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
Ⅰ. Estimated Macro-economic Environment for 1996 1996 is the first year of the 9th Five-Year Plan, the state will further deepen its system reforms and inject new vitality toward economic development. Economic growth...Ⅰ. Estimated Macro-economic Environment for 1996 1996 is the first year of the 9th Five-Year Plan, the state will further deepen its system reforms and inject new vitality toward economic development. Economic growth will maintain balanced, inflation will be more展开更多
During the past few decades, time series analysis has become one popular method for solving stock forecasting problem. However, depending only on stock index series makes the performance of the forecast not good enoug...During the past few decades, time series analysis has become one popular method for solving stock forecasting problem. However, depending only on stock index series makes the performance of the forecast not good enough, because many external factors which may be involved are not taken into consideration. As a way to deal with it, sentiment analysis on online textual data of stock market can generate a lot of valuable information as a complement which can be named as external indicators. In this paper, a new method which combines the time series of external indicators and the time series of stock index is provided. A special text processing algorithm is proposed to obtain a weighted sentiment time series. In the experiment, we obtain financial micro-blogs from some famous portal websites in China. After that, each micro-blog is segmented and preprocessed, and then the sentiment value is calculated for each day. Finally, an NARX time series model combined with the weighted sentiment series is created to forecast the future value of Shanghai Stock Exchange Composite Index(SSECI).The experiment shows that the new model makes an improvement in terms of the accuracy.展开更多
According to the statistics of the Ministry of Agriculture,the planting area of citrus would increase steadily,and the yield would decline slightly,2. 556 7 million ha and 36. 168 million t,respectively. Compared with...According to the statistics of the Ministry of Agriculture,the planting area of citrus would increase steadily,and the yield would decline slightly,2. 556 7 million ha and 36. 168 million t,respectively. Compared with 2015,the planting area would increase by 1. 97% and the yield would increase by 1. 17%. According to the production scheduling of Chongqing Agricultural Commission,the citrus production in Chongqing in 2016 would continue to maintain a steady and rapid growth,the estimated area and yield were 0. 206 7 million ha and 2. 8 million t,increasing by 4. 27% and 4. 48% compared with 2015 respectively. By the end of November 2016,most of mature citrus products in Chongqing would show different degree of rise in purchasing price,while the purchasing price of red orange and some processed raw material fruits would show different amplitude of decline. On the whole,the production and marketing situation of Chongqing citrus would become better.展开更多
Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent t...Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.展开更多
In this paper, the adaptive forecast and control of the market economic system with fuzzy inputs is discussed. A new method which is adapted for the adaptive forecast and control of this kind of system is introduced. ...In this paper, the adaptive forecast and control of the market economic system with fuzzy inputs is discussed. A new method which is adapted for the adaptive forecast and control of this kind of system is introduced. Through a living example the better result is explained concretly.展开更多
Power supply and demand inJanuary-September, 2007Since 2007, the national economy developed continu-ously, showing a situation of rapid growth, more optimizedstructure, increased efficiency and improvement of people&#...Power supply and demand inJanuary-September, 2007Since 2007, the national economy developed continu-ously, showing a situation of rapid growth, more optimizedstructure, increased efficiency and improvement of people'slivelihood. In the first three quarters, GDP achieved 16.6043trillion Yuan, and its year-on-year growth rate was 11.5%;展开更多
In recent 10 years, global NdFeB magnetic materials industry develops at the increasing speed over 20% every year, which strongly stimulates the fast production improvement of neodymia and neodymium metal. Thereinto, ...In recent 10 years, global NdFeB magnetic materials industry develops at the increasing speed over 20% every year, which strongly stimulates the fast production improvement of neodymia and neodymium metal. Thereinto, production of Chinese NdFeB enhances the most rapidly. In 2004, output of Chinese sintered NdFeB reached 25,000 tons, up 82.5% over previous year. 1. 2004 Chinese Neodymia Production (1) Production of Southern Ore According to statistics, total 30,000 tons of展开更多
This paper is intended to forecast the demand of the car market and the quantity of car possession in our country in 2000 by means of two statistics methods, i.e. tendency inference and regression analysis, and then i...This paper is intended to forecast the demand of the car market and the quantity of car possession in our country in 2000 by means of two statistics methods, i.e. tendency inference and regression analysis, and then its future growth tendency and market demand in our country are analyzed, according to the strategy requirement and the actual facts of our car industry development.展开更多
In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper intr...In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.展开更多
Stocks that are fundamentally connected with each other tend to move together.Considering such common trends is believed to benefit stock movement forecasting tasks.However,such signals are not trivial to model becaus...Stocks that are fundamentally connected with each other tend to move together.Considering such common trends is believed to benefit stock movement forecasting tasks.However,such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data.Motivated by this observation,we propose a framework that incorporates the inter-connection of firms to forecast stock prices.To effectively utilize a large set of fundamental features,we further design a novel pipeline.First,we use variational autoencoder(VAE)to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure(fundamentally clustering).Second,a hybrid model of graph convolutional network and long-short term memory network(GCN-LSTM)with an adjacency graph matrix(learnt from VAE)is proposed for graph-structured stock market forecasting.Experiments on minute-level U.S.stock market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline methods.The proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.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Ⅰ. Estimated Macro-economic Environment for 1996 1996 is the first year of the 9th Five-Year Plan, the state will further deepen its system reforms and inject new vitality toward economic development. Economic growth will maintain balanced, inflation will be more
基金the National Natural Science Foundation of China(No.61375053)
文摘During the past few decades, time series analysis has become one popular method for solving stock forecasting problem. However, depending only on stock index series makes the performance of the forecast not good enough, because many external factors which may be involved are not taken into consideration. As a way to deal with it, sentiment analysis on online textual data of stock market can generate a lot of valuable information as a complement which can be named as external indicators. In this paper, a new method which combines the time series of external indicators and the time series of stock index is provided. A special text processing algorithm is proposed to obtain a weighted sentiment time series. In the experiment, we obtain financial micro-blogs from some famous portal websites in China. After that, each micro-blog is segmented and preprocessed, and then the sentiment value is calculated for each day. Finally, an NARX time series model combined with the weighted sentiment series is created to forecast the future value of Shanghai Stock Exchange Composite Index(SSECI).The experiment shows that the new model makes an improvement in terms of the accuracy.
