Stock Price Forecasting and Hypothesis Testing Using Neural Networks
arXiv:1908.11212v11 citations
AI Analysis
This work addresses stock market forecasting for financial analysts, but it is incremental as it applies existing neural network methods to this domain.
The authors tackled stock price prediction using RNNs and MLPs on historical data from major exchanges, achieving competitive results, and then applied these predictions to statistically test the efficient-market hypothesis.
In this work we use Recurrent Neural Networks and Multilayer Perceptrons to predict NYSE, NASDAQ and AMEX stock prices from historical data. We experiment with different architectures and compare data normalization techniques. Then, we leverage those findings to question the efficient-market hypothesis through a formal statistical test.