Stock Trend Prediction Research Articles

Digger-Guider: High-Frequency Factor Extraction for Stock Trend Prediction

Digger-Guider: High-Frequency Factor Extraction for Stock Trend Prediction

Enhancing Portfolio Optimization: A Two-Stage Approach with Deep Learning and Portfolio Optimization

The portfolio selection problem has been a central focus in financial research. A complete portfolio selection process includes two stages: stock pre-selection and portfolio optimization. However, most existing studies focus on portfolio optimization, often overlooking stock pre-selection. To address this problem, this paper presents a novel two-stage approach that integrates deep learning with portfolio optimization. In the first stage, we develop a stock trend prediction model for stock pre-selection called the AGC-CNN model, which leverages a convolutional neural network (CNN), self-attention mechanism, Graph Convolutional Network (GCN), and k-reciprocal nearest neighbors (k-reciprocal NN). Specifically, we utilize a CNN to capture individual stock information and a GCN to capture relationships among stocks. Moreover, we incorporate the self-attention mechanism into the GCN to extract deeper data features and employ k-reciprocal NN to enhance the accuracy and robustness of the graph structure in the GCN. In the second stage, we employ the Global Minimum Variance (GMV) model for portfolio optimization, culminating in the AGC-CNN+GMV two-stage approach. We empirically validate the proposed two-stage approach using real-world data through numerical studies, achieving a roughly 35% increase in Cumulative Returns compared to portfolio optimization models without stock pre-selection, demonstrating its robust performance in the Average Return, Sharp Ratio, Turnover-adjusted Sharp Ratio, and Sortino Ratio.

A Novel NLP-based Stock Market Price Prediction and Risk Analysis Framework

Stock market prediction is an interesting and complex problem that has recently been in the limelight, thanks to the significant accuracy achieved by deep learning models. However, a complete platform with prediction and risk analysis ability is unavailable. In the current work, we present a novel framework for investment analysis designed to create ease for investors and provide a confidence measure along with the stock price to depict the risk involved in investing in stocks of a particular company. The model integrates two different approaches successfully to improve accuracy significantly. The model inputs two sources – a stock price dataset depicting the original scores as numerals and textual data extracted from Reddit news articles. The traditional problem of stock price prediction is dealt with using LSTMs on individual stock prices. At the same time, the confidence is represented by a risk value calculated intelligently using XGBoost and LSTM output. We have deployed natural language processing techniques for performing sentiment and subjectivity analyses, which are then used to extract features for further investigation in the study. The results show that an accuracy of 94% for stock trend prediction can be achieved using PCA as the feature extractor with tuned parameters for XGBoost and around 76% accuracy for stock price prediction with a tuned LSTM. It removes the hassle for investors to research the project or company they want to invest in and provides all relevant analysis and data.

Technology Company Stock Trend Prediction Based on ARIMA Model

With the development of finance, more and more people are paying attention to stock price prediction, hoping to gain economic benefits from it. From the literature review, due to the complexity of stock trends, it is necessary to study the analysis of industry stocks. This paper studies the effectiveness of the autoregressive moving average model (ARIMA) for 5 stocks in the technology industry. Then, we will compare and parameterize the ARIMA model by using the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). After the data processing, we find that the development prospects of four head technology companies have steadily increased due to years of economic accumulation and stable operation. However, as a technology company that has emerged in recent years, OpenAI is extremely susceptible to external factors such as academic fraud and AI replacing manual labor. Its stock trend has fluctuated greatly and its prospects are sluggish.

Opinion mining for stock trend prediction using deep learning

Opinion mining for stock trend prediction using deep learning

Optimized Deep Learning Model Architecture for the Feature Extraction to Predict Trend in Stock Market

Predicting stock trends is a complex task influenced by various factors such as market sentiment, economic indicators, and company performance. Analysts often employ technical analysis, studying historical price patterns and trading volumes, as well as fundamental analysis, assessing financial statements and industry trends. Deep Learning models have also gained popularity for predicting stock trends, using algorithms to identify patterns and relationships in large datasets. Deep learning algorithms, particularly neural networks, excel at recognizing intricate patterns and relationships within complex datasets, making them well-suited for predicting stock prices, identifying trends, and managing risk. Hence, this paper proposed a Bird Swarm Optimization ARIMA LSTM (BSO-ARIMA-DL) model for stock trend prediction. The proposed BSO-ARIMA-DL model performance is applied in the company datasets Apple, Amazon, and Infosys for stock trend prediction. With the proposed BSO-ARIMA-DL model features are optimized for the identification of features in the dataset for the evaluation of optimal features. Upon the estimation of features, the ARIMA model with the LSTM architecture is implemented for the stock trend analysis. The proposed BSO-ARIMA-DL model deep learning model is implemented for the stock trend prediction in the companies. The results demonstrated that the proposed BSO-ARIMA-DL model exhibits a minimal error of ~10% minimal to the conventional ARIMA model.

