio and the Modified Information Ratio, which considers the maximum drawdown and the sign of the annualized return compounded (ARC). LSTM models' performance was compared to benchmark strategies using the SMA, MACD, RSI, and Buy&Hold strategies. We rejected the hypothesis that algorithmic investment strategy using signals from LSTM model consisting only from daily returns in its input layer is more efficient. However, we could not reject the hypothesis that signals generated by LSTM model combining daily returns and technical indicators in its input layer are more efficient. The LSTM Extended model that combined daily returns with MACD and RSI in the input layer generated a better result than Buy&Hold and other strategies using a single technical indicator. The results of the sensitivity analysis show how sensitive this model is to inputs like sequence length, batch size, technical indicators, and the length of the rolling train - test window.

Keywords: algorithmic investment strategies; machine learning; testing architecture; deep learning; recurrent neural networks; LSTM; technical indicators; forecasting financial-time series; technical indicators; hyperparameter tuning S&P 500 Index (search for similar items in EconPapers)
JEL-codes: C15 C45 C52 C53 C58 C61 G14 G17 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2022
New Economics Papers: this item is included in nep-big, nep-cmp and nep-fmk
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.wne.uw.edu.pl/download_file/2345/0 First version, 2022 (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:war:wpaper:2022-29

Access Statistics for this paper

More papers in Working Papers from Faculty of Economic Sciences, University of Warsaw Contact information at EDIRC.
Bibliographic data for series maintained by Marcin Bąba ().