Neural Networks in Finance: Design and Performance

Irene Aldridge, Marco Avellaneda

Research output: Contribution to journalArticlepeer-review


Neural networks have piqued the interest of many financial modelers, but the concrete applications and implementation have remained elusive. This article discusses a step-by-step technique for building a potentially profitable financial neural network. The authors also demonstrate a successful application of the neural network to investing based on daily and monthly financial data. The article discusses various components of neural networks and compares popular neural network activation functions and their applicability to financial time series. Specifically, use of the tanh activation function is shown to closely mimic financial returns and produce the best results. Incorporating additional inputs, such as the S&P 500 prices, also helps improve neural networks’ forecasting performance. Longer training periods deliver strategies that closely mimic common technical analysis strategies, such as moving-average crossovers, whereas shorter training periods deliver significant forecasting power. The resulting neural network-based daily trading strategies on major US stocks significantly and consistently outperform the buy-and-hold positions in the same stocks.

Original languageEnglish (US)
Pages (from-to)39-62
Number of pages24
JournalJournal of Financial Data Science
Issue number4
StatePublished - Sep 1 2019

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Information Systems
  • Finance
  • Business and International Management
  • Strategy and Management
  • Business, Management and Accounting (miscellaneous)
  • Information Systems and Management


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