Stock Price Fluctuations Research Articles

Stock Price Time Series Data Forecasting Using the Light Gradient Boosting Machine (LightGBM) Model

In the world of stock investment, one of the things that commonly happens is stock price fluctuations or the ups and downs of stock prices. As a result of these fluctuations, many novice investors are afraid to play stocks. However, on the other hand, stocks are a type of investment that can be relied upon during disasters or economic turmoil, such as in 2019, namely the Covid-19 pandemic. For stock price fluctuations to be estimated by investors, it is necessary to carry out a forecasting activity. This study builds stock price forecasting using the Light Gradient Boosting Machine (LightGBM) algorithm, which has high accuracy and efficiency. To forecast stock price time series, the model used is the LightGBM ensemble. At the same time, they were optimizing the determination of hyperparameters using Grid Search Cross Validation (GSCV). This study will also compare the LGBM algorithm with other algorithms to see which model is optimal in forecasting price stock data. In this study, the test used the RMSE metric by comparing the original data (testing data) with the predicted results. The experimental results show that the LightGBM model can compete with and outperform boosting-based forecasting models like XGBoost, AdaBoost, and CatBoost. In comparing forecasting models, the same dataset is used so that the results are accurate, and the comparisons are equivalent. In future research, paying attention to the data during pre-processing is necessary because it has many outliers. In addition, it is necessary to include exogenous variables and external variables, which are determined to involve many parties.

The Impact of the Deregulation of Covid-19 on the Pharmaceutical Industry

The current COVID-19 has lasted for a long time, and the Chinese government has relaxed the control of the pandemic after many considerations. Numerous researches about the COVID-19's effects on the economy and business are currently available. But few studies are on the pharmaceutical industry and the Deregulation. Therefore, this paper selects the Pharmaceutical and Biological Full Benefit as the research object. And then uses the ARIMA model to perform time series fitting and prediction of the closing price series in the past period. By comparing the predicted and real values, how the Deregulation affected the stock price of the biopharmaceutical sector could be determined. After modeling analysis, it is concluded that a positive effect has been produced by the Deregulation on the stock price of the pharmaceutical industry. Further study shows that there is a short-term lag in the impact, and the relative growth rate is not large. And the impact has been observed with certain short-term nature through the prediction results. This paper innovatively uses stock price fluctuations as an insertion point to explore the impact of the Deregulation on the pharmaceutical industry, which has profound practical guiding significance. For Chinese policy makers, it is suggested that the country should cooperate with the improvement and upgrading of the pharmaceutical industry. For investors, it is suggested that they should maintain a more stable and positive wait-and-see attitude towards the pharmaceutical industry.

Study on the Applicability of LSTM for Predicting Stock Price when Facing Extreme Events

The analysis of stock price fluctuations holds considerable significance in the field of economics, particularly given the present environment characterized by unpredictability and rapid changes. Previously, the long short-term memory (LSTM) model has been employed effectively in addressing time series problems, including stock market forecasting. However, in the current dynamic landscape, the ability of LSTM to adapt to volatile conditions and provide accurate predictions is an area that merits further investigation. This study gathers stock data from prominent and representative companies, namely Apple, Google, Amazon, and Microsoft, spanning from January 2012 to March 2023. Specifically, two significant events are examined: the impact of the Covid-19 outbreak on the US stock market on February 26, 2020, and the Russia-Ukraine conflict occurring on February 26, 2022. By dividing the stock data surrounding these events into training and test sets, this research aims to evaluate the differential performance of LSTM in scenarios where it possesses no prior knowledge of these events versus situations where it has already assimilated the influence exerted by them.

Predicting Tencent's Stock Price: A Comparative Analysis of Machine Learning Algorithms

Chinese internet companies, such as Tencent, are bringing a new energy to the global market. As one of the largest internet companies in China, Tencent's stock price fluctuations hold significant importance for investors and the market. Accurate forecasting of stock prices is of great importance to make high-yield decisions and manage risk. This paper aims to provide investors with effective and scientific references for decision-making by analyzing various forecasting methods. The dataset China-Techgiant-Stock-Data-in-HK-Market-2022 from Kaggle is used in this paper. This study utilizes various machine learning and time series analysis methods, including linear regression, SVM, random forest, LSTM neural network, and ARIMA, to predict stock prices using historical data. The models' accuracy and performance are compared, with traditional machine learning methods (linear regression, SVM, and random forest) contrasted against deep learning and time series analysis methods (LSTM neural network and ARIMA). It turns out that LSTM has the best prediction results. This can better guide investors in their stock decisions.

