In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price predictions.Several review papers in the literat...In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price predictions.Several review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market forecasting.However,no survey study has explored feature selection and extraction techniques for stock market forecasting.This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications.We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022.We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles.We also describe the combination of feature analysis techniques and ML methods and evaluate their performance.Moreover,we present other survey articles,stock market input and output data,and analyses based on various factors.We find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.展开更多
Quantitative stock selection has become a research hotspot in the field of investment decision. As the data mining technology becomes mature, quantitative stock selection has made great progress. From the perspective ...Quantitative stock selection has become a research hotspot in the field of investment decision. As the data mining technology becomes mature, quantitative stock selection has made great progress. From the perspective of value investment, this paper selects top 200 stocks of A share in terms of market value. With the random forest (RF), financial characteristic variables with significant impact on SVR are screened out. At the same time with quantum genetic algorithm (QGA) superior to the traditional genetic algorithm (GA), SVR parameters are deeply and dynamically sought for, so as to build the RF-QGA-SVR model for year-to-year stock ranking. The quantitative stock selection model is built, and the empirical analysis of its stock selection performance is conducted. The conclusion is as follows: 1) Optimizing SVR with QGA has higher precision than the traditional genetic algorithm, and is more excellent than the traditional GA optimization;2) SVR after RF optimization of characteristic variables more significantly improves the accuracy of stock ranking and prediction;3) In the stock ranking obtained from the RF-QGA-SVR model, the yields of top stock portfolios are much higher than the market benchmark yield. At the same time, the yields of the top 10 stock portfolios are the highest, and the top 30 stock portfolios are the most stable. This study has positive reference significance on quantitative stock selection in the field of quantitative investment.展开更多
Forecasting stock returns is extremely challenging in general,and this task becomes even more difficult given the turbulent nature of the Chinese stock market.We address the stock selection process as a statistical le...Forecasting stock returns is extremely challenging in general,and this task becomes even more difficult given the turbulent nature of the Chinese stock market.We address the stock selection process as a statistical learning problem and build crosssectional forecast models to select individual stocks in the Shanghai Composite Index.Decile portfolios are formed according to rankings of the forecasted future cumulative returns.The equity market’s neutral portfolio-formed by buying the top decile portfolio and selling short the bottom decile portfolio-exhibits superior performance to,and a low correlation with,the Shanghai Composite Index.To make our strategy more useful to practitioners,we evaluate the proposed stock selection strategy’s performance by allowing only long positions,and by investing only in Ashare stocks to incorporate the restrictions in the Chinese stock market.The longonly strategies still generate robust and superior performance compared to the Shanghai Composite Index.A close examination of the coefficients of the features provides more insights into the changes in market dynamics from period to period.展开更多
This article attempted to construct a multi-factor quantitative stock selection model,analyze the financial indicators and transaction data of listed companies in detail via the big data statistical test method,and to...This article attempted to construct a multi-factor quantitative stock selection model,analyze the financial indicators and transaction data of listed companies in detail via the big data statistical test method,and to find out the alpha excess return relative to the market in the case of short stock index futures as a hedge in the Chinese market.展开更多
Securities investment fund is one of the most important institutional investors in the securities market. The quality of the management of the securities investment fund by the fund management company not only directl...Securities investment fund is one of the most important institutional investors in the securities market. The quality of the management of the securities investment fund by the fund management company not only directly affects its survival and development,but also plays a decisive role in the stable operation of securities market and even the macroeconomy. As China is in rapid development of funds,scientific and effective investment strategies will be the only road for the long-term development of wellestablished fund management companies. We attempt to combine the value investment strategies with quantitative investment strategies to build a system of optimized investment strategies with the use of analytic hierarchy process so as to improve business performance of fund management companies and to foster the concept of value investment. Moreover,it possesses certain significance in enriching investment strategies for securities investment fund and promoting the healthy growth of the Chinese securities investment fund.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
Decision-making of investors at the stock exchange can be based on the fundamental indicators of stocks, on the technical indicators, or can exist as a combination of these two methods. The paper gives emphasis to the...Decision-making of investors at the stock exchange can be based on the fundamental indicators of stocks, on the technical indicators, or can exist as a combination of these two methods. The paper gives emphasis to the domain of technical analysis. In the broader sense the technical analysis enables the dynamics of the expected future values of the shares estimation. This can be performed on the basis of the data on historical trends of the revenues, profits and other indicators from the balance sheet, but also on the basis of historical data on changes in the values of the shares. Companies generally belong to the different sectors that have different presumptions of development resulting from the global market trends, technology and other characteristic. Processing of historical data values of the outstanding shares of the Zagreb Stock Exchange （ZSE） is origination of this research. Investors are interested to know the estimation of future returns for the stocks as well as the size of the risk associated with the expected returns. Research task in this paper is finding the optimal portfolio at the ZSE based on the concept of dominant portfolio by Markowitz approach. The portfolio is created by solving non-linear programming problem using the common software tools. The results of obtained optimal portfolios contain relevant conclusions about the specifics of the shares as well as the characteristics of the industrial sectors but also provide a further knowledge about diverse sectors treatment at the stock exchange in a multi-year period.展开更多
This paper uses unique data on the shareholdings of both institutional and individual investors to directly investigate whether institutional investors have better stock selection ability than individual investors in ...This paper uses unique data on the shareholdings of both institutional and individual investors to directly investigate whether institutional investors have better stock selection ability than individual investors in China.Controlling for other factors,we find that institutional investors increase(decrease)their shareholdings in stocks that subsequently exhibit positive(negative)short-and long-term cumulative abnormal returns.In contrast individual investors decrease(increase)their shareholdings in stocks that subsequently exhibit positive(negative)short-and long-term cumulative abnormal returns.These findings indicate that institutional investors have superior stock selection ability in China.展开更多
基金funded by The University of Groningen and Prospect Burma organization.
