Stock Price Fluctuations Research Articles

Pengaruh Kebijakan Investasi, Kebijakan Dividen, dan Profitabilitas terhadap Nilai Perusahaan pada Perusahaan Sektor Perbankan di Bursa Efek Indonesia (BEI) Periode 2021-2023

The rapid development of globalization compels companies to enhance the quality of their products and services to compete in the market. The primary goal of companies is to maximize corporate value, which reflects the well-being of shareholders and attracts investors’ interest. Corporate value is often gauged by the stock price in the capital market, making it crucial for companies to plan sound financial strategies. This research focuses on the banking sector listed on the Indonesia Stock Exchange (IDX) from 2021 to 2023, evaluating the influence of investment policy, dividend policy, and profitability on corporate value. The study employs a descriptive quantitative method using secondary data from the companies’ financial reports. Multiple linear regression analysis is used to assess the relationships between these variables. The results indicate that investment policy and profitability have a positive but not significant effect on corporate value, while dividend policy has a significant positive impact. Corporate value is also affected by stock price fluctuations, which are often inconsistent. These findings affirm that dividend policy is a crucial factor in enhancing corporate value, while investment policy and profitability, although important, do not have a significant impact within the study period. Overall, these independent variables collectively have a significant influence on corporate value in the banking sector on the IDX. This research provides insights for company management in formulating effective strategies to increase corporate value.

Application of Sentiment Analysis to Explore the Connection Between Public Sentiment and Stock Price Movement by Python

This study aims to research the relationship between sentiments of public and the trend of the stock. Specially, it focuses on analyzing how positive or negative sentiment correlated to the trend of stocks. In methodology, this research investigates the predictive power of public sentiment on stock market trend by analyzing users comments on Reddit by utilizing Python Reddit API Wrapper (PRAW) to systematically web-scrape relevant financial discussion from a subreddit called stocks. After the data collecting, preprocessing step is conducted to removing noise and standardize text. Eventually, sentiment analysis is applied to analyze the sentiments of comments by using Natural Language Processing (NLP) techniques. This approach allows for an in-depth examination of the correlation between the sentiments expressed in Reddit comments and subsequent stock price fluctuations by comparing the outcome and the actual price movement. Eventually, through the comparison between the sentiment analysis result and actual stocks movement, the sentiment of public comments shares the same trend with the market movement.

A Study of the Impact of Solvency Indexes on Stock Price Volatility in the Energy Industry

In recent years, the energy industry has developed rapidly, opportunities and competition coexist in the industry, and energy stock price fluctuations are frequent. The article takes the listed enterprises in the energy industry in 2018-2023 as the research object, constructs the evaluation system of debt service indexes, and analyzes the relationship between debt service indexes and stock price volatility in the energy industry by using the fixed effect regression analysis method. The results found that: the gearing ratio and current ratio of energy enterprises are significantly positively correlated with their stock price volatility, and the nature of the enterprise is a new energy enterprise or a traditional energy enterprise, the correlation between their solvency index and stock price fluctuation also has a different strength. The stock price volatility of traditional energy enterprises is more affected by their solvency indexes, while that of new energy enterprises is comparatively less affected.

Examining Dividend Policy as a Mediating Variable in Stock Price Trends: Evidence from the Indonesian Basic Materials

This research investigates the impact of financial ratios, including profitability, liquidity and solvency, on stock prices, with dividend policy as a mediator, in the Indonesian raw materials sector. The analysis covers 100 samples from 25 of companies listed on the Indonesia Stock Exchange (BEI) from 2019 to 2022. The research aims to explore stock price fluctuation trends and examine the relationship between financial ratios and stock prices, as well as the role of dividends policy in mediating this relationship. The findings show that profitability and liquidity are important factors in determining stock prices, while solvency does not influence stock prices, nor does dividend policy play an important role in mediating the relationship between variables. It is hoped that these findings will provide insight for investors, policy makers and industry stakeholders in navigating Indonesia's dynamic construction materials market.