PENERAPAN MODEL GEOMETRIC BROWNIAN MOTION DAN PERHITUNGAN NILAI VALUE AT RISK PADA SAHAM BANK CENTRAL ASIA TBK

Stock price fluctuations are difficult to predict, resulting in uncertain profits. Therefore, a mathematical model is needed to predict future stock prices, namely the Geometric Brownian Motion (GBM) model based on a stochastic process. Stocks are also accompanied by risks that have potential for loss. The risk can be measured using Value at Risk (VaR) which can estimate the maximum loss that may happen from an investment at a certain level of confidence and period of time. The purpose of this research is to implement the GBM model in predicting stock prices and estimating the maximum loss of stock investment using VaR. This research analyzes the daily closing stock price of PT Bank Central Asia (BBCA) for the period November 1, 2021, to December 31, 2022. The stock price predictions with the GBM model are used to estimate the VaR value. Based on the analysis results, GBM is highly accurate model with an average MAPE value of 5.77% and the smallest MAPE value of 1.45%. The VaR values obtained at the 80%, 90%, 95% and 99% confidence level are 1,17%, 1,74%, 2,19% and 2,86% of the total fund investment for the next one-day period, respectively.

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.

Analyzing the Impact of Financial News Sentiments on Stock Prices—A Wavelet Correlation

This study investigates the complex interplay between public sentiment, as captured through news titles and descriptions, and the stock prices of three major tech companies: Microsoft (MSFT), Tesla (TSLA), and Apple (AAPL). Leveraging advanced analytical methods including Pearson correlation, wavelet coherence, and regression analysis, this research probes the degree to which stock-price fluctuations can be attributed to the polarity of media sentiment. The methodology combines statistical techniques to assess sentiment’s predictive power for stock opening and closing prices, while wavelet coherence analysis unveils the temporal dynamics of these relationships. The results demonstrate a significant correlation between sentiment polarity and stock prices, with description polarity affecting Microsoft’s opening prices, title polarity influencing Tesla’s opening prices, and a positive impact of title polarity on Apple’s closing prices. However, Tesla’s stock showed no significant coherence, indicating a potential divergence in how sentiment affects stock behavior across companies. The study highlights the importance of sentiment analysis in forecasting stock-market trends, revealing not only direct correlations but also lagged influences on stock prices. Despite its focus on large-cap tech firms, this research provides a foundational understanding of sentiment’s financial implications, suggesting further investigation into smaller firms and other market sectors.

The influence of grain futures market on stock price fluctuation of agricultural listed companies

Based on the data from June 13, 2019 to September 1, 2023, this paper analyzes the impact of the grain futures market on the stock price volatility of listed agricultural companies. The results show that the composite price index of grain futures and the composite stock price index of listed agricultural companies are granger reasons for each other in the short term. The cointegration test shows that there is a long-term equilibrium relationship between them. Based on this, a vector error correction model is constructed for impulse response analysis, and the correlation between the two is further verified.

A Conceptual Analysis of Price Movement, Stock Determinants and Their Effects on Economic Growth in Nigeria

This study explores the effects price movements in stocks and its determinants on growth in Nigerian economy. The study in specific terms model’s stock price fluctuations against economic growth and insecurity, exchange rate, interest rate, inflation rate, stock price movement against GDPGR in Nigeria. To achieve the above objectives, the study employs the ex-post facto research design. Quarterly time series data were obtained from the Central Bank of Nigeria (CBN) statistical Bulletins using already available data from 1985 - 2019. The data were analyzed via the aids of econometric techniques such as the descriptive statistics test, the Augmented Dickey-fuller (ADF) unit root test, Correlation matrix and Heteroskedasticity ARCH effect, granger causality, the GARCH (1,1) model and ARDL model for estimations. Findings from the analysis shows price fluctuations had positive and significant effect on growth of the Nigerian Economy. Also, it was opined that insecurity, interest rate, inflation rate, exchange rate, and GDPGR have long-run significant effects on share price volatilities in Nigeria. Finally, it was revealed insecurity, interest rate, inflation, exchange rate and GDPGR have a long-run significant effects on share price fluctuations in Nigeria. On the bases of this finding studies recommended that Authority through its agencies should formulate working document that would stabilize macroeconomic variables and by extension reduce the fluctuation of the stock price as this is required to enhance confidence amongst investors in the market. Also, that the CBN should design and implement policy instruments that would stabilize interest rate to promote significant short run investments and sustain the long run benefits of investments in Nigeria. Government should expedite efforts to reduce the rate of inflations and stabilize the price of goods and services as this promises greater savings and encourages investments which will translate into economic growth. Lastly, policies to tackle cases of corruption in the security framework should be formulated, while the Government should be proactive about its fight against insecurity in Nigeria as this would help stabilize share prices and enhance economic growth in Nigeria.

