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A Machine Learning Framework for Stock Selection
The effectiveness of the stock selection strategy is validated in Chinese stock market in both statistical and practical aspects, showing that: 1) Stacking outperforms other models reaching an AUC score of 0. 972; 2) Genetic Algorithm picks a subset of 114 features and the prediction performances of all models remain almost unchanged after the selection procedure, which suggests some features are indeed redundant; 3) LR and DNN are radical models; RF is risk-neutral model; Stacking is somewhere between DNN and RF.
Language Distribution Prediction based on Batch Markov Monte Carlo Simulation with Migration
To model the randomness of language spreading, we propose the Batch Markov Monte Carlo Simulation with Migration(BMMCSM) algorithm, in which each agent is treated as a language stack.
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