Predicting Stock Trends Using Web Semantics and Feature Fusion

Stock data are characterized by high dimensionality and sparsity, making stock trend prediction highly challenging. Although the Light Gradient Boosting Machine (LightGBM), based on web semantics, excels at capturing global features and efficiently performs in stock trend prediction, it does not consider the issue of declining prediction performance caused by the changing distribution of stock data over time (concept drift phenomenon). Accordingly, this work introduces the Convolutional Neural Network (CNN) into the prediction model to leverage its ability to effectively capture local features. Additionally, local features are combined with global features to obtain a comprehensive set of feature information. Lastly, the model processes new data in real-time, continuously learns new knowledge, updates model parameters, and effectively addresses the decline in model performance caused by concept drift. Experimental results demonstrate that the proposed model outperforms other models indicating its ability to efficiently perform well in stock trend prediction.

Correction to: MFDAT: a stock trend prediction of the doublegraph attention network based on multisource information fusion

Correction to: MFDAT: a stock trend prediction of the doublegraph attention network based on multisource information fusion

Research on stock trend prediction based on grey wolf optimized RF and SVM models

The volatility of the stock market has a profound impact on the stability of the financial market, and then has a significant effect on the overall economic situation, so the accurate prediction of stock trend has always been the focus of research in the financial field. In this paper, the raw minute-level stock data of SSE 50 and CSI 300 in 2021 are normalized, and the historical training and test sets of daily stocks for model training and testing are established based on the datasets. The RF-SVM model based on the gray wolf optimization algorithm is constructed to compare the traditional support vector machine and random forest model, and the relevant performance indexes are calculated. In this study, the RF-SVM model based on gray wolf optimization is highly correlated with the characteristics of stock data, and the prediction accuracy of stock index data is more than 85%. The cross-validation score is over 0.77 and the overall weighted score is as high as 0.98. Compared with the SVM and random forest models under the default parameters, the RF-SVM model based on gray wolf optimization has an average advantage of 25% in performance indicators. The generalization ability is increased by about 26%; The fitting error was reduced by about 51.15%; The overall average score advantage is about 34%. These results prove that the RF-SVM model of gray wolf optimization has superior performance and significant optimization effect in stock prediction, and compared with the traditional grid search optimization algorithm, gray wolf optimization has excellent adaptability and can gradually obtain the global optimal parameter configuration.

Stock Trend Prediction with Machine Learning: Incorporating Inter-Stock Correlation Information through Laplacian Matrix

Predicting stock trends in financial markets is of significant importance to investors and portfolio managers. In addition to a stock’s historical price information, the correlation between that stock and others can also provide valuable information for forecasting future returns. Existing methods often fall short of straightforward and effective capture of the intricate interdependencies between stocks. In this research, we introduce the concept of a Laplacian correlation graph (LOG), designed to explicitly model the correlations in stock price changes as the edges of a graph. After constructing the LOG, we will build a machine learning model, such as a graph attention network (GAT), and incorporate the LOG into the loss term. This innovative loss term is designed to empower the neural network to learn and leverage price correlations among different stocks in a straightforward but effective manner. The advantage of a Laplacian matrix is that matrix operation form is more suitable for current machine learning frameworks, thus achieving high computational efficiency and simpler model representation. Experimental results demonstrate improvements across multiple evaluation metrics using our LOG. Incorporating our LOG into five base machine learning models consistently enhances their predictive performance. Furthermore, backtesting results reveal superior returns and information ratios, underscoring the practical implications of our approach for real-world investment decisions. Our study addresses the limitations of existing methods that miss the correlation between stocks or fail to model correlation in a simple and effective way, and the proposed LOG emerges as a promising tool for stock returns prediction, offering enhanced predictive accuracy and improved investment outcomes.

MF-DAT: a stock trend prediction of the double-graph attention network based on multisource information fusion

Stock forecasting research, which aims to predict the future price movement of stocks, has been the focus of investors and scholars. This is important for practical applications related to human-centric computing and information sciences. Previous research has generally only considered market information other than the relationship between stocks, and it is challenging to learn a better representation of stock characteristics by considering the relationship between stocks. In the existing methods of combining market information with stock relationship modeling, most of them use predefined industry relationships to construct stock relationship diagrams, which inevitably ignores the potential interactions between stocks, especially the hidden relationships between stock groups. To this end, a new dual-graph attention model (MF-DAT) based on multisource information fusion is designed. Specifically, first, multiple features are fused by the LMF module, then the long-term and short-term state characteristics of stocks are learned through the first layer of the graph attention layer, and finally the node representation of the stock relationship network constructed by the mining stock cluster structure through community detection is updated. Our model takes into account both stock time-series information and potential relationships between stocks. Experiments on the S &P 500 and NASDAQ datasets show that our MF-DAT has better performance than the 8 SOTA methods that are now more popular.

