Stock Price Changes Research Articles

The Impact of the Federal Funds Rate Hike on Walmart Stock Price: A Short-term and Dynamic Perspective

The relationship between interest rates and stock prices has been a topic of debate among scholars. Most scholars agree that interest rates are negatively correlated with stock prices in general, but there are differences in specific countries, industries, and stocks themselves. This paper take Wal-Mart's stock price before the Federal's interest rate hike on 17 March 2022 as a time series, and use ARIMA models to predict the future price, and test whether the interest rate hike affects Wal-Mart's stock price in the ultrashort term and the short term by examining the difference between the model's predicted price and the actual price. The finding of this paper is that interest rate hikes have a slight negative impact on Wal-Mart's stock price in the ultra-short term and no significant impact in the short term. Since previous studies on interest rates and stock prices have dealt less with short-term price changes of individual stocks, this paper provides direction for related research in this area and serves to fill this gap. The advice given to investors in this paper is that when interest rates rise, investors can go short on ultra-short investment period within a week, but if the investment period is longer than nine days, they should be careful to observe that other factors, because interest rate hike do not have a significant impact.

The Impact of Tax Aggressiveness, Enterprise Value, Thin Capital, and Company Performance on Share Price on Jakarta Islamic Index Category Companies

This research is part of a continuation of previous research, namely about the Jakarta Islamic Index, but currently researchers want to know the level of influence of JII category stock prices can be influenced by Tax Aggressiveness, Company Value, Thin Capital and Company Performance of the Jakarta Islamic Index Issuers. The analysis used in this study is a Quantitative Method, with the analysis used by conducting normality tests and multiple regression tests, so that from the test results can be known the level of influence of these independent variables on the JII stock price variable. The conclusion of the study is that partially or simultaneously the variable Change in Stock Prices of issuers indexed as the Jakarta Islamic Index is positively and cynically influenced by all independent variables which include tax aggressiveness, Enterprise Value, Thin Capital and Company Performance.

ANÁLISIS DE FACTORES MACROECONÓMICOS QUE AFECTAN LA COTIZACIÓN DE ACCIONES DE LAS COMPAÑÍAS AÉREAS

Change in stock prices and development of the market vary depending on decisions made by investors. Decisions made by investors are determined by corporate performance, the country’s economic situation, political developments and changes in macroeconomic factors. In this study, the relationship between stock prices of airlines and macroeconomic variables was analyzed. To accomplish this, daily macroeconomic data of 14 airlines covering the years 2009-2018 were analyzed by Toda Yomamato and HatemiJ asymmetric causality tests, and macroeconomic factors (Brent oil price, dollar exchange rate and interest rate) determining stock prices were identified. According to the results of the Toda and Yomamato causality test, it was found that there was a significant relationship among variables of the dollar exchange rate, the price of oil and stock prices of airlines. Except for one airline, there was no significant relationship between interest rate and stock price. According to the results of the Hatemi-J asymmetric causality test, it was found that there were significant relationships among variables of the dollar exchange rate, oil price, interest rate, and airline stock prices. In light of these findings, innovation and adoption of alternative fuel technology by the aviation industry and airlines can be a successful alternative to oil price risk. Results on exchange rate and interest rate changes indicate that airlines and related governments should focus on policies that increase growth of the aviation industry

Assessment of the impact of big data analysis on decision-making in stock trading processes

This paper aims to construct a trader model tailored to offer individualized investment strategies for users engaged in the US stock market. The approach taken involves two key aspects: forecasting stock prices and discerning users' personality traits to formulate optimal trading recommendations. The framework is realized through the integration of neural networks and the Moving Averages algorithm. This combined approach allows forecasting upcoming changes in stock prices and generating appropriate trading signals. Complementing this, a user risk assessment is administered, and its outcomes are leveraged to furnish personalized trading advice, offering invaluable support for informed decision-making amid intricate real-time scenarios. By synergizing predictive modeling and user-specific insights, this comprehensive system contributes to enhancing trading precision and user empowerment. This work can be useful in decision making for a wide range of stock market participants, as well as for institutions specialized for this area of operation.

