To address the limitations of existing abnormal traffic detection methods,such as insufficient temporal and spatial feature extraction,high false positive rate(FPR),poor generalization,and class imbalance,this study p...To address the limitations of existing abnormal traffic detection methods,such as insufficient temporal and spatial feature extraction,high false positive rate(FPR),poor generalization,and class imbalance,this study proposed an intelligent detection method that combines a Stacked Convolutional Network(SCN),Bidirectional Long Short-Term Memory(BiLSTM)network,and Equalization Loss v2(EQL v2).This method was divided into two components:a feature extraction model and a classification and detection model.First,SCN was constructed by combining a Convolutional Neural Network(CNN)with a Depthwise Separable Convolution(DSC)network to capture the abstract spatial features of traffic data.These features were then input into the BiLSTM to capture temporal dependencies.An attention mechanism was incorporated after SCN and BiLSTM to enhance the extraction of key spatiotemporal features.To address class imbalance,the classification detection model applied EQL v2 to adjust the weights of the minority classes,ensuring that they received equal focus during training.The experimental results indicated that the proposed method outperformed the existing methods in terms of accuracy,FPR,and F1-score and significantly improved the identification rate of minority classes.展开更多
Shanghai-Hong Kong Stock Connect Program,which is a new starting point for the opening up of the mainland capital market,still has many uncertainties.Research on the benefits and market volatility of such policies can...Shanghai-Hong Kong Stock Connect Program,which is a new starting point for the opening up of the mainland capital market,still has many uncertainties.Research on the benefits and market volatility of such policies can provide investors with time to invest in such policies,fluctuations in the underlying stocks of the Chinese stock market,and decision support for the formulation and revision of relevant policies.This paper studies whether there is significant abnormal rate of return in the selected stocks which are in the Shanghai Stock Connect Program within the specified period,the excess return gap between the stocks which are in the program and which are not in the program,and the impact of the Shanghai Stock Connect Program on the volatility of the relevant stocks.Based on the CAPM model and the Fama-French 3-factor model,this paper uses t test to study the significance of the abnormal rate of return.By establishing a difference-in-difference(DID)model,the regression of the abnormal rate of return is tested,and the sample volatility is analyzed according to the influence of the fund transaction.The study found that the stocks in the program have significant abnormal rate of returns during the window period.The Shanghai Stock Connect has brought about a huge change in transaction amount,and policy makers need to improve related and similar policies.展开更多
Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate.The random walk hypothesis and efficient market hypothesis essentially state that it is not poss...Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate.The random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematically,reliably predict future stock prices or forecast changes in the stock market overall.Nonetheless,machine learning(ML)techniques that use historical data have been applied to make such predictions.Previous studies focused on a small number of stocks and claimed success with limited statistical confidence.In this study,we construct feature vectors composed of multiple previous relative returns and apply the random forest(RF),support vector machine(SVM),and long short-term memory(LSTM)ML methods as classifiers to predict whether a stock can return 2% more than its index in the following 10 days.We apply this approach to all S&P 500 companies for the period 2017-2022.We assess performance using accuracy,precision,and recall and compare our results with a random choice strategy.We observe that the LSTM classifier outperforms RF and SVM,and the data-driven ML methods outperform the random choice classifier(p=8.46e^(-17) for accuracy of LSTM).Thus,we demonstrate that the probability that the random walk and efficient market hypotheses hold in the considered context is negligibly small.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.62102449).
文摘To address the limitations of existing abnormal traffic detection methods,such as insufficient temporal and spatial feature extraction,high false positive rate(FPR),poor generalization,and class imbalance,this study proposed an intelligent detection method that combines a Stacked Convolutional Network(SCN),Bidirectional Long Short-Term Memory(BiLSTM)network,and Equalization Loss v2(EQL v2).This method was divided into two components:a feature extraction model and a classification and detection model.First,SCN was constructed by combining a Convolutional Neural Network(CNN)with a Depthwise Separable Convolution(DSC)network to capture the abstract spatial features of traffic data.These features were then input into the BiLSTM to capture temporal dependencies.An attention mechanism was incorporated after SCN and BiLSTM to enhance the extraction of key spatiotemporal features.To address class imbalance,the classification detection model applied EQL v2 to adjust the weights of the minority classes,ensuring that they received equal focus during training.The experimental results indicated that the proposed method outperformed the existing methods in terms of accuracy,FPR,and F1-score and significantly improved the identification rate of minority classes.
文摘Shanghai-Hong Kong Stock Connect Program,which is a new starting point for the opening up of the mainland capital market,still has many uncertainties.Research on the benefits and market volatility of such policies can provide investors with time to invest in such policies,fluctuations in the underlying stocks of the Chinese stock market,and decision support for the formulation and revision of relevant policies.This paper studies whether there is significant abnormal rate of return in the selected stocks which are in the Shanghai Stock Connect Program within the specified period,the excess return gap between the stocks which are in the program and which are not in the program,and the impact of the Shanghai Stock Connect Program on the volatility of the relevant stocks.Based on the CAPM model and the Fama-French 3-factor model,this paper uses t test to study the significance of the abnormal rate of return.By establishing a difference-in-difference(DID)model,the regression of the abnormal rate of return is tested,and the sample volatility is analyzed according to the influence of the fund transaction.The study found that the stocks in the program have significant abnormal rate of returns during the window period.The Shanghai Stock Connect has brought about a huge change in transaction amount,and policy makers need to improve related and similar policies.
基金funded by The University of Groningen and Prospect Burma organization.
文摘Forecasting changes in stock prices is extremely challenging given that numerous factors cause these prices to fluctuate.The random walk hypothesis and efficient market hypothesis essentially state that it is not possible to systematically,reliably predict future stock prices or forecast changes in the stock market overall.Nonetheless,machine learning(ML)techniques that use historical data have been applied to make such predictions.Previous studies focused on a small number of stocks and claimed success with limited statistical confidence.In this study,we construct feature vectors composed of multiple previous relative returns and apply the random forest(RF),support vector machine(SVM),and long short-term memory(LSTM)ML methods as classifiers to predict whether a stock can return 2% more than its index in the following 10 days.We apply this approach to all S&P 500 companies for the period 2017-2022.We assess performance using accuracy,precision,and recall and compare our results with a random choice strategy.We observe that the LSTM classifier outperforms RF and SVM,and the data-driven ML methods outperform the random choice classifier(p=8.46e^(-17) for accuracy of LSTM).Thus,we demonstrate that the probability that the random walk and efficient market hypotheses hold in the considered context is negligibly small.
