Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a mo...Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a more reliable reference in terms of artificial intelligence index investment,this paper selects the NASDAQ CTA Artificial Intelligence and Robotics(AIRO)Index as the research target,and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics.Specifically,this paper uses the ensemble empirical mode decomposition(EEMD)method and the modified iterative cumulative sum of squares(ICSS)algorithm to decompose the index returns and identify the structural breakpoints.Furthermore,it combines the least-square support vector machine approach with the particle swarm optimization method(PSO-LSSVM)and the generalized autoregressive conditional heteroskedasticity(GARCH)type models to construct innovative hybrid forecasting methods.On the one hand,the empirical results indicate that the AIRO index returns have complex structural characteristics,and present time-varying and nonlinear characteristics with high complexity and mutability;on the other hand,the newly proposed hybrid forecasting method(i.e.,the EEMD-PSO-LSSVM-ICSS-GARCH models)which considers these complex structural characteristics,can yield the optimal forecasting performance for the AIRO index returns.展开更多
Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy-or-hold strategies so that they may increase profitability.However,obtai...Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy-or-hold strategies so that they may increase profitability.However,obtaining accurate and reliable predictions is challenging,noting that accuracy does not equate to reliability,especially when financial time-series forecasting is applied owing to its complex and chaotic tendencies.To mitigate this complexity,this study provides a comprehensive method for forecasting financial time series based on tactical input–output feature mapping techniques using machine learning(ML)models.During the prediction process,selecting the relevant indicators is vital to obtaining the desired results.In the financial field,limited attention has been paid to this problem with ML solutions.We investigate the use of feature selection with annealing(FSA)for the first time in this field,and we apply the least absolute shrinkage and selection operator(Lasso)method to select the features from more than 1000 candidates obtained from 26 technical classifiers with different periods and lags.Boruta(BOR)feature selection,a wrapper method,is used as a baseline for comparison.Logistic regression(LR),extreme gradient boosting(XGBoost),and long short-term memory are then applied to the selected features for forecasting purposes using 10 different financial datasets containing cryptocurrencies and stocks.The dependent variables consisted of daily logarithmic returns and trends.The mean-squared error for regression,area under the receiver operating characteristic curve,and classification accuracy were used to evaluate model performance,and the statistical significance of the forecasting results was tested using paired t-tests.Experiments indicate that the FSA algorithm increased the performance of ML models,regardless of problem type.The FSA hybrid models showed better performance and outperformed the other BOR models on seven of the 10 datasets for regression and classification.FSA-based models also outperformed Lasso-based models on six of the 10 datasets for regression and four of the 10 datasets for classification.None of the hybrid BOR models outperformed the hybrid FSA models.Lasso-based models,excluding the LR type,were comparable to the best models for six of the 10 datasets for classification.Detailed experimental analysis indicates that the proposed methodology can forecast returns and their movements efficiently and accurately,providing the field with a useful tool for investors.展开更多
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
An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into...An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine(SVM).Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities,the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately.The applicability and validity of MWSVM for stock returns forecasting is confirmed through experiments on real-world stock data.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support from National Natural Science Foundation of China(Nos.71774051,72243003)National Social Science Fund of China(No.22AZD128)the seminar participants in Center for Resource and Environmental Management,Hunan University,China.
文摘Forecasting returns for the Artificial Intelligence and Robotics Index is of great significance for financial market stability,and the development of the artificial intelligence industry.To provide investors with a more reliable reference in terms of artificial intelligence index investment,this paper selects the NASDAQ CTA Artificial Intelligence and Robotics(AIRO)Index as the research target,and proposes innovative hybrid methods to forecast returns by considering its multiple structural characteristics.Specifically,this paper uses the ensemble empirical mode decomposition(EEMD)method and the modified iterative cumulative sum of squares(ICSS)algorithm to decompose the index returns and identify the structural breakpoints.Furthermore,it combines the least-square support vector machine approach with the particle swarm optimization method(PSO-LSSVM)and the generalized autoregressive conditional heteroskedasticity(GARCH)type models to construct innovative hybrid forecasting methods.On the one hand,the empirical results indicate that the AIRO index returns have complex structural characteristics,and present time-varying and nonlinear characteristics with high complexity and mutability;on the other hand,the newly proposed hybrid forecasting method(i.e.,the EEMD-PSO-LSSVM-ICSS-GARCH models)which considers these complex structural characteristics,can yield the optimal forecasting performance for the AIRO index returns.
基金supported by THE SCIENTIFIC AND TECHNOLOGICAL RESEARCH COUNCIL OF TURKIYE.
文摘Stock market and cryptocurrency forecasting is very important to investors as they aspire to achieve even the slightest improvement to their buy-or-hold strategies so that they may increase profitability.However,obtaining accurate and reliable predictions is challenging,noting that accuracy does not equate to reliability,especially when financial time-series forecasting is applied owing to its complex and chaotic tendencies.To mitigate this complexity,this study provides a comprehensive method for forecasting financial time series based on tactical input–output feature mapping techniques using machine learning(ML)models.During the prediction process,selecting the relevant indicators is vital to obtaining the desired results.In the financial field,limited attention has been paid to this problem with ML solutions.We investigate the use of feature selection with annealing(FSA)for the first time in this field,and we apply the least absolute shrinkage and selection operator(Lasso)method to select the features from more than 1000 candidates obtained from 26 technical classifiers with different periods and lags.Boruta(BOR)feature selection,a wrapper method,is used as a baseline for comparison.Logistic regression(LR),extreme gradient boosting(XGBoost),and long short-term memory are then applied to the selected features for forecasting purposes using 10 different financial datasets containing cryptocurrencies and stocks.The dependent variables consisted of daily logarithmic returns and trends.The mean-squared error for regression,area under the receiver operating characteristic curve,and classification accuracy were used to evaluate model performance,and the statistical significance of the forecasting results was tested using paired t-tests.Experiments indicate that the FSA algorithm increased the performance of ML models,regardless of problem type.The FSA hybrid models showed better performance and outperformed the other BOR models on seven of the 10 datasets for regression and classification.FSA-based models also outperformed Lasso-based models on six of the 10 datasets for regression and four of the 10 datasets for classification.None of the hybrid BOR models outperformed the hybrid FSA models.Lasso-based models,excluding the LR type,were comparable to the best models for six of the 10 datasets for classification.Detailed experimental analysis indicates that the proposed methodology can forecast returns and their movements efficiently and accurately,providing the field with a useful tool for investors.
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.
基金the Hunan Natural Science Foundation(No. 09JJ3129)the Hunan Key Social Science Foundation (No. 09ZDB04)the Hunan Social Science Foundation (No. 08JD28)
文摘An admissible manifold wavelet kernel is proposed to construct manifold wavelet support vector machine(MWSVM) for stock returns forecasting.The manifold wavelet kernel is obtained by incorporating manifold theory into wavelet technique in support vector machine(SVM).Since manifold wavelet function can yield features that describe of the stock time series both at various locations and at varying time granularities,the MWSVM can approximate arbitrary nonlinear functions and forecast stock returns accurately.The applicability and validity of MWSVM for stock returns forecasting is confirmed through experiments on real-world stock data.
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.
