Stock Price Fluctuations Research Articles

Deep Convolutional Transformer Network for Stock Movement Prediction

The prediction and modeling of stock price movements have been shown to possess considerable economic significance within the finance sector. Recently, a range of artificial intelligence methodologies, encompassing both traditional machine learning and deep learning approaches, have been introduced for the purpose of forecasting stock price fluctuations, yielding numerous successful outcomes. Nonetheless, the identification of effective features for predicting stock movements is considered a complex challenge, primarily due to the non-linear characteristics, volatility, and inherent noise present in financial data. This study introduces an innovative Deep Convolutional Transformer (DCT) model that amalgamates convolutional neural networks, Transformers, and a multi-head attention mechanism. It features an inception convolutional token embedding architecture alongside separable fully connected layers. Experiments conducted on the NASDAQ, Hang Seng Index (HSI), and Shanghai Stock Exchange Composite (SSEC) employ Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), accuracy, and Matthews Correlation Coefficient (MCC) as evaluation metrics. The findings reveal that the DCT model achieves the highest accuracy of 58.85% on the NASDAQ dataset with a sliding window width of 30 days. In terms of error metrics, it surpasses other models, demonstrating the lowest average prediction error across all datasets for MAE, MSE, and MAPE. Furthermore, the DCT model attains the highest MCC values across all three datasets. These results suggest a promising capability for classifying stock price trends and affirming the DCT model’s superiority in predicting closing prices.

The impact of social media discourse on financial performance of e-commerce companies listed on Borsa Istanbul

This research investigates how changes in public discourse on social media (particularly Twitter, now X) influence financial performance of companies listed on the e-commerce XTCRT index of Borsa Istanbul amid the COVID-19 pandemic. Utilising a unique 4-layer network structure, the study integrates daily closing prices, highest and lowest values, and trading volume data of these companies with Twitter discussions on six categories: culture, economy, health, politics, technology, world affairs. By using advanced computational methods such as multilayer network analysis, long short-term memory architecture, nonlinear autoregressive distributed lag analysis, we find a high responsiveness of the e-commerce index to social media content, particularly in discussions related to health, technology, world affairs. The study underscores that social media discussions significantly shape index composition, resulting in noticeable shifts in market dynamics, including fluctuations in stock prices, trading volumes, and market sentiment. Our study advocates for a more advanced approach to market analysis that incorporates the dynamics of online public discussions.

THE IMPACT OF THE PRESIDENTIAL ELECTION ON IDX COMPOSITE PREDICTIONS USING LONG SHORT TERM MEMORY

An analysis of the performance of Indonesia's capital market, or Indonesia Stock Exchange (IDX), shows significant growth in recent years, with market capitalization increasing dramatically from IDR 679.95 trillion in 2004 to IDR 11,674.06 trillion by 2023. The IDX plays an important role in the Indonesian economy by facilitating capital formation and providing opportunities for investors to diversify their portfolios. However, the capital market is vulnerable to political events, such as presidential elections, which can affect national stability and economic performance. An analysis of the stock index performance before the presidential election showed a significant bullish trend. Still, given the considerable impact of political events, such as presidential elections, on financial markets, this study aims to analyze and forecast the performance of the IDX Composite by examining historical data from past election years, we provide insights and predictions in highlighting how the LSTM model accommodates these political factors in its forecasts. IDX Composite closing price forecasting was conducted using the bidirectional LSTM model to anticipate the impact. The analysis results show that this model can predict the weekly closing price of the IDX Composite with an error of 1.04%, with estimated stock price fluctuations in the next 8 weeks in the range of 6619.755 to 6812.722

News-Driven Volatility: A Deep Dive into Leading Nifty Healthcare Stocks

This study conducts a comprehensive analysis of stock price volatility within the healthcare sector, concentrating on five prominent companies: Sun Pharma, Dr. Reddy, Cipla, Apollo Hospitals, and Divis Laboratories. Leveraging annual daily data from the Nifty Healthcare Index for 2023, the research delves into the intricate relationship between stock price fluctuations and market-sensitive news events. Employing a robust methodology that integrates slope calculations, ANOVA, Tukey’s HSD tests, moving averages, and standard deviation metrics, the study evaluates the differential impact of these events on stock volatility. The findings reveal significant volatility, with Apollo Hospitals demonstrating the highest levels of price variability. The research highlights the profound influence of regulatory shifts, financial performance metrics, and macroeconomic conditions on stock behavior. These insights are pivotal for investors and financial analysts seeking to navigate the complex dynamics of the healthcare market. The application of moving averages further refines the analysis, offering a sophisticated lens through which to assess and interpret market trends, ultimately enhancing the strategic decision-making process within this critical sector.

