Stock Price Prediction Research Articles

Technical Indicator based Stock Market Prediction using Recurrent Neural Network

The Indian stock market is highly volatile and complex by nature. However, notion of stock price predictability is typical, many researchers suggest that the Buy & Sell prices are predictable and investor can make above-average profits using Stock Technical Indicator (STIs).Most of the earlier prediction models predict individual stocks and the results are mostly influenced by company’s reputation, news, sentiments and other fundamental issues. In this work, architecture of project is given. As a part of prediction model the Recurrent Neural Network (RNN) Deep Learning Algorithm is used to predict future prices of Stocks Using Technical Indicators (STIs) and also buy sell signals are generated. The project will be carried on National Stock Exchange (NSE) Stocks of India.

Stock Market Prediction and Visualisation

Abstract: Stock price forecasting is a widely discussed and significant topic in both financial and academic circles. The stock market is inherently unpredictable, lacking clear rules for estimating or predicting share prices. Various methods, including technical analysis, fundamental analysis, time series analysis, and statistical analysis, have been employed to forecast stock prices. However, none of these methods consistently serve as reliable prediction tools. In this paper, we explore the implementation, prediction, and analysis of stock market prices. Artificial Neural Networks and Machine Learning prove effective for forecasting stock prices, returns, and modeling stock behavior. By conducting statistical analyses, we establish relationships between selected factors and share prices, contributing to more accurate predictions. While the stock market remains inherently uncertain, this paper aims to apply data analysis and prediction concepts to forecast stock prices. In the era of global digitization, stock market prediction has undergone significant technological advancements, transforming traditional trading models. As market capitalization continues to rise, stock trading becomes a focal point for financial investors. Analysts and researchers have developed tools and techniques to predict stock price movements, aiding decision-making. Advanced models leverage non-traditional textual data from social platforms for market prediction. Machine learning approaches, including text data analytics and ensemble methods, have significantly improved prediction accuracy. However, analyzing and predicting stock markets remains challenging due to dynamic, erratic, and chaotic data. This study explores machine learning-based approaches for stock market prediction, emphasizing a generic framework. By critically analyzing findings from the last decade (2011–2021) from digital libraries like ACM and Scopus, we provide insights for emerging researchers to delve into this promising area.

A Comparative study of the Envisaged and Definite Stock Prices of BSE SMEs Using RNN during the COVID-19 Pandemic

The stock market is unstable, but the use of machine learning algorithms allows to predict its future dynamics before spending. The most popular area of scientific research right nowadays is machine learning, which involves enabling computers to perform tasks that often require human intelligence. The purpose of this paper is to construct a model using a network of Long-Short Term Memory model (LSTM) to forecast future stock market values. The paper presents the advantages and disadvantages of machine learning for assessing and forecasting the stock market. A review of literature on the application of machine learning models in key areas of finance using methodological model assessment and data manipulation is also available. This paper focuses on the losses of the SME sector due to COVID-19 by doing a comparative study using secondary data collection between the predicted closed stock prices and actual stock prices of the BSE SME IPO index for the period from 1 January 2018 to 30 April 2021.The LSTM network of Recurrent Neural Networks (RNNs) most effective deep learning model, is used to predict stock prices. The study provides insight and direction on where lockdown has a massive impact on the stock prices of BSE SME IPOs. The authors developed a model for predicting the future value of stock in the market, the application of which gave some positive results, demonstrating the need for machine learning and how it can change the world of finance. The novelty of the study is that in India, machine learning and deep learning methods in the field of finance are used much less often than in other countries.

