Stock Price Research Articles

STOCK VALUATION OF PT BANK SYARIAH INDONESIA, TBK

Indonesia is the country with the second largest Muslim population in the world. According to the State of the Global Islamic Economy (SGIE) Report, Indonesia's Islamic market will be the third largest globally by 2023, behind Malaysia and Saudi Arabia. Therefore, to accelerate the halal ecosystem's growth, the government, through the Ministry of State-Owned Enterprises (BUMN), is establishing PT Bank Syariah Indonesia Tbk (BSI). The government through the Ministry of SOEs is encouraging BSI to expand overseas. After opening a branch in Dubai, United Arab Emirates, BSI plans to open a branch in Saudi Arabia soon. Strategic investors are needed for BSI to be able to access the Middle East financial market, especially Saudi Arabia. Investors need information and analysis regarding the potential benefits that will be obtained before deciding to invest in BSI. This study uses qualitative and quantitative methods. The first objective of this study is to compare BSI's performance with the banking industry using the competitor analysis matrix method. Second, to analyze macroenvironmental aspects using the PESTLE and Porter's Five Forces methods. Third, to evaluate BSI shares using the excess return valuation method, asset-based valuation to determine the intrinsic value and relative valuation using the PER and PBV comparison. The results of the competitor analysis matrix study, BSI got a score of 4.61 which means BSI's performance is above the industry average. The intrinsic value of BRIS in the best scenario was IDR 2.949, the moderate was IDR 2.435, and the worst was IDR 2.101. Based on the calculation of intrinsic value using the asset-based model, the resulting intrinsic value is IDR 2.616 per share. The next analysis is relative valuation using the Price-to-earning (P/E) Ratio and price-to-book value (PBV) of PT Bank Syariah Indonesia compared to the industry average. Based on the results of the Monte Carlo simulation, the peak point was obtained at around 2.000–2.500, indicating that most simulation results predict stock prices in this range. The mean value of all simulation results is IDR 2.718. This is the central estimate of the most likely stock valuation in the average scenario. If the current market price is close to this value, the stock can be considered fairly valued. Share price BRIS per 11 Nov 2024 of IDR 2.820 per share, based on simulation upside probability 35%.

The Effects of Financial Ratios on The Stock Prices of Digital Banks

Digital bank stocks are one of the investment options in the capital market. However, most digital banks have volatile stock price movements. In addition, digital banks also have unstable and poor financial capabilities, especially in profitability, activity, and solvency. This study aimed to examine the effect of financial ratios on the stock price of digital banks. This study uses panel data regression with research objects consisting of 7 digital banks listed on the Indonesia Stock Exchange from 2018 to 2023. The results showed that Return On Assets, Total Assets Turnover, and Operating Expenses Operating Income have no effect on the stock price of digital banks. Meanwhile, Debt Equity Ratio has a negative effect and Loan Deposit Ratio has a positive effect on digital bank stock prices. This study is expected to be a reference for making investment decisions in digital bank stocks for investors and can be a reference for making policy directions for digital bank management. Abstrak Saham bank digital merupakan salah satu opsi investasi di pasar modal. Namun, sebagian besar bank digital memiliki pergerakan harga saham yang fluktuatif. Selain itu, bank digital juga memiliki kemampuan finansial yang belum stabil dan cenderung buruk, khususnya pada profitabiltas, aktivitas, dan solvabilitas. Tujuan penelitian ini adalah meneliti pengaruh rasio keuangan terhadap harga saham bank digital. Penelitian ini menggunakan regresi data panel dengan objek penelitian yang terdiri dari 7 bank digital yang terdaftar di Bursa Efek Indonesia pada tahun 2018 hingga 2023. Hasil penelitian menghasilkan Return on Assets, Total Assets Turnover, dan Beban Operasi Pendapatan Operasi tidak berpengaruh terhadap harga saham bank digital. Sedangkan, Debt Equity Ratio berpengaruh negatif dan Loan Deposit Ratio berpengaruh positif terhadap harga saham bank digital. Penelitian ini diharapkan dapat menjadi referensi pengambilan keputusan investasi saham bank digital bagi investor, serta dapat menjadi referensi pengambilan arah kebijakan bagi pihak manajemen bank digital.

