Combining robust dynamic neural networks with traditional technical indicators for generating mechanic trading signals

Pier Giuseppe Giribone, Simone Ligato () and Francesco Penone ()
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Pier Giuseppe Giribone: University of Genoa, Department of Economics, 16126 Genova GE, Italy*CARIGE Bank, Financial Engineering, Italy
Simone Ligato: #x2020;Azzoaglio Bank, Risk Management, Italy
Francesco Penone: #x2021;Independent Financial Analyst, Italy

International Journal of Financial Engineering (IJFE), 2018, vol. 05, issue 04, 1-44

Abstract: Forecasting assets’ prices is the aim of each trader, although the trading approaches employed may vary a lot. The development of machine learning techniques has brought the opportunity to design mechanic trading systems based on dynamic artificial neural networks. The aim of this paper is to combine traditional technical indicators [such as exponential weighted moving average (EWMA), percentage volume oscillator (PVO) and stochastic indicator — %K and %D] with the nonlinear autoregressive networks (NAR and NARX). The first part of the paper describes how neural networks designed for forecasting time series work, the second one performs a deeper validation of the code and the third one combines the dynamic networks with traditional technical indicators in order to generate reliable mechanic signals. The article ends with a back testing of the trading system performed on Dow Jones Industrial Average and on Nasdaq Composite Indexes.

Keywords: Machine learning; artificial intelligence; feedforward neural network; dynamic artificial neural network; NAR; NARX; time series forecasting; FinTech; automatic trading system; mechanic trading signals; technical analysis (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S2424786318500378

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