This is a summary of links featured on Quantocracy on Sunday, 07/16/2023. To see our most recent links, visit the Quant Mashup. Read on readers!
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Quant_rv: more exploration of strategy parameters [Babbage9010]There is grave danger in tying your strategy to one selected set of parameters, particularly if those parameters are cherry picked to give more exciting results than other possible choices. Im trying to working to avoid that in quant_rv. So far, quant_rv has two main parameters that can vary: the lookback_period for calculating realized volatility, and the volatility threshold used as a cutoff
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Quant And Machine Learning Links: 20230716 [Machine Learning Applied]Financial Machine Learning Bryan T. Kelly, Dacheng Xiu We survey the nascent literature on machine learning in the study of financial markets. We highlight the best examples of what this line of research has to offer and recommend promising directions for future research. This survey is designed for both financial economists interested in grasping machine learning tools, as well as for
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Are Sustainable Investors Compensated Adequately? [Alpha Architect]While sustainable investing continues to gain in popularity, economic theory suggests that if a large enough proportion of investors choose to favor companies with high sustainability ratings and avoid those with low sustainability ratings (sin businesses), the favored companys share prices will be elevated and the sin stock shares will be depressed. In equilibrium, the screening out of certain
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A model for bond risk premia and the macroeconomy [SR SV]An empirical analysis of the U.S. bond market since the 1960s emphasizes occasional abrupt regime changes, as defined by yield levels, curve slopes, and related volatility metrics. An arbitrage-free bond pricing model illustrates that bond risk premia can be decomposed into two types. One is related to continuous risk factors, traditionally summarized as the level, slope, and curvature of the