Stock Price Forecasting Research Articles

A Comprehensive Study on Trend Analysis of Selected Stocks by Using Selected Technical Indicators

In this research, we delve into an in-depth exploration of the Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), and pivot points indicators to gauge their efficacy in forecasting stock price trends. The study draws upon a rich dataset sourced from reputable financial platforms, covering the period from January 2023 to April 2024. To ensure a representative sample, we adopt a stratified sampling approach, meticulously selecting five major companies based on their market capitalization. These companies—Reliance Industries, Tata Consultancy Services, HDFC Bank, ICICI Bank, and Infosys—serve as focal points for our analysis, offering insights into various sectors of the economy. By offering a nuanced understanding of the interplay between technical indicators and market dynamics, our study endeavors to contribute meaningfully to the discourse on stock price forecasting and investment strategies. Key Words: Stock price prediction, Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Data analysis, Statistical methods, Market capitalization,

ARIMA Model-Based Research on Stock Price Prediction

Securities trading has always been a high-risk, high-return domain. Investors seek high returns while endeavoring to minimize risks as much as possible. Therefore, stock price prediction has become a popular and immensely valuable research topic. This paper will use the ARMA model to forecast stock prices. Firstly, an analysis was conducted on selected stock, determining that the price sequence exhibits no seasonal effects but does display volatility effects. The trend is essentially linear, and the relationship between volatility effects and trends fits an additive model. Based on this, preprocessing was conducted by taking the three-day moving average sequence of the series to eliminate the volatility effects, yielding a clean sequence trend. Then, the trend was differenced once to obtain a stationary sequence. Subsequently, the appropriate ARIMA model order was determined by the (partial) autocorrelation plot of this stationary sequence, and the model was fitted to the stock for prediction, yielding satisfactory results. This indicates that the model can accurately forecast long-term trends, but the filtering of volatility effects prevents the prediction results from sensitively reflecting short-term fluctuations.

ARIMA-GARCH MODEL IN OVERCOMING HETEROSCHEDSDATICITY IN STOCK PRICE PREDICTION (CASE STUDY: PT INDOFOOD, TBK (INDF))

Ensuring access to future stock price information holds significant weight for investors in formulating investment strategies. The Indonesian capital market serves pivotal economic and financial roles within the economy, offering various instruments, such as stocks. Among these, blue-chip stocks are recognized for their minimal risk exposure. The impact of the COVID-19 pandemic is anticipated to influence stock price dynamics, including those of blue-chip stocks. Statistical methodologies, such as Autoregressive Integrated Moving Average (ARIMA), are commonly utilized for stock price prediction. However, the efficacy of ARIMA is contingent upon the fulfillment of homoscedasticity assumptions. Failure to meet this assumption due to fluctuating stock price developments poses a challenge. Consequently, an ARIMA-GARCH hybrid model has been developed to address heteroskedasticity concerns in stock price forecasting. This study focuses on INDF stock data, exemplifying a blue-chip stock with positive performance. Results indicate that combining ARIMA-GARCH models, particularly the ARIMA(0,1,3)-GARCH(1,3) model, yields optimal predictions for subsequent stock prices. The MAPE value of the ARIMA-GARCH model stands at 1.41%, indicating superior performance compared to standalone ARIMA modeling. These findings are expected to serve as a valuable resource for investors navigating investment decisions.

SF-Transformer: A Mutual Information-Enhanced Transformer Model with Spot-Forward Parity for Forecasting Long-Term Chinese Stock Index Futures Prices.

The complexity in stock index futures markets, influenced by the intricate interplay of human behavior, is characterized as nonlinearity and dynamism, contributing to significant uncertainty in long-term price forecasting. While machine learning models have demonstrated their efficacy in stock price forecasting, they rely solely on historical price data, which, given the inherent volatility and dynamic nature of financial markets, are insufficient to address the complexity and uncertainty in long-term forecasting due to the limited connection between historical and forecasting prices. This paper introduces a pioneering approach that integrates financial theory with advanced deep learning methods to enhance predictive accuracy and risk management in China's stock index futures market. The SF-Transformer model, combining spot-forward parity and the Transformer model, is proposed to improve forecasting accuracy across short and long-term horizons. Formulated upon the arbitrage-free futures pricing model, the spot-forward parity model offers variables such as stock index price, risk-free rate, and stock index dividend yield for forecasting. Our insight is that the mutual information generated by these variables has the potential to significantly reduce uncertainty in long-term forecasting. A case study on predicting major stock index futures prices in China demonstrates the superiority of the SF-Transformer model over models based on LSTM, MLP, and the stock index futures arbitrage-free pricing model, covering both short and long-term forecasting up to 28 days. Unlike existing machine learning models, the Transformer processes entire time series concurrently, leveraging its attention mechanism to discern intricate dependencies and capture long-range relationships, thereby offering a holistic understanding of time series data. An enhancement of mutual information is observed after introducing spot-forward parity in the forecasting. The variation of mutual information and ablation study results highlights the significant contributions of spot-forward parity, particularly to the long-term forecasting. Overall, these findings highlight the SF-Transformer model's efficacy in leveraging spot-forward parity for reducing uncertainty and advancing robust and comprehensive approaches in long-term stock index futures price forecasting.