基金Supported by Modern Agricultural Technology System with Characteristic Benefit for Late-maturing Citrus in Chongqing Municipality
文摘According to the statistics of the Ministry of Agriculture,the planting area of citrus would increase steadily,and the yield would decline slightly,2. 556 7 million ha and 36. 168 million t,respectively. Compared with 2015,the planting area would increase by 1. 97% and the yield would increase by 1. 17%. According to the production scheduling of Chongqing Agricultural Commission,the citrus production in Chongqing in 2016 would continue to maintain a steady and rapid growth,the estimated area and yield were 0. 206 7 million ha and 2. 8 million t,increasing by 4. 27% and 4. 48% compared with 2015 respectively. By the end of November 2016,most of mature citrus products in Chongqing would show different degree of rise in purchasing price,while the purchasing price of red orange and some processed raw material fruits would show different amplitude of decline. On the whole,the production and marketing situation of Chongqing citrus would become better.
文摘Electricity price forecasting has become an important aspect of promoting competition and safeguarding the interests of participants in electricity market. As market participants, both producers and consumers intent to contribute more efforts on developing appropriate price forecasting scheme to maximize their profits. This paper introduces a time series method developed by Box-Jenkins that applies autoregressive integrated moving average (ARIMA) model to address a best-fitted time-domain model based on a time series of historical price data. Using the model’s parameters determined from the stationarized time series of prices, the price forecasts in UK electricity market for 1 step ahead are estimated in the next day and the next week. The most suitable models are selected for them separately after comparing their prediction outcomes. The data of historical prices are obtained from UK three-month Reference Price Data from April 1st to July7th 2010.
文摘In this paper, the adaptive forecast and control of the market economic system with fuzzy inputs is discussed. A new method which is adapted for the adaptive forecast and control of this kind of system is introduced. Through a living example the better result is explained concretly.
文摘Power supply and demand inJanuary-September, 2007Since 2007, the national economy developed continu-ously, showing a situation of rapid growth, more optimizedstructure, increased efficiency and improvement of people'slivelihood. In the first three quarters, GDP achieved 16.6043trillion Yuan, and its year-on-year growth rate was 11.5%;
文摘In recent 10 years, global NdFeB magnetic materials industry develops at the increasing speed over 20% every year, which strongly stimulates the fast production improvement of neodymia and neodymium metal. Thereinto, production of Chinese NdFeB enhances the most rapidly. In 2004, output of Chinese sintered NdFeB reached 25,000 tons, up 82.5% over previous year. 1. 2004 Chinese Neodymia Production (1) Production of Southern Ore According to statistics, total 30,000 tons of
文摘This paper is intended to forecast the demand of the car market and the quantity of car possession in our country in 2000 by means of two statistics methods, i.e. tendency inference and regression analysis, and then its future growth tendency and market demand in our country are analyzed, according to the strategy requirement and the actual facts of our car industry development.
文摘In power market, electricity price forecasting provides significant information which can help the electricity market participants to prepare corresponding bidding strategies to maximize their profits. This paper introduces the models of autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) which are applied to the price forecasts for up to 3 steps 8 weeks ahead in the UK electricity market. The half hourly data of historical prices are obtained from UK Reference Price Data from March 22nd to July 14th 2010 and the predictions are derived from a sliding training window with a length of 8 weeks. The ARIMA with various AR and MA orders and the ANN with different numbers of delays and neurons have been established and compared in terms of the root mean square errors (RMSEs) of price forecasts. The experimental results illustrate that the ARIMA (4,1,2) model gives greater improvement over persistence than the ANN (20 neurons, 4 delays) model.
文摘Stocks that are fundamentally connected with each other tend to move together.Considering such common trends is believed to benefit stock movement forecasting tasks.However,such signals are not trivial to model because the connections among stocks are not physically presented and need to be estimated from volatile data.Motivated by this observation,we propose a framework that incorporates the inter-connection of firms to forecast stock prices.To effectively utilize a large set of fundamental features,we further design a novel pipeline.First,we use variational autoencoder(VAE)to reduce the dimension of stock fundamental information and then cluster stocks into a graph structure(fundamentally clustering).Second,a hybrid model of graph convolutional network and long-short term memory network(GCN-LSTM)with an adjacency graph matrix(learnt from VAE)is proposed for graph-structured stock market forecasting.Experiments on minute-level U.S.stock market data demonstrate that our model effectively captures both spatial and temporal signals and achieves superior improvement over baseline methods.The proposed model is promising for other applications in which there is a possible but hidden spatial dependency to improve time-series prediction.
