首页|Studies from Capital University of Economics and Business Have Provided New Data on Machine Learning (Identifying Factors Via Automatic Debiased Machine Learnin g)
Studies from Capital University of Economics and Business Have Provided New Data on Machine Learning (Identifying Factors Via Automatic Debiased Machine Learnin g)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing have been published. According to news originating from Beijing, People's Re public of China, by NewsRx correspondents, research stated, "Identifying risk fa ctors that have significant explanatory power for the cross-sectional asset retu rns is fundamental in asset pricing. We adopt a novel automatic debiased machine learning (ADML) method proposed by Chernozhukov, Newey, and Singh (2022) to rob ustly estimate partial pricing effect of a certain factor controlling for a larg e number of confounding factors under a nonlinear stochastic discount factor (SD F) assumption." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), Beijing Municipal Social Science Foundation, Public Computin g Cloud at Renmin University of China. Our news journalists obtained a quote from the research from the Capital Univers ity of Economics and Business, "The ADML resolves biased estimation, non-robustn ess, and overfitting issues that are common to traditional machine learning appr oaches. We find that the most significant factors selected by the ADML outperfor m the Fama-French sparse factors and factors identified via the double-selection LASSO method under a linear factor model assumption. Out of a high-dimensional zoo of US stock market factors commonly tested in the finance literature, we ide ntify approximately 30 to 50 factors having significant but declining pricing po wer in explaining the cross-section of stock returns."
BeijingPeople's Republic of ChinaAsi aCyborgsEmerging TechnologiesMachine LearningRisk and PreventionCapita l University of Economics and Business
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