Introduction:Nowadays,the most significant challenges in the stock market is to predict the stock prices.The stock price data represents a financial time series data which becomes more difficult to predict due to its ...Introduction:Nowadays,the most significant challenges in the stock market is to predict the stock prices.The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature.Case description:Support Vector Machines(SVM)and Artificial Neural Networks(ANN)are widely used for prediction of stock prices and its movements.Every algorithm has its way of learning patterns and then predicting.Artificial Neural Network(ANN)is a popular method which also incorporate technical analysis for making predictions in financial markets.Discussion and evaluation:Most common techniques used in the forecasting of financial time series are Support Vector Machine(SVM),Support Vector Regression(SVR)and Back Propagation Neural Network(BPNN).In this article,we use neural networks based on three different learning algorithms,i.e.,Levenberg-Marquardt,Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared.Conclusion:All three algorithms provide an accuracy of 99.9%using tick data.The accuracy over 15-min dataset drops to 96.2%,97.0%and 98.9%for LM,SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.展开更多
Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic.With the objective of constructing an effective prediction model,both linear and machine learni...Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic.With the objective of constructing an effective prediction model,both linear and machine learning tools have been investigated for the past couple of decades.In recent years,recurrent neural networks(RNNs)have been observed to perform well on tasks involving sequence-based data in many research domains.With this motivation,we investigated the performance of long-short term memory(LSTM)and gated recurrent units(GRU)and their combination with the attention mechanism;LSTM+Attention,GRU+Attention,and LSTM+GRU+Attention.The methods were evaluated with stock data from three different stock indices:the KSE 100 index,the DSE 30 index,and the BSE Sensex.The results were compared to other machine learning models such as support vector regression,random forest,and k-nearest neighbor.The best results for the three datasets were obtained by the RNN-based models combined with the attention mechanism.The performances of the RNN and attention-based models are higher and would be more effective for applications in the business industry.展开更多
Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing ca...Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing capital from the investors to the business houses, which consequently leads to the availability of funds for business expansion. In this paper, we investigate to predict the daily excess returns of Bombay Stock Exchange (BSE) indices over the respective Treasury bill rate returns. Initially, we prove that the excess return time series do not fluctuate randomly. We are applying the prediction models of Autoregressive feed forward Artificial Neural Networks (ANN) to predict the excess return time series using lagged value. For the Artificial Neural Networks model using a Genetic Algorithm is constructed to choose the optimal topology. This paper examines the feasibility of the prediction task and provides evidence that the markets are not fluctuating randomly and finally, to apply the most suitable prediction model and measure their efficiency.展开更多
This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems,...This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.展开更多
传统的用户离网预测方法都是针对用户信息、消费行为等数据,通过数据挖掘的方法进行预测。为此,在分析移动用户离网原因的基础上提出了一种基于营销策略的用户离网预测模型(CPMP:Churn Predictionbased on Market Plan)。该模型针对用...传统的用户离网预测方法都是针对用户信息、消费行为等数据,通过数据挖掘的方法进行预测。为此,在分析移动用户离网原因的基础上提出了一种基于营销策略的用户离网预测模型(CPMP:Churn Predictionbased on Market Plan)。该模型针对用户主动离网的主要原因是其他运营商推出新的营销策略这一事实,通过比较用户在不同营销策略下可能发生的行为,进而预测用户离网的可能性。实验结果表明,基于CPMP模型对不同营销方案比较所得出差异,直接影响用户离网率。通过对营销策略的对比分析,可有效控制离网率。展开更多
A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employ...A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns.The empirical results show that 1)the Search Frequency of Baidu Index(SFBI)can predict next day’s price changes;2)the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks;3)the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs.These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.展开更多
文摘Introduction:Nowadays,the most significant challenges in the stock market is to predict the stock prices.The stock price data represents a financial time series data which becomes more difficult to predict due to its characteristics and dynamic nature.Case description:Support Vector Machines(SVM)and Artificial Neural Networks(ANN)are widely used for prediction of stock prices and its movements.Every algorithm has its way of learning patterns and then predicting.Artificial Neural Network(ANN)is a popular method which also incorporate technical analysis for making predictions in financial markets.Discussion and evaluation:Most common techniques used in the forecasting of financial time series are Support Vector Machine(SVM),Support Vector Regression(SVR)and Back Propagation Neural Network(BPNN).In this article,we use neural networks based on three different learning algorithms,i.e.,Levenberg-Marquardt,Scaled Conjugate Gradient and Bayesian Regularization for stock market prediction based on tick data as well as 15-min data of an Indian company and their results compared.Conclusion:All three algorithms provide an accuracy of 99.9%using tick data.The accuracy over 15-min dataset drops to 96.2%,97.0%and 98.9%for LM,SCG and Bayesian Regularization respectively which is significantly poor in comparison with that of results obtained using tick data.
