As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitat...As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.展开更多
This study maps the academic literature on Stock Price Forecasting with Long-Term Memory Artificial Neural Networks—RNA LSTM. The objective is to know if it is suitable for time series studies, especially for stock p...This study maps the academic literature on Stock Price Forecasting with Long-Term Memory Artificial Neural Networks—RNA LSTM. The objective is to know if it is suitable for time series studies, especially for stock price projection. Through bibliometric analysis and systematic literature review, it is observed that 333 authors wrote on the topic between 2018 and March 2022, and the journals Expert Systems with Applications, IEEE Access, Big Data Journal and Neural Computing and Applications, published the most relevant articles. Of the 99 articles published in this period, 43 are associated with Chinese institutions, the most cited being that of Kim and Won, who studies the volatility of returns and the market capitalization of South Korean stocks. The basis of 65% of the studies is the comparison between the RNN LSTM and other artificial neural networks. The daily closing price of shares is the most analyzed type of data, and the American (21%) and Chinese (20%) stock exchanges are the most studied. 57% of the studies include improvements to existing neural network models and 42% new projection models.展开更多
The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock m...The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock market’s prediction of the trend of stock prices helps in grasping the operation law of the stock market and the influence mechanism on the economy.The autoregressive integrated moving average(ARIMA)model is one of the most widely accepted and used time series forecasting models.Therefore,this paper first compares the return on investment(ROI)of Apple and Tesla,revealing that the ROI of Tesla is much greater than that of Apple,and subsequently focuses on ARIMA model’s prediction on the available time series data,thus concluding that the ARIMA model is better than the Naïve method in predicting the change in Tesla’s stock price trend.展开更多
Stock market is volatile and predicting stock prices is a challenging task.Stock prices are influenced by multiple factors,and prediction using only numerical or image features is ineffective.To solve this problem,we ...Stock market is volatile and predicting stock prices is a challenging task.Stock prices are influenced by multiple factors,and prediction using only numerical or image features is ineffective.To solve this problem,we propose a Hybrid Channel Stock model that incorporates multiple features of basic stock data,K-line charts and technical indicator factors for predicting the closing price of a stock on day n+1.The model combines multiple aspects of data and uses a multi-channel structure including improved CNN-TW,bidirectional LSTM and Transformer network.First,we construct the multi-channel branches of the multi-faceted feature fusion input network model;second,in this paper,we will use the concatenate method to stitch the output of each branch as the input of the rest of the network;the last layer in the network is the fully connected layer,which combines the linear activation function regression to output the predicted prices.Finally,we conducted extensive experiments on the Dow 30,SSH 50 and CSI100 indices.The experimental results show that the Hybrid Channel Stock method has the best performance with the smallest MSE,RMSE,MAE and MAPE compared with existing models.in addition,the experiments on different trading days validate the stability and effectiveness of the model,providing an important reference for investors to make stock investment decisions.展开更多
The new energy industry is strongly supported by the state,and accurate forecasting of stock price can lead to better understanding of its development.However,factors such as cost and ease of use of new energy,as well...The new energy industry is strongly supported by the state,and accurate forecasting of stock price can lead to better understanding of its development.However,factors such as cost and ease of use of new energy,as well as economic situation and policy environment,have led to continuous changes in its stock price and increased stock price volatility.By calculating the Lyapunov index and observing the Poincarésurface of the section,we find that the sample of the China Securities Index Green Power 50 Index has chaotic characteristics,and the data indicate strong volatility and uncertainty.This study proposes a new method of stock price index prediction,namely,EWT-S-ALOSVR.Empirical wavelet decomposition extracts features from multiple factors affecting stock prices to form multiple sub-columns with features,significantly reducing the complexity of the stock price series.Support vector regression is well suited for dealing with nonlinear stock price series,and the support vector machine model parameters are selected using random wandering and picking elites via Ant Lion Optimization,making stock price prediction more accurate.展开更多
Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics.At the same time,stock price forecasting that supports trading strategies is considered one ...Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics.At the same time,stock price forecasting that supports trading strategies is considered one of the most challenging tasks.Therefore,this study analyzes and extracts news media data,expert comments,social opinion data,and pandemic text data using natural language processing,and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices.An interval constraint-based trading strategy is constructed.Using data from several typical stocks in the Chinese stock market during the COVID-19 period,the empirical studies and trading simulations show,first,that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting.Second,the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus,can assist investors in decision-making.展开更多
