Fostering AI resilience in the EU labour market - epc.eu
<a href="https://news.google.com/rss/articles/CBMihgFBVV95cUxOekdIcVc3QWh0NzZqRFAzQTlLbGJrV1dwaWo2Q3Q4aGhBd2NZU3o4eTBoeFJ4NU1qUzhscEJ3R1V5UzMtSTlUQmgxSWdyeTQ0VHZLOEN6Z0xiSEVvSFRTc25DcXZKTzNEODZ3em9xQWdSNDZjMHVDdXYyUkJvN3N1VW5tc0hNQQ?oc=5" target="_blank">Fostering AI resilience in the EU labour market</a> <font color="#6f6f6f">epc.eu</font>
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Quant Factor Research in Practice: IC, IR, and the Barra Multi-Factor Model
Why Does Your Backtest Look Great But Lose Money Live? Classic quant beginner story: find an "interesting" indicator → backtest → great results → trade live → lose money. Why? Because "looks effective" and "statistically significant" are two very different things. Quantitative factor research has a rigorous evaluation framework: IC, IR, and Barra risk neutralization. Skip this and your backtest is just mining noise. Part 1: IC — The Measuring Stick for Factor Effectiveness IC (Information Coefficient) = correlation between current-period factor exposure and next-period stock returns. Use RankIC (Spearman) over Pearson IC in practice—it's more robust to outliers. import scipy.stats as stats def calc_rank_ic ( factor_series , return_series ): rank_factor = factor_series . rank () rank_return
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