Stock Market Prediction Using Machine Learning Techniques

The prediction of stock market prices holds significant importance within the contemporary economic landscape. Consequently, there has been a notable surge in scholarly interest directed towards exploring novel avenues for enhancing stock market prediction capabilities. Recent research endeavors have illuminated the potential predictive value inherent in various data streams, including historical stock data and user-generated content sourced from platforms such as Twitter and web news. These investigations have revealed a discernible relationship between social mood, as reflected in online discourse, and future stock price movements. However, prior studies have often overlooked the incorporation of such sentiment-derived information, thus presenting an information gap. In the present study, we address this gap by proposing an effective methodology for the integration and analysis of multiple information sources to facilitate more accurate stock price predictions. Leveraging Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models, we conduct a comprehensive analysis of disparate data sources. Furthermore, we employ an ensemble approach, incorporating Weighted Average and Differential Evolution techniques, to refine the predictive accuracy of our models. Our findings demonstrate the efficacy of the proposed methodology in generating highly accurate stock price predictions across varying future time horizons, including one-day, seven-day, 15-day, and 30-day intervals. These predictions offer valuable insights for investors seeking to make informed decisions regarding their investment strategies and enable companies to gauge their anticipated performance within the stock market landscape.

Research on Stock Price Prediction Model Based on LSTM

Stock prices are a non-linear, long-term series of data. The research topic of this paper is to use the Long Short-Term Memory Network (LSTM) model to analyze the historical stock price data of BYD (BYD) to predict the future stock price trend, and to provide intelligent reference for investors by evaluating the model performance, aiming to improve the accuracy of stock price prediction. The accuracy and consistency of stock price prediction can be increased by using this method, which successfully takes advantage of the time characteristics in historical data. After explaining the idea and architecture of LSTM, this article creates a stock price prediction model based on it, using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) as evaluation metrics. At the same time, network structures with different layers were designed to verify the effectiveness of the model, and the experimental results were shown. The advantages and limitations of the model are highlighted, and the feasibility of deep learning in the stock financial market and the credibility and advantages of strategic decision-making are demonstrated.

Predicting Google Stock Prices Using Machine Learning Models

Stock price prediction could help investors decide to sell or buy the stock, which returns and minimizes risk. Precise forecasts can bring substantial financial gains, while unprecise forecasts can bring significant financial losses. During the volatile financial markets, a reliable stock price prediction is a valuable source for traders, investors, and financial analysts. The research uses different models to compare and analyze Google's stock price prediction. The research uses machine learning models to predict Google's stock price. The researcher aims to find the best model to predict the stock price using the Mean Square Error (MSE) result. The researcher chose Google as the topic because of Google's importance in technology and the stock market. The research uses Goggle's stock data from 2016-2021. The data includes opening, closing, and high, and low prices. The study used three methods, linear regression, decision tree, and random forest, to predict Google's stock market price.

Comparison of Different Machine Learning Approaches for Forecasting Stock Prices

Predicting stock prices is a crucial task that has significant implications for investment decisions, business strategies, and financial market stability. Accurate predictions can help investors make informed decisions, capture opportunities, and minimize risks. Understanding financial markets, economic data, company-specific elements, and a variety of statistical and analytical approaches are all necessary for making accurate stock price predictions. This paper employs three machine learning methods to forecast stock price data from a three-year time series dataset. Stock prediction plays a fundamental role in investment decisions, and accurate predictions are crucial. While various methods for predicting stock trends exist, different stock datasets may require different prediction models. Using three years of stock data, including gold and bitcoin, from the 2022 MCM Problem C, this study applies Support Vector Machines (SVM), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN) to make predictions. Bayesian hyperparameter optimization is employed, and a comparative analysis is conducted. Ultimately, the results indicate that CNN outperforms the other methods, exhibiting relatively low error metrics and a high R-squared value, with no apparent signs of overfitting.

