Influence Stock Prices Research Articles

Assessing the Effects of Cyber Attacks on Financial Markets

Cyber-attacks on financial markets have emerged as a significant threat to global economic stability, disrupting market operations, compromising sensitive data, and undermining investor confidence. This study explores the multifaceted impact of cyber-attacks on financial markets, examining both the immediate and long-term effects on market performance, regulatory response, and overall market resilience. By analyzing recent incidents, this research highlights how cyber-attacks influence stock prices, trading volumes, and market volatility, shedding light on the vulnerabilities within financial systems. Findings suggest that cyber threats not only impact individual institutions but also ripple through interconnected markets, amplifying financial risk on a broader scale. The study concludes with recommendations for strengthening cybersecurity measures in financial markets to enhance resilience and mitigate potential losses from future cyber incidents.

Predicting Market Share Patterns using LSTM

Forecasting stock prices is a significant challenge in the financial sector, marked by its complexity and the impact of numerous variables. In this paper, we undertake a comprehensive investigation into how machine learning algorithms can be effectively applied to address this challenge. Our main goal is to clarify the methodologies and strategies that can enhance the accuracy of stock market predictions. We explore the complex process of predicting stock values and scrutinize the various machine learning algorithms that have been suggested and employed for this purpose. By critically evaluating these algorithms, we aim to provide insights into their individual strengths and weaknesses, ultimately helping the reader make an informed decision about the most appropriate algorithm for their specific forecasting requirements. Beyond algorithm selection and attribute analysis, our review also considers external factors that can significantly influence stock prices. These factors include a broad range of variables, such as economic conditions, geopolitical events, corporate news, and market sentiment. Understanding the interaction between these external elements and stock market dynamics is essential for developing more robust and reliable prediction models.

Decoding Stock Price Movements: How Net Profit Margin and Debt-to-Equity Ratio Drive Value, with Earnings per Share as the Game-Changer

This research investigates the relationships between Net Profit Margin (NPM), Debt-to-Equity Ratio (DER), Earnings per Share (EPS), and stock prices within the context of the Indonesian stock market. Utilizing a quantitative analysis approach, the study employs panel data regression to assess the statistical significance of these financial metrics on stock valuations. The findings reveal that NPM has a meaningful and statistically significant positive impact on stock prices, indicating that higher profit margins are associated with increased investor confidence and market valuation. Conversely, the results show that DER does not significantly influence stock prices, suggesting that investors may perceive high debt levels as a risk that diminishes stock value. Furthermore, the study finds that EPS does not moderate the relationship between NPM and stock prices, nor does it strengthen the effect of DER on stock valuations. These conclusions contribute to understanding financial determinants in stock price movements, emphasizing the importance of profit margins while highlighting the limited role of leverage and earnings per share in this context. The research provides recommendations for investors and corporate managers and suggests avenues for future studies, including broader geographic comparisons and integrating qualitative factors into stock valuation analysis.

A Stock Prediction Method Based on Multidimensional and Multilevel Feature Dynamic Fusion

Stock price prediction has long been a topic of interest in academia and the financial industry. Numerous factors influence stock prices, such as a company’s performance, industry development, national policies, and other macroeconomic factors. These factors are challenging to quantify, making predicting stock price movements difficult. This paper presents a novel deep neural network framework that leverages the dynamic fusion of multi-dimensional and multi-level features for stock price prediction, which means we utilize fundamental trading data and technical indicators as multi-dimensional data and local and global multi-level information. Firstly, the model dynamically assigns weights to multi-dimensional features of stocks to capture the impact of each feature on stock prices. Next, it applies the Fourier transform to the global features to capture the long-term trends of the global environment and dynamically fuses these with local and global features of the stocks to capture the overall market environment’s impact on individual stocks. Finally, temporal features are captured using an attention layer and an RNN-based model, which incorporates historical price data to forecast future prices. Experiments on stocks from various industries within the Chinese CSI 300 index reveal that the proposed model outperforms traditional methods and other deep learning approaches in terms of stock price prediction. This paper proposes a method that facilitates the dynamic integration of multi-dimensional and multi-level features in an efficient manner and experimental results show that it improves the accuracy of stock price predictions.