Identification and Ranking of Financial, Non-Financial, and Behavioral Components Influencing Earnings Response Coefficient in the Iranian Capital Market (Data Mining Approach)

Objective: The relationship between accounting profit and investor reactions, as well as stock price changes, is measured using the Earnings Response Coefficient (ERC). Previous research indicates a variation in ERC between companies and over time, which is attributed to specific company factors and characteristics. The aim of this study is to identify and rank the financial, non-financial, and behavioral components affecting companies' ERC. Methodology: Using data from 153 companies listed on the Tehran Stock Exchange over the period 2013 to 2022, and employing data mining techniques and two methods: stepwise forward regression and regression decision tree, the influential components on ERC were identified and ranked. Findings: The results of the stepwise forward regression method indicated that the components of auditor's opinion type, earnings per share (EPS), stock liquidity, growth opportunities, earnings stability, inflation rate, sales growth, operating profit ratio, relative strength index per share, and market index return respectively influence ERC. The regression decision tree method results also showed that the components of earnings stability, stock liquidity, EPS, stock price synchronicity, stock trading imbalance, adjusted trading volume, psychological line index, relative strength index per share, earnings conservatism, and financial statement restatements respectively influence ERC. The Wilcoxon test results also showed that the ranking of components influencing ERC is not the same in the two methods. Additionally, comparing the mean absolute error of the two methods indicated that the regression decision tree method identifies and ranks the influential components on ERC more accurately. Conclusion: The results of both methods confirm the high impact of EPS, stock liquidity, earnings stability, and relative strength index per share on the ERC of companies.

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.

A study of the impact of financial news media coverage patterns on stock price fluctuations

Abstract This paper examines the impact of media communication on the stock market in the context of the digital age, particularly its role in stock price volatility. With the significant increase in the speed, breadth and depth of media communication, the efficiency of stock market information transmission has been dramatically improved, significantly impacting stock price volatility. The article first explores the specific impact of media coverage characteristics and methods on stock price volatility by quantitatively analyzing media coverage’s breadth, manner and content. Then, the article utilizes multiple linear regression models to deeply explore the impact of various news texts on stock price volatility in different reporting modes (e.g., live reporting, television, and text reporting) in the Shanghai and Shenzhen stock markets. The findings show a significant association between the breadth of media coverage and stock prices at the 1% significance level. In addition, the regression coefficients of content and manner of coverage on stock price changes are 0.096 and 0.229, respectively, which are both significant at the 5% significance level. The research results of this paper not only reveal the close relationship between media communication and stock market volatility and provide theoretical support for predicting dramatic stock market volatility, which helps to promote the construction of a healthy and orderly capital market order.

Factors Influencing Stock Prices in Commercial Banks of Nepal

This study explores the factors that influence stock prices in commercial banking business in Nepal. Descriptive and causal-comparative research design and purposive sampling technique used in this research examine how critical financial variables affect market prices per share (MPPS), including book value per share, earnings per share, dividend per share, and price-earnings ratio. There is a statistically significant but relatively small positive correlation between book value per share. The results show that MPPS and earnings per share, dividend per share, and price-earnings ratio are significantly positively correlated. The model demonstrates its robustness in capturing stock price changes by accounting for around 86.1% of the variation in MPPS. These observations offer insightful recommendations for experts and investors involved in Nepal's commercial banking sector.