Studying investor rationality on stock price: A case of Bank Syariah Indonesia

Purpose – This study aims to analyze the behavior of investors' investment decisions, as reflected in the volatility of stock prices, especially at PT Bank Syariah Indonesia Tbk (BSI). The analysis here compares aspects of rationality and irrationality as well as their relationship with the stock prices of Bank Syariah Indonesia.Methodology – This study uses the Autoregressive Distributed Lag (ARDL) method to test aspects of rationality proxied by Price-to-Book Value (PBV) and Price-to-Earnings Ratio (PER), while aspects of irrationality proxied by mood sentiment (SWAV). Data were collected from February 2021 to February 2024 on a monthly basis, resulting in 37 research samples.Findings – The results show that PBV, PER, and mood sentiment have a significant effect on BSI stock prices. In the long term, PBV has a positive effect, while PER and mood sentiment have negative effects on BSI stock prices. In this situation, mood sentiment has the greatest influence on BSI stock prices, indicating that the volatility of BSI stocks is driven by irrationality.Implications – As the stock price is driven mostly by irrationality, investors must have fundamental and macroeconomic analysis skills as well as expertise when buying and selling stocks in order to minimize losses due to the irrational behavior. In addition, regulators and companies must provide transparent and accurate information to investors to reduce the irrational behavior.Originality – This study modifies the objective of Islamic banks’ stock prices in Indonesia by elaborating rational and irrational factors as the determinants of stock prices.

Greening or greenwashing? Corporate green bonds and stock pricing efficiency in China

Greening or greenwashing? Corporate green bonds and stock pricing efficiency in China

Does Diversification Strengthen Or Weaken A Firm’s Performance On Stock Price?

This study examines the impact of diversification on a firm’s performance, specifically in relation to its stock price. The primary aim is to assess whether diversification strengthens or weakens the effect of financial performance on stock prices.The research uses Price-to-Book Value (PBV) as a measure of stock price and Return on Assets (ROA) as a measure of financial performance. The sample consists of 292 companies, with data analyzed through descriptive statistics, regression analysis, and ANOVA. The findings suggest that financial performance, as measured by ROA, has a significant positive effect on PBV, indicating that better financial performance tends to enhance a company's stock price. However, the study also explored whether diversification both international and industrial moderates this relationship. The results showed that while diversification strategies were prevalent in the sample, they did not significantly weaken or strengthen the relationship between ROA and PBV. These findings imply that while financial performance plays a crucial role in determining stock prices, diversification, in this context, does not have a notable moderating effect. This study contributes to the ongoing debate regarding the impact of diversification on firm performance, suggesting that, although diversification may reduce risk, it does not necessarily amplify the financial performance’s impact on stock prices. Further research could explore other factors influencing this relationship and test these findings across different industries and time periods.

Stock Liquidity and Corporate Investment Efficiency

For a long time, investment has played a key role in stimulating the economic growth of our country, but the low investment efficiency of listed companies has hindered listed companies from entering the stage of high-quality development. With the reform of financial market and the rapid development of China's capital market, the impact of capital market on the investment efficiency of listed companies has been paid more and more attention. Relevant studies have shown that stock liquidity can have an obvious effect on internal factors such as stock price information content, agency cost, management compensation incentive, etc., and then can have a certain effect on its investment efficiency. This paper selects A-share non-financial listed companies from 2013 to 2022 for empirical test, and finally draws A relatively robust conclusion: stock liquidity has a promoting effect on enterprise investment efficiency.

Independent Directors' Opposition, Earnings Management and the Risk of Stock Price Crash

The purpose of this article is to explore the factors affecting subsidy relaxation and the measures that the new-energy vehicle industry should take in response to the continued weakening of subsidy policies. Examination of relevant literature and case studies revealed that domestic and international research on the impact and shortcomings of the vehicle purchase subsidy policy itself has made some progress, but there are still other problems and challenges. This article explains the profound significance of the release of the new energy vehicle purchase subsidy policy and subsidy weakening, and how the new energy vehicle industry should respond to the subsidy weakening and others Finally, the article summarises the main results and conclusions of the study, and provides a forecast on the research direction for the development of the new energy vehicle market.