Stock Market Prediction

The prediction of stock market trends is a challenging yet critical task in the financial sector, given its significant implications for investors, traders, and financial institutions. This research leverages the Long Short-Term Memory (LSTM) algorithm, a type of recurrent neural network (RNN), to develop a robust model for forecasting stock prices. The study utilizes historical stock market data sourced from Yahoo Finance, accessed via the yfinance package in Python. The primary objectives are to preprocess the data, implement the LSTM model, and evaluate its performance against traditional models such as Random Forest and Linear Regression. Data preprocessing involved handling missing values, normalizing the dataset, and transforming it into sequences suitable for LSTM training. The model's architecture includes multiple LSTM layers designed to capture temporal dependencies in the data. The study evaluates the model's performance using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and prediction accuracy. Comparative analysis shows that the LSTM model outperforms both Random Forest and Linear Regression models, with lower MSE and RMSE values and higher accuracy in predicting stock prices. This research discovered that LSTM's ability to retain long-term dependencies makes it particularly effective for stock market prediction, where historical trends and patterns significantly influence future prices. The results indicate that the LSTM model provides more reliable and precise predictions, which can enhance decision-making in trading and investment. This research highlights the potential of advanced neural network architectures in financial forecasting, offering a valuable tool for investors aiming to optimize their strategies and mitigate risks. The significance of this study lies in its practical application in the financial industry, demonstrating that machine learning models, particularly LSTM, can substantially improve the accuracy of stock market predictions. Future research could explore the integration of additional features, such as macroeconomic indicators and sentiment analysis, to further enhance model performance. This study underscores the importance of continuous innovation and the adoption of sophisticated algorithms to navigate the complexities of financial markets.

Working of Stock Price Prediction Using LSTM

Our proposal centers on the development of a robust tool designed to forecast stock prices by leveraging a sophisticated combination of advanced techniques. We're merging spatial features, which essentially encapsulate the distinctive attributes of individual stocks, with the powerful capabilities of long-and-short-term memory networks (LSTM). These networks excel at recognizing and interpreting patterns and trends over extended periods. By integrating these two elements, our model gains the ability to capture both short-lived fluctuations and enduring trends within stock prices.This fusion of spatial features and LSTM networks represents a significant advancement in stock price prediction methodologies. We're confident that this innovative approach will surpass existing methods in terms of accuracy and reliability. To validate our confidence, we're conducting rigorous testing and refinement of the system using diverse sets of data. This ensures that our model performs effectively across various market conditions and scenarios, thereby instilling trust among investors seeking dependable insights into stock market trends. Key Word: Long and Short-Term Memory, Time-Series Data, Yahoo Finance.

Stock Price Analysis and Forecasting of Bank of China Based on ARIMA Model

The stock market is of great importance to the development of the national economy. The price changes of bank stocks have an important impact on the whole stock market. Therefore, it is of practical value to model the changes in bank stock prices and forecast the future trends. Currently, there is a limited number of studies examining the utilization of the ARIMA model in forecasting stock prices. It is crucial to investigate its level of accuracy. This study employs the ARIMA model to conduct an empirical analysis on the time series data of Bank of China's opening price from January 1st, 2024, to January 31st, 2024. The objective is to predict the stock price for the subsequent 28 days and compare it with actual values. The findings reveal that short-term stock price predictions based on the ARIMA model exhibit a high degree of precision. In conclusion, the ARIMA model can offer valuable insights and serve as a theoretical foundation for investors and decision-makers to some extent. This paper will provide reference for the subsequent research on stock price forecasting and ARIMA model, and fill the existing research gaps.

A Study on Stock Price Prediction Using LSTM and RNN

Stock price prediction remains a critical task in financial markets, influencing investment decisions and portfolio management. This research paper explores the efficacy of Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) in forecasting stock prices.

