Stock Fluctuations Research Articles

An A-Share Board Chasing Strategy Based on the JoinQuant Platform

The price limit system sets a limit on the price fluctuation of stock prices to ensure the stable operation of the securities market. Current research mainly focuses on the impact of various price limit policies on the stock market, but research on short-term investment strategies and methods for price limit stocks is still insufficient. Since the proportion of people who can get great investment returns in the Chinese stock market is very low, this paper aims to explore the stocks with good liquidity on the stopping stocks to construct investment strategies. In this paper, five important stock factors are identified through the multi-factor stock selection method, and then through the Join Quant platform with a back testing method, we finally get a good excess return and determine the value range of these five factors. Based on the above research, people can provide a good stop trading strategy for individual investors. This strategy can be called as a board chasing strategy, which can help investors to get high investment returns in a short period of time.

Investment Research on Individual Stocks in Large Research Industry Enterprises

As technology continues to develop, the influence of the technology industry on the market cannot be ignored. This study aims to explore the trends and fluctuations of key representative technology stocks, specifically focusing on Advanced Micro Devices (AMD), Oracle Corporation (ORCL), and Qualcomm Incorporated (QCOM). By meticulously analyzing the publicly available balance sheets and income statements of these companies, we will extract pertinent financial indices and conduct a comprehensive financial analysis. The analysis will include key financial metrics such as revenue growth, profit margins, return on equity (ROE), and debt-to-equity ratios, among others. These metrics will be used to evaluate the financial health and performance trends of each company over a specified period. Additionally, we will compare these metrics across the three companies to provide insights into their relative market positions and competitive advantages. The objective of this analysis is to furnish investors with a reliable and data-driven market analysis that can inform their investment decisions. By understanding the financial underpinnings of these technology giants, investors can better assess the potential risks and returns associated with investing in these stocks. This study aims to bridge the gap between financial theory and practical investment strategies, offering a valuable resource for both individual and institutional investors interested in the technology sector.

Stock Market Price Prediction Using ARIMA Model

Stock market price prediction involves forecasting future stock values using techniques like technical analysis, fundamental analysis, and machine learning. Technical analysis looks at historical price movements, while fundamental analysis evaluates a company's financial health. Machine learning models use large datasets to identify patterns. This paper introduces a vital approach to time series analysis and forecasting, emphasizing its widespread practical applications. Time series data consists of an ordered sequence of data points recorded at equal time intervals. The stock market, one of the most complex financial systems, experiences significant fluctuations in stock prices over time. Stock market forecasting aims to uncover market trends and help investors maximize returns while minimizing risks. Given the market's sensitivity to rapid changes, this research focuses on developing innovative approaches to forecast stocks for high profits. Specifically, this paper analyzes the time-series data of the Indian stock market and builds a statistical model to predict future stock prices efficiently

Share Price Performance of Banking Issuers After Dividend Cum-Date

The primary objective of this analysis is to examine the fluctuations in stock prices post dividend distribution, and to evaluate how dividends affect stock performance over a one-year period following their allocation. Statistical analysis using the Paired Sample T-Test is employed to assess time series data in this study. The sample used consists of seven banking issuers, namely BBCA, BBNI, BBRI, BMRI, BTPN, MEGA, and BNGA. The study findings suggest that there is a slight decrease in the performance of banking stock prices the day following the dividend cumdate, though not enough to be considered significant. However, a noticeable difference in share price performance among banking issuers is evident between three months and a year. This was obtained after conducting a paired sample t-test with a result of 0.032. The contraction of stock price performance at cum date in some companies at a certain time, resulting in stock prices falling within a few days. Some of the decline in stock prices after the cum date occurred only a few days or even less than one month, but there were several companies that experienced a longer decline or tended to experience a negative trend for a long time.

Managing medicines in decentralisation: discrepancies between national policies and local practices in primary healthcare settings in Indonesia.

