Stock Price Research Articles

Comprehensive Analysis of Risk and Return Associated with Equity Stock Investment

This study provides a comprehensive analysis of the risk and return profiles of six prominent Indian automobile stocks: TVS Motor, Ashok Leyland, Bajaj Auto, MRF Ltd, Hero MotoCorp, and Mahindra & Mahindra, covering the period from 2020 to 2024. The findings reveal significant growth across the sector, with varying degrees of volatility. Key metrics such as stock price growth, earnings per share (EPS), return on equity (ROE), and price-to- earnings (P/E) ratios were analyzed, highlighting the importance of understanding both growth potential and associated risks

Faktor-Faktor yang Memengaruhi Harga Saham pada Bisnis Subsektor Makanan dan Minuman yang Terdaftar di IDX

This study aims to investigate the effects of the Current Ratio (CRA), Debt-to-Equity Ratio (DERA), and Return on Equity (ROEQ) on the stock prices (HS) of companies in the food and beverage sector listed on the Indonesia Stock Exchange (IDX) during the period from 2017 to 2022. The analysis utilizes data sourced from the published annual financial statements of the relevant companies, employing a purposive sampling method to select 17 companies from the food and beverage subsector as the research sample. This research employs a quantitative descriptive approach and multiple linear regression analysis processed with Eviews Version 12. The findings indicate that CRA does not have a significant effect on stock prices when analyzed separately. Conversely, ROEQ has a positive and significant impact on stock prices, while DERA shows a significant negative effect. These findings affirm that the three variables, namely CRA, DERA, and ROEQ, contribute to stock price fluctuations both individually and collectively. Therefore, this research recommends that companies in the food and beverage sector enhance their financial performance, particularly through better debt management and increased profits, in order to strengthen the attractiveness of their stock prices in the market and attract greater interest from investors.

Feature Expansion Effect Approach for Improving Stock Price Prediction Performance

Feature Expansion Effect Approach for Improving Stock Price Prediction Performance

The Value Relevance of Accounting Information in Iraq

The paper tests the value relevance of accounting information extracted from financial statements of 15 Iraqi companies listed on the Iraq Stock Exchange (ISX) for period 2010–2020. The relationships among accounting information (measured as earnings, book value, and cash flows) and company value (measured as stock prices) have been analyzed via descriptive statistics, correlation and regression. It is found that all these relationships are positive and significant in different levels. Compared with book value, earnings have the highest influence on stock price, while cash flows have the lowest influence on stock price. This study participates in the understanding of value relevance of accounting information in ISX as an emerging market and presenting peer insights to enhance the financial reporting standards in this country and in other similar countries in Middle East region.

The Impact of Mandatory ESG Disclosure on Share Price

Abstract: The importance of sustainable development has attracted public attention. To achieve the global sustainable development goals, copious countries have recently begun formulating and implementing mandatory ESG (Environment, Social and Governance) disclosure policies. This has prompted the research community to investigate the impact of compulsory disclosure on the interests of corporate stakeholders, such as share price. According to stakeholder theory, long-term value-based theory, resource-based theory, and efficient market hypothesis, ESG mandatory disclosure will have short-term and long-term impacts on stock prices. Specifically, from the short-term perspective, good ESG reports will lead to rapid growth. Then the price will return to normal level. Moreover, in the long term, this has no significant impact on share prices. This article will use event research methods and linear regression models to specifically study the impact of Apple's ESG report disclosure on stock prices. Finally, research has found that in a normal environment, mandatory ESG disclosure will have a positive short-term impact on share price according to the good quality of ESG reports, while this will have a negative influence on stock price due to the poor quality of ESG reports. However, in special circumstances such as the 2022-2022 global epidemic period and long-term perspective, the disclosure of ESG reports has no significant impact on stock prices.