Unraveling Financial Markets: Deep Neural Network-Based Models for Stock Price Prediction

This paper delves into the potential and challenges of leveraging deep neural networks (DNNs) in stock price forecasting. Traditional econometric models often grapple with the complexities of financial time series data, leading to the exploration of DNNs, especially architectures like Long Short-Term Memory (LSTM) networks, to capture intricate patterns in such data. While these networks present promising results, challenges such as model interpretability, non-stationarity of data, overfitting, and computational demands remain. The financial sector's increasing digitization and influx of alternative data offer a unique opportunity for DNNs, emphasizing their capability for automatic feature extraction. However, the integration of DNNs necessitates a multi-disciplinary approach, involving financial experts, data scientists, and computational specialists. The paper concludes with an optimistic outlook for the synergy between deep learning and traditional financial theories, aiming for a harmonious blend of modern computational techniques with foundational financial principles.

Machine learning methods for financial forecasting and trading profitability: Evidence during the Russia–Ukraine war

PurposeThis study aims to evaluate the effectiveness of machine learning models to yield profitability over the market benchmark, notably in periods of systemic instability, such as the ongoing war between Russia and Ukraine.Design/methodology/approachThis study made computational experiments using support vector machine (SVM) classifiers to predict stock price movements for three financial markets and construct profitable trading strategies to subsidize investors’ decision-making.FindingsOn average, machine learning models outperformed the market benchmarks during the more volatile period of the Russia–Ukraine war, but not during the period before the conflict. Moreover, the hyperparameter combinations for which the profitability is superior were found to be highly sensitive to small variations during the model training process.Practical implicationsInvestors should proceed with caution when applying machine learning models for stock price forecasting and trading recommendations, as their superior performance for volatile periods – in terms of generating abnormal gains over the market – was not observed for a period of relative stability in the economy.Originality/valueThis paper’s approach to search for financial strategies that succeed in outperforming the market provides empirical evidence about the effectiveness of state-of-the-art machine learning techniques before and after the conflict deflagration, which is of potential value for researchers in quantitative finance and market professionals who operate in the financial segment.

Decoding Stocks Patterns Using LSTM

Decoding stocks is extensively utilized in the financial sector by numerous organizations. It is volatile in nature, so it’s tough to predict the prices of stock. Numerous methodologies exist for tackling this task, including logistic regression, support vector machines (SVM), autoregressive conditional heteroskedasticity (ARCH) models, recurrent neural network (RNN), convolutional neural networks (CNN), backpropagation, Naïve Bayes, among others. Among these, Long Short-Term Memory (LSTM) stands out as particularly adept at handling time series data. The primary aim is to discern prevailing market trends and achieve accurate stock price forecasts. Leveraging LSTM and RNN , we strive for error free stock price predictions, with promising results.

Comparative Analysis of Stock Price Prediction Accuracy: A Machine Learning Approach with ARIMA, LSTM, And Random Forest Models

This research investigates the comparative effectiveness of three distinct predictive models – ARIMA (Auto Regressive Integrated Moving Average), LSTM (Long Short-Term Memory), and Random Forest – in forecasting stock prices. Focusing on Tata Motors and Infosys stocks, historical data spanning a significant timeframe is collected using the finance library. These models are trained on a diverse set of features including open, close, high, and low prices to capture the underlying market dynamics. The evaluation of model performance is centred on their ability to forecast stock prices over varying prediction horizons. Metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) are utilized to quantify the accuracy and reliability of the predictions. Through rigorous analysis, this study provides insights into the strengths and limitations of each model, offering valuable guidance to investors and market analysts. The findings underscore the significance of selecting appropriate predictive models in financial forecasting and contribute to advancing the understanding of predictive modelling techniques in stock market analysis. Additionally, the research delves into the implications of long-term dependencies in stock price forecasting, shedding light on the challenges and opportunities inherent in predicting market trends over extended periods.

An Innovative Study on Stock Price Prediction for Investment Decision Through ARIMA and LSTM with Recurrent Neural Network

The securities market is extremely volatile and difficult to prognosticate. Stock prices are depending upon numerous factors. To reduce the risk of volatility, it is very important to apply an accurate mechanism to forecast stock prices. The importance of share price prediction forecasting in finance and economics has sparked researchers’ interest in creating more reliable forecasting models over time. In this research paper, the researchers try to explore two different applications based on linear and nonlinear (RNN) functions. The criteria for the stock price predictions are evolved using Auto Regressive Integrated Moving Average (ARIMA) which takes the linearity function from the past share prices. The ARIMA model assumes the future prices usually be similar to past. Sudden changes may not reflect in this model. The nonlinearity Recurrent Neural Network (RNN) is going to be applied for share price prediction so that it can be taken into account the quick changes that are occurring in the market environment. To test the RNN, the study used the Long Short Term Memory (LSTM) model which takes the support of Artificial Intelligence. Taking the sample of share prices of banks listed in the NIFTY index, the ARIMA and LSTM have been performed and analyzed. Stock price predictions for banks listed in the NIFTY bank index are found better with the ARIMA model than with the LSTM model.