文摘In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price predictions.Several review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market forecasting.However,no survey study has explored feature selection and extraction techniques for stock market forecasting.This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications.We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022.We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles.We also describe the combination of feature analysis techniques and ML methods and evaluate their performance.Moreover,we present other survey articles,stock market input and output data,and analyses based on various factors.We find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.
文摘Quantitative stock selection has become a research hotspot in the field of investment decision. As the data mining technology becomes mature, quantitative stock selection has made great progress. From the perspective of value investment, this paper selects top 200 stocks of A share in terms of market value. With the random forest (RF), financial characteristic variables with significant impact on SVR are screened out. At the same time with quantum genetic algorithm (QGA) superior to the traditional genetic algorithm (GA), SVR parameters are deeply and dynamically sought for, so as to build the RF-QGA-SVR model for year-to-year stock ranking. The quantitative stock selection model is built, and the empirical analysis of its stock selection performance is conducted. The conclusion is as follows: 1) Optimizing SVR with QGA has higher precision than the traditional genetic algorithm, and is more excellent than the traditional GA optimization;2) SVR after RF optimization of characteristic variables more significantly improves the accuracy of stock ranking and prediction;3) In the stock ranking obtained from the RF-QGA-SVR model, the yields of top stock portfolios are much higher than the market benchmark yield. At the same time, the yields of the top 10 stock portfolios are the highest, and the top 30 stock portfolios are the most stable. This study has positive reference significance on quantitative stock selection in the field of quantitative investment.
文摘Forecasting stock returns is extremely challenging in general,and this task becomes even more difficult given the turbulent nature of the Chinese stock market.We address the stock selection process as a statistical learning problem and build crosssectional forecast models to select individual stocks in the Shanghai Composite Index.Decile portfolios are formed according to rankings of the forecasted future cumulative returns.The equity market’s neutral portfolio-formed by buying the top decile portfolio and selling short the bottom decile portfolio-exhibits superior performance to,and a low correlation with,the Shanghai Composite Index.To make our strategy more useful to practitioners,we evaluate the proposed stock selection strategy’s performance by allowing only long positions,and by investing only in Ashare stocks to incorporate the restrictions in the Chinese stock market.The longonly strategies still generate robust and superior performance compared to the Shanghai Composite Index.A close examination of the coefficients of the features provides more insights into the changes in market dynamics from period to period.
基金Supported by National Natural Science Foundation of China(11961005)Guangdong Province General University Characteristic Innovation Project(2018KTSCX253).
文摘This article attempted to construct a multi-factor quantitative stock selection model,analyze the financial indicators and transaction data of listed companies in detail via the big data statistical test method,and to find out the alpha excess return relative to the market in the case of short stock index futures as a hedge in the Chinese market.
文摘Securities investment fund is one of the most important institutional investors in the securities market. The quality of the management of the securities investment fund by the fund management company not only directly affects its survival and development,but also plays a decisive role in the stable operation of securities market and even the macroeconomy. As China is in rapid development of funds,scientific and effective investment strategies will be the only road for the long-term development of wellestablished fund management companies. We attempt to combine the value investment strategies with quantitative investment strategies to build a system of optimized investment strategies with the use of analytic hierarchy process so as to improve business performance of fund management companies and to foster the concept of value investment. Moreover,it possesses certain significance in enriching investment strategies for securities investment fund and promoting the healthy growth of the Chinese securities investment fund.
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.
文摘Decision-making of investors at the stock exchange can be based on the fundamental indicators of stocks, on the technical indicators, or can exist as a combination of these two methods. The paper gives emphasis to the domain of technical analysis. In the broader sense the technical analysis enables the dynamics of the expected future values of the shares estimation. This can be performed on the basis of the data on historical trends of the revenues, profits and other indicators from the balance sheet, but also on the basis of historical data on changes in the values of the shares. Companies generally belong to the different sectors that have different presumptions of development resulting from the global market trends, technology and other characteristic. Processing of historical data values of the outstanding shares of the Zagreb Stock Exchange （ZSE） is origination of this research. Investors are interested to know the estimation of future returns for the stocks as well as the size of the risk associated with the expected returns. Research task in this paper is finding the optimal portfolio at the ZSE based on the concept of dominant portfolio by Markowitz approach. The portfolio is created by solving non-linear programming problem using the common software tools. The results of obtained optimal portfolios contain relevant conclusions about the specifics of the shares as well as the characteristics of the industrial sectors but also provide a further knowledge about diverse sectors treatment at the stock exchange in a multi-year period.
文摘This paper uses unique data on the shareholdings of both institutional and individual investors to directly investigate whether institutional investors have better stock selection ability than individual investors in China.Controlling for other factors,we find that institutional investors increase(decrease)their shareholdings in stocks that subsequently exhibit positive(negative)short-and long-term cumulative abnormal returns.In contrast individual investors decrease(increase)their shareholdings in stocks that subsequently exhibit positive(negative)short-and long-term cumulative abnormal returns.These findings indicate that institutional investors have superior stock selection ability in China.