THE EFFECT OF CAPITAL STRUCTURE ON STOCK PRICES MEDIATED BY PROFITABILITY

This study aims to determine the effect of profitability and capital structure on stock prices, the effect of profitability on capital structure and the effect of capital structure on stock prices by mediating profitability. The approach in this study is quantitative research using secondary data in the form of financial reports published on the Indonesia Stock Exchange with data analysis techniques using SPSS. The research method used is purposive sampling with a sample of 28 Property & Real Estate Sector Companies Listed on the Indonesia Stock Exchange in 2019-2021. The results of this study indicate that (1) profitability does not affect stock prices, (2) Profitability does not affect capital structure, (3) Capital structure does not affect stock prices, (4) Profitability is unable to mediate the relationship between Capital structure and stock prices.

Corporate social responsibility and stock price informativeness: evidence from the global banking industry

ABSTRACT This study explores the impact of corporate social responsibility (CSR) performance on stock price informativeness (SPI) in the global banking industry. Drawing on a dataset of 217 banks from 45 countries between 2009 and 2020, we find that CSR performance significantly enhances SPI by ensuring that more bank-specific information is reflected in stock prices. These results align with signalling theory, which suggests that CSR performance reduces information asymmetry by signalling a firm’s commitment to long-term sustainability and ethical practices, thereby improving market efficiency. The effect is stronger in countries with mandatory CSR disclosure regulations, underscoring the role of regulatory frameworks in amplifying the benefits of CSR activities. Furthermore, we find that the relationship between CSR performance and SPI is more pronounced in investment banks than in commercial banks, reflecting the greater market sensitivity to CSR performance in institutions with higher risk profiles. Our findings provide evidence of a critical link between CSR performance and improved market transparency, highlighting the role of CSR initiatives in fostering more accurate stock price reflections.

Valentine's Day and Abnormal Returns in Southeast Asia

The market becomes inefficient as a result of market anomalies such as Valentine's Day, which creates positive emotions and leads investors to act irrationally when making decisions. These irrational actions result in fluctuations in stock prices, which offer investors opportunities for returns. This study aims to investigate the impact of Valentine's Day on market index returns in Southeast Asia from 2014 to 2024. The ARCH-GARCH (1,1) model was used in this study and the results indicate that Valentine's Day has an impact on market index returns in Southeast Asia (specifically Malaysia, Thailand, and Singapore), but not in Indonesia, the Philippines, or Vietnam. These results suggest that investors who are influenced by positive emotions are more likely to feel optimistic when making risky decisions. Therefore, market anomalies like Valentine's Day can be used as a consideration for investors seeking abnormal stock returns.

Can Company radiation Announcement Influence Investor Sentiment and Predict Abnormal Returns? Tunisia Evidence

The purpose of this paper is to observe whether the announcement of company radiation can affect their abnormal return on the Tunisian stock exchange. The sample includes 5 non financial listed stocks in Tunisia Stock Exchange during the years 2012-2021. Company decides to announce its radiation to signal the market that the firm is now processing future prospects, which will result in changing its stock prices. The findings yield qualitatively consistent with the previous research. The result shows that the stock prices move upward significantly after dividend announcements. Abnormal return (AR) and cumulative abnormal return (CAR) from the market model are statistically significantly revealed. Moreover, most event windows show that the stock return becomes lower when the company delisted from stock market. The result is limited to the absence of an important number of firms listed in TSE from this period. Investors in Tunisia react negatively to firms’ radiation and show their preference for dividend to self control, satisfaction and increase their profit. This document provides evidence of the phenomenon of firms’ radiation from markets, we document the impact of delisting announcement on stock returns. Even though the results indicate that Tunisian market react to the announcement of companies’ radiation by managers. Moreover, the timing of delisting decision is affected by some different factor related to the firms’ situation and institutional environment. Tunisian investors react negatively to the announcement to firms delisting from Tunisian market.

