The main rationale for applying ML to trading is to obtain predictions of asset fundamentals, price movements or market conditions. A strategy can leverage multiple ML algorithms that build on each other. Downstream models can generate signals at the portfolio level by integrating predictions about the prospects of individual assets, capital market expectations, and the correlation among securities. Alternatively, ML predictions can inform discretionary trades as in the quantamental approach outlined above. ML predictions can also target specific risk factors, such as value or volatility, or implement technical approaches, such as trend following or mean reversion:

• In Chapter 3, Alternative Data for Finance, we illustrate how to work with fundamental data to create inputs to ML-driven valuation models
• In Chapter 13, Working with Text Data, Chapter 14, Topic Modeling, and Chapter 15, Word Embeddings we use alternative data on business reviews that can be used to project revenues for a company as an input for a valuation exercise.
• In Chapter 8, Time Series Models, we demonstrate how to forecast macro variables as inputs to market expectations and how to forecast risk factors such as volatility
• In Chapter 18, Recurrent Neural Networks we introduce recurrent neural networks (RNNs) that achieve superior performance with non-linear time series data.