Stock Price Prediction Research Articles

Research on the Feasibility of Machine Learning Methods in Stock Price Prediction

Abstract: Stock price prediction has always been an important topic in financial research. Accurate price prediction can not only help investors make informed investment decisions but also enhance market stability and reduce systemic risk. In recent years, with advancements in computing technology and data science, machine learning methods have been increasingly applied in the financial field. Compared to traditional methods, machine learning methods can better handle high-dimensional, nonlinear, and large datasets, thus demonstrating higher prediction accuracy and applicability in stock price prediction.This paper reviews relevant literature and selects five models for empirical research: Support Vector Machine (SVM), Long Short-Term Memory network (LSTM), LightGBM, a combination of LSTM and LightGBM, and Convolutional Neural Network (CNN). The effectiveness of these models in predicting the stock price of Meituan-W (3690) was analyzed and compared in detail. The experimental results show that the LightGBM model performs best in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE), proving its significant advantages in handling large-scale, high-dimensional, and nonlinear data. By comparing the prediction results of different models, this paper explores the strengths and weaknesses of each model and their feasibility and effectiveness in practical applications. Machine learning methods have significant potential in stock price prediction, model selection needs to comprehensively consider data characteristics, computational resources, and practical application scenarios.

Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach

This study explores the comparative performance of cutting-edge AI models, i.e., Finaance Bidirectional Encoder representations from Transsformers (FinBERT), Generatice Pre-trained Transformer GPT-4, and Logistic Regression, for sentiment analysis and stock index prediction using financial news and the NGX All-Share Index data label. By leveraging advanced natural language processing models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment scores, and predict market price movements. This research highlights global AI advancements in stock markets, showcasing how state-of-the-art language models can contribute to understanding complex financial data. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed the more computationally intensive FinBERT and predefined approach of versatile GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. The GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was resource-demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of the practical applications of AI approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 representing emerging tools with potential for future exploration and innovation in AI-driven financial analytics.

Stock Price Analysis Based on Time Range as a Basis for Determining the Optimal Forecasting Method

The modeling of stock prices for telecommunications companies in Indonesia (TBIG.JK, TLKM.JK, XL.JK, ISAT.JK, TOWR.JK) is examined in this study by considering three time periods: 1 year, 5 years, and 10 years. The analysis results indicate that the distribution of stock prices for each company and time period varies, with some stocks exhibiting a distribution close to normal while others show high kurtosis. These findings suggest that the assumption of normal distribution may not be appropriate for all cases, making it essential to select a stock price prediction model that takes into account the specific distribution characteristics for each company and time period

Deep Learning-Based Stock Price Prediction Using LSTM Model

The stock market is a vital component of the broader financial system, with its dynamics closely linked to economic growth. The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets. By examining historical transaction data, latent opportunities for profit can be uncovered, providing valuable insights for both institutional and individual investors to make more informed decisions. This study focuses on analyzing historical transaction data from four banks to predict closing price trends. Various models, including decision trees, random forests, and Long Short-Term Memory (LSTM) networks, are employed to forecast stock price movements. Historical stock transaction data serves as the input for training these models, which are then used to predict upward or downward stock price trends. The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements. The LSTM-based deep neural network model, in particular, demonstrates a commendable level of predictive accuracy. This conclusion is reached following a thorough evaluation of model performance, highlighting the potential of LSTM models in stock market forecasting. The findings offer significant implications for advancing financial forecasting approaches, thereby improving the decision-making capabilities of investors and financial institutions.

Measuring Impact of Market Sentiment on Stock Prices

This study considers the impact of investor sentiment on stock market prices, it is when something beyond traditional economic factors, such as emotions or perceptions that play dominating role in driving psychological and real pricing. This behaviour in investor sentiment often leads to irrational decision making, always influenced by news, social media and the movements of markets. This will make stocks price more volatile. Through sentiment analysis, historical stock data and behavioural finance models the study examines times when there were significant movements in the market. These trends suggest that happy days can cause major prices to rise (along with the sun) and bad news can punch holes in a report even if business hasn't really looked any worse. The regulators and the investors should understand that sentiment can improve the efficiency of the stock price-prediction algorithms as well as bring anomalies to light in the market. The study of market sentiment and its effect on the stock price. In the given paper of research, it is easy to notice the impact of interaction between investor sentiment and stock price.

A Stock Prediction Method Based on Multidimensional and Multilevel Feature Dynamic Fusion

Stock price prediction has long been a topic of interest in academia and the financial industry. Numerous factors influence stock prices, such as a company’s performance, industry development, national policies, and other macroeconomic factors. These factors are challenging to quantify, making predicting stock price movements difficult. This paper presents a novel deep neural network framework that leverages the dynamic fusion of multi-dimensional and multi-level features for stock price prediction, which means we utilize fundamental trading data and technical indicators as multi-dimensional data and local and global multi-level information. Firstly, the model dynamically assigns weights to multi-dimensional features of stocks to capture the impact of each feature on stock prices. Next, it applies the Fourier transform to the global features to capture the long-term trends of the global environment and dynamically fuses these with local and global features of the stocks to capture the overall market environment’s impact on individual stocks. Finally, temporal features are captured using an attention layer and an RNN-based model, which incorporates historical price data to forecast future prices. Experiments on stocks from various industries within the Chinese CSI 300 index reveal that the proposed model outperforms traditional methods and other deep learning approaches in terms of stock price prediction. This paper proposes a method that facilitates the dynamic integration of multi-dimensional and multi-level features in an efficient manner and experimental results show that it improves the accuracy of stock price predictions.

