Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to...Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.展开更多
There is an imperfection in the accuracy indicators system and the technical parameters system in IEC standards 62053-21 implemented for watt-hour meters currently. This article quoted some parts of OIML MI-003 and R4...There is an imperfection in the accuracy indicators system and the technical parameters system in IEC standards 62053-21 implemented for watt-hour meters currently. This article quoted some parts of OIML MI-003 and R46 contents to provide a reference of the integrity comparison between the accuracy system and technical parameter system. The comparison result shows that the watt-hour meter accuracy technical indicators system of OIML R46 is more integrated. First reason is that it determines all the accuracy （basic error） defining rules just by using the technical parameter I efficiently; secondly, it can solve the contradiction that no error regulation of the range from starting current to minimum current in the process of providing, handing over and checking watt-hour meter products＇ qualities efficiently. Please pay close attention to the R46 official version which will be released soon, as it is still in the Committee Draft （CD6） stage currently.展开更多
Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the au...Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the authors combine semi-varying coefficient model with technical analysis and statistical learning,and propose semi-varying coefficient panel data model with individual effects to explore the dynamic relations between the stock returns from five companies:CVX,DFS,EMN,LYB,and MET and five technical indicators:CCI,EMV,MOM,ln ATR,ln RSI as well as closing price(ln CP),combine semi-parametric fixed effects estimator,semi-parametric random effects estimator with the testing procedure to distinguish fixed effects(FE) from random effects(RE),and finally apply the estimated dynamic relations and the testing set to predict stock returns in December 2020 for the five companies.The proposed method can accommodate the varying relationship and the interactive relationship between the different technical indicators,and further enhance the prediction accuracy to stock returns.展开更多
Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficien...Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.展开更多
Forex(foreign exchange)is a special financial market that entails both high risks and high profit opportunities for traders.It is also a very simple market since traders can profit by just predicting the direction of ...Forex(foreign exchange)is a special financial market that entails both high risks and high profit opportunities for traders.It is also a very simple market since traders can profit by just predicting the direction of the exchange rate between two currencies.However,incorrect predictions in Forex may cause much higher losses than in other typical financial markets.The direction prediction requirement makes the problem quite different from other typical time-series forecasting problems.In this work,we used a popular deep learning tool called“long short-term memory”(LSTM),which has been shown to be very effective in many time-series forecasting problems,to make direction predictions in Forex.We utilized two different data sets—namely,macroeconomic data and technical indicator data—since in the financial world,fundamental and technical analysis are two main techniques,and they use those two data sets,respectively.Our proposed hybrid model,which combines two separate LSTMs corresponding to these two data sets,was found to be quite successful in experiments using real data.展开更多
The idea of this study is derived from observing the profitability of stock investments following the phenomena of continuously rising(or falling)prices of stocks and continuously overbought(or oversold)signals emitte...The idea of this study is derived from observing the profitability of stock investments following the phenomena of continuously rising(or falling)prices of stocks and continuously overbought(or oversold)signals emitted by technical indicators.We employ the standard event study approach and technical trading strategies to explore whether investors would exploit profits in trading the constituent stocks of the Korea Composite Stock Price Index 50 and Shanghai Stock Exchange 50 when the aforementioned continuous phenomena occur.We find that both the Korean and Chinese stock markets are not fully efficient;this finding may enhance the robustness of the existing literature.In addition,we reveal that contrarian strategies are appropriate for the trading stocks listed on the Korean stock market for all the cases investigated in this study.However,momentum strategies are appropriate for the Chinese stock market when continuously rising stock prices and overbought signals are simultaneously observed.These findings imply that the difference in investor behaviors between the Korean and Chinese stock markets might result in dissimilar trading strategies being employed for these two markets.展开更多
As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empi...As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical analysis.Researchers in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock trend.This study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend prediction.This study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random forest.As the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as input.Then,the parameter combination of the model is optimized through random parameter search.The experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine model.Combined with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.展开更多
基金Funding is provided by Taif University Researchers Supporting Project Number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.
文摘There is an imperfection in the accuracy indicators system and the technical parameters system in IEC standards 62053-21 implemented for watt-hour meters currently. This article quoted some parts of OIML MI-003 and R46 contents to provide a reference of the integrity comparison between the accuracy system and technical parameter system. The comparison result shows that the watt-hour meter accuracy technical indicators system of OIML R46 is more integrated. First reason is that it determines all the accuracy （basic error） defining rules just by using the technical parameter I efficiently; secondly, it can solve the contradiction that no error regulation of the range from starting current to minimum current in the process of providing, handing over and checking watt-hour meter products＇ qualities efficiently. Please pay close attention to the R46 official version which will be released soon, as it is still in the Committee Draft （CD6） stage currently.
