Factor Feature Engineering: Cleaning, Scaling, Smoothing, and Transforming Data
Summary
The document explains factors as measurable characteristics of assets that can serve as inputs to quantitative models and investment strategies. It outlines four common feature-engineering steps: cleaning missing or erroneous observations, standardizing values across scales, smoothing noise, and transforming distributions. Examples include removing missing rows, applying Z-score or min-max scaling, using a moving average, and applying a logarithmic transform. A Python illustration demonstrates these operations on a small sample series.
The material is introductory rather than a complete research workflow. It does not compare methods empirically or show that the transformations improve prediction or returns. Some choices require care: dropping missing observations can change the sample, smoothing can introduce lag, and logarithms require suitable positive inputs. In time-series or cross-sectional investment data, preprocessing should also be fitted using information available at the time to avoid look-ahead leakage. The document presents feature engineering as a way to prepare factor data, not as evidence that any particular factor will be profitable.
Key ideas
- Factors represent measurable asset characteristics that can be used as model inputs.
- Feature engineering can include cleaning, standardization, smoothing, and distribution transforms.
- Z-score and min-max scaling are two ways to put factor values on comparable scales.
- Moving averages can reduce short-term variation but may also add lag.
- Preprocessing choices need to respect data timing and the assumptions of each transformation.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.