Unveiling the Nexus Between Crises, Investor Sentiment, and Volatility of Tourism-Related Stocks: Empirical Findings From Pakistan

This study investigates the predictive power of sentiment metrics concerning the volatility of tourism-related stocks listed on the Pakistan Stock Exchange (PSX) during recent crises. We employed univariate and multivariate GARCH-family econometric models to analyze daily data collected from March 2020 to April 2023. The data is categorized into two periods: A (COVID-19 until February 2022) and B (the vote of no-confidence motion until April 2023). The findings indicate that Market Sentiment (MS) plays a more significant role than individual sentiment in predicting stock price volatility and negatively impacts return volatility. During period A, the Fear Index (FI) and in B, economic policy uncertainty (EPU) were identified as factors contributing to increased fluctuation in tourism-related stocks. Additionally, MS exhibited a heightened level of bearishness compared to the COVID-19 era. The multivariate BEKK-GARCH analysis revealed that MS past volatility negatively spillover onto the current conditional volatility of PSX firms. This highlights the paramount importance of MS in understanding stock price fluctuations in the PSX. Interestingly, the technology sector exhibited lower sensitivity across the analysis. This study provides valuable insights for decision-makers by elucidating the principles of behavioral finance theory within the context of the PSX. They should prioritize long-term strategies to institutionalize investors, enhance their emotional intelligence, and mitigate psychological biases in the market.

Menguji Dampak Omicron terhadap Harga Saham dan Volume Transaksi: Studi Kasus pada Indeks Saham LQ-45 di Bursa Efek Indonesia

Rapid stock price movements, especially during the ongoing pandemic, demand heightened attention from investors. The unpredictability of stock prices and market reactions to COVID-19 and its variants poses a challenge in predicting their future trends. Beyond stock prices, examining the trading volume of listed companies is crucial. This research aims to analyze potential differences in stock prices and transaction volumes within the LQ-45 Stock Index following the Omicron announcement in Indonesia. The study focuses on the 14 days preceding and following the Omicron announcement, utilizing a saturated sampling approach. The data is analyzed through the Wilcoxon Signed Rank Test for hypothesis testing. Findings suggest significant disparities in stock prices and transaction volumes before and after the Omicron announcement. The research concludes that news or announcements related to the Omicron variant adversely impact the capital market, aligning with event study principles. Such announcements influence market responses, resulting in fluctuations in stock prices and transaction volumes.

Analysis of Investor Reactions to The Right Issue at The IDX Before and During Covid-19

The COVID-19 pandemic has resulted in fluctuations in movement stock prices, and greatly influenced economic conditions as well as reactions of investors regarding the rights issue policy which will influence buying and selling interest shares. The rights issue is an event where information is published as an announcement that can be used to see market reactions. This research was conducted to analyze investor reactions to the Rights Issue announcement. Using 50 samples of companies that carried out rights issues during 2017 - 2020. The inquiry about was carried out utilizing the occasion consider strategy with an advertising show approach. The ordinariness test is the Kolmogorov – Smirnov test. The statistical test used in this research is the difference between two means tests (Wilcoxon Signed Rank Test). The comes about of this inquiry appears that there's a noteworthy contrast in excess returns within the period sometime recently and after the declaration of the rights issue, be that as it may, this inquiry appears that there's no distinction in stock liquidity sometime recently and after the declaration of the rights issue. there's no distinction between the bid-ask spread sometime recently and after the declaration of the rights issue, both sometime recently and amid the widespread COVID-19 implies that the declaration of the rights issue cannot be utilized as a reference choice in an exertion to extend the liquidity of the company offers within the long-term brief, this can be since the declaration of the rights issue isn't sufficient contains data that can impact venture choices.

The Construction of Fuzzy Prediction Model of Stock Price Rise and Fall Based on Machine Learning Technology

In recent years, the use of smart data analysis method to predict the stock price is financial technology; important issues in the field of finch. However, there are many technical indicators and human subjective factors will affect the stock price forecast, so we must effectively grasp the important influence indicators to improve the accuracy of stock price forecast. Therefore, this study uses four machine learning algorithms to predict and analyze the stock price fluctuation through the screening process of technical indicators, and then selects the important technical indicators. In addition, due to the uncertainty and fuzziness of the attributes of technical indicators and human subjective judgment, this study uses the fuzzy inference method to construct the fuzzy inference system to predict the rise and fall of stock price, and proposes the prediction method of the range of the rise and fall of stock price. Finally, this paper makes an empirical analysis on the stock price data of three companies. The results show that the accuracy of stock price forecast is more than 82.13%, and the average accuracy of stock price forecast is more than 83%. Therefore, the fuzzy inference prediction system proposed in this study not only has the theoretical basis, but also can effectively predict the trend and range of stock price, which has practical value and contribution to investors.