Stock Price Prediction Using Convolutional Neural Networks on Various Time Frames

The stock market is a place where stocks can be transferred, traded, and circulated. It has a history of 400 years and can be used as a channel for companies to raise funds. In this study, two Convolutional Neural Network (CNN) models are developed to forecast stock market prices, catering to distinct investment strategies: a short-term weekly model and a medium-term approximately three-month model. The weekly model utilizes a structure to predict Friday closing prices based on the daily closing prices from the preceding week, intending to capture the weekly trends. In contrast, the medium-term model is designed to comprehend the broader market movements by predicting the closing price on a day nearly twelve weeks ahead, using the past twenty days’ prices. Both models incorporate a Conv1D layer followed by batch normalization, pooling, and dense layers, calibrated to their respective temporal frameworks. The study’s findings demonstrate the challenges and intricacies of stock price prediction over varying time frames and propose enhancements for future models, including the incorporation of a wider array of market indicators and economic factors.

Stock Market Prediction and Analysis using Supervised Learning

The Stock Market Remains a Captivating Subject for Stockbrokers and Investors Seeking Financial Success Through Strategic Equity Trading. Informed Decisions Are Pivotal in Navigating the Dynamic Landscape of Stock Investments, Prompting the Adoption of Various Predictive Techniques. This Study Introduces a Novel Prediction Algorithm That Elucidates the Intricate Relationship Between Independent Variables—Comprising Opening and Closing Prices, High and Low Stock Values, and Trading Volume—and the Dependent Variable, Which Is the Stock Price. Leveraging a Deep Learning System, We Demonstrate the Efficacy of Generating Precise Stock Price Forecasts. Our Research Ambit Encompasses a Comprehensive Exploration of Diverse Deep-Learning Architectures Tailored to Anticipate Stock Prices for Global Conglomerates and Indian Enterprises. A Primary Objective Is to Conduct a Comparative Analysis of These Architectures, Discerning Their Respective Performances in Stock Price Prediction Scenarios. Notably, Long Short-Term Memory (Lstm) Algorithms Are Instrumental in Achieving Heightened Accuracy and Robust Prediction Outcomes. The Methodology Entails Meticulous Data Collection From Historical Stock Market Datasets Spanning Various Companies of Global and Indian Origin. Subsequent Data Preprocessing Involves Addressing Missing Values, Standardizing Features, and Structuring the Data Into Sequences Conducive to Lstm Model Input. The Lstm Architecture, Characterized by Its Adeptness in Capturing Long-Term Dependencies and Temporal Patterns, Forms the Cornerstone of Our Prediction Model. Through This Research Endeavor, We Aim to Provide Valuable Insights Into the Potential of Deep Learning Algorithms, Particularly Lstm, in Facilitating Informed Decision-Making for Investors and Stock Market Participants

Predicting stock price crash risk in China: A modified graph WaveNet model

The stock price of a firm is dynamically influenced by its own factors as well as those of its peers. In this study, we introduce a Graph Attention Network (GAT) integrated with WaveNet architecture—termed the GAT-WaveNet model—to capture both time-series and spatial dependencies for forecasting the stock price crash risk of Chinese listed firms from 2012 to 2023. Utilizing node-rolling techniques to prevent overfitting, our results show that the GAT-WaveNet model significantly outperforms traditional machine learning models in prediction accuracy. Moreover, investment portfolios leveraging the GAT-WaveNet model substantially exceed the cumulative returns of those based on other models.

A Machine Learning Approach for Investigating the Determinants of Stock Price Crash Risk: Exploiting Firm and CEO Characteristics

This study uses machine learning to investigate the effects of firm and CEO characteristics on stock price crash risk by collecting massive data on publicly listed firms in China. The results show that eXtreme Gradient Boosting (XGBoost) is the most effective model for predicting stock price crash risk, with relatively satisfactory performance. Meanwhile, the SHapley Additive exPlanations (SHAP) method is used to interpret the importance of features. The results show that the average weekly return of a firm over a year (RET) contributes the most and is negatively associated with crash risk, followed by Sigma, IPO age, and firm size. We also found that, among CEO characteristics, CEO pay contributes substantially to crash risk at the firm level. Our findings have important implications for research into the impact of firm and CEO characteristics on stock price crash risk and provide a novel way for investors to plan their investment decisions and risk-taking behavior rationally.