Enhancing Stock Trend Prediction Using BERT-Based Sentiment Analysis and Machine Learning Techniques

Predicting stock trends with precision in the ever-evolving financial markets continues to be a formidable challenge. This research investigates an innovative approach that amalgamates the capabilities of BERT (Bidirectional Encoder Representations from Transformers) for sentiment classification (Pang et al., 2002; ?) with supervised machine learning techniques to elevate the accuracy of stock trend prediction. By harnessing the natural language processing process of BERT and its capacity to understand context and sentiment in textual data, coupled with established machine learning methodologies, we aim to provide a robust solution to the intricacies of stock market prediction. By leveraging BERT's natural language processing capabilities, we extract sentiment features from financial news articles. These sentiment scores, combined with traditional financial indicators, form a comprehensive set of features for our predictive model. We aggregate daily net sentiment, among other metrics, and demonstrate its statistically significant predictive efficacy concerning subsequent movements in the stock market. We employed a machine learning model to establish a quantitative relationship between the aggregation of daily net sentiment and trends in stock market movements. Which improved the state-of-the-art performance by 15 percentage points. This research contributes to the ongoing effort to improve stock trend prediction methods, ultimately aiding market participants in making informed investment choices.

Deep Reinforcement Learning Based Optimization and Risk Control of Trading Strategies

Deep reinforcement learning (DRL) based optimization leverages advanced machine learning techniques to solve complex decision-making problems in various domains. Deep learning in trading involves the application of sophisticated neural network architectures to analyze financial data and make predictions in stock markets, forex, commodities, and other trading domains. Deep learning algorithms, such as convolutional neural networks (CNNs) or recurrent neural networks (RNNs), traders can extract valuable insights from vast amounts of historical market data, including price movements, trading volumes, and market sentiment. These models can learn complex patterns and relationships within the data, enabling them to forecast future market trends, identify potential trading opportunities, and manage risks more effectively. Deep learning in trading has shown promise in improving decision-making processes, enhancing trading strategies, and achieving higher returns, although challenges such as data scarcity, model interpretability, and overfitting remain areas of ongoing research and development. In this paper evaluated the stock market to achieve the financial gain to achieve the significant improvement in prediction with the stock trading. The data related to the stock market are optimized with stock trend data of 7,000 stocks situated in the United States. The estimated stock trade data were computed and processed with the Multiagent Q-learning model for the data detection and classification. A deep Q-network is developed to adaptively pick distinct action subsets and train the market making agent based on the inventory states. The experimental findings demonstrate the effectiveness of our suggested methodology in feature learning and superiority in accuracy improvement when compared to deep learning-based modelling of frequency trading patterns and standard signal processing approaches for stock trend prediction.

Stock Trend Prediction and Analysis Based on Machine Learning

Stock trend prediction is a critically important activity in the financial sector. Investors aim to gain a better understanding of market dynamics, make wiser investment decisions, and manage investment risks more effectively by forecasting stock price trends. This is the focus of our upcoming research. This paper aims to utilize machine learning methods such as Support Vector Machines (SVM), based on spatial interval maximization strategies, Random Forest, based on multi-decision tree parallel learning strategies, and eXtreme Gradient Boosting (XGBoost), based on classification functions, to devise accurate classification function strategies for predicting and analyzing fluctuations in Dow Jones stock prices. Principal Component Analysis (PCA) will then be used to select some suitable features, identifying key factors with the greatest impact and minimal inter-correlation related to stock prices. Through a combination of various classification models, the research aims to forecast the future rise and fall of Dow Jones stocks. It has been observed that performing feature curation through PCA in advance enhances the predictive accuracy of different machine learning methods. Notably, the combination of PCA and XGBoost achieves the highest predictive rate. This approach provides a robust and scientifically grounded method for future stock price predictions and the formulation of trading strategies.