In Indonesia, primary health centres (PHCs) are mandated to provide essential medicines to ensure equal access to medication for all Indonesians, as stated in the national medicine policy. However, limited information is available regarding the actual practices of health workers within the context of decentralised governance. This paper investigates the discrepancies between national policies and local practices in two Indonesian districts, shedding light on coping mechanisms employed in each phase of medicine management within PHCs. The mixed-method study began by identifying pertinent policies addressing medicine management in PHCs. Subsequently, panel data on patient visits to tuberculosis, maternal and neonatal health (MNH) and non-communicable disease (NCD) services were collected from 2019 to 2022. After analysing the panel data, interviews were conducted with 56 health workers including physicians, nurses, pharmacists, midwives, and public health programme managers regarding their views on fluctuations in medicine stocks and the patient visits data. These participants included pharmacists and programme managers specialising in tuberculosis, MNH, and NCD care and were affiliated with PHCs and district health offices (DHO). Our findings highlight the occasional unavailability of essential medicines in PHCs, with stockouts being attributed to supplier shortages at provincial and national levels and to variations in the capacity of the local health system. Low-skilled pharmaceutical staff are a contributing factor in each phase of medicine management. Additionally, health workers employ coping mechanisms, such as deviating from policy on the use of capitation funds to purchase medicines, to manage temporary stockouts. To tackle systemic stockouts, central government should prioritise capacity-building among health workers, by establishing a continuous and easily accessible local learning system.

Analyzing financial interdependencies: The cointegration of Jordan and BRIC stock markets

This study examines the financial integration between Jordan and the BRIC economies (Brazil, Russia, India, and China) to determine whether long-term equilibrium relationships exist and to assess implications for portfolio diversification and policy. Drawing on daily stock index data from 01 January 2014, to 31 August 2024, the study employs econometric techniques, including Granger Causality tests, Johansen Cointegration, and Vector Autoregression (VAR). The stationarity of stock indices at the first difference level is confirmed through unit root testing. Results indicate minimal long-term cointegration between Jordan and BRIC markets, pointing to low integration and potential diversification benefits for institutional investors. However, short-term causal links—particularly between Jordan and the Russian and Indian markets—highlight these countries’ influence on Jordan’s stock fluctuations. The findings suggest that, in the absence of long-term cointegration, investors may mitigate risk by investing in less correlated markets, such as Jordan, while leveraging short-term partnerships with Russia and India. Additionally, the study provides valuable insights for business leaders considering strategic alliances with BRIC counterparts in sectors like technology, agriculture, and energy, and calls for future research into factors like regulatory frameworks and geopolitical stability that may limit long-term financial integration. These results have significant implications for institutional investors, business executives, and policymakers, suggesting targeted strategies for financial stability, risk mitigation, and economic collaboration.

Commentary: Demographic response of osprey within the lower Chesapeake Bay to fluctuations in menhaden stock

Commentary: Demographic response of osprey within the lower Chesapeake Bay to fluctuations in menhaden stock

EFFICIENT MARKET HYPOTHESIS DURING COVID-19 PANDEMIC: BRICS-T COUNTRIES

The impact of COVID-19 on financial markets has become a significant topic of research in the literature. Particularly, the declaration of the outbreak as a global pandemic by the World Health Organization on March 11, 2020, altered investors' risk perceptions and led to major fluctuations in stock markets. In this study, the response of stock indices in BRICS-T countries (Brazil, Russia, India, China, South Africa, and Türkiye) to the pandemic declaration was analyzed using the Event Study method. The event window was set as -5 to +5 days, and the estimation window was selected as the 90 days preceding the event. As a result of the analysis based on the comparison of normal and abnormal returns, it was found that all BRICS-T stock markets, except China, were not semi-strong form efficient. In addition, the results of the paired sample t-test applied to all stock markets demonstrated that the difference between abnormal returns calculated before and after COVID-19 was statistically significant. In this context, it was concluded that, unlike previous outbreaks, the COVID-19 pandemic led to significant fluctuations in financial markets. This study provides an important contribution to understanding the effects of the pandemic on emerging markets and examining investor behavior.