The Impact of Pre Delisting Crisis on the Stock Price of Chinese Stocks --The Case of "Bilibili"

Against the backdrop of the introduction of the US Foreign Company Accountability Act, the threat of trade tensions between the US and China to the fate of corporations has once again been exposed. The US increased regulatory scrutiny of Chinese stocks and then signing of an agreement between Chinese and US regulators regarding audit and regulatory cooperation. Although the risk of Chinese stocks being delisted has decreased in the short term, in the medium to long term, the risk of delisting and the pressure brought by trade frictions between China and the United States still exist for the company. This paper uses the event study method to analyze the degree of abnormality in the share price volatility of Bilibili, when the first pre-delisting list is announced and the company itself is on the list, reflecting the magnitude of the pre-delisting event's impact on Chinese stocks. The findings demonstrate that not only do the listed companies have abnormal fluctuations in stock prices, but the entire Chinese concept stock market has short-term negative impacts when the pre-delisting list is announced. Finally, based on the research results and previous literature, this article will provide relevant suggestions from the perspectives of the country, government, and enterprises.

The Influence of COVID-19 Epidemic on the Financial Market of China Energy Industry

Aim to reveal the changes in the financial market of the energy industry in China under the influence of the COVID-19 epidemic by use the fluctuation of the stock price of the energy industry. In this paper, ARIMA, a time series autoregressive moving average model, is used to model the fluctuation of stock prices in the energy industry, it shows the impacst on the financial market of energy industry before and after the outbreak of epidemic, in order to understand the energy crisis under the COVID-19 epidemic more clearly through this study. This research found that based on the COVID-19 pandemic, the stock price of the energy industry fluctuated. The epidemic caused investors' lack of confidence in the market, which caused the stock price to fall. Later, due to the government's policy to save the market, the stock price rebounded. Different from other research based on global nature, this paper focuses on energy enterprises in China. This study is meaningful. For investors, by understanding the changes in the financial market, they can regain confidence in the market. For leaders, this study can let them know the serious impact of epidemic on the market, respond to the rescue more quickly, and introduce the rescue policy.

The Impact of Information Asymmetry on Investment Behavior in the Stock Market

The stock market is the main component of the capital market and plays an important role in promoting the development of the national economy. Information asymmetry, as a typical issue in China’s stock market, triggers stock price fluctuations, increases stock market risks and largely influences investors’ investment behavior in stock market. Therefore, improving the information asymmetry problem is of great significance for the stable development of the stock market. This paper depends on the nature of information asymmetry, explores its specific manifestation in the stock market, and then analyses the process of information asymmetry issues on investment behavior. Studies indicate that information asymmetry leads to misdirected investment from three aspects, which include judgment on individual stock, assessment of the market value of the company and expectations of stock market developments, resulting in investor losses. Based on the above conclusions, this paper puts forward recommendations for improvement in terms of market regulation and the legal system: to improve the transparency of stock market transactions through the disclosure of information by regulators and companies. To complete the legislation of the stock market and establish strict punishment mechanisms to ease the problem of asymmetric information and maintain the stable operation of the stock market.

Research on the Quantitative Causal Transmission of Stock Price Fluctuations of Listed Companies in the Rare Earth Industry Chain

Rare earth is an important strategic mineral resource, and the study of the causes of stock price fluctuations of rare earth listed companies and their transmission can enable all parties in the market to have a clearer understanding of current market relationships. In this study, the quantitative causality analysis model and complex network method are used to analyze each link within the rare earth industry chain and the relationship between regions from the quantitative causality of stock price fluctuations among listed companies in the rare earth industry. The main transmission paths of fluctuations in each link between companies are also given. It is found that the quantitative causal relationship between the price fluctuations of rare earth industry stocks has positive causation, negative causation, and unclear causation, and the dominant causal relationship is mostly positive causation. The positive causal relationship between enterprises in the same region is significantly greater than that between enterprises in different regions. The negative causal relationship and unclear causal relationship between enterprises in different regions are significantly greater than the negative causal relationship and unclear causal relationship between enterprises in the same region.