Stock Trend Prediction based on Wide & Deep Asymmetrical Bidirectional Legendre Memory Units

Stock Trend Prediction based on Wide & Deep Asymmetrical Bidirectional Legendre Memory Units

Stock prediction and analysis based on machine learning algorithms

The stock market has consistently remained a focal point of substantial concern for investors. Nevertheless, due to the intricate, tumultuous, and often noisy nature of the stock market, forecasting stock trends presents a formidable obstacle. To augment the accuracy of stock trend predictions, the author adopts a combination of the Long Short-Term Memory (LSTM) neural network and a noise reduction technique known as Ensemble Empirical Mode Decomposition (EEMD). This composite model is employed to develop predictions for the daily stock price increases, aiming to provide more precise insights into market behavior. The framework is capable of generating the daily stock price change trend curve based on the training outcomes. EEMD, standardization, and other data preprocessing methods can effectively reduce the noise of the stock market. In this paper, three U.S. stocks from 2010 to 2023 are chosen as the research subjects. After the training is completed, the prediction curve generated by the model closely aligns with the actual curve. Furthermore, three commonly used evaluation metrics were utilized to assess the models performance. Based on all those experimental outcomes, this model adeptly forecasts the stocks trend.

A large-scale microblog dataset and stock movement prediction based on Supervised Contrastive Learning model

The integration of Deep Neural Networks (DNN) with Natural Language Processing (NLP) technologies has opened new avenues in financial market prediction, particularly through the utilization of textual information. This study represents a significant advancement, which offers two primary contributions to stock trend prediction: (i) the exploitation of textual data (news, comments, microblogs) using advanced DNN architectures, enhancing market information utilization; (ii) significant improvement of the accuracy of predicting the direction of stock volatility by integrating textual and neural network technologies. Meanwhile, we have crawled, filtered, and constructed a large-scale microblog dataset. This dataset includes approximately 114,992 microblog textual data from 40 Science and Technology Innovation Board (STIB) companies in China during 2021. We conducted a comprehensive analysis using various DNN techniques, including Feedback Neural Networks (FNN), Supervised Contrastive Learning (SCL), Cross Entropy (CE), and Dual Contrastive Learning (DualCL), in conjunction with bag of words models, BERT, and Roberta compilers. Our findings reveal that the SCL method, when combined with microblog data, significantly increases prediction accuracy, particularly during the COVID-19 period. Furthermore, we discovered that using a cross-stock dataset enhances the accuracy of all prediction methods, and random allocation of microblog data leads to better results than sequential allocation. Additionally, we compared the efficacy of traditional models like the CAPM, three-factor, and five-factor models against neural network-based methods. Our results suggest a notable superiority of the SCL method in increasing prediction accuracy. Finally, applying our findings to real-world trading strategies, we demonstrated the practical advantages of using the SCL method in trading, evidenced by significant improvements across all performance indicators.

Proposed Bayesian Optimization Based LSTM-CNN Model for Stock Trend Prediction

Proposed Bayesian Optimization Based LSTM-CNN Model for Stock Trend Prediction

Research on Stock Trend Prediction Method in Financial Markets Based on Support Vector Machines

Research on Stock Trend Prediction Method in Financial Markets Based on Support Vector Machines

A portfolio trading system using a novel pixel graph network for stock selection and a mean-CDaR optimization for portfolio rebalancing

Due to the complexity and dynamic nature of financial markets, portfolio management tasks require continuous adaptation with market intelligence. Furthermore, to make profitable trading decisions, it is essential to predict the future performance of the market. To that end, this study proposes a dynamic portfolio trading system in two stages: stock trend prediction and portfolio optimization. In the first stage, although many researchers proposed powerful classification models to predict BUY/SELL trading signals, some scholars only focused on converting time series' features into multichannel images without considering any spatial relationships between features. Considering the relational capacity of graphs, this study proposes a novel image-based classification model, called Pixel Graph Network (PGN), to analyze the constructed images with propagating information from all neighboring pixels, build up a representation of the images as special graphs, and get advantages of a graph-based learning algorithm. In the second stage, stocks with predicted BUY trading signals in the following days, are fed into the Mean–Conditional Drawdown at Risk (M-CDaR) optimization model to prevent any significant drawdown of the investment portfolio. Moreover, a hierarchical feature selection algorithm has been proposed, combined with a continuous trend labeling method to improve the efficiency of the training process. To demonstrate the superiority of our proposed PGN model in comparison with benchmarks, 18 stocks from the New York Stock Exchange (NYSE) are used for numerical experiments. The obtained results show the improvement and effectiveness of the proposed model in both stock trend prediction and portfolio optimization. In terms of financial performance, the proposed PGN+M-CDaR portfolio-based trading system achieves an average annual return of 112%, an average annual Sortino ratio of 10.78, and an average annual Sharpe ratio of 2.79 which are promising to achieve substantial profits while mitigating downside risk.