Stock Price Dynamics Research Articles

Dynamics of Stock Prices and Exchange Rate with Structural Breaks and Asymmetry: Evidence From Türkiye

This study investigates the impacts of the nominal exchange rate on Turkish stock prices using a structural break cointegration test with endogenously determined multiple structural breaks and an asymmetric cointegration test for the period of 2002-2021. The study differs from previous research on this relation in two respects. First, it takes into account structural breaks in relation to both regimes and trends (C/S/T). Second, it extends the asymmetric cointegration with multiple structural breaks. The findings of structural break cointegration capture the break dates in line with the Turkish economics dynamics and reveal the negative effects of the exchange rates on stocks, with their significance and magnitude differing in regimes. Similarly, NARDL results indicate that negative and positive exchange rate shocks exhibit asymmetric effects on stocks for both the whole period and regimes. The overall findings demonstrate that exchange rate variations have distinctive impacts on stock prices when considering structural break and asymmetrical dynamics. In this background, policymakers and foreign investors need to take into account these dynamics when dealing with Turkish financial markets.

Application of the Fractal Brownian Motion to the Athens Stock Exchange

The Athens Stock Exchange (ASE) is a dynamic financial market with complex interactions and inherent volatility. Traditional models often fall short in capturing the intricate dependencies and long memory effects observed in real-world financial data. In this study, we explore the application of fractional Brownian motion (fBm) to model stock price dynamics within the ASE, specifically utilizing the Athens General Composite (ATG) index. The ATG is considered a key barometer of the overall health of the Greek stock market. Investors and analysts monitor the index to gauge investor sentiment, economic trends, and potential investment opportunities in Greek companies. We find that the Hurst exponent falls outside the range typically associated with fractal Brownian motion. This, combined with the established non-normality of increments, disfavors both geometric Brownian motion and fractal Brownian motion models for the ATG index.

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

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

Peran Profitabilitas, Likuiditas, Aktivitas, dan Leverage dalam Harga Saham

This research stems from the need to investigate the influence of financial factors, such as profitability, liquidity, activity, and leverage, on the share price of a company. The growing complexity of financial markets drives the need for a deeper understanding of the contribution of each factor to stock price dynamics. This research aims to identify and analyze the role of each financial factor of profitability, liquidity, activity and leverage in determining stock price movements. The research method used involves multivariable statistical analysis using historical financial data of companies listed on the stock market. This quantitative approach will help in dissecting the complex relationship between profitability, liquidity, activity, leverage, and stock price. This research produces significant findings regarding the impact of each financial factor on stock prices. An in-depth analysis will show to what extent profitability, liquidity, activity, and leverage influence a company's stock valuation. These results can be the basis for developing investment strategies that are smarter and responsive to market dynamics.

Unraveling Indonesia's Stock Market Dynamics for Global Insights

This study examines stock price dynamics in the LQ-45 index on the Indonesia Stock Exchange from 2017 to 2021, focusing on factors like inflation, interest rates, Earning Per Share (EPS), and Price Earning Ratio (PER). Using quantitative methods and data from annual reports and official sources, the research found that inflation and interest rates had no significant impact on stock prices, while EPS and PER showed a positive and significant influence. These findings contribute to understanding stock market behavior and can guide companies and investors in making informed decisions. Highlight: Stock Price Trends: LQ-45 index insights on market dynamics. Economic Influences: Examining inflation and interest rates' impact. EPS and PER Impact: Significant role in stock valuation revealed. Keyword: Stock Prices, LQ-45 Index, Indonesia Stock Exchange, Inflation, Earning Per Share (EPS)

Market Reaction to Environmental Disasters

Despite the fact that environmental issues are coming to the fore today, the market reaction to environmental disasters is not strong enough. This article examines the impact of a number of major industrial disasters on the companies’ stock performance, depending on financial health of the companies involved in an accident. We assessed the impact of financial indicators such as: financial leverage, profitability, balance value per share, capital expenditures, market capitalization and revenue on the amplitude of cumulative average abnormal returns (CAAR). The sample consists of 32 companies fromthe oil, chemical, mining and energy industries of developed countries involved in 80 major accidents between 2000 and 2020. The majority of disasters occurred in North America (47.5%) and in Europe (26.3%). Using the event study method to assess shareholders’ reaction and regression analysis, we proved that the financial leverage, profitability and book value per share has a positive impact on the amplitude of CAAR, while the ratio of capital expenditures to revenue has a negative impact on cumulative returns. The results showed that market capitalization and revenue growth do not affect the dynamics of stock prices after industrial disasters. In general, our study shows that the impact of all financial indicators on CAARis small (<1%). That is, despite the mandatory publication of climate risk reports, investors did not actively sell shares of companies guilty of industrial disasters. The results of the study are useful in several areas. On the one hand, by forming a diversified investment portfolio, investors taking into account the type of companies that are more sensitive to disasters. On the other hand, knowing such a market reaction, the state should provide financial players with strict rules and penalties for companies responsible for accidents.