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.

Retraction Note: Grey Wolf optimization-Elman neural network model for stock price prediction

Retraction Note: Grey Wolf optimization-Elman neural network model for stock price prediction

Stock Price Prediction Research Based on CNN-LSTM

In recent years, neural network technology has been widely used in the field of stock forecasting, especially in the aspect of LSTM, which has become a research hotspot because of its unique advantages in dealing with time series problems in stock forecasting. By analyzing and summarizing the recent research literature based on LSTM and its extended model, this paper aims to deeply explore the effect and potential limitations of LSTM in capturing the dynamic and nonlinear characteristics of the stock market. By constructing different combination models combining LSTM and CNN, the paper uses normalization to preprocess the split data and realize the generalization prediction of stock price. Compared with the experimental results, a suitable combination model of LSTM and CNN is found. This hybrid model can significantly improve the accuracy and stability of prediction, and also shows the advantages in multi-input data processing. The results of this study not only provide a new technical means for the direction of stock prediction, but also explore a new direction for the further application of deep learning technology in the financial field.

Research on Stock Price Prediction Model Based on Sentiment Factor and Multi-Core Bagging Algorithm

Stock price prediction model is one of the key research points in the quantitative financial field. With the rapid development of data acquisition and storage technology, the data of emotional factors based on nonlinear structure is increasing. How to combine this part of information with common European technology factors to increase the ability of the model is a problem that needs to be solved. Based on this, this paper proposes a method of uncertainty quantification that combines European-and non-European data. Specifically, on the one hand, the Gaussian process regression model is used to capture the unknown relationship between predictive variables and price; on the other hand, the multi-kernel learning technology based on European and non- European kernel functions is used to combine the effective information of European and non- European predictive variables. Moreover, the proposed method can weigh the bias of the prediction model against the variance by using the Bagging algorithm. The simulated data analysis shows that the proposed method not only improves the prediction performance of the European Gaussian process, but also works in the presence of irrelevant predictive variables. Finally, the actual data analysis shows that the proposed method has achieved good prediction results in the task of predicting the rise and fall of stock prices.

Determinants JCI of the Indonesian Stock Exchange (Comparison of IHSG Projection of OLS Method and ARIMA and GARCH Methods for the Period 2015-2023)

This study aims to examine the effect of interest rate, exchange rate, inflation, gross domestic product and LQ-45 index variables on the movement of the Composite Stock Price Index using the ordinary least square method. Comparison in predicting the movement of the stock price index through three models, and then compared with the projection results with OLS, ARIMA and GARCH methods. This research was conducted using monthly data on JCI, interest rates, exchange rates, inflation, gross domestic product and the LQ-45 index for the period May 2015 - December 2023. From the results it can be concluded that interest rates have a significant negative effect on JCI, exchange rates have a significant negative effect on JCI, inflation has a significant negative effect on the value of JCI, gross domestic product has a significant negative effect on JCI and the LQ-45 index has a positive effect on JCI. The results of the comparison of JCI projections with 3 models showed that the OLS model offers more accurate results and has the lowest MAPE value in predicting stock price movements.

Machine Learning Applications in Stock Price Prediction

With economic fluctuations, investors focus on the stock market where scientific investment strategies become increasingly crucial. This article utilizes linear regression to predict stock price trends, achieving an R value close to 1, indicating high accuracy in the model's predictions. Additionally, logistic regression and random forest classification models were employed to predict stock price movements, revealing a superior performance with the random forest model achieving an AUC value of 0.64. By employing a combination of regression and classification algorithms, this study offers a diverse perspective on stock price prediction, highlighting the importance of scientific forecasting in financial markets. The findings demonstrate that integrating multiple predictive models enhances the accuracy and reliability of stock price predictions, providing investors with precise market information to facilitate rational investment decisions. Furthermore, this research contributes valuable insights for future studies in stock market prediction, promoting continuous innovation and development in the theoretical and methodological aspects of this field. The results underscore the potential of advanced machine learning techniques in financial forecasting, offering a robust framework for understanding market dynamics and improving investment strategies. This study serves as a reference point for both academic research and practical applications, driving forward the evolution of predictive modeling in finance.