Enhancing Investment Strategies: A Comprehensive Technical Analysis Approach for Informed Buy-Sell-Hold Decisions in NEPSE (Nepal Stock Exchange)

Stock price forecasting is a type of time series issue that anticipates a company's condition based on an analysis of time values. To address the Buy-Sell-Hold problem, investors need to make accurate predictions about future indices or stock price changes. In this study, data was collected for 6 months (February 2023 to July 2023), and technical trading tools such as candlesticks, Bollinger Band, and MACD were used. While personal views of respondents were taken from Google Form Questionnaire Surveys to determine the Buy-Sell-Hold in NEPSE. A questionnaire survey yielded 62 responses. A combined chart of Candle Sticks, BB, and MACD gives better results compared to the analysis of a single technical indicator. This data shows that 70.97% agreed that the technical analysis gives the right path and decision, and the rest 29.03% did not agree with the technical analysis to predict the Buy-Sell-Hold decisions. However, seeing the statically, real distribution of the "Buy-Sell-Hold" would be helpful for a fuller analysis and understanding of the data. These analyses can be favourable to the good economic and political condition of Nepal.

Factors Influencing Stock Prices in Commercial Banks of Nepal

This study explores the factors that influence stock prices in commercial banking business in Nepal. Descriptive and causal-comparative research design and purposive sampling technique used in this research examine how critical financial variables affect market prices per share (MPPS), including book value per share, earnings per share, dividend per share, and price-earnings ratio. There is a statistically significant but relatively small positive correlation between book value per share. The results show that MPPS and earnings per share, dividend per share, and price-earnings ratio are significantly positively correlated. The model demonstrates its robustness in capturing stock price changes by accounting for around 86.1% of the variation in MPPS. These observations offer insightful recommendations for experts and investors involved in Nepal's commercial banking sector.

The Influence of Markov Chain and Properties of Principal Component Solutions in The Analysis of Share Price Movements for Stock Market

The stock market performance and operation has been widely recognized as a significantly viable investment field in financial markets. Therefore, this paper studied stochastic analysis of Markov chain and PCA in the closing share price data of Access and Fidelity banks (2016-2022) through Nigeria Stock Exchange. The share prices were transformed into 3-steps transition probability matrix solution to cover this number of years. The striking focus of this is on two merged banks where their transition probability matrix was considered. The future share prices changes were known. The criteria of obtaining four share prices which formed from the two merged banks 2x2 matrices were given and analytical solution of principal component were considered for future stock price changes. From the solution matrix of two merged banks showed that the has the best probability of price increasing in the near future: 12%, best probability of reducing in future by 22% and best probability of no-change in the near future by 21% which is a tool for proper decision making in the day-to-day management of the bank; which shows it is profit making organization and are hopeful for future investment plans both short or

Using mergers and acquisitions to increase stock returns in the banking sector: A case study on the Indonesian stock exchange

This study aims to determine the impact of merger and acquisition (mergers and acquisitions) activity in the banking sector on stock performance. It focuses on banking companies listed on the Indonesia Stock Exchange (IDX) that carried out mergers and acquisitions from 2019 to 2021. A 15-day period was observed, consisting of seven days before and seven days after the mergers and acquisitions event and the day of the event itself. The data was analyzed using market study methods and event studies. Stock price data was used to analyze the stock returns during mergers and acquisitions events, and market studies were used to measure the reactions through changes in stock prices after certain events. The results revealed that stock returns and abnormal stock returns increased across all events (E1, E2, and E3). Additionally, cumulative abnormal returns showed a positive effect on events E1 and E3, while event E2 showed a negative effect. Overall, the findings indicated that the market responded positively to mergers and acquisitions activity in the Indonesian banking sector, and the acquired banks performed well after the mergers and acquisitions. The study's results have implications for the internal management of companies, as it suggests that mergers and acquisitions could potentially lead to profit gains in the banking sector. Moreover, investors can use this insight to make more informed decisions when considering investments in the banking sector.