Stock price fluctuation and prediction is a problem that has attracted much attention.There exist many mathematical and statistical problems behind it.In essence,the key to solving this problem lies in capturing the l...Stock price fluctuation and prediction is a problem that has attracted much attention.There exist many mathematical and statistical problems behind it.In essence,the key to solving this problem lies in capturing the linear and nonlinear characteristics in the time series to predict future price movements.This study investigates the predictive capabilities of two distinct methodologies—Long Short-Term Memory(LSTM)networks and Autoregressive Integrated Moving Average(ARIMA)models—using Apple Inc.(AAPL)stock price data spanning 2016 to 2024.By synthesizing theoretical frameworks with empirical analysis,the research evaluates how each model captures linear trends and nonlinear fluctuations,ultimately proposing a hybrid ARIMA-LSTM architecture to enhance forecasting accuracy.Finally,according to the principal characteristics of the two models,the ARIMA-LSTM hybrid model is constructed.The results show that the hybrid model significantly outperforms single models in terms of RMSE and directional accuracy.Combined with error distribution visualization and volatility analysis,the hybrid model demonstrates efficient performance in achieving prediction optimization through the decomposition of linear and nonlinear components.It provides a new methodological perspective for financial time series modeling.展开更多
In view of the breakthrough progress of the depth learning method in image and other fields,this paper attempts to introduce the depth learning method into stock price forecasting to provide investors with reasonable ...In view of the breakthrough progress of the depth learning method in image and other fields,this paper attempts to introduce the depth learning method into stock price forecasting to provide investors with reasonable investment suggestions.This paper proposes a stock prediction hybrid model named ISI-CNN-LSTM considering investor sentiment based on the combination of long short-term memory(LSTM) and convolutional neural network(CNN).The model adopts an end-to-end network structure,using LSTM to extract the temporal features in the data and CNN to mine the deep features in the data can effectively improve the prediction ability of the model by increasing investor sentiment in the network structure.The empirical part makes a comparative experimental analysis based on Shanghai stock index in China.By comparing the experimental prediction results and evaluation indicators,it verifies the prediction effectiveness and feasibility of ISI-CNN-LSTM network model.展开更多
文摘As financial markets grow increasingly complex and volatile,timeseriesbased stock price forecasting has become a critical research focus in the field of finance.Traditional forecasting methods face significant limitations in handling nonlinear and high-dimensional data,while neural networks(NNs)have demonstrated great potential due to their powerful feature extraction and pattern recognition capabilities.Although several existing surveys discuss the applications of NNs in stock forecasting,they often lack a detailed examination of models that use time-series data as input and fail to cover the latest research developments.In response,this paper reviews relevant literature from 2015 to 2025 and classifies timeseriesbased stock forecasting methods into four categories:NNs,recurrent NNs(RNNs),convolutional NNs(CNNs),Transformers and other models.We analyze their performance under different market conditions,highlight strengths and limitations,and identify recent trends in model design.Our findings show that hybrid architectures and attention-based models consistently achieve superior forecasting stability and adaptability across volatile market scenarios.This survey offers a systematic reference for researchers and practitioners and outlines promising future research directions.
文摘This study maps the academic literature on Stock Price Forecasting with Long-Term Memory Artificial Neural Networks—RNA LSTM. The objective is to know if it is suitable for time series studies, especially for stock price projection. Through bibliometric analysis and systematic literature review, it is observed that 333 authors wrote on the topic between 2018 and March 2022, and the journals Expert Systems with Applications, IEEE Access, Big Data Journal and Neural Computing and Applications, published the most relevant articles. Of the 99 articles published in this period, 43 are associated with Chinese institutions, the most cited being that of Kim and Won, who studies the volatility of returns and the market capitalization of South Korean stocks. The basis of 65% of the studies is the comparison between the RNN LSTM and other artificial neural networks. The daily closing price of shares is the most analyzed type of data, and the American (21%) and Chinese (20%) stock exchanges are the most studied. 57% of the studies include improvements to existing neural network models and 42% new projection models.
文摘The stock market is an important economic information center.The economic benefits generated by stock price prediction have attracted much attention.Although the stock market cannot be predicted accurately,the stock market’s prediction of the trend of stock prices helps in grasping the operation law of the stock market and the influence mechanism on the economy.The autoregressive integrated moving average(ARIMA)model is one of the most widely accepted and used time series forecasting models.Therefore,this paper first compares the return on investment(ROI)of Apple and Tesla,revealing that the ROI of Tesla is much greater than that of Apple,and subsequently focuses on ARIMA model’s prediction on the available time series data,thus concluding that the ARIMA model is better than the Naïve method in predicting the change in Tesla’s stock price trend.
