Stock Market Analysis Research Articles

Probability Distributions for Modeling Stock Market Returns—An Empirical Inquiry

Investing in stocks and shares is a common strategy to pursue potential gains while considering future financial needs, such as retirement and children’s education. Effectively managing investment risk requires thoroughly analyzing stock market returns and making informed predictions. Traditional models often utilize normal variance distributions to describe these returns. However, stock market returns often deviate from normality, exhibiting skewness, higher kurtosis, heavier tails, and a more pronounced center. This paper investigates the Laplace distribution and its generalized forms, including asymmetric Laplace, skewed Laplace, and the Kumaraswamy Laplace distribution, for modeling stock market returns. Our analysis involves a comparative study with the widely-used Variance-Gamma distribution, assessing their fit with the weekly returns of the S&P 500 Index and its eleven business sectors, drawing parallel inferences from international stock market indices like IBOVESPA and KOSPI for emerging and developed economies, as well as the 20+ Years Treasury Bond ETFs and individual stocks across varied time horizons. The empirical findings indicate the superior performance of the Kumaraswamy Laplace distribution, which establishes it as a robust alternative for precise return predictions and efficient risk mitigation in investments.

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

Web Trade Analytics

This research introduces an innovative web application developed using the MERN (MongoDB, Express.js, React, Node.js) stack, enhanced with fundamental machine learning algorithms, designed to address the complexities of stock market analysis. The central focus is on creating a user-customizable dashboard, allowing investors to select specific stocks for real-time analysis, sentiment tracking, and future price prediction. The methodology integrates historical stock data with sentiment analysis sourced from news and social media. The machine learning algorithms leverage this data to generate buy or sell recommendations and forecast future price trends. Our findings underscore the practicality of this approach, enhancing investors' abilities to navigate the unpredictable stock market landscape and make informed decisions with an eye on the future. Keywords— Stock Market, MERN Stack, Predictive Analysis, Future Forecasting

Cross-Correlation Analysis of Crude Oil-Related Stock Markets in China Caused by the Conflict Between Russia and Ukraine

Cross-Correlation Analysis of Crude Oil-Related Stock Markets in China Caused by the Conflict Between Russia and Ukraine

Practical Analysis of Stock Market Under ESG

In today's era, climate change brought about by the greenhouse effect is gradually bringing more and more economic losses and natural disasters. In order to mitigate the negative impact of climate change, many countries have jointly formulated a 1.5°C climate scenario, which has brought a lot of impact on the global economy and industry development (Henderson et al., 2020). At the same time, my client commissioned me to build a portfolio that complies with the 1.5°C climate scenario, and the shares must be included in the S&P 500 Net Zero 2050 Climate Transition ESG Index as of 30/09/2022. The total portfolio is $100,000, and the benchmark designates the S&P 500 Net Zero 2050 Climate Transition ESG Index. Because the investment horizon is short and only six weeks, I chose semi-active management in my portfolio management process, and the rebalancing method was selected as percentage rebalancing. Finally, based on macro and industry analysis and relative valuation and technical analysis of the corresponding stocks, I selected ten stocks: Starbucks Corporation, Tesla, Inc., Amgen Inc., Cisco Systems, Inc., Eversource Energy, The Coca-Cola Company, and Expeditors International of Washington, Inc., Trans Digm Group Incorporated， Netflix, Inc., S&P Global Inc. I calculated their respective shares using the Markowitz model and bought and rebalanced, but unfortunately, my portfolio did not work as expected. The sector index fell by 4.87% in the week of the investment term, but my portfolio fell by 7.45% and performed poorly. Among them, the collapse of Tesla due to special events and my previous judgment of Tesla were greatly affected, and although its share price was only 9.94%, its share price fell by 33.74%. Although the late-stage rebalancing gave me hope for the better, the short investment horizon became a significant obstacle to the improvement of my portfolio.

Financial Crisis Prediction and Stock Market Analysis: A Systematic Review

Abstract: It is common known that attempting to foresee the movements of the stock market is a huge task that calls for a great lot of focus and concentration. Because of this, precisely predicting stock prices might potentially result in attractive rewards if the necessary decisions are made. This is the reason why this is the case. The non-stationary, noisy, and chaotic data that is accessible makes it difficult to generate correct forecasts about the stock market. This is because accurate predictions are difficult to produce. Because of this, it is difficult for investors to generate accurate forecasts in order to invest their money and earn a return from their investments. Various strategies are developed within the framework of the existing methodologies in order to forecast stock market movements. An overview of research articles arguing for different approaches is the goal of this study. In addition to being great academic publications, these research articles provide calculation methodologies, machine learning algorithms, performance factors, and other information. To address this recognized need, this study attempts to provide a comprehensive and current evaluation of the stock market forecasting approaches now in use. This research will include the categorization, analysis, and comparison of various approaches. In the end, the process of training and testing the model is evaluated using machine learning models. Based on the data, it is abundantly evident that logistic regression is better than any other approach that is presently being used.