Digitalization and predictability in the BRICS countries: what can be learned from information about the dynamics of stock prices

The study aims to identify the impact of digitalization on predictability in the BRICS countries’ stock markets. It is based on an analysis of the dynamics of stock markets volatility during the 1990-2023 period. The paper seeks to prove that the standard deviation of stock returns is determined by the volume of incoming new information, and higher volatility of returns indicates lower predictability. Digitalization may cause a reduction in uncertainty as it uses more data, improves their quality and develops data analysis methods. On the other hand, digitalization may lead to increased uncertainty due to the emergence and development of new industries, in which it is more difficult to predict cash flows in comparison with traditional industries because of increased complexity of supply chains and technologies. Based on quantitative analysis, it has been revealed that digitalization has led to a statistically significant decrease in the volatility of stock markets in the BRICS countries. In 2015-2023, relative to the period of the 1990s-2006, volatility in Russia decreased by 1 percentage point, in India by 0.4–0.5 percentage points, in China by 0.8 percentage points, in the UAE (Dubai) by 0.5-0.7 percentage points. Statistically insignificant decreases in volatility were observed in Brazil and South Africa. In the developed capital markets, the decrease in volatility between these two periods was also statistically insignificant, amounting to less than 0.1 percentage points. These findings may indicate that the processes of digitalization in the BRICS countries contributed to an accelerated increase in predictability thanks to an increase in the volume and quality of information and the emergence of new methods of analysis. At the same time, part of the decrease in volatility may be explained by further development and improved efficiency of capital markets. The joint influence of these effects on the complexity of forecasting turned out to be more significant than the impact of innovative technologies and new industries.

Brain activity of professional investors signals future stock performance

A major aspiration of investors is to better forecast stock performance. Interestingly, emerging "neuroforecasting" research suggests that brain activity associated with anticipatory reward relates to market behavior and population-wide preferences, including stock price dynamics. In this study, we extend these findings to professional investors processing comprehensive real-world information on stock investment options while making predictions of long-term stock performance. Using functional MRI, we sampled investors' neural responses to investment cases and assessed whether these responses relate to future performance on the stock market. We found that our sample of investors could not successfully predict future market performance of the investment cases, confirming that stated preferences do not predict the market. Stock metrics of the investment cases were not predictive of future stock performance either. However, as investors processed case information, nucleus accumbens (NAcc) activity was higher for investment cases that ended up overperforming in the market. These findings remained robust, even when controlling for stock metrics and investors' predictions made in the scanner. Cross-validated prediction analysis indicated that NAcc activity could significantly predict future stock performance out-of-sample above chance. Our findings resonate with recent neuroforecasting studies and suggest that brain activity of professional investors may help in forecasting future stock performance.

Behavior Among Stock Price, Financial Performance, and Financial Distress Evidence from the Australian and New Zealand Stock Exchanges

Objective: The main objective of this study is to investigate the behavior among stock price, financial performance, and financial distress in Australian and New Zealand Stock Exchanges. Similar studies have been done on United States and European markets, but none on Australian and New Zealand Stock Exchanges. Our finding suggested that PE Ratio is not a reliable measure of financial performance in Australian and New Zealand Stock Exchanges. Method: Altman’s Z-score is applied to reflect the financial distress of the large listed firms in Australian Stock Exchange and New Zealand Stock Exchange while expected return on total investment minus the market premium, which is associated with the overall risk of a firm, is used as a proxy to stock price performance. In addition, this study also investigates the various effects on profitability, financial leverage, liquidity, growth, and size factors on stock performance. Results: Regression models are applied to investigate the stock performance upon independent variables’ effects. Results of the study show that all selected variables are reliable to use to measure performance, except P/E ratio. Moreover, Z-score, current ratio, P/E ratio, and ROE generally have significant positive relationship with stock price performance, while total assets, in general, have significant negative relationship with stock price performance. Conclusion: This paper has a potential to contribute in knowledge of stock price and financial performance. It also examines financial distress of the market. The objective of the study is to examine the dynamics of stock prices using market value ratios and financial performance using financial ratios under financial distress of selective countries particularly. Using the empirical data from listed companies from the stock exchanges, this study determines the stock performance in a unique way with the combination of market value ratios and financial ratios.