基金supported by the Natural Science Foundation of CQ CSTC under Grant No.cstc.2018jcyj A2073Chongqing Social Science Plan Project under Grant No.2019WT59+3 种基金Science and Technology Research Program of Chongqing Education Commission under Grant No.KJZD-M202100801Mathematic and Statistics Team from Chongqing Technology and Business University under Grant No.ZDPTTD201906Open Project from Chongqing Key Laboratory of Social Economy and Applied Statistics under Grant No.KFJJ2022056Chongqing Graduate Research Innovation Project under Grant No.CYS23568。
文摘Accurately predicting stock returns is a conundrum in financial market.Solving this conundrum can bring huge economic benefits for investors and also attract the attention of all circles of people.In this paper the authors combine semi-varying coefficient model with technical analysis and statistical learning,and propose semi-varying coefficient panel data model with individual effects to explore the dynamic relations between the stock returns from five companies:CVX,DFS,EMN,LYB,and MET and five technical indicators:CCI,EMV,MOM,ln ATR,ln RSI as well as closing price(ln CP),combine semi-parametric fixed effects estimator,semi-parametric random effects estimator with the testing procedure to distinguish fixed effects(FE) from random effects(RE),and finally apply the estimated dynamic relations and the testing set to predict stock returns in December 2020 for the five companies.The proposed method can accommodate the varying relationship and the interactive relationship between the different technical indicators,and further enhance the prediction accuracy to stock returns.
基金Analysis and Research on Online Learning in Higher Vocational Colleges Based on Kirkpatrick Model-Taking the Course of Physiology as an Example(Project No.:D/2021/03/91)The excellent teaching team of Physiology of Suzhou Vocational College of Health Science and Technology in 2019(Project number:JXTD201912).
文摘Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.
文摘Forex(foreign exchange)is a special financial market that entails both high risks and high profit opportunities for traders.It is also a very simple market since traders can profit by just predicting the direction of the exchange rate between two currencies.However,incorrect predictions in Forex may cause much higher losses than in other typical financial markets.The direction prediction requirement makes the problem quite different from other typical time-series forecasting problems.In this work,we used a popular deep learning tool called“long short-term memory”(LSTM),which has been shown to be very effective in many time-series forecasting problems,to make direction predictions in Forex.We utilized two different data sets—namely,macroeconomic data and technical indicator data—since in the financial world,fundamental and technical analysis are two main techniques,and they use those two data sets,respectively.Our proposed hybrid model,which combines two separate LSTMs corresponding to these two data sets,was found to be quite successful in experiments using real data.
文摘The idea of this study is derived from observing the profitability of stock investments following the phenomena of continuously rising(or falling)prices of stocks and continuously overbought(or oversold)signals emitted by technical indicators.We employ the standard event study approach and technical trading strategies to explore whether investors would exploit profits in trading the constituent stocks of the Korea Composite Stock Price Index 50 and Shanghai Stock Exchange 50 when the aforementioned continuous phenomena occur.We find that both the Korean and Chinese stock markets are not fully efficient;this finding may enhance the robustness of the existing literature.In addition,we reveal that contrarian strategies are appropriate for the trading stocks listed on the Korean stock market for all the cases investigated in this study.However,momentum strategies are appropriate for the Chinese stock market when continuously rising stock prices and overbought signals are simultaneously observed.These findings imply that the difference in investor behaviors between the Korean and Chinese stock markets might result in dissimilar trading strategies being employed for these two markets.
基金National Natural Science Foundation of China,Grant/Award Numbers:61673084,National Natural Science Foundation of ChinaThe Fundamental Research Foundation for Universities of Heilongjiang Province,Grant/Award Number:LGYC2018JC017。
文摘As a complex hot problem in the financial field,stock trend forecasting uses a large amount of data and many related indicators;hence it is difficult to obtain sustainable and effective results only by relying on empirical analysis.Researchers in the field of machine learning have proved that random forest can form better judgements on this kind of problem,and it has an auxiliary role in the prediction of stock trend.This study uses historical trading data of four listed companies in the USA stock market,and the purpose of this study is to improve the performance of random forest model in medium-and long-term stock trend prediction.This study applies the exponential smoothing method to process the initial data,calculates the relevant technical indicators as the characteristics to be selected,and proposes the D-RF-RS method to optimize random forest.As the random forest is an ensemble learning model and is closely related to decision tree,D-RF-RS method uses a decision tree to screen the importance of features,and obtains the effective strong feature set of the model as input.Then,the parameter combination of the model is optimized through random parameter search.The experimental results show that the average accuracy of random forest is increased by 0.17 after the above process optimization,which is 0.18 higher than the average accuracy of light gradient boosting machine model.Combined with the performance of the ROC curve and Precision–Recall curve,the stability of the model is also guaranteed,which further demonstrates the advantages of random forest in medium-and long-term trend prediction of the stock market.