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

MACROECONOMIC ASPECTS IN INFLUENCING STOCK PRICE MOVEMENT ON THE INDONESIAN SHARIA STOCK INDEX 2018-2022

This study aims to analyze the influence of macroeconomic variables and world oil prices in influencing stock price movements on the Indonesian Sharia Stock Index (ISSI) in 2018-2022. In this case, stock price movements can be influenced by several factors, one of which is macroeconomics. The research methodology used in this study is to use a quantitative research approach and secondary data type, with quarterly samples from each year 2018 to 2022 which amounted to. The research model carried out is log linear regression panel data analysis conducted through the chow test and classical assumptions using Eviews 10 tools. Based on the results of this study, it states that partially shows the variables of Inflation, BI Interest Rate Economic Growth (GDP) and Money Supply. While simultaneously all variables did not have a significant effect on the Indonesian Sharia Stock Index (ISSI).

How does finance and accounting supervision affect stock price crash risk?

As an essential component of China's national supervision system, finance and accounting supervision plays an important role in regulating financial order, promoting economic development, and maintaining social stability. This study selects the special action of finance and accounting supervision as a quasi-natural experiment and conducts a difference-in-differences estimation to alleviates the problem of endogeneity. Using a sample of A-share listed firms from 2007 to 2013, this study examines the effect of finance and accounting supervision on stock price crash risk in the context of China. Our results indicate that firms subject to finance and accounting supervision are less likely to suffer a stock price crash. We explore the underlying mechanism and find that finance and accounting supervision mitigates firms' information asymmetry, thereby reducing future stock price crash risk. We also find that the negative effect of finance and accounting supervision on stock price crash risk is more pronounced for highly financialized firms, firms with larger management shareholding, and firms with poorer stockholder checks and balances. This study has significance for enriching research on the factors influencing stock price crash risk and assessing the effectiveness of finance and accounting supervision in the capital market.

The Link Effect of ESG Score, Stock Price Volatility, and Tax Payment: Doing Well while Doing Good

This study aims to identify how environmental, social and governance (ESG) performance influences stock price volatility, explicitly focusing on the moderating role of tax engagement. ESG performance is measured by an ESG Score calculated from the weighting of three dimensions: environmental, social and governance. Stock price volatility is measured by the degree of price variations over 12 months based on the last 52 weeks’ prices. A sample of Indonesia-listed firms is used, with 770 observations from 2023. The results show that the ESG Score negatively impacts stock price volatility, which is more significant in the social dimension than in the environmental and governance dimensions. In addition, the tax payment variable moderates the relationship and increases the effect of the ESG Score on stock price volatility. These findings suggest that ESG practices and tax transparency are ethical elements and critical components for financial stability, promoting the high-quality development of listed firms. This study is significant for firms, regulators, policymakers and investors. Overall, it underrates the importance of firms adopting ESG activities and engaging in tax management to mitigate risks and maintain viability in the contemporary business environment. This study provides new empirical evidence regarding the factors driving corporate stock price volatility. In addition, it offers pertinent policy recommendations for businesses and governments regarding the significance of ESG investments.

Pengaruh Earning Per Share, Return on Equity dan Price to Book Value Terhadap Harga Saham Periode 2019-2021 (Studi Kasus pada Perusahaan Sub Sektor Otomotif dan Komponenya di BEI)

The phenomenon that occurred during the Covid-19 pandemic that spread in Indonesia in early 2020 has an impact on all aspects of life, including the capital market. The spread of Covid-19 in Indonesia has caused fear for investors so that many of them choose to leave risky assets. This affects the movement of the Composite Stock Price Index (IHSG). The problem in this study aims to determine the effect of EPS (Earning Per Share), ROE (Return on Equity) and PBV (Price to Book Value) on stock prices in automotive companies listed on the IDX for the period 2019 - 2021. The type of research used is quantitative research with secondary data sources. The population in this study is automotive companies listed on the Indonesia Stock Exchange for the period 2019 - 2021 as many as 15 companies. Sampling using purposive sampling technique as many as 10 companies that meet the criteria. Data analysis techniques using multiple linear regression analysis techniques. The results of the study indicate that EPS can influence stock prices, and ROE also influences stock prices, and PBV influences company value. Simultaneously, the variables EPS (Earning Per Share), ROE (Return on Equity) and PBV (Price to Book Value) influence stock prices.