An optimal investment consumption model for retirees with no health insurance

Retirees meet a number of problems as they are growing older which needs persistent attention. Hence, without a doubt, the outcomes of the financial markets influence the choices that people make when nearing retirement. In our model, the stock price dynamics follow Geometric Brownian motion (GBM) and our goal was to optimize the expected discounted utility of consumption and terminal wealth whilst considering health expenses. The investment return process comprises risk free asset and risky assets, and the health expenses. We choose power utility functions where comprehensive solutions for Hyperbolic Absolute Risk Aversion (HARA) utility functions are obtained and optimal investment, consumption and health expenditure strategies are derived by applying dynamic programming and variable change technique on the Hamilton-Jacobi-Bellman (HJB) equations. In our numerical results it showed various effects of some economic and market parameters on the optimal investment, consumption and health expense strategies. The inflation price market risk governs the amount invested in stock, bond and also how much to be put in health to sustain a given period of the retiree's lifetime. As the health welfare rate R increases, the proportion of wealth invested in the stock increases. We also investigated the effects of the high correlation coefficients and low correlation coefficients on consumption and income rate respectively. As the constant variance discounting coefficient increases, seasoned enterprise annuity retirees decrease their allocation to the risky assets. Finally, a numerical example is presented to depict the effects of financial parameters on the optimal investment strategy with health expenditure.

Optimization and Improvement of Recurrent Neural Networks in Stock Price Prediction

Recurrent Neural Networks (RNNs) are a significant branch of deep learning, particularly suitable for handling data with temporal dependencies. In the financial domain, stock price prediction is a highly focused problem. Traditional stock price prediction methods, often based on linear models, fail to fully capture the nonlinear dynamics of stock prices. RNNs, with their memory capabilities, can capture the long-term dependencies of stock prices, hence offering great application potential in stock price prediction. However, RNNs also face challenges when processing stock price data, as stock prices are influenced by numerous factors and are highly complex and uncertain. Additionally, stock price data often contains noise and outliers, impacting the model’s predictive performance. To address these issues, incremental improvements in prediction accuracy and model generalization capabilities are achieved through optimizing the model structure and improving data preprocessing methods. As technology advances and algorithms continue to innovate, the application of RNNs in stock price prediction will become more widespread and in-depth.

Sentiment's Stock Impact: Company Size's Role In Stock Price Dynamics

This study aims to present empirical evidence on the impact of investor sentiment on stock prices, test the moderating effect of firm size on the relationship between investor sentiment and stock prices, and examine potential differences in this relationship across firms of varying sizes. The sample for this study consists of 246 companies listed on the main board of the Indonesia Stock Exchange from 2018 to 2022. EViews 12 were used for data processing. The findings of the study suggest that investor sentiment has a positive impact on the stock prices of firms traded on the Indonesia Stock Exchange. Additionally, the relationship between investor sentiment and stock prices is not moderated by company size, although the effect of investor sentiment on stock prices varies across different company sizes.

Measures of Model Risk for Continuous-Time Finance Models

Abstract Measuring model risk is required by regulators in financial and insurance markets. We separate model risk into parameter estimation risk (PER) and model specification risk (MSR), and we propose expected shortfall type model risk measures applied to Lévy jump, affine jump-diffusion, and multifactor models. We investigate the impact of PER and MSR on the models’ ability to capture the joint dynamics of stock and option prices. Using Markov chain Monte Carlo techniques, we implement two methodologies to estimate parameters under the risk-neutral probability measure and the real-world probability measure jointly.

Variance of entropy for testing time-varying regimes with an application to meme stocks

Shannon entropy is the most common metric for assessing the degree of randomness of time series in many fields, ranging from physics and finance to medicine and biology. Real-world systems are typically non-stationary, leading to entropy values fluctuating over time. This paper proposes a hypothesis testing procedure to test the null hypothesis of constant Shannon entropy in time series data. The alternative hypothesis is a significant variation in entropy between successive periods. To this end, we derive an unbiased sample entropy variance, accurate up to the order O(n-4)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(n^{-4})$$\\end{document} with n the sample size. To characterize the variance of the sample entropy, we first provide explicit formulas for the central moments of both binomial and multinomial distributions describing the distribution of the sample entropy. Second, we identify the optimal rolling window length to estimate time-varying Shannon entropy. We optimize this choice using a novel self-consistent criterion based on counting significant entropy variations over time. We corroborate our findings using the novel methodology to assess time-varying regimes of entropy for stock price dynamics by presenting a comparative analysis between meme and IT stocks in 2020 and 2021. We show that low entropy values correspond to periods when profitable trading strategies can be devised starting from the symbolic dynamics used for entropy computation, namely periods of market inefficiency.

ТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ ИНФОРМАЦИОННОЙ СРЕДЫ МЕДИАКОМПАНИЙ: ИНСТРУМЕНТАЛЬНЫЙ КОМПЛЕКС LDA-TF-IDF

The paper investigates the impact of text processing, topic modeling and machine learning on predicting the dynamics of company stock prices in the information environment. It is found that the use of methods such as TF-IDF and LDA can identify key topics that influence the public dialog about companies in the media and entertainment industry. This allows us to assess the current state of news coverage and predict the impact of thematic aspects on company characteristics. Additionally, the use of gradient-based boosting based on the CatBoost algorithm and its optimization with GridSearchCV shows the potential to effectively predict whether news articles belong to specific clusters. This is an important step in analyzing the impact of the information environment on companies' performance and their performance in the media environment.

Analyzing the Financial Resilience of Islamic Banks in Indonesia Amid the COVID-19 Pandemic: A Comparative Study of Stock Price Dynamics

Analyzing the Financial Resilience of Islamic Banks in Indonesia Amid the COVID-19 Pandemic: A Comparative Study of Stock Price Dynamics

Defaultable perpetual American put option in a last passage time model

In this article we provide a valuation formula for a defaultable perpetual American put option in the Black-Schole’s market where the default time is modelled using the last passage time of the set [L,∞) by the stock price dynamics. In other words, we suppose that financial default happens if the stock price can no longer recover and rebound above the level L.

A New Solution to the Fractional Black–Scholes Equation Using the Daftardar-Gejji Method

The main objective of this study is to determine the existence and uniqueness of solutions to the fractional Black–Scholes equation. The solution to the fractional Black–Scholes equation is expressed as an infinite series of converging Mittag-Leffler functions. The method used to discover the new solution to the fractional Black–Scholes equation was the Daftardar-Geiji method. Additionally, the Picard–Lindelöf theorem was utilized for the existence and uniqueness of its solution. The fractional derivative employed was the Caputo operator. The search for a solution to the fractional Black–Scholes equation was essential due to the Black–Scholes equation’s assumptions, which imposed relatively tight constraints. These included assumptions of a perfect market, a constant value of the risk-free interest rate and volatility, the absence of dividends, and a normal log distribution of stock price dynamics. However, these assumptions did not accurately reflect market realities. Therefore, it was necessary to formulate a model, particularly regarding the fractional Black–Scholes equation, which represented more market realities. The results obtained in this paper guaranteed the existence and uniqueness of solutions to the fractional Black–Scholes equation, approximate solutions to the fractional Black–Scholes equation, and very small solution errors when compared to the Black–Scholes equation. The novelty of this article is the use of the Daftardar-Geiji method to solve the fractional Black–Scholes equation, guaranteeing the existence and uniqueness of the solution to the fractional Black–Scholes equation, which has not been discussed by other researchers. So, based on this novelty, the Daftardar-Geiji method is a simple and effective method for solving the fractional Black–Scholes equation. This article presents some examples to demonstrate the application of the Daftardar-Gejji method in solving specific problems.

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.

Integrating technical indicators, chip factors and stock news for enhanced stock price predictions: A multi-kernel approach

In the field of stock price forecasting, we are actively seeking to integrate various information to more accurately grasp market dynamics. Although historical stock prices and financial news have been widely used in previous studies, it is relatively rare to find research considering news-based, technical, and chip factors simultaneously and evaluating their combined effect. In this study, we innovatively propose a multi-kernel model that not only combines news-based, technical, and chip factor analysis but also utilizes market data provided by the Taiwan Stock Exchange, including institutional trading situations and stock price technical indicators. The aim is to further enhance the prediction accuracy of stock price dynamics. Based on the frequency of word occurrences, we design a new discriminant index to extract features highly correlated with stock prices from financial news. The empirical results show that our multi-kernel model significantly surpasses the single-kernel model in prediction accuracy. However, we also find that although financial news is somewhat correlated with stock price dynamics, information such as chip factors and stock price technical indicators contribute more significantly in our model. This further validates that our multi-kernel learning algorithm can effectively handle multifaceted data sources and give appropriate weights according to the importance of each data point, thereby enhancing the comprehensiveness of prediction. Through this research, we hope to bring new perspectives and inspirations to the field of stock price forecasting.