Predicting land cover changes and carbon stock fluctuations in Fuzhou, China: A deep learning and InVEST approach

Predicting the spatiotemporal dynamics of land cover and its carbon stock holds significant importance in guiding regional sustainable development, enhancing regional carbon stocks, and addressing global climate change. However, there is insufficient research on the quantitative relationships between various land cover types and carbon stock changes, as well as their future spatial predictions. Focusing on the core area of Fuzhou City, China, this study constructs a streamlined framework by coupling deep learning and the InVEST model to predict urban land cover and carbon stock changes in 2025 and 2035. The results show that: (1) The prediction model for land cover change has high applicability and can produce the simulated images with high accuracy. Impervious surface is expected to increase by 53 km2 in 2025 and 131 km2 in 2035 compared to 2020, resulting in considerable reductions in forest and cropland. (2) Carbon stocks of the study area are expected to decrease by 1.68 × 106t in 2035 compared to 2020 due to large amounts of high-carbon-density forests and croplands being converted into low-carbon-density impervious surfaces. (3) Multiple regression analysis reveals that forests have the largest impact on carbon stocks in the area, with a magnitude 5.25 times greater than impervious surfaces and 11.5 times greater than cropland, whereas impervious surfaces are the second most influential land cover type on carbon stock changes. Therefore, expanding forest areas becomes an essential initiative as forests could offset the carbon stock loss caused by impervious surface growth. This study provides scientific references for optimizing land-use planning and formulating policies for the development of low-carbon cities.

Stock market prediction-COVID-19 scenario with lexicon-based approach

Stock market forecasting remains a difficult problem in the economics industry due to its incredible stochastic nature. The creation of such an expert system aids investors in making investment decisions about a certain company. Due to the complexity of the stock market, using a single data source is insufficient to accurately reflect all of the variables that influence stock fluctuations. However, predicting stock market movement is a challenging undertaking that requires extensive data analysis, particularly from a big data perspective. In order to address these problems and produce a feasible solution, appropriate statistical models and artificially intelligent algorithms are needed. This paper aims to propose a novel stock market prediction by the following four stages; they are, preprocessing, feature extraction, improved feature level fusion and prediction. The input data is first put through a preparation step in which stock, news, and Twitter data (related to the COVID-19 epidemic) are processed. Under the big data perspective, the input data is taken into account. These pre-processed data are then put through the feature extraction, The improved aspect-based lexicon generation, PMI, and n-gram-based features in this case are derived from the news and Twitter data, while technical indicator-based features are derived from the stock data. The improved feature-level fusion phase is then applied to the extracted features. The ensemble classifiers, which include DBN, CNN, and DRN, were proposed during the prediction phase. Additionally, a SI-MRFO model is suggested to enhance the efficiency of the prediction model by adjusting the best classifier weights. Finally, SI-MRFO model’s effectiveness compared to the existing models with regard to MAE, MAPE, MSE and MSLE. The SI-MRFO accomplished the minimal MAE rate for the 90th learning percentage is approximately 0.015 while other models acquire maximum ratings.

Substantiating the reliability conditions for the production process at metallurgical enterprises through the fault-tolerant functioning of the system «extraction of raw materials – technological railroad routes – metallurgical production»

The object of this study is the process of formation of insurance reserves at enterprises in the metallurgical industry. Under conditions of uneven supply of raw materials to metallurgical enterprises due to disruption of the transportation process or other reasons, there is a need to create insurance stocks in order to ensure the continuity of production. At the same time, it is necessary to take into account existing restrictions, such as the limited capacity of railroad sections and the impossibility of organizing parallel movement of trains, etc. The presence of these limitations makes it impossible to use classical methods for solving similar problems, such as linear programming. Therefore, to resolve the task, a simulation model was built, based on the discrete-event principle in the AnyLogic University Researcher environment using Oracle libraries and the Java SE compiler. With the help of the model, the process of rotation of dispatch routes at the railroad yard with multiple suppliers and one consignee was formalized. The optimization criterion was chosen to be the minimum deviations of fluctuations in reserves of iron ore concentrate and coke. Analysis of the simulation results revealed that the optimal size of the fleet of railroad routes on the selected rotation polygon is 30 units; at the same time, their utilization rate will be 65 %. It was also established that fluctuations in raw material stocks have a «natural character», which is confirmed by the normal distribution of the density of stock volumes. Under these conditions, the value of fluctuations in the volumes of the main raw materials will be ±13115 t/day for iron ore concentrate, and ±5298 t/day for coke. Reducing the range of fluctuation of raw materials volumes could make it possible to optimize the costs of creating stocks and streamline the transport work of the enterprise for providing raw materials

The Influence of Earning Per Share (EPS) and Price Earnings Ratio (PER) on Stock Prices in the Food and Beverage Sector on the Indonesian Stock Exchange (BEI) for the 2015-2019 Period