基金supported by NRPU Project No.20-16091awarded by Higher Education Commission,PakistanThe title of the project is“University Education and Occupational Skills Mismatch (A Case Study of SMEs in Khyber Pakhtunkhwa)”,by the National Natural Science Foundation of China (Grant No.61370073)the National High Technology Research and Development Program of China,the project of Science and Technology Department of Sichuan Province (Grant No.2021YFG0322).
文摘Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic.With the objective of constructing an effective prediction model,both linear and machine learning tools have been investigated for the past couple of decades.In recent years,recurrent neural networks(RNNs)have been observed to perform well on tasks involving sequence-based data in many research domains.With this motivation,we investigated the performance of long-short term memory(LSTM)and gated recurrent units(GRU)and their combination with the attention mechanism;LSTM+Attention,GRU+Attention,and LSTM+GRU+Attention.The methods were evaluated with stock data from three different stock indices:the KSE 100 index,the DSE 30 index,and the BSE Sensex.The results were compared to other machine learning models such as support vector regression,random forest,and k-nearest neighbor.The best results for the three datasets were obtained by the RNN-based models combined with the attention mechanism.The performances of the RNN and attention-based models are higher and would be more effective for applications in the business industry.
文摘Stock Market is the market for security where organized issuance and trading of Stocks take place either through exchange or over the counter in electronic or physical form. It plays an important role in canalizing capital from the investors to the business houses, which consequently leads to the availability of funds for business expansion. In this paper, we investigate to predict the daily excess returns of Bombay Stock Exchange (BSE) indices over the respective Treasury bill rate returns. Initially, we prove that the excess return time series do not fluctuate randomly. We are applying the prediction models of Autoregressive feed forward Artificial Neural Networks (ANN) to predict the excess return time series using lagged value. For the Artificial Neural Networks model using a Genetic Algorithm is constructed to choose the optimal topology. This paper examines the feasibility of the prediction task and provides evidence that the markets are not fluctuating randomly and finally, to apply the most suitable prediction model and measure their efficiency.
基金Project supported by the Key Program for the National Natural Science Foundation of China(Grant No.61333003)the General Program for the National Natural Science Foundation of China(Grant No.61273104)
文摘This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.
文摘传统的用户离网预测方法都是针对用户信息、消费行为等数据,通过数据挖掘的方法进行预测。为此,在分析移动用户离网原因的基础上提出了一种基于营销策略的用户离网预测模型(CPMP:Churn Predictionbased on Market Plan)。该模型针对用户主动离网的主要原因是其他运营商推出新的营销策略这一事实,通过比较用户在不同营销策略下可能发生的行为,进而预测用户离网的可能性。实验结果表明,基于CPMP模型对不同营销方案比较所得出差异,直接影响用户离网率。通过对营销策略的对比分析,可有效控制离网率。
基金This work is supported by the National Natural Science Foundation of China(71320107003 and 71532009).
文摘A number of studies have investigated the predictability of Chinese stock returns with economic variables.Given the newly emerged dataset from the Internet,this paper investigates whether the Baidu Index can be employed to predict Chinese stock returns.The empirical results show that 1)the Search Frequency of Baidu Index(SFBI)can predict next day’s price changes;2)the stock prices go up when individual investors pay less attention to the stocks and go down when individual investors pay more attention to the stocks;3)the trading strategy constructed by shorting on the most SFBI and longing on the least SFBI outperforms the corresponding market index returns without consideration of the transaction costs.These results complement the existing literature on the predictability of Chinese stock returns and have potential implications for asset pricing and risk management.