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.

Pengaruh Return On Assets (ROA), Return On Equity (ROE), Earning Per Share (EPS) dan Debt to Equity Ratio (DER) Terhadap Harga Saham

This research aims to analyze the influence of Return On Assets (ROA), Return On Equity (ROE), Earning Per Share (EPS), and Debt to Equity Ratio (DER) on share prices in 34 LQ45 index companies during 2018–2020. The research method used is associative with the population of companies listed on the LQ45 index of the Indonesian Stock Exchange. Data collection was carried out through annual financial reports from 34 companies during the 2018-2020 period. The sampling method uses purposive sampling with certain criteria, and data analysis is carried out using multiple linear regression using SPSS software. The research results show that these four variables simultaneously influence stock prices. Specifically, Earning Per Share (EPS) has a positive influence, while Debt to Equity Ratio (DER) has a negative influence. Return On Assets (ROA) and Return On Equity (ROE) do not have a significant effect on stock prices.

Pengaruh ROA, ROE, dan EPS Terhadap Harga Saham Perbankan Buku III Yang Tercatat di BEI

The investment in the capital market is currently increasing among the public, emphasizing the urgency of providing financial literacy to enable individuals to make informed and intelligent investment decisions in the capital market. BUKU III was selected due to the relatively low per-share stock prices of the existing banks, allowing the public to have higher purchasing power. ROA, ROE, and EPS were chosen as the factors under review due to their widespread availability in financial reports, serving as a reference for the public in determining their investment choices. Previous studies focused on the impact of ROA, ROE, and EPS on stock prices in the BUKU IV (Tahir, Djuwarsa, and Mayasari, 2021). Their findings revealed that ROA had no significant independent influence on stock prices. Conversely, ROE and EPS, independently, significantly influenced stock prices in BUKU IV banks. Additionally, the simultaneous impact of ROA, ROE, and EPS affected the stock prices of BUKU IV banks. This study aims to explore whether ROA, ROE, and EPS exert influence on the BUKU III banks stock prices listed on the Indonesia Stock Exchange. The research method employed is the hypotheticodeductive method, utilizing quarterly data from 2018 to 2022, with a sample size of 5 companies. Data were collected from Stockbit, and the analysis methods used included t-tests, F-tests, and multiple linear regression analysis using Microsoft Office Excel and SPSS tools. The results indicate that ROA, ROE, and EPS do not have a partial or simultaneous impact on the stock prices of BUKU III banks.

Resilience and Adaptation: Investment Metrics of Indonesian Healthcare Companies Before and During the COVID-19 Pandemic

The impact of dividend policy and stock valuation (price earning proportion and price to book value) on the prices of healthcare industry stocks listed on the Indonesia Stock Trade somewhere in the range of 2016 and 2021 is examined in this study. The research data used in this quantitative analysis are secondary data. This study included eighteen healthcare sector companies in its sample. Purposive sampling is the methodology used in this study, and SPSS 24 is the analysis tool. We explained the relationship between stock prices, dividend policy, and stock valuation. We order the variables into pre-pandemic and during-pandemic categories subsequent to testing them for like clockwork. That's what the experimental results demonstrate, for all periods, the earning-per-share notation has a significant impact on stock prices. Stock valuation, on the other hand, has a significant impact on stock prices, as does the final dividend policy with dividend payout proportion and dividend per share. Meanwhile, results from the pre-pandemic time frame indicate that EPS and DPR didn't significantly influence results; results from the pandemic time frame indicate that stock valuation didn't significantly influence stock price. This research provides fresh insights into what the recovery of economic exposure means for corporate conduct in Indonesia, where persistent financial and economic pressures from stock market unpredictability give substantial sources of uncertainty.