Fluctuations in Stock Prices of Companies in Indonesia as an Impact of Organizing Presidential Elections

The objective of this study was to determine how non-economic events, such as the presidential election affect the Composite Stock Price Index. A descriptive quantitative method was used to collect secondary data from the Indonesia Stock Exchange (IDX) website. The data used is a historical data set from 1999 to 2019. The data was taken from the daily closing price of LQ45 on the JCI (Composite Stock Price Index). Analysis techniques using time series analysis. Data analysis used ARIMA (Autoregressive Integrated Moving Average) model. The study found that company stock prices changed due to presidential elections in 1999, 2004, 2009, and 2014. Suggestions for further research are to find techniques used as well as to include other elements that further influence investor decisions.

The stock price forecast under the failure of silicon valley bank based on the ARIMA model

The collapse of Silicon Valley Bank on March 10, 2023, had a profound impact on the stock prices of many companies in the United States. This study aims to examine the response of other banks in the US to this event by utilizing the Autoregressive Integrated Moving Average (ARIMA) model to forecast their stock prices. The research demonstrates that the ARIMA model effectively predicts the general trend of these banks' stock prices, with Root Mean Squared Error (RMSE) values below 1 for four out of six major US banks. These findings indicate that the proposed method is a promising tool for managing sudden fluctuations in stock prices, outperforming traditional linear regression models. Consequently, this research provides valuable insights for investors and financial institutions in managing and mitigating risks associated with abrupt market changes. Additionally, the study contributes to a greater understanding of the effects of bank collapses on the stock market. Overall, the research highlights the significance of incorporating advanced forecasting methods, such as ARIMA, in analyzing and predicting stock price movements in volatile market conditions.

The antecedents of financial performance and their implications for firm value in mining sector companies listed on the Indonesia Stock Exchange

Indonesia is a nation characterized by its substantial endowment of natural resources. Based on the most recent data provided by the World Bank, Indonesia holds the fourth position in terms of global copper production, ranks fifth among the major nickel producers worldwide, stands at the eighth position in terms of global gold production, and is recognized as the largest coal producer globally. The allure of Indonesia's abundant natural resources serves as a magnet for potential investors. Furthermore, the valuation of mining industry stocks serves as a crucial metric for investors to allocate their capital and pursue potential profitability in forthcoming periods.
 The mining sector firms encompass a collection of enterprises that are publicly traded on the Indonesia Stock Exchange. These companies operate within several sub-sectors of mining, quarrying, and other related mining services. The swings observed in mining stock prices, characterized by both increases and losses, signify a dynamic and volatile market environment. The fluctuation in stock prices can be attributed to the interplay of supply and demand, which is influenced by a multitude of factors encompassing both macroeconomic and microeconomic considerations.
 The findings of this study indicate that the first model demonstrates a positive and statistically significant relationship between ownership structure, intellectual capital, and financial performance. Moreover, the moderation of intellectual capital in the context of the interaction between capital structure and financial performance demonstrates a noteworthy and unfavorable impact on financial performance. In contrast, it has been observed that both capital structure and exchange rates do not exert a substantial influence on financial performance. The findings of the second model indicate that various factors, such as capital structure, financial performance, ownership structure, and intellectual capital, have a statistically significant and positive impact on business value, with the exception of the exchange rate. In the present study, the findings pertaining to indirect influence through mediation reveal that the financial performance of firms plays a mediating role in the association between capital structure, ownership structure, exchange rates, and firm value.
 The findings of the analysis indicate that the proposed hypotheses can be accepted, with the exception of a few hypotheses that were rejected due to the lack of substantial effects observed in the results. The validation of many hypotheses within the initial framework provides empirical support for the theoretical propositions and conceptual frameworks posited by Jensen and Meckling (1976), Sveiby (2010), and Stewart and Ruckdeschel (1998). The acceptance of the hypothesis in the second model aligns with the viewpoints expressed by Brigham et al. (2015), Freeman (2015), and Jensen & Meckling (1976). The internal role of a firm plays a crucial function in attaining optimal financial performance and determining the value of a mining company.