基金supported by these three foundation programs:the Science and Technology Research Project(Youth)of Chongqing Municipal Education Commission(KJQN202201142)the Chongqing Research Program of Basic Research Frontier Technology(CSTB2022BSXM-JCX0069CCCC)the Training Program of the National Natural Science Foundation of China and National Social Science Fund of China of Chongqing University of Technology(2022PYZ030)。
文摘Stock market is volatile and predicting stock prices is a challenging task.Stock prices are influenced by multiple factors,and prediction using only numerical or image features is ineffective.To solve this problem,we propose a Hybrid Channel Stock model that incorporates multiple features of basic stock data,K-line charts and technical indicator factors for predicting the closing price of a stock on day n+1.The model combines multiple aspects of data and uses a multi-channel structure including improved CNN-TW,bidirectional LSTM and Transformer network.First,we construct the multi-channel branches of the multi-faceted feature fusion input network model;second,in this paper,we will use the concatenate method to stitch the output of each branch as the input of the rest of the network;the last layer in the network is the fully connected layer,which combines the linear activation function regression to output the predicted prices.Finally,we conducted extensive experiments on the Dow 30,SSH 50 and CSI100 indices.The experimental results show that the Hybrid Channel Stock method has the best performance with the smallest MSE,RMSE,MAE and MAPE compared with existing models.in addition,the experiments on different trading days validate the stability and effectiveness of the model,providing an important reference for investors to make stock investment decisions.
基金Key Research Project in Universities of Henan Province(No.24B480012)Science and Technology of Henan Province of China(No.182400410419)+1 种基金the Foundation for Fostering the National Foundation of Pingdingshan University(No.PXY-PYJJ-2016006)National Science and Technology Council,Taiwan(MOST 111-2410-H-161-001).
文摘The new energy industry is strongly supported by the state,and accurate forecasting of stock price can lead to better understanding of its development.However,factors such as cost and ease of use of new energy,as well as economic situation and policy environment,have led to continuous changes in its stock price and increased stock price volatility.By calculating the Lyapunov index and observing the Poincarésurface of the section,we find that the sample of the China Securities Index Green Power 50 Index has chaotic characteristics,and the data indicate strong volatility and uncertainty.This study proposes a new method of stock price index prediction,namely,EWT-S-ALOSVR.Empirical wavelet decomposition extracts features from multiple factors affecting stock prices to form multiple sub-columns with features,significantly reducing the complexity of the stock price series.Support vector regression is well suited for dealing with nonlinear stock price series,and the support vector machine model parameters are selected using random wandering and picking elites via Ant Lion Optimization,making stock price prediction more accurate.
基金partly supported by the National Natural Science Foundation of China under Grants No.72171223,No.71801213,and No.71988101the National Key R&D Program of China under Grants No.2021ZD0111204。
文摘Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics.At the same time,stock price forecasting that supports trading strategies is considered one of the most challenging tasks.Therefore,this study analyzes and extracts news media data,expert comments,social opinion data,and pandemic text data using natural language processing,and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices.An interval constraint-based trading strategy is constructed.Using data from several typical stocks in the Chinese stock market during the COVID-19 period,the empirical studies and trading simulations show,first,that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting.Second,the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus,can assist investors in decision-making.
文摘Stock price fluctuation and prediction is a problem that has attracted much attention.There exist many mathematical and statistical problems behind it.In essence,the key to solving this problem lies in capturing the linear and nonlinear characteristics in the time series to predict future price movements.This study investigates the predictive capabilities of two distinct methodologies—Long Short-Term Memory(LSTM)networks and Autoregressive Integrated Moving Average(ARIMA)models—using Apple Inc.(AAPL)stock price data spanning 2016 to 2024.By synthesizing theoretical frameworks with empirical analysis,the research evaluates how each model captures linear trends and nonlinear fluctuations,ultimately proposing a hybrid ARIMA-LSTM architecture to enhance forecasting accuracy.Finally,according to the principal characteristics of the two models,the ARIMA-LSTM hybrid model is constructed.The results show that the hybrid model significantly outperforms single models in terms of RMSE and directional accuracy.Combined with error distribution visualization and volatility analysis,the hybrid model demonstrates efficient performance in achieving prediction optimization through the decomposition of linear and nonlinear components.It provides a new methodological perspective for financial time series modeling.
基金Supported by Open Research Fund of the State Key Laboratory for Management and Control of Complex Systems(20200103)Doctoral Research Start-Up Fund of Anhui University of Finance&Economics(85051)。
文摘In view of the breakthrough progress of the depth learning method in image and other fields,this paper attempts to introduce the depth learning method into stock price forecasting to provide investors with reasonable investment suggestions.This paper proposes a stock prediction hybrid model named ISI-CNN-LSTM considering investor sentiment based on the combination of long short-term memory(LSTM) and convolutional neural network(CNN).The model adopts an end-to-end network structure,using LSTM to extract the temporal features in the data and CNN to mine the deep features in the data can effectively improve the prediction ability of the model by increasing investor sentiment in the network structure.The empirical part makes a comparative experimental analysis based on Shanghai stock index in China.By comparing the experimental prediction results and evaluation indicators,it verifies the prediction effectiveness and feasibility of ISI-CNN-LSTM network model.
