{'fraction_non_alphanumeric': 0.020689655172413793, 'fraction_numerical': 0.0, 'mean_word_length': 5.1525423728813555, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Is Your Alpha Pure and Your Beta Fit for Purpose?

Quantitative investors produce high backtested alpha, however, sometimes “less is more”. A beta that is unfit for purpose will favor factors with inflated alpha putting investment models at risk. Purifying alpha does not maximize past returns. It reduces unwanted risks and increases the probability of aligning live performance with backtests. Because there is no statistical procedure to choose one beta over another, the literature is filled with “better betas”. We suggest alpha purity should be the key differentiator between betas and we study how to bring alpha closer to normality. Practitioners fearful of blindly maximizing backtests will appreciate this approach.