Enhancing Stock Market Predictions through Artificial Intelligence

This paper explores the integration of Artificial Intelligence (AI) techniques in stock market analysis and prediction. Traditional methods have limitations in capturing the complexities of market dynamics, leading to inaccuracies in forecasting. By leveraging AI algorithms such as machine learning, deep learning, and natural language processing, significant advancements have been made in predicting stock trends, volatility, and optimal trading strategies. This paper reviews various AI models and approaches used in stock market analysis, discusses their strengths and limitations, and presents case studies demonstrating their effectiveness. Furthermore, it discusses the ethical implications and challenges associated with AI in stock market prediction, including data privacy concerns and algorithmic biases. Ultimately, this paper aims to provide insights into how AI can revolutionize stock market analysis and empower investors with more accurate decision-making tools

Utilizing Supervised Machine Learning for Stock Market Analysis

The abstract describes an attempt to apply machine learning algorithms to predict future stock prices in the stock market. The paper aims to simplify the complexity of the stock market, which involves various entities such as small ownerships, brokerage corporations, and the banking sector. The motivation for using machine learning is to enhance predictability in this intricate business model.

Speculation of Stock Marketing Using Advanced Recursive Techniques

In the current scenario, the economic status of countries is dependent on stock markets. However, predicting the future prices of any stock is a multifaceted task, as the nature of data is complex and unstructured in nature, which is difficult understand. The focus of the study relies on applying deep neural techniques with regression-based application to discover knowledge from financial databases. The authors have applied LSTM, an advanced version of RNN, and regression-based methods such as ARIMA for predicting future prices of stocks. The study was supported by implementing the techniques on real-world data that was captured from SBI for 6 years. The data has significant opening and closing prices of stock markets. To implement the current study approach, the authors have utilized Python language, where result predicts various performance parameters such as MAE, MSE, RMSE, and bias for both LSTM as well as ARIMA. The performance matrix of LSTM and ARIMA were compared for MAE (mean absolute error) for LSTM, which is 4.32, whereas for ARIMA is 3.83. Also, MSE (mean squared error) value for LSTM is 29.52, for ARIMA was 24.53, and RMSE (root mean squared error) for LSTM and ARIMA are 5.43 and 4.95. The overall accuracy of both of the algorithm were widely applied for real-world prediction among the stock market analysis.

Survey on Stock Price Forecasting

This research project delves into the challenging domain of stock price prediction by harnessing the capabilities of neural networks. In an ever-evolving financial landscape, the project seeks to develop a robust model that can analyze historical stock data, identifying intricate patterns and trends to inform future price movements. Through the convergence of data science and financial analysis, this endeavor aims to provide investors with valuable insights, aiding them in navigating the complexities of the stock market. Employing machine learning algorithms, particularly neural networks, the model scrutinizes extensive sets of historical stock data, considering factors such as past price trends, trading volumes, and relevant financial indicators. Techniques such as regression, time-series analysis, and neural networks are leveraged to unveil complex relationships within the data, enhancing the model's predictive capabilities. The model undergoes meticulous training on historical data to glean insights from past market behaviors. Subsequently, it undergoes rigorous testing on unseen data to evaluate its predictive accuracy. Continuous refinement and optimization strategies are implemented to ensure the model's adaptability to the ever-changing dynamics of the market. Ultimately, this project aspires to furnish investors with a powerful tool for making informed decisions in the face of the unpredictable nature of financial markets. By integrating the precision of neural networks with the intricacies of stock market analysis, the research aims to contribute to the advancement of stock price forecasting methodologies.