Impact of Macroeconomic Variables on Stock Market Prices in Sub-Saharan Africa

Purpose: The study assessed macroeconomic variables' impact on Sub-Saharan Africa's stock prices. Specifically, the study examined the impact of the inflation rate, interest rate, exchange rate, and money supply on stock price. Methodology: The study employed a causal research design to show a causal relationship between variables. Monthly data were collected from six countries, i.e Tanzania, Kenya, Malawi, Botswana, Ghana, and Nigeria, from 2005 to 2023. The collected data were analyzed through descriptive statistics and multiple regression analysis. Findings: Findings revealed that the inflation rate and exchange rate both have statistically significant and positive effects on stock prices, with coefficients of 0.4222 (p < 0.001) and 1.3522 (p < 0.001), respectively, showing that increases in these factors are associated with higher stock prices. The coefficient for interest rate (0.0120) is not statistically significant (p = 0.133), implying no strong relationship. These results, therefore, show that inflation and the exchange rate positively and insignificantly affect stock prices, suggesting that a shock increase in either or both economic variables is positively associated with increased stock market values. This study provides a theoretical contribution by integrating Arbitrage Pricing Theory, the Efficient Market Hypothesis, Monetarist Theory, and Purchasing Power Parity to explain the intricate relationship between macroeconomic variables and stock price movements. Unique Contribution to Theory, Policy and Practice: By drawing from these theories, it highlights how variables like money supply, inflation, interest rates, and exchange rates influence stock prices through risk sensitivities, market efficiency, and purchasing power dynamics. From this perspective, it would be prudent for policy framers to consider implications for the stock market while adopting policies relating to inflation and currency exchange. Interest rates do not strongly correlate with stock prices; it would be worth monitoring, though, as interest rate policies sometimes have broader economic impacts. Investors should, therefore, focus on inflation and exchange rate trends to make strategic investment decisions that capitalize on these significant relationships.

The Role of Good Corporate Governance in Stock Price Determination: Evidence from BRI

Corporate governance refers to the mechanisms and processes that direct and control a company to ensure its operations are efficient and aligned with its objectives. In the context of BRI Bank, the corporate lending program poses a significant risk to its financial stability, especially amidst the ongoing economic crisis. The deterioration in the quality of non-performing loans (NPLs) has implications for evaluating asset and debt risk quality. This study employs a quantitative research method to measure data and generalize the results from a sample to the broader population. The sample consists of BRI Bank companies for the period 2017–2023. A census technique is applied, and the data is analyzed using panel data analysis to assess the relationship between corporate governance and stock prices. The findings reveal that good corporate governance has a significant effect on stock prices when considered collectively. However, when the corporate governance variable is examined individually, it does not show a significant impact on stock prices. These results suggest that while good corporate governance contributes to stock price performance in a combined context, its isolated effect is not as pronounced. The findings highlight the importance of comprehensive governance practices in influencing financial outcomes, particularly in the face of economic challenges.

THE IMPACT OF GREEN BANKING PRACTICES ON BANKS’ COST OF CAPITAL; EVIDENCE FROM A DEVELOPING COUNTRY

Purpose: This study aims to explore whether green banking practices by Pakistani banks have any impact on their cost of equity and cost of debt. Design/Methodology: We constructed a specifically designed composite green banking index to measure the extent of green banking practices from banks within the sample. Cost of capital is represented by the cost of debt and cost of equity. Banks listed on Pakistan’s KSE-100 Index over a period of the 10 years i.e. from 2010 to 2019 is used as our study sample. Panel Data Regression analysis is used to test the hypothesized relationships. Findings: Green banking is still in its evolution phase among Pakistani banks. While the debt market is stricter and is incorporating these practices in advancing financing to the banks, there is still a need for investor education and awareness at the equity market level, which has not yet been incorporated in the pricing of the banking stocks. Originality: Green banking is still in its evolution phase among Pakistani banks. While the debt market is stricter and is incorporating these practices in advancing financing to the banks, there is still a need for investor education and awareness at the equity market level, which has not yet been incorporated in the pricing of the banking stocks.

