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
Based on the atmospheric self_memorization principle, a complex memory function was introduced and the spectral form of atmospheric self_memorial equation was derived. Setting up and solving the equation constitute a ...Based on the atmospheric self_memorization principle, a complex memory function was introduced and the spectral form of atmospheric self_memorial equation was derived. Setting up and solving the equation constitute a new approach of the numerical weather prediction. Using the spectral model T42L9 as a dynamic kernel, a global self_memorial T42 model (SMT42) was established, with which twelve cases of 30_d integration experiments were carried out. Compared with the T42L9, the SMT42 is much better in 500 hPa forecast not only for daily circulation but also for monthly mean circulation. The anomaly correlation coefficient (ACC) of forecast for monthly mean circulation has been improved to 0.42, increased by 0.05, and the root_mean_square error (RMSE) has been reduced from 6.09 to 4.03 dagpm.展开更多
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.
文摘Based on the atmospheric self_memorization principle, a complex memory function was introduced and the spectral form of atmospheric self_memorial equation was derived. Setting up and solving the equation constitute a new approach of the numerical weather prediction. Using the spectral model T42L9 as a dynamic kernel, a global self_memorial T42 model (SMT42) was established, with which twelve cases of 30_d integration experiments were carried out. Compared with the T42L9, the SMT42 is much better in 500 hPa forecast not only for daily circulation but also for monthly mean circulation. The anomaly correlation coefficient (ACC) of forecast for monthly mean circulation has been improved to 0.42, increased by 0.05, and the root_mean_square error (RMSE) has been reduced from 6.09 to 4.03 dagpm.