Algorithm-Based Low-Frequency Trading Using a Stochastic Oscillator and William%R: A Case Study on the U.S. and Korean Indices

Using stochastics in stock market analysis is widely accepted for index estimation and ultra-high-frequency trading. However, previous studies linking index estimation to actual trading without applying low-frequency trading are limited. This study applied William%R to the existing research and used fixed parameters to remove noise from stochastics. We propose contributing to stock market stakeholders by finding an easy-to-apply algorithmic trading methodology for individual and pension fund investors. The algorithm constructed two oscillators with fixed parameters to identify when to enter and exit the index and achieved good results against the benchmark. We tested two ETFs, SPY (S&P 500) and EWY (MSCI Korea), from 2010 to 2022. Over the 12-year study period, our model showed it can outperform the benchmark index, having a high hit ratio of over 80%, a maximum drawdown in the low single digits, and a trading frequency of 1.5 trades per year. The results of our empirical research show that this methodology simplifies the process for investors to effectively implement market timing strategies in their investment decisions.

REVIEW OF AI TECHNIQUES IN FINANCIAL FORECASTING: APPLICATIONS IN STOCK MARKET ANALYSIS

This scholarly inquiry delves into the burgeoning intersection of Artificial Intelligence (AI) and financial forecasting, particularly within the stock market domain. The study's backdrop is set against the rapid evolution of AI techniques, which have significantly altered the landscape of financial analysis. The primary aim is to dissect and evaluate the impact of AI on stock market predictions, juxtaposing its capabilities against traditional forecasting methods while navigating through the ethical and practical complexities inherent in AI implementation. The scope of the paper encompasses a comprehensive review of AI's evolution in financial analysis, its comparative effectiveness, and the sector-specific applications in stock markets. Methodologically, the study employs a systematic review of existing literature, focusing on peer-reviewed articles that shed light on the performance, challenges, and future prospects of AI in stock market forecasting. The findings reveal AI's profound potential in enhancing market efficiency and volatility understanding, albeit tempered by challenges such as data quality issues, model interpretability, and the need for robust regulatory frameworks. The main conclusions underscore AI's transformative role in financial forecasting, highlighting its ability to analyze vast datasets and predict market trends with heightened accuracy. However, the study also acknowledges the limitations within AI models, emphasizing the necessity for a balanced approach that integrates AI with traditional methods and continuous algorithmic refinement. Recommendations advocate for collaborative efforts between technologists, ethicists, and financial experts to develop ethically sound, transparent, and effective AI applications. In summary, this paper offers a panoramic view of AI's role in financial forecasting, serving as a guidepost for future explorations in this field. It underscores the immense possibilities and intricate challenges of AI in the dynamic landscape of stock market analysis, paving the way for a new era of data-driven decision-making in finance.
 Keywords: Artificial Intelligence, Financial Forecasting, Stock Market Prediction, Machine Learning, Ethical Considerations, Regulatory Frameworks.

Stock price forecasting in Indonesia stock exchange using deep learning: a comparative study

In 2022, the Indonesia stock exchange (IDX) listed 825 companies, making it challenging to identify low-risk companies. Stock price forecasting and price movement prediction are vital issues in financial works. Deep learning has previously been implemented for stock market analysis, with promising results. Because of the differences in architecture and stock issuers in each study report, a consensus on the best stock price forecasting model has yet to be reached. We present a methodology for comparing the performance of convolutional neural networks (CNN), gated recurrent units (GRU), long short-term memory (LSTM), and graph convolutional networks (GCN) layers. The four layers types combination yields 11 architectures with two layers stacked maximum, and the architectures are performance compared in stock price predicting. The dataset consists of open, highest, lowest, closed price, and volume transactions and has 2,588,451 rows from 727 companies in IDX. The best performance architecture was chosen by a vote based on the coefficient of determination (R2), mean squared error (MSE), root mean square error (RMSE), mean absolute percent error (MAPE), and f1-score. TFGRU is the best architecture, producing the finest results on 315 companies with an average score of RMSE is 553.327, MAPE is 0.858, and f1-score is 0.456.

Performance Analysis of Share Market by using ML Technique

This research investigates the transformative impact of machine learning (ML) on stock market analysis, assessing its potential benefits and confronting associated challenges. The study employs various ML models, including regression, classification, clustering, and natural language processing, to analyze extensive datasets comprising historical stock prices, financial indicators, economic data, and sentiment from social media. Key findings reveal that ML models exhibit superior accuracy in forecasting market movements and stock prices compared to traditional methods. Automated anomaly detection algorithms demonstrate proficiency in identifying unusual market behaviour, offering timely warnings for potential market shifts and fraudulent activities. ML-powered risk assessment tools showcase the capacity to personalize investment strategies based on individual preferences, augmenting decision-making for investors. Despite challenges such as data quality, model selection, and ethical considerations, the research underscores the undeniable potential of ML in stock market analysis. Rigorous methodologies, including data preprocessing, feature engineering, and model evaluation, contribute to the robustness of the findings. Ethical considerations, including algorithmic biases and transparency are thoroughly explored to ensure responsible application in the financial domain.