THE INFLUENCE OF FOREIGN AND DOMESTIC INVESTORS ON THE PRICE AND VOLUME OF STOCKS THE INDONESIAN STOCK EXCHANGE: TESTING JANUARY EFFECT

This research aims to test and find empirical theoretical evidence regarding the influence of January on changes in stock prices and volume by foreign and domestic investors. The research method uses a quantitative method. The researcher uses sample data on the LQ-45 company's stock return report on January 2, 2020 to 2023 from Indonesia Stock Exchange by using Multiple Regression Analysis with IBM SPSS 20. This research has found that partially foreign and domestic investor have significant impact on volume of stock while Domestic investor have significant impact on price stock but Foreign Investor does not significant impact on price stock. Simultaneously Foreign and domestic investor have significant impact on price and volume of stock. The contribution and benefit of this research is to expand the scientific and theoretical models of the January effect based on trading prices and share volume by foreign and domestic investors

Digital Currency and Financial Markets in Nigeria: Impact and Policy Implications

<p class="MsoNormal" style="margin-top: 12pt; text-align: justify;"><span lang="EN-US" style="font-family: arial, helvetica, sans-serif;">The rise of privately issued digital currencies, which primarily serve as alternative investment assets poses a challenge to the traditional financial instruments traded in the financial market. This study examines the dynamic relationship between the major privately issued digital currency (Bitcoin) and two financial market securities in Nigeria. The paper employed Vector Autoregressive (VAR) model and presents three relevant findings. First, the impulse response function indicates the absence of a significant response of the Nigerian financial market to shocks emanating from the Bitcoin market, implying lower connectedness between the two markets. Secondly, the outcome of the variance decomposition reveals a lower contribution of Bitcoin to changes in stock prices and treasury bills, however, stock prices and treasury bills contributed higher impact to each other compared to the contribution of Bitcoin. Thirdly, a weak bi-directional causality between the Bitcoin and treasury bills was observed and a uni-directional causality running from treasury bills and stocks, implying the existence of portfolio rebalancing from the fixed income to the equities market. Despite the weak connection between digital currency and the financial market, the paper recommends that the Central Bank of Nigeria and the Securities and Exchange Commission should maintain monitoring the development of crypto exchanges and continue reviewing the existing policy restricting cryptocurrency transactions through banks to avoid its unsavoury effects.</span></p>

Sensitivity Analysis Based on Portfolio Option Models Using Tesla and Nio as Examples

The electric vehicle (EV) industry is undergoing a significant transformation, driven by environmental concerns, advancements in battery technology, and changing consumer preferences. Companies like Tesla and China's NIO are at the forefront of this revolution, showcasing the global potential of EVs. This study undertook a sensitivity analysis of these two leading EV manufacturers, Tesla and NIO, to understand the dynamics of their stock prices and implications for investment strategies. Data was meticulously collected from various financial databases to capture daily stock prices, which were then used to compute monthly returns. The study also delved into options, focusing on the strike prices and volatility of Tesla and NIO options. Using a combination of stock prices, strike prices, and volatility, the intrinsic and time value of these options were analyzed. The research employed a composite option model to describe the price variation of an option, considering factors like asset price volatility, risk-free rate, and dividend yield. The findings revealed that stock prices and option prices exhibited certain sensitivities to market variables. For instance, Tesla's option prices showed an upward trend with rising stock prices, while NIO's option prices displayed varied responses to stock price changes. The study underscores the importance of understanding these sensitivities for informed investment decisions in the evolving EV market.

The impact of news about COVID-19 on the dynamics of the Russian stock market

Subject. The article addresses the Russian stock market during the COVID-19 pandemic. Objectives. The study aims at empirical substantiation of the influence of tonality of news about COVID-19 published in official sources and social networks, on the Russian stock market. Methods. We employ a set of methods and procedures to assess the sentiment of downloaded news texts and tweets and to model the dynamics of general and sectoral stock indices (bag-of-words method, Markov-switching GARCH models, evaluation of text tonality using EcSentiThemeLex dictionary). Results. The paper reveals the influence of the tone of coverage of events related to COVID-19 pandemic in news sources and social networks on changes in stock prices of Russian companies. We substantiated the existence of industry specifics in terms of the degree of influence of the tone of news and tweets on stock price dynamics. The findings can be used by investors and issuers to model and predict changes in securities prices, and complement the theory, by underpinning the significance of the tonality of messages in the news and social networks for the dynamics of the Russian stock market. Conclusions. Changes in the emotional tonality of news and social media posts about COVID-19 impacted the stock market of the Russian Federation. The models enabled to prove that in a volatile economy, not only the information published about the coronavirus is significant for the stock market, but also the dynamics of the number of cases.