Predicting Stock Price by Bidirectional-Lstm with Dual Attention Network

Predicting Stock Price by Bidirectional-Lstm with Dual Attention Network

Data Visualization and Machine Learning Trend Analysis Forecasting Based on Roblox Stock Data

The stock market, as a highly complex, unstable and dynamically changing system, is affected by a wide range of factors, such as political, economic, social and technological, in terms of price changes. Therefore, stock price prediction is a challenging problem, but also a problem of great importance. In order to provide a more accurate and reliable method for stock closing price prediction, this paper aims to reduce investment risk and improve investment returns by comparing the prediction effects of different regression models for investors to make investment strategies and decisions. In this paper, Open, High, Low, and Adj Close are used as input parameters, Close is used as the target parameter, the final closing price of the stock is determined from the various stock data, the training set, validation set, and test set are divided in accordance with 6:2:2, and the predictive effect of the model is evaluated using the parameters of MSE, RMSE, MAE, MAPE, and R2. After the comparison of multiple machine learning algorithms, XGBoost regression model and Random Forest Regression model perform the best in the prediction of stock closing price, and their MSEs are 0.061 and 0.072 respectively; followed by LightGBM regression model and decision tree regression model, the MSEs of the two models are 0.113 and 0.299 respectively, but the prediction results of BP neural network regression are poor. By comparing the prediction results of different regression models, it can provide investors with a more accurate and reliable method to predict the closing price of stocks, help investors better formulate investment strategies and decisions, reduce investment risks and improve investment returns. This is of great significance to both individual and institutional investors.

Optimized Ensemble Support Vector Regression Models for Predicting Stock Prices with Multiple Kernels

Optimized Ensemble Support Vector Regression Models for Predicting Stock Prices with Multiple Kernels

ARIMA-BPNN based Stock Price Prediction Model Based on Fusion News Sentiment Analysis

In recent years, the prediction of stocks has mainly focused on improving and combining stock prediction algorithms, or analyzing news sentiment tendencies to simulate subjective investor consciousness. However, both methods have shortcomings in practicality and comprehensiveness. Therefore, based on the use of stock data, the sentiment propensity of vocabulary in the article was processed, and a new algorithm model was obtained by combining the differential integration moving average autoregressive model and backpropagation feedforward neural network model. Finally, sentiment propensity was integrated into the combination model to obtain an algorithm model that integrates sentiment analysis. After optimizing the sentiment vocabulary of news. The algorithm has improved its ability to recognize emotional tendency words, while traditional algorithms have been improved to improve the accuracy of stock prediction, further verifying the relationship curve between emotional tendency and stock prediction fluctuations. The experimental results show that the combined model of sentiment analysis is close to the true value in predicting stock results, with an error of less than 1.5%. The accuracy and stability of the model’s prediction results are significantly better than the uncombined model and traditional prediction models. The new combination model provides better judgment basis for investors through experimental prediction results, creating conditions for investors to avoid stock market risks and improve investment value.

Enhanced stock price forecasting through a regularized ensemble framework with graph convolutional networks

Stock prediction is a prominent and challenging topic of interest for both investors and researchers. With the introduction of Graph Convolutional Networks (GCNs) to financial data analysis, many researchers have attempted to use GCNs to exploit the interrelationships among stock data. However, a notable research gap remains in the application of GCNs to establish associations among transaction data, which inherently exhibit more stable and well-defined correlations. Hence, this study proposes a regularized ensemble framework with graph convolutional networks for stock price prediction. The framework employs multiple multi-graph convolutional networks to construct an ensemble graph convolutional network, elucidating spatiotemporal relationships within transaction data across various time scales. Additionally, the Variational Modal Decomposition (VMD) method is utilized to extract features from each dimensional sequence of the transaction data, enabling the capture of features across different time scales and mitigating non-stationarity and noise in the data. Furthermore, a regularization term incorporating trend accuracy is introduced to strike a balance between numerical precision and trend accuracy, thereby enhancing the overall model performance. Finally, this study presents a specialized investment strategy designed for stock price prediction. Our approach yields substantial improvements on SSE and DJIA datasets, with ACC and R2 improving by over 15% and 10%, respectively, accompanied by reductions of more than 40% in RMSE, MAE, and MAPE compared to the baseline. These results underscore the effectiveness, robustness, and adaptability of our proposed methodology. Our codes and data can be accessed at https://github.com/WonderNothing/REGCN/.