The Impact of General Elections on Stock Market Volatility in Indonesia (2004–2023)

Elections are events that play an important role in the economic aspects of a country. Elections create uncertainty and potentially affect investor sentiment, causing fluctuations in Indonesian stock prices. A few months before the election, stock prices in Indonesia showed a significant increase in the Indonesia Composite Stock Price Index (IHSG). This occurs due to an increase in purchasing power for campaign needs and an increase in the government budget. The ARCH/GARCH model is a relevant analysis instrument in looking at market volatility. This study forecasts the close value of IHSG in the election year 2024 and analyzes the effect of elections on stock price volatility in Indonesia. This research resulted in the best model is GARCH (1,1) with RMSE 604.74, MAE 484.27, and MAPE 13.37. The forecasting results from November 23, 2023 - February 29, 2023 tend to increase, the average increase is 0.097%, the biggest increase from the end of November to the beginning of the first week of December 2023 by 0.229%. The relationship between IHSG value and political month is positive. The regression equation shows the IHSG value in months containing political events is 743,53 greater than in non-political months.

DO GREEN ACCOUNTING AND CARBON EMISSION DISCLOSURE AFFECT STOCK RETURN?

Environmental issues such as climate change can be an external factor that influences stock price fluctuations and stock returns. This research aims to analyze the impact of carbon emission disclosure, green accounting practices, and profitability on the share returns of food and beverage sector companies listed on the Indonesia Stock Exchange during the period 2020 to 2022. This research uses a quantitative approach, multiple linear regression analysis methods, and a sample of 54 companies. The results of this study indicate that profitability has a significant impact on stock returns. However, carbon emission disclosure and green accounting do not significantly impact stock returns. The results of this research can be a reference for companies and investors who want to pay closer attention to environmental conditions.

Exploring Different Dynamics of Recurrent Neural Network Methods for Stock Market Prediction - A Comparative Study

ABSTRACT The intricate and unpredictable nature of stock markets underscores the importance of precise forecasting for timely detection of downturns and subsequent rebounds. Various factors, including news, rumors surrounding events or companies, market sentiments, and governmental policies, can significantly impact stock prices. Nevertheless, the precision of current methods remained insufficient until the adoption of artificial neural network architectures like long short-term memory (LSTM). The aim of this study is to create a precise AI-driven platform tailored for both the Indian and international stock markets. This platform is designed to assist retail investors in navigating digital environments by employing various LSTM algorithms. Its primary goals include predicting stock price fluctuations, pinpointing potential investment prospects, and refining trading strategies. The application aims to leverage advanced LSTM algorithms to analyze historical market data, recognize patterns, and provide real-time insights. It will take past price and process it through LSTM algorithms to take a logical decision. In the quest to broaden retail participation in the capital markets, the effort is to develop an application for novice investors who either have no time in research or are the victims of financial mis-selling and enable them to leverage the technology to their advantage.

Application of Stochastic Processes in Financial Market Models

This paper explores the significant role of stochastic processes in financial modeling, tracing the evolution from basic Brownian motion to sophisticated stochastic differential equations used in modern financial markets. Beginning with the historical development of Brownian motion, identified by Robert Brown and later mathematically modeled by Louis Bachelier for stock price fluctuations, the paper outlines its foundational influence on the Efficient Market Hypothesis and the random walk theory. The extension of these concepts in the Black-Scholes model for option pricing highlights the practical applications of these theories in predicting financial outcomes. The discussion progresses to geometric Brownian motion (GBM) and its crucial role in stock price modeling, emphasizing its use in Monte Carlo simulations for option pricing. The limitations of the Black-Scholes model and GBM in dealing with real market conditions such as stochastic volatility and jump-diffusion processes are addressed, showcasing the evolution of more complex models like the Heston model and GARCH. Interest rate models like the Vasicek and Cox-Ingersoll-Ross models are evaluated for their real-world applicability, particularly in scenarios of low and negative interest rates. This comprehensive review not only underscores the theoretical advancements in financial modeling but also its practical implications in contemporary financial markets.

Selective genetic algorithm labeling: A new data labeling method for machine learning stock market trading systems

Investing in the stock market involves high risks and equivalent returns. Many investors tend to be loss averse and suffer significant losses due to information asymmetry and emotion-based investing. In order to minimize such losses, various studies are attempting to predict the stock market's future and automate investment decisions. However, the stock market is affected by many factors and poses a challenge in prediction. Also, many researchers who develop stock trading systems are interested primarily in improving prediction model performance. Particularly, most studies set up-down labels in the data labeling phase. However, up-down labeling views all stock price fluctuations as identical, resulting in inefficient model training by including substantial noise in the learning data. Therefore, this study proposes SGA (Selective Genetic Algorithm) labeling to overcome such concerns. A trading system was developed using the proposed method, and a machine learning model and ensemble method supported the label prediction phase. The proposed trading system was analyzed empirically with 38 NASDAQ stocks and 33 KOSPI stocks, and a comparative experiment with up-down labeling method was conducted. In this study, SGA labeling performance exceeded that of up-down labeling. Additionally, instance selection and ensemble methods affected the trading system's performance.