The objective of this study is to examine the impact of earnings per Share (EPS) and Price Earnings Ratio (PER) on the stock prices of companies in the food and beverage industry listed on the Indonesia Stock Exchange (BEI) from 2015 to 2019. EPS is utilized as a metric to gauge the profitability of each share, whereas PER characterizes the market's assessment of the earnings generated per share. The research employed a descriptive verification analysis method, utilizing a sample of seven prominent organizations within the researched sector.The data utilized is derived from the yearly financial reports of each company, specifically secondary data. The investigation employed multiple regression approaches to examine the hypothesis regarding the impact of EPS and PER on stock prices. The test utilizes the coefficient of determination to ascertain the extent to which the regression model can account for the fluctuations in stock prices.The research findings indicate a substantial correlation between EPS (Earnings Per Share), PER (Price Earnings Ratio), and firm share prices within the food and beverage industry on the IDX (Indonesia Stock Exchange). The R^2 value of 0.753 suggests that 75.3% of the changes in stock prices can be accounted for by the independent variables, EPS and PER. In this industry, share prices are primarily influenced by the profitability per share and market valuation.In addition, this study emphasizes that additional variables not investigated in this research can significantly impact stock prices, including macroeconomic conditions, market sentiment, government regulations, and other external factors. Stable macroeconomic conditions can lead to more favorable circumstances for increased stock values, whereas government policy or market sentiment alterations can profoundly influence a company's stock performance.The recommendations derived from this study are intended for investors and corporate executives operating in the food and beverage industry on the IDX. Investors are advised to consider the earnings per share (EPS) and price-to-earnings ratio (PER) components of their fundamental analysis to make better-informed investment choices. It is recommended that companies prioritize enhancing their financial performance and ensuring openness in their financial reporting to boost market trust and raise the value of their shares.Subsequent investigations might broaden the range by including supplementary variables like Dividend Yield or Return on Equity (ROE) while doing a more extensive analysis of qualitative elements that impact market perceptions of EPS and PER. This research contributes to understanding the factors that impact share prices in the Indonesian capital market, particularly within the food and beverage industry.

Stock market prediction employing ensemble methods: the Nifty50 index

<span lang="EN-US">Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions.</span>

Relational Fusion-based Stock Selection with Neural Recursive Ordinary Differential Equation Networks

Stock selection attempts to rank a list of stocks for optimizing investment decision making, aiming at minimizing investment risks while maximizing profit returns. Recently, researchers have developed various (recurrent) neural network-based methods to tackle this problem. Without exceptions, they primarily leverage historical market volatility to enhance the selection performance. However, these approaches greatly rely on discrete sampled market observations, which either fail to consider the uncertainty of stock fluctuations or predict continuous stock dynamics in the future. Besides, some studies have considered the explicit stock interdependence derived from multiple domains (e.g., industry and shareholder). Nevertheless, the implicit cross-dependencies among different domains are under-explored. To address such limitations, we present a novel stock selection solution—StockODE, a latent variable model with Gaussian prior. Specifically, we devise a Movement Trend Correlation module to expose the time-varying relationships regarding stock movements. We design Neural Recursive Ordinary Differential Equation Networks (NRODEs) to capture the temporal evolution of stock volatility in a continuous dynamic manner. Moreover, we build a hierarchical hypergraph to incorporate the domain-aware dependencies among the stocks. Experiments conducted on two real-world stock market datasets demonstrate that StockODE significantly outperforms several baselines, such as up to 18.57% average improvement regarding SR.

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.

A stock border compensation technique for gaseous energy scheduling in steel enterprises under uncertainty

This study proposes a stock boundary compensation method to address the uncertainty of gaseous energy in integrated steel plants. The uncertainty disrupts the balance of temporary storage, resulting in energy waste, environmental pollution, and disturbances in steel production. The method accurately compensates for the impact of gaseous energy uncertainty on storage. Firstly, this paper takes oxygen and byproduct gas systems as the examples to describe the gas energy system and its scheduling model. The storage deviation caused by gaseous energy uncertainty is quantitatively analyzed using formulas. Subsequently, this study formulates an optimization problem to determine the optimal margin for compensating the storage boundary using a data-driven approach. Comparative experiments demonstrate that the proposed method not only significantly enhances the safety of gaseous energy storage subject to uncertainty, but also outperforms traditional robust optimization and stock fluctuation optimization methods in terms of system robustness against uncertainty and operating cost optimality.