Apakah Inovasi Lingkungan Perusahaan dan Profitabilitas Mempengaruhi Risiko Jatuhnya Harga Saham

This study investigates the effect of environmental innovation and profitability on the risk of stock price drops in manufacturing companies listed on the Indonesia Stock Exchange (IDX). Using quantitative methods, the research examines annual financial reports for 2021-2022. The findings reveal that both environmental innovation and profitability significantly impact stock price risk. Companies that implement effective environmental innovation strategies reduce long-term risks and enhance investor confidence, while high profitability strengthens financial performance and lowers volatility. These results provide critical insights for investors and managers to mitigate financial risks.

RESEARCH ON THE IMPACT OF EXECUTIVES’ OPPORTUNISTIC REDUCTION OF HOLDINGS ON CORPORATE STOCK PRICE VOLATILITY RISK

The stock market is an important part of the national economy, and its stable operation is indispensable for the prevention and control of financial market risks. How to effectively regulate the share reduction of senior executives is a major issue facing the high-quality development of my country's capital market. This paper selects China's A-share non-financial listed companies from 2007 to 2022 as the research sample. The mechanism analysis shows that the opportunistic reduction of executives can positively affect the risk of corporate stock price volatility through financing constraints. This paper also looks at how economic policy uncertainty affects the link between opportunistic firing of executives and the risk of corporate stock price volatility. This not only adds to the current research, but it also makes some good suggestions for how to stop opportunistic firing of executives and stop the risk of corporate stock price volatility.

The Impact of Monetary Policy on Stock Price Index Movement

This study aims to analyze the impact and effectiveness of monetary policy on the decline of the Composite Stock Price Index (IHSG) during the COVID-19 pandemic. The research employs a quantitative approach using Ordinary Least Squares (OLS) with equations 2017-2024 to evaluate the relationship between monthly IHSG and variables such as BI Rate and Exchange Rate. The findings reveal that IHSG significantly affects JUB, con-sistent with previous research indicating that monetary expansion by Bank Indonesia en-hances market liquidity and raises IHSG. However,during the pandemic, global uncertainty disrupted the relationship between IHSG and other economic variables, as evidenced by the insignificance of IHSG concerning BI Rate andthe significant impact of Exchange Rate in the first equation, and Dow Jones in the second. The significant influence of Dow Jones on IHSG highlights global market integration and its effect on investor sentiment in Indonesia. The study emphasizes the importance of understanding the relationship be-tween IHSG and economic variables amid global uncertainty.

Impact of Operational and Financial Efficiency on Aviation Stock Prices: A Machine Learning Model with SHAP Interpretability

Using a machine learning approach, this study examines how operational and financial efficiency metrics influence stock prices in the aviation industry. A CatBoost regression model enhanced with SHapley Additive exPlanations (SHAP) was developed using data from 65 global aviation companies collected between 2015 and 2023. The model predicts stock prices based on various operational and financial indicators, including Total Revenue per Available Seat Mile (ASM), Passenger Load Factor, liquidity ratios, and debt-to-assets ratios. The findings suggest that operational efficiency metrics, particularly Total Revenue per ASM and Passenger Load Factor, play a significant role in predicting stock prices within the aviation sector. Financial metrics, such as the Quick Ratio and Debt-to-Assets Ratio, also contribute to the model but appear to have a secondary influence compared to operational factors. SHAP values provided interpretable insights into the model's predictions, allowing for a better understanding of the relative importance of different features. Furthermore, the study's findings offer support for the semi-strong form of the Efficient Market Hypothesis (EMH), demonstrating that operational and financial metrics are reflected in stock prices. These results indicate that aviation companies demonstrating higher operational efficiency may be better positioned for favorable stock market performance, although financial health remains important. This study contributes to the existing literature by integrating operational and financial metrics into a machine learning framework, offering a comprehensive and interpretable model for stock price prediction in the aviation industry.