Determining the Ebullient Phases in EFTA Stock Markets

Previous empirical literature supports the idea that stock bubbles have impacts on the efficient allocation of wealth. Researchers targeted various economies in the past using various methods to explore the bubble phenomenon. This study uses the generalized SADF test, which is admitted by empirical literature to be the most successful technique, to explore stock bubbles in three countries included in the European Free Trade Association (EFTA) that have not been studied before. This paper takes the lead and tests for the existence of bubbles in the monthly end index prices of respective countries based on the latest available time series data from January 2001 to September 2019. Based on empirical results, it is concluded that the stock markets of all three countries experienced multiple bubbles during the study period. The case of Iceland is worse, where comparatively more fluctuations in stock prices are seen. To avoid the occurrence of further stock price bubbles in these countries, policy recommendations are provided as well.

Web Application for Company Stock Price Prediction

This study contributes to predicting stock prices. In this paper, we have provided a novel machine learning and deep learning technique for stock prediction, which capitalizes on the complex emotional patterns of investors that can be extracted from Twitter data. By joining two separate datasets, such as Twitter sentiments and the associated stock prices, we have applied complex algorithms and evaluated this method, which classifies a user’s sentiment into a positive and negative one. It was possible to address the complexity of investor behavior within the highly unstable stock market environment. One of the most important problems that are encountered by developers is gaining the possibility to properly capture stock price fluctuations in the temporal dimension. It implies the necessity to utilize such reliable evaluation metrics as precision-recall factors for classification purpose and root mean squared error for regression purpose. by using these metrics, the performance of the predictive model can be evaluated in detail, which allows to draw meaningful permanent conclusions within the context of how well it reflects the complex relationship between investor sentiment and market dynamics. The results of the evaluation conducted as part of the process of validating the proposed hypothesis show that, indeed, the predictive accuracy improves significantly from the use of machine learning and deep learning algorithms if paired with Twitter users’ emotional sentiment data. This combination enhances the predictive capacity of the model and provides meaningful insights into how the changes in stock prices are impacted by the sentiment of investors. Moreover, in the process of this study, the explanation and detail consideration of the algorithms for feature engineering and data processing were provided, which stands crucial for increasing the predictive model’s accuracy The study has a valuable implication for both the financial analysis and technical spheres due to the resolution of these methods and presents a model for further research activities in stock prediction. To conclude, this interdisciplinary strategy that combines sentiment analysis and stock prediction not only advances our understanding of the investor behavior but also brings the opportunity for novel applications in the emerging field at the juncture of technology and finance. As a result, the current research sheds light on the intricate connection between the dynamics of the stock market and investor sentiment, thus offering new insights that can better inform predictive modeling research in the financial industry.

Research on the application of deep learning techniques in stock market prediction and investment decision-making in financial management

Introduction: This paper introduces a deep learning approach based on Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and attention mechanism for stock market prediction and investment decision making in financial management. These methods leverage the advantages of deep learning to capture complex patterns and dependencies in financial time series data. Stock market prediction and investment decision-making have always been important issues in financial management.Methods: Traditional statistical models often struggle to handle nonlinear relationships and complex temporal dependencies, thus necessitating the use of deep learning methods to improve prediction accuracy and decision effectiveness. This paper adopts a hybrid deep learning model incorporating CNN, BiLSTM, and attention mechanism. CNN can extract meaningful features from historical price or trading volume data, while BiLSTM can capture dependencies between past and future sequences. The attention mechanism allows the model to focus on the most relevant parts of the data. These methods are integrated to create a comprehensive stock market prediction model. We validate the effectiveness of the proposed methods through experiments on real stock market data. Compared to traditional models, the deep learning model utilizing CNN, BiLSTM, and attention mechanism demonstrates superior performance in stock market prediction and investment decision-making.Results and Discussion: Through ablation experiments on the dataset, our deep learning model achieves the best performance across all metrics. For example, the Mean Absolute Error (MAE) is 15.20, the Mean Absolute Percentage Error (MAPE) is 4.12%, the Root Mean Square Error (RMSE) is 2.13, and the Mean Squared Error (MSE) is 4.56. This indicates that these methods can predict stock market trends and price fluctuations more accurately, providing financial managers with more reliable decision guidance. This research holds significant implications for the field of financial management. It offers investors and financial institutions an innovative approach to better understand and predict stock market behavior, enabling them to make wiser investment decisions.