Erratum: Response of cetaceans to fluctuations of pelagic fish stocks and environmental conditions within the Celtic Sea ecosystem

Erratum: Response of cetaceans to fluctuations of pelagic fish stocks and environmental conditions within the Celtic Sea ecosystem

The loss of political connections and the fluctuation of corporate stock prices: Moderating effect based on media attention

This article examines the impact and mechanism of political connections on stock price fluctuations after the resignation of independent directors with “official” status, based on the exogenous influence of Document No. 18 of the Central Organization Department. Using panel data of A-share listed companies on the Shanghai and Shenzhen stock markets from 2012 to 2020, the experimental group and control group for political affiliation deficiency events were determined through propensity score matching (PSM), and a quasi natural experiment was constructed using differences in differences (DID) for empirical research. By examining the relationship between regional financial development, lack of political connections, and stock price fluctuations, we found that regions with higher levels of financial development are more prone to stock price fluctuations due to the lack of political connections, which is related to higher levels of debt financing in regions with higher levels of financial development. In addition, the increasing level of debt exacerbates conflicts and inconsistencies among stakeholders, which is not conducive to the stability of the company's stock price. Through the above research, relevant suggestions have been provided to enterprises, media, and governments. For example, enterprises should clarify the boundary between government and enterprise, focus on long-term strategic goals, build core competitiveness, and thereby enhance their own value; The media should play a correct role in information transmission and public opinion guidance, and play a positive role in the economic development of the industry; The government should promote market-oriented construction and establish positive government enterprise relationships.

Hedging Strategies with Derivatives after 2020

This paper examines the changes in the futures market and how Chinese companies have hedged in recent years. As is known to all, the world has experienced lots of events, such as the COVID-19 pandemic, the Ukraine Conflict, the economic recession, and the rising interest rate in the U.S. Under the risky circumstances, the foreign exchange market and commodity futures market have shown a larger volatility. During times of global economic turmoil, the investors in the financial markets are seeking opportunities to protect their investment portfolios and assets. Historically, the futures market can be seen as a safe haven for funds, mitigating losses caused by fluctuations in stock or commodity prices through proper hedging strategies. This paper analyses what the Chinese futures and spot markets are like and how Chinese companies use hedging tools to lock profits. Then, the paper gives suggestions and prospects for futures hedging and the development of markets.

Modeling of Machine Learning-Based Extreme Value Theory in Stock Investment Risk Prediction: A Systematic Literature Review.

The stock market is heavily influenced by global sentiment, which is full of uncertainty and is characterized by extreme values and linear and nonlinear variables. High-frequency data generally refer to data that are collected at a very fast rate based on days, hours, minutes, and even seconds. Stock prices fluctuate rapidly and even at extremes along with changes in the variables that affect stock fluctuations. Research on investment risk estimation in the stock market that can identify extreme values is nonlinear, reliable in multivariate cases, and uses high-frequency data that are very important. The extreme value theory (EVT) approach can detect extreme values. This method is reliable in univariate cases and very complicated in multivariate cases. The purpose of this research was to collect, characterize, and analyze the investment risk estimation literature to identify research gaps. The literature used was selected by applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and sourced from Sciencedirect.com and Scopus databases. A total of 1107 articles were produced from the search at the identification stage, reduced to 236 in the eligibility stage, and 90 articles in the included studies set. The bibliometric networks were visualized using the VOSviewer software, and the main keyword used as the search criteria is "VaR." The visualization showed that EVT, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, and historical simulation are models often used to estimate the investment risk; the application of the machine learning (ML)-based investment risk estimation model is low. There has been no research using a combination of EVT and ML to estimate the investment risk. The results showed that the hybrid model produced better Value-at-Risk (VaR) accuracy under uncertainty and nonlinear conditions. Generally, models only use daily return data as model input. Based on research gaps, a hybrid model framework for estimating risk measures is proposed using a combination of EVT and ML, using multivariable and high-frequency data to identify extreme values in the distribution of data. The goal is to produce an accurate and flexible estimated risk value against extreme changes and shocks in the stock market. Mathematics Subject Classification: 60G25; 62M20; 6245; 62P05; 91G70.