Effect of integrated financial reporting on firm value with the role of adjusting corporate governance in companies listed on the Iraqi stock exchange

The issue of conflict of interests between shareholders and managers and the mechanism of corporate governance as a solution to reduce agency costs associated with it is one of the important issues that has attracted the attention of many researchers and experts due to its effect on the performance of companies. The purpose of this research is the effect of integrated financial reporting on company value with the role of adjusting corporate governance in companies listed on the Iraqi Stock Exchange. The current research is applied in terms of purpose and descriptive and correlational in nature. The time domain of the research covers a period of years, from the beginning of 2016 to the end of 2022. The studied sample included 10 companies that were selected by systematic elimination method. The results of the test of research hypotheses indicated that corporate governance does not have a modifying role in the effect of integrated financial reporting on the value of the company. It seems that in the Iraqi capital market, integrated financial reporting tools cannot help the shareholders in evaluating the company's value, and other factors affecting the stock price (company's value) are considered by the shareholders.

Stock price nowcasting and forecasting with deep learning

Stock price nowcasting and forecasting with deep learning

Market Predictability Before the Closing Bell Rings

This study examines the predictability of the last 30 min of intraday stock price movements within the US financial market. The analysis encompasses several potential explanatory variables, including returns from each 30 min intraday trading session, overnight returns, the federal reserve fund rate decision days and the subsequent three days, the US dollar index, month effects, weekday effects, and market volatilities. Market-adaptive trading strategies are developed and backtested on the basis of the study’s insights. Unlike the commonly employed multiple linear regression methods with Gaussian errors, this research utilizes a Bayesian linear regression model with Student-t error terms to more accurately capture the heavy tails characteristic of financial returns. A comparative analysis of these two approaches is conducted and the limitations inherent in the traditionally used method are discussed. Our main findings are based on data from 2007 to 2018. We observed that well-studied factors such as overnight effects and intraday momentum have diminished over time. Some other new factors were significant, such as lunchtime returns during boring days and the tug-of-war effect over the days after a federal fund rate change decision. Ultimately, we incorporate findings derived from data spanning 2022 to 2024 to provide a contemporary perspective on the examined components, followed by a discussion of the study’s limitations.

A Review of Prediction Techniques used in the Stock Market

The prediction of stock market movements is a critical task for investors, financial analysts, and researchers. In recent years, significant advancements have been made in the field of stock prediction, driven by the integration of machine learning and data analysis techniques. Though stock market predictions are highly desired, there are many factors contributing towards volatility of the market. There is a need for extensive study and concentration on various predictive techniques to investigate different scenarios triggering such volatility. This paper reviews the latest methodologies employed for predicting stock prices, with a particular focus on the Australian stock market. Key techniques such as time series analysis like ARIMA & GARCH, machine learning models like SVM, LSTM & Neural Network, and sentiment analysis are discussed, highlighting their applications, key strengths, and some limitations.

10‐K complexity, analysts' forecasts, and price discovery in capital markets

AbstractThis paper examines the behaviour of stock prices and analysts' earnings forecasts following firms' increasingly complex 10‐K filings. Our evidence suggests that greater information uncertainty outweighs greater analyst ability, and the net effect drives greater underreaction in analysts' earnings forecasts following the release of more complex 10‐Ks. Further analysis suggests that analyst underreaction mitigates market overreaction to the information in low‐complexity 10‐K filings, and overwhelms market overreaction to the information in high‐complexity 10‐K filings thus compromising price discovery of the earnings implications of more complex 10‐Ks.

Artificial Intelligence and Deep Learning in Stock Prediction: A Bibliometric Review

Artificial intelligence (AI) and deep learning (DL) are advancing in stock market prediction, attracting the attention of researchers in computer science and finance. This bibliometric review analyzes 525 articles published from 1991 to 2024 in Scopus-indexed journals, utilizing VOSviewer software to identify key research trends, influential contributors, and burgeoning themes. The bibliometric analysis encompasses a performance analysis of the most prominent scientific contributors and a network analysis of scientific mapping, which includes co-authorship, co-occurrence, citation, bibliographical coupling, and co-citation analyses enabled by the VOSviewer software. Among the 693 countries, significant hubs of knowledge production include China, the US, India, and the UK, highlighting the global relevance of the field. Various AI and DL technologies are increasingly employed in stock price predictions, with artificial neural networks (ANN) and other methods such as long short-term memory (LSTM), Random Forest, Sentiment Analysis, Support Vector Machine/Regression (SVM/SVR), among the 1399 keyword counts in publications. Influential studies such as LeBaron (1999) and Moghaddam (2016) have shaped foundational research in 8159 citations. This review offers original insights into the bibliometric landscape of AI and DL applications in finance by mapping global knowledge production and identifying critical AI methods advancing stock market prediction. It enables finance professionals to learn about technological developments and trends to enhance decision-making and gain market advantage.