An Analysis of the Correlation between Stock Price Fluctuations and Social Media Traction

This paper aims to identify the correlation between stock performance and social media traction during 2020. It analyzes data from three different investment-focused communities within the social media platform Reddit. This data is compared to the historical financial data of six stocks across six different industries in the year 2020. The number of posts with the name of the stock within the original post each week is compared to the absolute percent change between market open to close for that week. As a control, data from the same time frame on the SPDR S&P 500 ETF Trust is used for comparison with the experimental data. All of the stock price information was taken from Yahoo! Finance. This study reveals that there is a statistically significant positive correlation between stock price change and number of Reddit posts each week. The observed correlation between stock performance and social media patterns has a significant impact in determining future stock volatility and predicting short-term investor sentiment.

The Effect of Short-Sale Restrictions on Corporate Managers

This paper studies the effect of short selling on corporate managers from 2002 through 2010. We examine how the exemption of short-sale uptick tests due to the Regulation SHO pilot program affects managers’ decisions to abandon value-reducing acquisition attempts. We find that when deciding whether to abandon value-reducing acquisition attempts during the program, managers of pilot firms, whose stocks are less subject to short-selling impediments, are more sensitive to stock price changes than managers of nonpilot firms. We find no difference in managers’ sensitivity prior to nor post SHO. These results indicate that, despite their dislike of short sellers, managers believe that the level of informativeness from capital markets is superior when short sellers are less impeded.

The Stock Price Changes and Performance of JD after Second Listing in Hong Kong

In recent years, the trend of Chinese concept stocks returning to Hong Kong stocks market has been continuously strengthening due to multiple factors. This article analyzes the changes of JD's stock price after its second listing, as well as its financial performance after its second listing. According to the analysis, the changes in costs and benefits of JD after its second listing can to some extent explain the changes in its stock price after its second listing. In addition, this study investigates the macro market environment of JD after its second listing, and finds that bearish factors such as the power game between the Chinese and American governments and the Chinese government's antitrust policies for Internet industry also affect JD's stock price. Overall, this study to some extent supplements the current research gap and provides suggestions for other Chinese concept stocks planning to return to the Hong Kong stock market in the future.

COMPARATIVE ANALYSIS OF GEOMETRIC BROWNIAN MOTION, ARTIFICIAL NEURAL NETWORK AND NAIVE BAYESIAN TECHNIQUES USING NIGERIA STOCK EXCHANGE DATA

This paper presents a comparative analysis of three market price forecast models, namely Geometric Brownian Motion, Artificial Neural Network, and Naive Bayesian techniques, using data from the Nigeria Stock Exchange. The exploratory data analysis results indicate slight variations in the mean and median of the log stock price over a 5-year period. The data shows a relatively small spread from the mean and approximately symmetric distribution, as indicated by a skewness value close to zero. The normality test confirms that the log stock price data follows a normal distribution. The forecast using Artificial Neural Network (ANN) shows a minimal change in future stock price, suggesting low returns and moderate risk in the Nigerian stock market. The graphical representation of the ANN model demonstrates a constant path with little variation. Similarly, the Naive Bayesian technique provides a similar forecast to the ANN model, indicating limited profit potential. The Geometric Brownian Motion model also forecasts little variation in future stock prices, with 2023 showing slightly higher values. The accuracy of the forecast models is evaluated using the Mean Absolute Percentage Error (MAPE). The results indicate that the ANN model has an error of 4.60%, the Naive Bayesian model has an error of 9.29%, and the Geometric Brownian Motion model has an error of 12.67%. These findings suggest that the ANN model performs better in terms of accuracy compared to the other two models.