Research on Stock Price Prediction and Quantitative Stock Picking Strategy Based on Deep Learning

With the continuous development of the domestic stock market and the continuous improvement of the financial system system, and at the same time, the domestic stock market gradually rises in the financial system, based on the prediction research of the domestic stock market will become more and more important. In order to solve the problems of low precision and poor accuracy of short-term stock price prediction, this paper selects the bi-directional long- and short-term memory network of attention mechanism (WOA-BiLSTM-Attenion) model under the whale optimization algorithm for stock price prediction. The modeling of bi-directional long- and short-term memory network with attention mechanism can reduce the loss of historical information and increase the influence of important information. On this basis, Whale Optimization Algorithm (WOA) is then used for hyperparameter selection to reduce human interference. The experimental results show that compared with BP, LSTM, BiLSTM, BiLSTM-Attention, the WOA-BiLSTM-Attenion model has a better effect on stock closing price prediction, with a value of 13.9446, and the value of 0.9477, which has a higher accuracy, with a view to providing certain reference for the prediction research in other fields.

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.

The Application of Neural Network Models to Explain the Relationship Between Stock Value, Returns Value, and Information on Prices

This study aims to identify features for stock market value predictions based on time series grouping. This research uses various data sets for different activities. We have the balance sheet data set, which includes a series of quarterly balance sheets. Next, there is the ratio data set. The clustering data set consists of a series of daily prices. There are two data sets for testing activities: pilot forecasts and investments with daily data, projections, and investments. To speed up the development and exploration of different methods, testing begins with a subset of the data, with additional shares of witnesses. This research uses ARIMA and artificial neural networks to predict stock prices. Predicted results provide an investment strategy that compares the results obtained with what happens, resulting in generally positive profits. The capacity of artificial neural networks to manage non-linearities in financial data explains this. Second, many experiments demonstrate that not all data can be utilized using predicting techniques. These results indicate that improved estimations do not always result from more significant data. Forecast accuracy and performance on the test set will likely be negatively impacted by noise and competing signals introduced by data from unrelated stocks. Put another way, by including unrelated data, the information about the action supplied by the group of which it is a member is obscured. As such, the forecasts more closely align with the market's average behavior than with the performance of a specific stock.

Optimizing Banking Stock Price Prediction: Deep Learning Based Approach

Recently, the stock market has experienced significant instability, especially during the global pandemic which resulted in unprecedented economic impact. The main focus of this research is to develop an optimal prediction model using deep learning techniques for the projection of closing prices of bank stocks during the pandemic period. This study evaluates the performance of banking stocks, specifically ARTO, BRIS, BBNI, and BMRI in the Jakarta Composite Index (JCI). Data was extracted from Yahoo Finance and processed through LSTM and GRU algorithms, including data cleaning, normalisation, and descriptive statistical analysis. Scoring metrics such as MSE, RMSE, and MAPE are used to measure the effectiveness of the predictive models. The results show that the LSTM and GRU models can predict stock prices well. These findings provide a basis for trading strategies and improved decision-making in the stock market. Recent research confirms the importance of integrating deep learning methods such as LSTM and GRU in stock price prediction, helping to understand complex financial market fluctuations and improve prediction accuracy.