Factor Modeling, Portfolio Construction, Python, Log Returns, Finance.
= train_data[self.factors] train_model = self.build_factor_model().copy() # Predict log returns on test data test_factors = test_data[self.factors] test_results = {} for ticker in test_data.columns: predicted_returns = np.dot(np.column_stack( (np.ones(len(test_factors)), test_factors),