Corporate earnings, dividend payments, and stock price behavior of manufacturing firms listed in Nigeria

The purpose of the study is to investigate the effects of corporate earnings and dividend payments on the stock price behaviour of manufacturing firms in Nigeria between 2013 and 2022. The study adopted an ex-post facto research design, and deployed Generalised Method of Moments (sys-GMM) and Granger causality tests for the data analysis. The two-step system GMM is used to control for omitted variables such as bias, unobserved panel heterogeneity or heteroscedasticity, autocorrelation within panels or groups, endogeneity issues, and measurement errors in the data. Findings show that share price (SP) and its predictors yield valuable insights into the factors influencing stock valuation. Dividend per share (DPS) emerges as a significant positive predictor, suggesting a notable impact on share price. Firm size (FSZ) proves to be a statistically significant predictor, indicating that the size of a firm plays a role in influencing stock prices, with larger firms associated with a decrease in share price. Conversely, Earnings per Share (EPS) and Dividend Yield (DY3) do not exhibit statistical significance, suggesting limited predictive power in the model. By way of conclusion, the study provides an understanding of the factors influencing share price behavior within the Nigerian manufacturing sector and the positive impact of past performance, particularly lagged share prices, confirming the persistence of stock market trends. The practical implications of this study are that it underscores the understanding of financial dynamics in emerging economies, particularly in the context of manufacturing firms in Nigeria.

Developing a Dynamic Feature Selection System (DFSS) for Stock Market Prediction: Application to the Korean Industry Sectors

For several years, a growing interest among numerous researchers and investors in predicting stock price movements has spurred extensive exploration into employing advanced deep learning models. These models aim to develop systems capable of comprehending the stock market’s complex nature. Despite the immense challenge posed by the diverse factors influencing stock price forecasting, there remains a notable lack of research focused on identifying the essential feature set for accurate predictions. In this study, we propose a Dynamic Feature Selection System (DFSS) to predict stock prices across the 10 major industries, as classified by the FnGuide Industry Classification Standard (FICS) in South Korea. We apply 16 feature selection algorithms from filter, wrapper, embedded, and ensemble categories. Subsequently, we adjust the settings of industry-specific index data to evaluate the model’s performance and robustness over time. Our comprehensive results identify the optimal feature sets that significantly impact stock prices within each sector at specific points in time. By analyzing the inclusion ratios and significance of the optimal feature set by category, we gain insights into the proportion of feature classes and their importance. This analysis ensures the interpretability and reliability of our model. The proposed methodology complements existing methods that do not consider changes in the types of variables significantly affecting stock prices over time by dynamically adjusting the input variables used for learning. The primary goal of this study is to enhance active investment strategies by facilitating the creation of diversified portfolios for individual stocks across various sectors, offering robust models and feature sets that consistently demonstrate high performance across industries over time.

Pengaruh Current Ratio, Sales Growth, Net Profit Margin dan Firm Size. Terhadap Harga Saham: pada Perusahaan Pertambangan yang terdaftar di Bursa Efek Indonesia (BEI) periode 2016-2022

The purpose of this research is to ascertain how Return on Assets in Mining Companies Listed on the Indonesia Stock Exchange is affected by Current Ratio, Sales Growth, Net Profit Margin, and Firm Size between 2015 and 2022. This study employs a quantitative methodology that combines a verification and descriptive strategy. Various statistical tests are employed in hypothesis testing, including partial and simultaneous tests (t and f tests), multiple linear regression tests, Pearson product moment correlation coefficient tests, determination coefficient tests, and classical assumption tests. 56 mining companies' financial reports provided the sample data. Purposive sampling combined with nonprobability sampling is being used. IBM SPSS version 26.0 was the analysis tool used in this investigation. Current Ratio has a considerable impact on stock price, according to study data. A major factor influencing stock price is sales growth. The stock price is not influenced in part by firm size or net profit margin. According to the study's findings, the stock price is significantly impacted by the concurrently obtained current ratio, sales growth, net profit margin, and company size.