Teknologi Artisan Coffee Roasting Pada Pengusaha Café Probolinggo Dalam Upaya Menghasilkan Kopi Lokal Berkualitas

The potential of the coffee industry in Probolinggo City, especially in the Micro, Small, and Medium-sized Enterprises food and beverage sector, including coffee shops, shows promising growth. For example, the coffee shops in the Tisnonegaran Village have become a significant tourist destination contributing to the local economy. However, coffee industry stakeholders face several challenges. The availability of high-quality coffee beans is a major concern as they often need to be sourced from outside the city, leading to stock instability and price fluctuations due to high delivery costs. To address these challenges, artisan coffee roasting technology training plays a crucial role. This training provides a deep understanding of the coffee bean roasting process, enabling coffee business owners to produce high-quality beans with unique flavor profiles. Knowledge about coffee bean selection, temperature control, and roasting times equips participants with the ability to optimize their coffee products. Furthermore, a plan for the development of artisan nano coffee roaster machine technology is also proposed to enhance the quality of mature coffee beans. Through collaboration with students, this technology's design is expected to bring innovation and a deeper understanding of effective roasting equipment and techniques. By combining training, technological development, and collaboration among coffee industry stakeholders, students, and the government, Probolinggo City has the potential to become a high-quality and competitive hub for the coffee industry in Indonesia.

Stock Price Correlation Analysis with Twitter Sentiment Analysis Using The CNN-LSTM Method

The intricate interplay between stock prices, reflecting a company's intrinsic value, and multifaceted factors like economic conditions, corporate performance, and market sentiment, constitutes a vital research domain. Grounded in sentiment analysis, our study deciphers public opinions from vast textual data to gauge sentiment, leveraging Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. We focus on Bank Central Asia (BBCA), a prominent Indonesian banking institution, aiming to forecast stock price fluctuations by analyzing sentiment trends extracted from social media, especially Twitter. Meticulous experimentation, encompassing data segmentation, feature extraction, augmentation, and model refinement, yields significant enhancements in prediction accuracy. The CNN-LSTM model's performance improves from 73.41% to a robust 77.75% accuracy, with F1-scores rising from 73.00% to 75.42%. Importantly, strong correlations emerge between sentiment predictions and actual stock price movements, validated by Spearman correlation coefficients. Positive sentiment exhibits a substantial correlation of 0.745 with stock price changes, while negative sentiment exerts notable influence with a correlation coefficient of 0.691. In summary, our study advances the field of sentiment-driven stock price prediction, showcasing deep learning's effectiveness in extracting sentiment from social media narratives. The implications extend to understanding market dynamics and potentially integrating sentiment-aware strategies into financial decision-making. Future research directions could explore model transferability across financial contexts, real-time sentiment data integration, and interpretability techniques for enhanced practicality in sentiment-driven predictions.

Analysis of the Influence of Enterprise Mergers and Acquisitions on the Stock Market

The global M&A market has been growing rapidly in recent years. With the development of emerging markets, M&A can be seen in almost any event that affects the direction of the market, especially in stock market volatility, where the impact of M&A is sometimes a factor that cannot be ignored. In many different situations, M&A can have a different impact on overall stock market volatility. Through this process, we will show the intrinsic relationship between stock market turbulence and corporate M&A transactions through analysis to improve the in-depth measurement of M&A activities by companies. From several perspectives, we will use literature reading and analysis methods to conduct an analysis of the impact of corporate mergers and acquisitions. The study will show that the relationship between stock price fluctuations and M&A transactions is complementary, and that the development goals and status of the firm will determine whether M&A can have a positive impact on the stock market.

Application Analysis of Financial Time Series in Stock Market

Time series analysis is a basic tool in number investing. The time-series sequence is a sequence of variables measured in a fixed section sequence over some time. The fluctuation of stock price affects all aspects of social and economic life, so effectively predicting the trend of stock price changes has great economic and social value. Based on the existing research results on time series, this paper takes the stock market as an entry point, and sorts and summarizes the literature from the application fields and application methods of time series. The research results show that the application fields of time series involve the stock market, enterprises, macro economy, etc., among which the application of the stock market is the most commonly used and the most extensive. The research based on time series mainly innovates and corrects the research methods. The research in this paper can provide a theoretical reference value for further research on time series.