Unlocking Stock Price Prognostication Dual Attention-Infused Bi-Directional LSTM Empowered by Bayesian Optimization DA-Bi-LSTM-BO

Unlocking Stock Price Prognostication Dual Attention-Infused Bi-Directional LSTM Empowered by Bayesian Optimization DA-Bi-LSTM-BO

A Study on Stock Analysis of Automobile Industries

This study investigates the stock movement of automobile companies listed on the NIFTY50 index, focusing on five major firms: TVS Motor, MRF, Ashok Leyland, Maruti Suzuki, and Tata Motors. Using quarterly and annual data from January 2021 to December 2021, the analysis identifies key trends and volatility factors impacting stock prices in the automotive sector. Key findings suggest that fluctuations in stock prices are influenced by quarterly performance, industry trends, and macroeconomic conditions. Additionally, the report highlights the impact of the COVID-19 pandemic on the sector, with companies facing challenges such as reduced demand, increased costs, and supply chain disruptions. The study concludes that stock movements in this industry are volatile, urging investors to monitor these fluctuations closely before making investment decisions.

COMPARATIVE ANALYSIS OF REGRESSION MODELS FOR STOCK PRICE PREDICTION: LINEAR, SUPPORT VECTOR, POLYNOMIAL, AND LASSO

This paper investigates the performance of various regression-based machine learning techniques for short-term stock price prediction. Four regression algorithms – linear regression, support vector regression (SVR), polynomial regression and LASSO regression – were applied to Apple Inc’s historical price data for two-years ending on October 1st, 2024, to predict the next day’s closing stock price. Technical indicators such as moving average, relative strength index (RSI) and lag features were included in the regressions to improve prediction performance. Evaluation metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and R-squared (R²) were employed to assess the models’ performance. Results show that linear regression and LASSO regression were the most performant models, reaching R² values of more than 0.95, with minimal error values. While SVR yielded the poorest results when accounting for all metrics. Overall, the study highlights the predictive power of simpler regression models over more complex ones in stock price predictions and offers recommendations for model selection.

GEOPOLITICAL INSTABILITY AND ITS IMPACT ON FINANCIAL MARKET DYNAMICS: AN ARTIFICIAL INTELLIGENCE APPROACH AND SENTIMENT ANALYSIS

This paper investigates the impact of geopolitical instability, specifically the Russia-Ukraine conflict, on the dynamics of the Ukrainian financial market. The study focuses on key indices and companies, including the PFTS Index, Kernel Holding SA (KER), MHP SA DRC (MHPC), and Ukrnafta (UNAF). Utilizing advanced machine learning models—decision trees, Random Forest, and Long Short-Term Memory (LSTM) networks—the research predicts stock price changes in response to market volatility induced by geopolitical events. The analysis reveals a significant correlation between the onset of conflict and stock price fluctuations, particularly in the agricultural and energy sectors, with notable resilience differences across industries. The findings underscore the importance of incorporating predictive analytics for decision-making in turbulent market environments, offering valuable insights for investors and policymakers navigating uncertainty.

OFDI and stock price synchronicity: Evidence from China

AbstractUsing the data on outward foreign direct investment (OFDI) by listed companies in China, we find that OFDI significantly increases stock price synchronicity. This result indicates that OFDI releases more market/industry (systematic) information relative to private information. Reverse technology spillover and the host country/region institutional environment are two potential mechanisms through which OFDI increases synchronicity. We find that OFDI plays a role in screening firms with better transparency and corporate governance. The propensity score matching method and instrumental variable regressions are employed to rule out endogeneity issues. We also check the impact of stock return characteristics and macro shocks on this positive relationship.