Stock prices look very much like random walks: the signal-to-noise ratio is close to zero. Therefore, trying to model the prices directly to make investment decisions is extremely challenging. A different approach is to predict a company’s fundamental financial data (revenue, assets, debts, and so on). If you can know one year in advance what the financial health of a given company will be, then it obviously becomes much easier to make good investment decisions.
As was shown in “Improving Factor-Based Quantitative Investing by Forecasting Company Fundamentals,” a recent paper by John Alberg and Zachary C. Lipton presented at NIPS 2017, good predictions can be made using deep learning—more specifically using LSTM recurrent networks. Aurélien Géron explains how to forecast stock prices using the solution proposed in that paper, which is an excellent example of using LSTMs for time series forecasting. In the process, he details how LSTMs work, their advantages and limitations, and some alternatives. You’ll also learn how to perform cross-validation when dealing with time series, in particular to search for good hyperparameters.
Aurélien Géron is a machine learning consultant at Kiwisoft. Previously, he led YouTube’s video classification team and was founder and CTO of two successful companies (a telco operator and a strategy firm). Aurélien is the author of several technical books, including the O’Reilly book Hands-on Machine Learning with Scikit-Learn and TensorFlow.
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