This paper derives a new decomposition of stock returns using price extremes and proposes a conditional autoregressive shape(CARS)model with beta density to predict the direction of stock returns.The CARS model is con...This paper derives a new decomposition of stock returns using price extremes and proposes a conditional autoregressive shape(CARS)model with beta density to predict the direction of stock returns.The CARS model is continuously valued,which makes it different from binary classification models.An empirical study is performed on the US stock market,and the results show that the predicting power of the CARS model is not only statistically significant but also economically valuable.We also compare the CARS model with the probit model,and the results demonstrate that the proposed CARS model outperforms the probit model for return direction forecasting.The CARS model provides a new framework for return direction forecasting.展开更多
Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on f...Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.展开更多
基金Funding was provided by National Social Science Fund of China(Grant No.22BJY259)National Natural Science Foundation of China(Grant Nos.71971004,72271055)Research on Modeling of Return Rate Based on Mixed Distribution and Its Application in Risk Management(Grant No.19YB26).
文摘This paper derives a new decomposition of stock returns using price extremes and proposes a conditional autoregressive shape(CARS)model with beta density to predict the direction of stock returns.The CARS model is continuously valued,which makes it different from binary classification models.An empirical study is performed on the US stock market,and the results show that the predicting power of the CARS model is not only statistically significant but also economically valuable.We also compare the CARS model with the probit model,and the results demonstrate that the proposed CARS model outperforms the probit model for return direction forecasting.The CARS model provides a new framework for return direction forecasting.
文摘Big data analytic techniques associated with machine learning algorithms are playing an increasingly important role in various application fields,including stock market investment.However,few studies have focused on forecasting daily stock market returns,especially when using powerful machine learning techniques,such as deep neural networks(DNNs),to perform the analyses.DNNs employ various deep learning algorithms based on the combination of network structure,activation function,and model parameters,with their performance depending on the format of the data representation.This paper presents a comprehensive big data analytics process to predict the daily return direction of the SPDR S&P 500 ETF(ticker symbol:SPY)based on 60 financial and economic features.DNNs and traditional artificial neural networks(ANNs)are then deployed over the entire preprocessed but untransformed dataset,along with two datasets transformed via principal component analysis(PCA),to predict the daily direction of future stock market index returns.While controlling for overfitting,a pattern for the classification accuracy of the DNNs is detected and demonstrated as the number of the hidden layers increases gradually from 12 to 1000.Moreover,a set of hypothesis testing procedures are implemented on the classification,and the simulation results show that the DNNs using two PCA-represented datasets give significantly higher classification accuracy than those using the entire untransformed dataset,as well as several other hybrid machine learning algorithms.In addition,the trading strategies guided by the DNN classification process based on PCA-represented data perform slightly better than the others tested,including in a comparison against two standard benchmarks.
