Why Would You Utilize Neural Networks to Help You Trade When You Could Use Simple, Layered Approaches
Neural networks tend to be overconfident which leads to losses when trading; simple models can underfit, but layered approaches make up the difference
Trading in an efficient, complex market is not a cats versus dogs problem. Markets have short data series, are noisy, are random at times, and are not controlled under tight bounds. Price action is not even well understood by humans.
When 90% of human traders fail to make money in the stock market, how is one model — even if it is a neural network — supposed to make money, and not overfit, when markets are not well understood, non-deterministic, noisy, and have short data series?
Popularized applications of neural networks have similar stories: known rules (like chess or speech recognition), deterministic (operated machines), done well by humans (image classification), and lots of relevant data (machine translation).
Introduction
Simple, layered approaches are ideal for generating good trade signals. In a recent book titled Advances in Financial Machine Learning, Marcos Lopez de Prado, 2019 quant of the year, talked…