Debt To Asset Ratio, Debt To Equity Ratio, Return On Asset, Return On Equity, Net Profit Margin And The Effect On Stock Prices In Indonesia Energy Sector Companies

Abstract: The purpose of this study is to analyze the effect of Debt to Asset Ratio, Debt to Equity Ratio, Return On Asset, Return on Equity, and Net Profit Margin on stock prices, especially in energy sector companies in Indonesia. The data analysis methods used are descriptive statistical tests, classical assumption tests, multiple linear regression analysis tests, T-tests, F-tests, and determination coefficient tests. The sample in this study was 20 energy sector companies listed on the Bursa Efek Indonesia ( BEI ) during the 2017-2021 period and met the sample selection criteria. The sampling technique used was Purposive Sampling. The data used is secondary data obtained from the company's financial statements. The results of the study indicate that Return On Asset has a partial effect on stock prices while Debt to Asset Ratio, Debt to Equity Ratio, Return On Asset, Return on Equity, and Net Profit Margin do not affect stock prices in energy sector companies in Indonesia. Keywords Debt to Asset Ratio, Debt to Equity Ratio, Return on Assets, Return on Equity, Net Profit Margin, and Share Price

Forecasting Stock Market Indices Using Integration of Encoder, Decoder, and Attention Mechanism.

Accurate forecasting of stock market indices is crucial for investors, financial analysts, and policymakers. The integration of encoder and decoder architectures, coupled with an attention mechanism, has emerged as a powerful approach to enhance prediction accuracy. This paper presents a novel framework that leverages these components to capture complex temporal dependencies and patterns within stock price data. The encoder effectively transforms an input sequence into a dense representation, which the decoder then uses to reconstruct future values. The attention mechanism provides an additional layer of sophistication, allowing the model to selectively focus on relevant parts of the input sequence for making predictions. Furthermore, Bayesian optimization is employed to fine-tune hyperparameters, further improving forecast precision. Our results demonstrate a significant improvement in forecast precision over traditional recurrent neural networks. This indicates the potential of our integrated approach to effectively handle the complex patterns and dependencies in stock price data.

Utilizing Ensemble Learning and Dimension Reduction in Predicting Stock Prices: A Transparent Methodology with Insights from Explainable AI

Utilizing Ensemble Learning and Dimension Reduction in Predicting Stock Prices: A Transparent Methodology with Insights from Explainable AI

Are rare earth stocks efficient? Novel insights using asymmetric MF-DFA

This study investigates the weak-form efficiency and asymmetric multifractal scaling behavior of rare earth stock indices in the global, U.S. and Chinese markets during the trade war and the COVID-19 period. We examine the scaling behavior across overall, upward (bullish), and downward (bearish) market states from 2013 to 2021, employing an asymmetric multifractal detrended fluctuation analysis approach. Our findings indicate asymmetric multifractality in U.S. rare earth stock prices, caused by fat tails and long-range correlations. Weak-form price inefficiency and asymmetry in U.S. rare earth stock prices are prominent during market downturns, such as the trade war and COVID-19 periods. Chinese rare earth stocks demonstrate greater efficiency than U.S. and global stocks; thus, the latter markets provide arbitrage opportunities during upward and downward trends.

Impact of Artificial Intelligence on Stock Price Prediction in India

Impact of Artificial Intelligence on Stock Price Prediction in India

Stock Price Forecasting Using Long Short-Term Memory Networks: A Comprehensive Analysis of Financial Time-Series Data

In this study, we employ Long Short-Term Memory (LSTM) networks to predict stock prices using historical data from Tesla Inc. spanning 10 years. The research emphasizes the importance of LSTM's capability to model complex temporal dependencies in financial time-series data, outperforming traditional statistical methods. Various feature combinations and time steps are tested, identifying a 30-day window as the optimal setup. Key evaluation metrics, including Mean Squared Error (MSE), training loss, and validation loss, are utilized to assess model performance. The results show that LSTM networks are particularly effective for short-term stock price predictions, while longer-term forecasts experience decreasing accuracy. This work contributes to the growing body of knowledge on deep learning applications in financial markets, offering practical insights for investors and financial institutions