Forecasting Indian Trade Trends through LSTM- based Predictive Modeling

The efficacy of Long Short-Term Memory (LSTM) neural networks and attention-based models in predicting next-day closing prices of the MSFT 500index is meticulously examined. A comprehensive suite of nine carefully chosen predictors spanning fundamental market data, macroeconomic indicators, and technical metrics is amalgamated, fostering a holistic comprehension of market behavior. Through rigorous analysis, the research evaluates single-layer and multilayer LSTM architectures alongside attention- based LSTM variants, juxtaposed against traditional ARIMA models. Surprisingly, the single-layer LSTM consistently outperforms its multilayer counterpart, demonstrating superior accuracy and model fit. The integration of corporate accounting statistics augments predictive capabilities, enriching the models' efficacy. Notably, attention-based LSTM models, particularly the Attention-LSTM variant, exhibit markedly lower prediction errors and higherreturns in trading strategies compared to other methodologies. However, the heightened complexity of stacked-LSTM structures fails to surpass the predictive acumen of simpler LSTM architectures.This inquiry underscores the paramount importance of leveraging advanced AI techniques and comprehensive datasets in navigating the intricate nuances of modern financialmarkets, offering invaluable insights for both researchers and practitioners engaged in stock priceforecasting endeavors.

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.

Stock Price Prediction Using Machine Learning

Abstract: The "Stock Price Prediction Using Machine Learning" project aims to develop an advanced predictive model for forecasting stock prices in financial markets. The volatility and complexity of stock markets make accurate predictions challenging, and the utilization of machine learning techniques offers a promising approach to address this challenge. This project leverages historical stock data, technical indicators, and sentiment analysis to create a robust predictive model. The methodology involves collecting and preprocessing a vast dataset of historical stock prices and relevant financial indicators. Various machine learning algorithms, including but not limited to linear regression, decision trees, support vector machines, and neural networks, are employed to analyze patterns and relationships within the data. The project focuses on model evaluation and comparison to identify the most accurate and reliable prediction model. Performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and accuracy are utilized to assess the effectiveness of the models. Hyperparameter tuning and cross- validation are employed to enhance the models' generalization capabilities

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 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

Causality-driven multivariate stock movement forecasting.

Our study aims to investigate the interdependence between international stock markets and sentiments from financial news in stock forecasting. We adopt the Temporal Fusion Transformers (TFT) to incorporate intra and inter-market correlations and the interaction between the information flow, i.e. causality, of financial news sentiment and the dynamics of the stock market. The current study distinguishes itself from existing research by adopting Dynamic Transfer Entropy (DTE) to establish an accurate information flow propagation between stock and sentiments. DTE has the advantage of providing time series that mine information flow propagation paths between certain parts of the time series, highlighting marginal events such as spikes or sudden jumps, which are crucial in financial time series. The proposed methodological approach involves the following elements: a FinBERT-based textual analysis of financial news articles to extract sentiment time series, the use of the Transfer Entropy and corresponding heat maps to analyze the net information flows, the calculation of the DTE time series, which are considered as co-occurring covariates of stock Price, and TFT-based stock forecasting. The Dow Jones Industrial Average index of 13 countries, along with daily financial news data obtained through the New York Times API, are used to demonstrate the validity and superiority of the proposed DTE-based causality method along with TFT for accurate stock Price and Return forecasting compared to state-of-the-art time series forecasting methods.

Least squares parameter estimation for uncertain fractional differential equations and application to stock model

In recent years, uncertain fractional differential equations was proposed for the description of complex uncertain dynamic systems with historical characteristics. For wider applications of uncertain fractional differential equations, researches on parameter estimation for uncertain fractional differential equations are of great importance. In this paper, based on the thought of least squares estimation and uncertain hypothesis test, an algorithm of parameter estimation for uncertain fractional differential equations is discussed. Finally, we consider the application of uncertain fractional differential equations based model to predict the forecasting stock price of three major indexes of U.S. stocks and make a comparison between uncertain fractional differential equations, uncertain differential equations and stochastic differential equations.