Ai And ML For Portfolio Construction
Ai And ML For Portfolio Construction Our approach is to build a machine learning model based on the features from certain SEC filings.
We will use this data with our machine learning to predict the direction of a stock’s quarterly price movement. Moreover, a portfolio constructed of those stocks that have a positive prediction return to build our portfolio. Overall, our model constructed portfolio outperforms S&P 500 for an average of 12% in simulation.
Supervised Machine Learning Strategy
Next is to fit a model on our dataset. Our target is to optimize our portfolio’s return, and our approach is to predict the return and choose those that have the highest expected return. We already have the target value: quarterly return for each of our data starts at 45 days after one quarter end, so a supervised machine learning model would be an appropriate choice.
Feature Normalization can speed up the learning algorithms a lot for our dataset because we have features with very different scales (holding quantity and market value can vary from 10 to 100,000,000 while long fraction usually no bigger than 1). Since there is no evidence shows that our features are normally distributed. Moreover, feature scaling and mean normalization are useful to transform our features. By doing so, all our features are on a similar scale.
Cross-Validation Model Selection
In order to choose the best model. We split our data into three parts: training set, cross-validation set and test set and the proportion is 60%, 20% and 20% respectively. In conclusion, the reason we add a cross validation set is that we use this set to pre-test. And based on the pre-test result for model selection and adjustment. Then we use the test set to make predictions. So that our model is tested on a completely new set of data. And the result would reflect the ‘true’ accuracy of our model’s prediction.