Performance Analysis of Share Market by Using ML Technique

This research paper delves into the intricate world of stock market analysis, exploring the application of machine learning techniques to enhance performance evaluation. The study adopts multifaceted approach, combining historical stock market data, advanced machine learning algorithms, and rigorous back testing to derive insights into market dynamics. The findings aim to contribute to the growing body of literature on financial analytics, providing valuable implications for investors and financial professionals.

Machine Learning in Tesla’s Stock Price Prediction

With the development of new energy battery technology and people's attention to environmental issues, new energy vehicles have gradually become one of the important choices for people to travel, and stock investors have also begun to keep an eye on the field of new energy vehicles. However, since the new energy industry is an emerging industry, the current stock market analysis in this regard is not complete. Machine learning is one of the important means of stock forecasting at present. This article will use 6 machine learning models such as linear regression, polynomial regression, XGBoost, ARIMA, Prophet and LSTM to analyze and predict the stock of Tesla, a leading company in the field of new energy vehicles, so as to judge the investment prospects of new energy vehicles. The outcomes demonstrate that LSTM has the lowest error in predicting the price of Tesla’s stock.

Stock Forecasting Based on Linear Regression Model and Nonlinear Machine Learning Regression Model

To enhance the accuracy of stock price prediction for Netflix and provide individuals with a comprehensive understanding of stock trading prices, this study constructs a predictive model by employing three distinct approaches: a linear regression model, a Long Short-term Memory (LSTM) artificial neural network, and a Gated Recursive Unit (GRU) which serves as a component of the LSTM architecture. A prediction scheme is devised, utilizing historical stock data spanning from 2002 to 2022 for Netflix. The primary objective is to forecast the stock price of Netflix for the subsequent 20-day period. To evaluate the efficacy of the three models, a rigorous assessment is conducted employing robust evaluation indices. The outcomes of this analysis will enable a determination of the fitting adequacy of each model, thereby facilitating the identification of the most suitable approach for accurate stock price prediction in the context of Netflix. This research endeavors to contribute to the field of stock market analysis by leveraging advanced predictive modeling techniques for enhanced forecasting accuracy and insightful decision-making.

Performance Analysis of Stocks using Deep Learning Models

This paper explores into stock market analysis using computer models to predict stock price changes, particularly focusing on the AAPL dataset from Apple Inc. Accurate stock forecasts are vital for informed investment decisions. By comparing different models—Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU)—the study offers a comprehensive performance analysis of deep learning approaches. The use of different deep learning architectures allowed us to capture different aspects of the stock price data and assess their effectiveness in making predictions. The project's findings would enable us to make informed decisions about which model to choose for future stock price prediction tasks, benefiting traders, investors, and other stakeholders in the financial domain. LSTM exhibited the best predictive capability, capturing intricate patterns due to its ability to handle long-term dependencies GRU also performed well. However, the MLP model didn't do as well because it couldn't grasp the complex patterns in the data as effectively as the other two models. So, for forecasting stock prices, the LSTM model proved to be the most accurate and reliable choice. These findings offer valuable insights for financial practitioners aiming to enhance stock price prediction using deep learning techniques, aiding in smarter investment strategies.

Stock Market Development Opportunities and Factor Analysis

This article focuses on analyzing opportunities analysis for Vietnam stock market in recent years. Method: Methodology includes Qualitative research method: synthesis and inductive methods. Results: Authors stated opportunities for stock market development including but not limit to: current low deposit rates, M&A opportunities in market still rise; coming from policy innovation efforts from the government (local); positive economic growth (average 5.2% last 3 years); etc. From our above analysis, we suggest that to reduce fluctuation of stock market, we need policies supporting GDP growth as well as increasing liquidity if we want to have more fluctuation on market.

Survey Of AI Based Stock Market Prediction

The main aim of this study is to investigate and assess data to derive relevant findings. Substantial effort is invested in the background to collect data through techniques that guide us toward the specific data needed to address the core research question. This chapter explores various options for data analysis techniques available for our data and identifies the most suitable technique for analysing the data. The research delves into the analysis of stock market forecasting in India through AI application technology. We critically evaluate the effectiveness, challenges, and future directions of AI in forecasting stock prices and market movements, aiming to provide insights and guidance for researchers, practitioners, and investors.