PERBANDINGAN FUZZY TIME SERIES MARKOV CHAIN DAN FUZZY TIME SERIES CHENG

Investing is a hot and fast-growing topic right now. Stock.investing.is one of the most popular investments by the public. The JCI is an index that measures the performance of all stocks listed on the IDX. The closing price of the stock is published daily and can be used by investors as an investment benchmark. The occurrence of stock price fluctuations entails a high risk of loss. Predicting stock prices is one way to avoid this risk. Fuzzy time series is a time series model that can be used to minimize the risk of future occurrences. Time parameter data is not stationary, so this research uses fuzzy time series with Markov chains and Cheng's method to determine the JCI completion dates from January 2018 to June 2022. Calculating the accuracy level of the two method approaches using symmetric Mean Absolute Percentage Error (sMAPE), the sMAPE value obtained in the Fuzzy Time Series Markov Chain method is 2.11% and the Fuzzy Time Series Cheng method is 2.98%. The conclusion is that of the two methods the Markov Chain has a smaller value and can be said to be a better method compared to the Cheng method.

The Influence of Economic Variables on Stock Prices of Telecommunications Companies

The general background fluctuation of stock prices is a critical concern for investors, particularly in dynamic economic environments where various indicators can significantly impact financial performance. The specific background of this study focuses on the telecommunications sector, which has been increasingly influenced by economic variables such as the Inflation Rate, SBI Interest Rate, Earnings Per Share, Rupiah Exchange Rate, and Company Fundamentals. Despite existing research, there is a knowledge gap regarding how these factors collectively influence stock prices in publicly listed telecommunications companies. This study aims to investigate the effects of these economic variables on stock prices from 2020 to 2023, utilizing a quantitative approach with secondary data obtained from annual financial reports of 16 companies. The results demonstrate that all examined variables—Inflation Rate, SBI Interest Rate, Earnings Per Share, Rupiah Exchange Rate, and Company Fundamentals—significantly affect stock prices. The novelty of this research lies in its comprehensive analysis of the interplay between these economic indicators specifically within the telecommunications sector. The implications of these findings suggest that investors and policymakers should consider these economic factors when making investment decisions, thereby enhancing their financial strategies and contributing to a more robust understanding of stock market dynamics in the telecommunications industry. Highlights: All examined economic variables significantly affect stock prices. The study analyzes data from 16 publicly listed telecommunications companies. Findings enhance investment strategies and understanding of market dynamics. Keywords: Inflation, Interest Rate, Earnings Per Share, Exchange Rate, Stock Price

Stock Price Prediction and Investment Simulation - Based on SSA-CNN-LSTM Method

Stock price fluctuations, which are time-series in nature. At the same time, based on the machine learning Long Short-Term Memory Network (LSTM) has excellent processing ability in predicting long time series, this paper proposes a stock price prediction method using CNN-LSTM optimized for LSTM hyperparameters using Sparrow algorithm. The data used in this study covers a total of 3403 trading days from March 1, 2010 to March 29, 2024 and about 40 technical indicator factors were selected. CNN model was firstly used to obtain features in the data. And later, CNN-LSTM network using SSA for parameter optimization uses the extracted features for stock price prediction and simulated trading based on the predicted up and down signals. The experimental results show that the SSA-CNN-LSTM network is able to provide better prediction accuracy than the CNN-LSTM network for price derivation. This prediction method not only provides a better op-eration idea for actual trading, but also provides practical experience for scholars to study time se-ries in finance.