Empirical Study on BYD Stock Price Prediction Based on LSTM Model

This study employs Long Short-Term Memory (LSTM) neural network models for stock price prediction. LSTM models excel in managing nonlinearities and time-series dependencies in data, thereby enhancing the accuracy of stock price forecasts, reducing investment risks, and increasing returns. Investors can reasonably use the LSTM model to predict stock prices and obtain greater returns. The research utilizes eight years of historical data from BYD, including opening prices, closing prices, highest prices, lowest prices, and trading volumes, as training data. By adjusting various hyperparameters such as epochs, learning rates, and training-test ratios, the study analyzes their impact on the predictive accuracy of the model and identifies optimal configurations through comparative experimental methods. We found that the best parameters were when epoch number was 25, training ratio was 90:10 (minimum mean square error was 0.00175572) and 4 years of training data was selected as the training set (minimum mean square error was 0.00146477).

Deep Learning for Financial News Analysis and Stock Price Prediction: A Case Study of TCS

<span lang="EN-IN">Finance has been the most important aspect of people’s lives for making small to large decisions in life. Finances affect our ability to invest in opportunities, save for uncertain future events, and purchase necessities. In addition, money has a significant impact on reducing poverty, creating jobs, and advancing society. On the other side, deep learning is a growing field as it is transforming and revolutionizing various areas and industries. While realising the importance of both finance and deep learning in our lives for better decision-making, our study aims to find the connectedness between both so that understanding this relation and utilising it will create better systems for optimal decision-making and also to study its influence on the stock market, presenting various challenges and opportunities regarding this matter and unleashing the potential that comes with the advanced technologies.</span>

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.

A Comparative Study of NIFTY-50 Prediction Using Linear Regression and Regression Tree Models

It is important for investors to predict the tendency of stock. However, the stock market fluctuation is too hard to predict. A single stock may be inflected by many elements, so it would be much more feasible to study a stock index. A stock index may be decided by many stocks, which means that it would not be effected by some only one company. In this passage, NIFTY-50 would be analyzed to make a prediction. The linear regression and regression tree are used to predict the close price of NIFTY-50. To make sure the data size is big enough, the data includes the price from 2000 to 2021. By comparing two models, a proper model to predict stock index would be found. After the comparison of the MSE and R square index, it is easy to find that the regression tree is the model much more reliable. However, there is still long way to go before researchers find way to predict stock price precisely.

Statistical Model Comparison for the National Stock Exchange (NSE) Stock Price Prediction

The stock market is one of the most versatile sectors in the financial system, and the stock market plays an important role in economic development. It is also a platform for trading various securities and derivatives without barriers. This paper focuses on the comparison of different types of time series models to predict the stock prices of five companies by using machine learning techniques.

PERBANDINGAN FUZZY TIME SERIES MARKOV CHAIN DAN FUZZY TIME SERIES CHENG

Investing is a hot and fast-growing topic right now. Stock.investing.is one of the most popular investments by the public. The JCI is an index that measures the performance of all stocks listed on the IDX. The closing price of the stock is published daily and can be used by investors as an investment benchmark. The occurrence of stock price fluctuations entails a high risk of loss. Predicting stock prices is one way to avoid this risk. Fuzzy time series is a time series model that can be used to minimize the risk of future occurrences. Time parameter data is not stationary, so this research uses fuzzy time series with Markov chains and Cheng's method to determine the JCI completion dates from January 2018 to June 2022. Calculating the accuracy level of the two method approaches using symmetric Mean Absolute Percentage Error (sMAPE), the sMAPE value obtained in the Fuzzy Time Series Markov Chain method is 2.11% and the Fuzzy Time Series Cheng method is 2.98%. The conclusion is that of the two methods the Markov Chain has a smaller value and can be said to be a better method compared to the Cheng method.

A deep learning-based model for stock price prediction under consideration of financial news publishers

Stock price prediction with financial news is beneficial for making correct investment decisions. Recent researches mainly focus on extracting sentiments from news. The publishers as a crucial part of news, which can reflect the authority and reliability, but are usually neglected. In this paper, we propose a novel hybrid deep learning model for stock price prediction that considers the financial news publishers. Specifically, a novel feature about financial news publishers is constructed by classifying financial news publishers. Furthermore, a novel hybrid model named 2d-CNN-AM-LSTM is proposed by combining Convolutional Neural Network (CNN), Attention Mechanism (AM) and Long Short-term Memory, so that it can fuse textual and numerical data in-depth. To verify the superiority of the proposed model, numerous baselines are employed to conduct experiments with representative stock datasets. The experimental analysis demonstrates that the proposed model considering financial news publishers surpasses other benchmarks and achieves outstanding prediction performance.

A multi-stage machine learning approach for stock price prediction: Engineered and derivative indices

In this paper, a machine learning approach is proposed to predict the next day's stock prices. The methodology involves comprehensive data collection and feature generation, followed by predictions utilizing Multi-Layer Perceptron (MLP) networks. We selected 5,283 records of daily historical data, including open prices, close prices, highest prices, lowest prices, and trading volumes from four well-known stocks in the FTSE 100 index. A novel set of engineered and derivative indices is extracted from the original time series to enhance prediction accuracy. Two Multi-Layer Perceptron (MLP) are proposed to predict the next day's stock prices using the engineered discrete and continuous indices. The case study uses the daily historical time series of stock prices between January 1, 2000, and December 31, 2020. The proposed machine learning approach presents suitable applicability and accuracy, respectively.