Application of a Shallow Neural Network to Short-Term Stock Trading
This work addresses the challenge of using neural networks for short-term stock trading, though it is incremental as it applies a simple existing method to a specific domain.
The authors tackled the problem of applying neural networks to stock market trading by developing a single-layer neural network that recommends buy/sell decisions based on historical price comparisons, finding through chi-squared analysis that it can accurately make such decisions.
Machine learning is increasingly prevalent in stock market trading. Though neural networks have seen success in computer vision and natural language processing, they have not been as useful in stock market trading. To demonstrate the applicability of a neural network in stock trading, we made a single-layer neural network that recommends buying or selling shares of a stock by comparing the highest high of 10 consecutive days with that of the next 10 days, a process repeated for the stock's year-long historical data. A chi-squared analysis found that the neural network can accurately and appropriately decide whether to buy or sell shares for a given stock, showing that a neural network can make simple decisions about the stock market.