Research on Change Point Detection during Periods of Sharp Fluctuations in Stock Prices–Based on Bayes Method β-ARCH Models

Research on Change Point Detection during Periods of Sharp Fluctuations in Stock Prices–Based on Bayes Method β-ARCH Models

Research on the Dynamics of Word-of-Mouth Influencing Stock Prices

Word-of-mouth (WOM) can be considered one form of public opinion, reflecting consumers’ views towards product or service quality. With the development of social media, WOM holds influence over investors and subsequently impacts the stock prices of companies. The purpose of this study is to uncover the dynamics encompassing the factors that influence WOM, its propagation mechanisms, and the consequential impact on reality illustrated through fluctuations in stock prices. The mathematical model to describe the whole process can be separated into two components: information propagation model and stock price fluctuation model. The cumulative abnormal return (CAR) is used to demonstrate fluctuations in stock prices. Utilizing a Lyapunov function, it is proven that the equilibrium point of the model is globally asymptotically stable. As for the real world, it suggests that WOM will eventually prevail online, leading to fluctuations in stock prices. Based on the event study method, the empirical examination of 43 real events shows that our model can effectively predict the CAR and reflect the influence on stock prices caused by WOM in real-world scenarios.

Research on the Investment Value of BYD based on Financial Analysis and Valuation Analysis

With the promotion of China's dual-carbon strategic policy and the development of the new energy automobile industry in recent years, the new energy automobile industry has been widely concerned by the academic community and the industry, especially the fluctuation of BYD's stock price has also been widely discussed. This paper uses financial analysis and valuation analysis to study the investment value of BYD. The research results show that in terms of finance, BYD's profitability and operating capacity have shown a good upward trend, but BYD's liquidity and solvency have shown a downward trend year by year. In terms of valuation, by comparing BYD's EPS and P/E ratio with Tesla, it is found that BYD has more long-term investment value than Tesla. The research in this paper provides guidance for investors to make investment decisions, and contributes support and foundation for the future advancement of BYD and the entirety of the new energy vehicle industry.

A Financial Analysis and Valuation of Occidental Petroleum Corporation

Oil has always been an important political and strategic resource in a country. In recent times, the significant changes in the international situation and the mergers and acquisitions of American oil giants have intensified the changes in the oil industry, directly leading to fluctuations in American oil stock prices. Therefore, this article collects financial data from Occidental Petroleum Corporation in recent years and conducts comparative analysis to explore the development prospects of Occidental Petroleum Corporation and some changes in the petroleum industry. Research has found that Occidental Petroleum Corporation has good development prospects based on its own oil production capacity and environmentally friendly technology development, and its stock price may experience a significant increase. In addition, various macro factors such as the Russia Ukraine war and changes in the international situation may lead to a shock to the US oil industry and make a decrease in overall profits for it.

A study on the impact of investor sentiment on individual stocks based on text mining

This paper investigates the influence of investor sentiment on individual stock prices through text mining. By reviewing relevant theories and literature, we utilized web crawler technology to collect comment data from the Eastmoney Stock Bar, constructing a comprehensive index of investor sentiment and an index of investor sentiment for individual stocks. Employing the VAR model and Granger causality test, we analyzed the correlation and dynamic relationship between investor sentiment and stock price fluctuations. The study found that stocks of different types of financial institutions react differently to investor sentiment, and that investor sentiment significantly affects stock prices. This paper puts forward corresponding policy recommendations, which can help market investors make scientific decisions and promote the healthy development of the market.

Manipulating stock prices based on insider information

Purpose This paper aims to investigate the trading behavior of insider investors before and after information releases, identifying information-based manipulation in the stock market and the characteristics of companies whose stock prices are manipulated. Design/methodology/approach This paper employs logit regression method and an event study approach, utilizing hand-collected data from 2010 to 2022, with information categorized into negative and positive types. Findings The results show no evidence of insider trading or negative information-based manipulation in both high and low transparency firms. However, in highly transparent companies, the Board of Directors (BOD) avoids direct manipulation by using relatives to evade market supervisors. In low transparency companies, both the BOD and family members (FM) exploit positive information to benefit personally by buying shares before releasing favorable news, causing a sharp stock increase, and selling afterward. Continued buying by the BOD and FM also suggests likely positive news announcements. Practical implications The characteristics of information-based manipulation in companies, as provided by this study, help individual investors avoid investing in stocks that are highly susceptible to manipulation. Originality/value Empirical research on information-based manipulation is scarce due to limited secondary data. Our study uses transaction data from insider investors in a frontier market with low transparency and high information asymmetry. This enables us to analyze information-based stock price manipulation. We identify manipulation by comparing insiders' trading behavior with their market information releases, resulting in stock price fluctuations greater than 5%.

The Impact of Stock Price Fluctuations on the Financial Market

The Impact of Stock Price Fluctuations on the Financial Market

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

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

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

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

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

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

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

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

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

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

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

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