Capability of Hybrid Long Short-Term Memory in Stock Price Prediction: A Comprehensive Literature Review

Stocks are financial instruments representing ownership in a company. They provide holders with rights to a portion of the company's assets and earnings. The stock market serves as a means for companies to raise capital. By selling shares to the public, companies can obtain funds needed for expansion, research and development, as well as various other investments. Though significant, predicting stock prices poses a challenge for investors due to their unpredictable nature. Stock price prediction is also an intriguing topic in finance and economics due to its potential for significant financial gains. However, manually predicting stock prices is complex and requires in-depth analysis of various factors influencing stock price movements. Moreover, human limitations in processing and interpreting information quickly can lead to prediction errors, while psychological factors such as bias and emotion can also affect investment decisions, reducing prediction objectivity and accuracy. Therefore, machine processing methods become an alternative to expedite and reduce errors in processing large amounts of data. This study attempts to review one of the commonly used prediction algorithms in time series forecasting, namely hybrid LSTM. This approach combines the LSTM model with other methods such as optimization algorithms, statistical techniques, or feature processing to enhance the accuracy of stock price prediction. The results of this literature review indicate that the hybrid LSTM method in stock price prediction shows promise in improving prediction accuracy. The use of optimization algorithms such as GA, AGA, and APSO has successfully produced models with low RMSE values, indicating minimal prediction errors. However, some methods such as LSTM-EMD and LSTM-RNN-LSTM still require further development to improve their performance.

A novel trading system for the stock market using Deep Q-Network action and instance selection

Stock trading is a complex decision-making process that involves predicting market price movements. Many investors attempt to buy at low prices and sell at high prices, which can be difficult due to numerous factors that can influence stock prices. Consequently, researchers have developed trading systems designed to generate high stock market returns using machine learning or deep learning techniques. However, building a trading system using supervised learning can be challenging in real-time due to noisy stock price data. Reinforcement learning trading systems can play an active role in the stock market and their performance depends heavily on how the learning environment is constructed. Therefore, this study proposes a Deep Q-Network (DQN) Action Instance Selection Trading System (DAIS) to improve the limitations of both supervised learning and reinforcement learning trading systems. DAIS learns effective trading timing by labeling the behavior of DQN models and using instance selection to effectively handle noise in stock price data. Our proposed model is evaluated by testing 72 companies listed on Korean Composite Stock Price Indexes (KOSPI) and comparing the results with those obtained in previous studies using supervised and reinforcement learning trading systems. The results indicate that the DAIS trading system is more efficient than the compared systems.

How Capital Market Liberalization Influences Stock Price Crash Risk: The Intermediate Effect of Corporate Accounting Clarity

The introduction of the Shanghai-Hong Kong Stock Connect and Shenzhen-Hong Kong Stock Connect programs represents a pivotal juncture in the evolution of Chinas capital markets, signaling a significant move towards increased openness and integration. These initiatives have sparked considerable academic interest, underscoring their potential to transform the financial landscape. This research employs these programs as a quasi-natural experiment to investigate their profound impact on financial stability. Our analysis focuses on a comprehensive dataset encompassing A-share firms listed on both the Shanghai and Shenzhen Stock Exchanges over a thirteen-year period, from 2008 to 2021. Employing a robust difference-in-differences analytical approach, this study seeks to discern the effects of capital market liberalization on the incidence of stock price crashes. The empirical findings reveal a compelling association: greater openness in capital markets correlates significantly with a reduced risk of stock price crashes. This correlation is largely attributed to enhancements in corporate accounting transparency facilitated by these initiatives. Thus, these findings underscore the dual role of enhanced capital market openness in not only fostering stability in stock prices but also in mitigating market risks, safeguarding investor wealth, reducing systemic financial vulnerabilities, and fostering sustainable development across capital markets. This study contributes meaningfully to the existing literature on stock price crash risk by providing nuanced insights and actionable recommendations for policymakers and market participants alike, aiming to further bolster capital market openness and resilience.

Analysis of Cash Flow and Net Income on Stock Prices in Mining Companies in Indonesia

Information about a company's cash flow and net income is useful for readers of financial statements, especially investors, as a basis for analysing the company's ability to create profits because investors must see the stock price when making an investment. Based on these considerations, this study aims to examine the effect of cash flow and net income on stock prices in mining companies in Indonesia. The population of this study consisted of 19 companies, and a sample of 36 financial records from three coal mining companies was used using a purposive sampling technique. The author uses a multiple linear regression analysis method. The results showed that net income significantly affects stock prices, while cash flow has no significant effect individually. However, when considered together, both cash flow and net income influence stock prices. This study provides valuable insights for future investors and researchers by recommending sample expansion, a more in-depth literature review, and diverse research methods.