Using Artificial Neural Networks to Pick Stocks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Deep Learning for Forecasting Stock Returns in the Cross-Section (2018, background): “Besides those, Kryzanowski et al. [10] found that neural networks correctly classify 72% of the positive/negative returns to predict one-year-ahead stock returns by using financial ratios and macroeconomic variables.”
TAR-cointegration neural network model: An empirical analysis of exchange rates and stock returns (2010, methodology): “Krishnaswamy, Gilbert, and Pashley (2000), Kryzanowski, Galler, and Wright (1993), Refenes, Zapranis, and Francis (1995), uses NN as tools for finance and stock prediction, proving that NN models outperform linear regression models.”
A modern neural network model to do stock market timing on the basis of the ancient investment technique of Japanese Candlestick (2011, background): “Kryzanowski, Galler, and Wright (1993) adopted a NN to classify stock return as negative, neutral, or positive in the Canada stock market.”
The Efficacy of Neural Networks and Simple Technical Indicators in Predicting Stock Markets (2007, methodology): “These techniques include technical analysis, fundamental analysis, time series forecasting, and artificial intelligence techniques.”