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Neural networks in financial trading

  • S.I.: Networks and Risk Management
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Abstract

In this study, we generate 50 Multi-layer Perceptons, 50 Radial Basis Functions, 50 Higher Order Neural Networks and 50 Recurrent Neural Network and we explore their utility in forecasting and trading the DJIA, NASDAQ 100 and the NIKKEI 225 stock indices. The statistical significance of the forecasts is examined through the False Discovery Ratio of Bajgrowicz and Scaillet (J Financ Econ 106(3):473–491, 2012). Two financial everages, based on the levels of financial stress and the financial volatility respectively, are also applied. In terms of the results, we note that RNN have the higher percentage of significant models and present the stronger profitability compared to their Neural Network counterparts. The financial leverages doubles the trading performance of our models.

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Notes

  1. Backpropagation is the most common learning algorithm in MLPs and the most commonly used type in financial time series forecasting (Kaastra and Boyd 1996).

  2. As risk free rate we consider the effective federal funds rate. This is the interest rate at which depository institutions trade federal funds (balances held at Federal Reserve Banks) with each other overnight.

  3. Stock indices can be traded with three separate ways. A trader can buy or short all stocks that constitute the index, however this might lead to substantial transaction costs and liquidity issues. The second way is through futures and the third is through an Exchange Traded Fund (ETF) on the index. The transaction costs considered in this study are an approximation of the costs that large institutional investors will be facing if they decided to trade the stock indices under study with futures or ETFs.

  4. The finance literature is rich with financial stress indices. In our application, we need an index that is updated on a daily basis and covers USA and Japan. The OFR has these two characteristics. However, it should be noted that in the other advanced economies are included not only Japan but also the Eurozone countries. Although the fit is not perfect for this case, it is the closest to the best of our knowledge. Other stress indices have lower frequency or focus on a single economy.

  5. A brief description of the RiskMetrics volatility model is provided in "Appendix".

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Author information

Authors and Affiliations

  1. University of Glasgow Business School, University of Glasgow, Adam Smith Building, Glasgow, G12 8QQ, UK

    Georgios Sermpinis

  2. Dubai Business School, University of Dubai, Dubai, 14143, UAE

    Andreas Karathanasopoulos

  3. Business and Management Department, University of Oviedo, Campus de Viesques, s/n. 33204, Gijón, Spain

    Rafael Rosillo

  4. Business and Management Department, University of Oviedo, Campus de Viesques, s/n. 33204, Oviedo, Spain

    David de la Fuente

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  1. Georgios Sermpinis
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  2. Andreas Karathanasopoulos
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  3. Rafael Rosillo
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  4. David de la Fuente
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Correspondence to Georgios Sermpinis.

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Appendix: RiskMetrics volatility

Appendix: RiskMetrics volatility

The RiskMetrics volatility model is one of the simpler methods to forecast the financial volatility. Nevertheless, it is popular among quantitative finance professionals. Derived from the from the GARCH(1,1) model but with fixed coefficients, the daily RiskMetrics volatility forecast for day t is estimated as:

$$ RiskMetrics\; volatility_{t}^{2} = 0.94\sigma_{t - 1}^{2} + 0.06R_{t - 1}^{2} $$
(6)

where \( \sigma_{t - 1}^{2} \) is the volatility of the index at day t − 1 and \( R_{t - 1}^{2} \) is the squared logarithmic return of the index on the same day.

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Sermpinis, G., Karathanasopoulos, A., Rosillo, R. et al. Neural networks in financial trading. Ann Oper Res 297, 293–308 (2021). https://doi.org/10.1007/s10479-019-03144-y

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