The Evaluation of Impacts of Multi Factor Model on TTC Stock Price: a Case in Real Estate Industry in Vietnam

Objectives: The objective of this study is to analyze the impact of macroeconomic factors on the stock price of a real estate firm, specifically Thanh Cong TCC, in Vietnam from 2014 to 2020. The study aims to assess both positive and negative influences of five macroeconomic variables on stock prices and to understand the overall business health reflected by stock fluctuations. Methods: This study utilizes a data collection method involving statistics, analysis, synthesis, comparison, and quantitative analysis to generate qualitative comments and discussion. Econometric methods, particularly regression analysis, are employed to evaluate the quantitative results and assess the impact of macroeconomic factors on stock prices. Results: The quantitative research reveals several key findings. Firstly, the stock price of Thanh Cong TCC (Y) exhibits a negative correlation with the lending rate (R) and GDP growth, indicating that higher lending rates and lower GDP growth negatively impact stock prices. Conversely, there is a positive correlation between stock prices and the risk-free rate (Rf), consumer price index (CPI), and VNINDEX, suggesting that these factors positively influence stock prices. Furthermore, the study identifies GDP growth and lending rates as having the highest impact on firm stock prices. Conclusion: In conclusion, this study highlights the importance of considering macroeconomic factors in understanding stock price fluctuations in real estate firms in developing countries like Vietnam. The findings emphasize the significant impact of lending rates and GDP growth on stock prices, indicating their pivotal role in shaping the business health and overall economic conditions. By recognizing these influences, firms like Thanh Cong TCC can better navigate market dynamics and make informed strategic decisions to enhance their performance and resilience in the face of macroeconomic challenges.

Is corporate social responsibility a stock price stabilizer during the public emergency? An integrated view of government response

PurposeThe aim of this study is to investigate the role of Corporate Social Responsibility (CSR) in assisting firms in their response to public emergency crises under the integrated view of government emergency response.Design/methodology/approachUsing event study and survival analysis method, the authors examine whether CSR can act as a stock price stabilizer for companies from China by splitting the stock price fluctuations into two phases – CSR price insurance, which decrease the shock on stock prices during the emergency crisis, and CSR price recovery, which helps stock prices rebound faster during the postcrisis phase.FindingsThe authors’ empirical results confirm the stabilizer role of CSR during crisis and that effective government response can strengthen such effect. Furthermore, the authors examine the different aspects of the government’s response and the impact of multiple waves of public emergency.Originality/valueThis study provides empirical evidence on the topic of CSR and the government’s response to public emergency under the emerging context.

Random walk theory and application

This project presents an overview of Random Walk Theory and its applications, as discussed in the provided project work. Random Walk Theory posits that changes in elements like stock prices follow a distribution independent of past movements, making future predictions challenging. Originating from the work of French mathematician Louis Bachelier and later popularized by economist Burton Markiel, the theory finds extensive applications beyond finance, spanning fields such as psychology, economics, and physics. The project delves into various types of random walks, including symmetric random walks, and explores their implications in different spaces, from graphs to higher-dimensional vector spaces. It provides definitions, examples, and graphical representations to elucidate random walk concepts, highlighting their relevance in practical scenarios like particle movement and stock price fluctuations. Key concepts such as the reflection principle and the main lemma are discussed to provide a comprehensive understanding of random walks and their properties. Through examples and lemmas, the project elucidates the mathematical foundations of random walks, offering insights into their behavior and applications across diverse disciplines. In summary, this project contributes to a deeper comprehension of Random Walk Theory, serving as a fundamental framework for understanding stochastic processes and their real-world implications.

Analysis of the Correlation between Corporate Dividend Policy and Stock Price

Based on the basic premise and assumption of building a multiple linear regression model and the correlation theory between corporate dividend policy and stock price, this paper adopts the multiple linear regression method of cross-section data to analyze the correlation between corporate dividend policy and stock price of 1458 enterprises from Choice financial terminal after strictly eliminating outliers. It is concluded that the significant positive correlation between dividend per share and stock price is stronger than that between fundamental indicators such as company total market value, return on equity, turnover rate of trading indicators and dividend distribution ratio and stock price, respectively. It is concluded that dividend distribution ratio is helpful to stabilize stock price fluctuations and improve investor return mechanism is beneficial to the healthy development of A-share market.