Stock Prices Of Companies Research Articles

The Influence of Reference Coal Prices, Earnings per Share, and Dividend per Share on Stock Prices in Coal Industry Companies in 2017-2022

This study aimed to analyze the effect of Reference Coal Price (HBA), earnings per share, and dividends per share on stock prices in coal industry companies in 2017-2022. The study used energy sector companies in the coal production industry sub-sector listed on the IDX for the 2017-2022 period as the object of research, using a sample of 13 coal industry companies that met the predetermined sample criteria and an observation period of 6 years so that the observation data amounted to 50 data. The data collection method uses secondary data taken from the company's financial statements from the Indonesia Stock Exchange (IDX) and the official website of each company sampled. The analysis methods used in this research are descriptive statistical analysis, normality test, multiple linear regression analysis, and hypothesis testing. Based on the results of data analysis and hypothesis testing, it shows that the HBA variable has a negative effect on stock prices, while the EPS and DPS variables have a significant positive effect on stock prices.

THE IMPACT OF ACCOUNTING INFORMATION ON BANKING COMPANY STOCK PRICES

With the help of this study, we want to determine to what extent accounting information influences the stock prices of banking companies listed on IDX between the years 2017 and 2021. For the purposes of this study, the population includes all financial institutions listed on. the Indonesia Stock Exchange (IDX) during the period from 2017 to 2021. The use of the purpose sampling method was found to be the most appropriate method. According to the survey report, there were 26 different companies. The results of this study show that the Earnings Per Share (EPS) variable appears to have a significant impact on stock prices. On the other hand, the variables "Debt-to-Equity Ratio" (DER), "Price to Book Value" (PBV) and "Net Profit Margin" (NPM) have influence on share prices.

Can State-owned Shareholders' Participation Curb Stock Price Crash Risk? --Based on Empirical Evidence of A-share Private Listed Companies from 2008 to 2019

After state-owned shareholders buy shares in private listed companies, they will more or less bring about some changes in the development of private listed companies. The existence of state-owned shareholders can enable listed companies to obtain more resources, which will play a certain role in promoting the company's operation. These favorable conditions will also attract more attention from the outside world. In order to study whether these changes will affect or restrain the company's stock price crash risk during the company's development, we take the stock price data of private listed companies in A-shares from 2008 to 2019 as the object of analysis. This paper studies the influence of state-owned shareholders' participation on the risk of stock price collapse of private companies. Through the regression model analysis, it is found that there is a significant negative correlation between the state-owned shareholders' participation in the private listed company and the company's stock price crash risk, that is, the state-owned shareholders' participation in the private listed company can significantly restrain the company's stock price crash risk.

Share Price Impact Of Pt XL Axiata Tbk and PT Link Net Tbk Before and After The Acquisition

The purpose of this study is to be able to analyze the impact of PT XL Axiata's stock price before and after the Link Net acquisition to help readers understand the direct impact of the acquisition on the company's stock price. The research sample is the stock price before and after the acquisition of PT XL Axiata and PT Link Net from January 1, 2021 to May 1, 2023. Data collection in this study is secondary data. There is a significant difference between PT XL Axiata and PT Link Net and stock prices can affect market sentiment. PT LINK Net shows a significant difference in share price before and after the acquisition of XL Axiata. Keyword: Impact, Acquisition, Stock Price, Company Performance

Does the U.S.-China trade war stop? A novel event study on fake news and stock price in China

This study leverages the unique fake news event on the U.S.-China trade war, and adopts the difference-in-differences framework to identify its two-stage effect on stock prices in China. Our results reveal that the spread of fake news, proclaiming the end of the trade war, caused stock prices of high-tech companies to soar. After it was clarified, prices gradually decreased until the end of the first stage. But in the second stage, the stock prices of these companies grew higher than their counterparts, caused by speculation beliefs. The two-stage effect was more pronounced for the high-tech companies on the Entity List.

Giao dịch của người nội bộ và những ảnh hưởng lên giá cổ phiếu tại Việt Nam

The paper examines the effect of insider trading on stock prices of non-financial companies listed on the Vietnam stock market in the period of 2010-2019. Using event study methodology, the empirical results show that there are abnormal returns surrounding announcements of trading activities of insiders in Vietnam. More specifically, there are positive abnormal returns when buying announcements of insiders and there are negative abnormal returns when selling announcements of insiders. In addition, the study also shows factors affecting positively the abnormal returns around announcements of insider trading events, such as dividend per share, return on assets, insiders who are member of the Board of Directors and the number of insider trading stocks.

PENGARUH PRICE EARNINGS RATIO (PER), PRICE TO BOOK VALUE (PBV), DAN INFLASI TERHADAP HARGA SAHAM PERUSAHAAN FARMASI YANG TERCATAT DI BEI 2017-2021

The purpose of this research is to analyse the influence of Price Earnings Ratio, Price to Book Value, and Inflation toward Stock Prices of pharmacy companies listed on Indonesia Stock Exchange in the period of 2017-2021. The sample of this research consists of 8 pharmacy companies. The dependent variable is represented by the stock price index, while the independent variables in this study are Price Earnings Ratio, Price to Book Value, and inflation. The research method used is a quantitative method that takes into account the company's market ratios of financial reports obtained from the IDX website and the level of inflation in Indonesia. Partially, the results of this study indicate that during the 2017-2021 period Price to Book Value has a positive and significant influence toward pharmacy companies stock prices. While price earnings ratio and inflation didn’t have a significant influence toward pharmacy company stock prices.

Can commodity prices predict stock market returns? The case of dry bulk shipping companies

We explore the relationship between the returns of 45 dry bulk shipping company stock prices and the main 15 commodities that bulk carriers transport. Using a principal component analysis to reduce the dimensionality of the commodities dataset and a panel methodology, we find that a change in the commodity price principal component would result in a 0.6% change in the returns of the shipping stock prices. Minerals appear to have a stronger impact, as a 1% change in the minerals principal component results in a 1.1% change in the returns. This is mainly due to the fact that minerals account for larger trade volumes in the dry bulk market and they employ mostly bigger vessels, while the price of Brent oil is also an important factor affecting shipping stock prices.

Price to Book Value Drives Stock Prices in Indonesia, While DER and PER Falter

This study examines the impact of the Debt to Equity Ratio (DER), Price to Book Value (PBV), and Price Earning Ratio (PER) on stock prices of food and beverage companies listed on the Indonesia Stock Exchange (IDX) from 2016 to 2018. Using secondary data from financial reports and statistical analysis via SPSS 23, the research found that DER does not significantly affect stock prices, PBV positively influences stock prices, and PER has no significant impact. These findings suggest that investors prioritize market confidence and financial prospects (as indicated by PBV) over debt levels and earnings ratios when making investment decisions in this sector. Highlights: 1. DER Impact: Debt to Equity Ratio does not affect stock prices significantly.2. PBV Significance: Price to Book Value positively influences stock prices.3. PER Irrelevance: Price Earning Ratio does not impact stock prices significantly. Keywords: Debt to Equity Ratio, Price to Book Value, Price Earning Ratio, Stock Prices, Indonesia Stock Exchange

STOCK MARKET REACTIONS TO PUBLIC RELEASE OF ENVIRONMENTAL RANKINGS OF HEAVY POLLUTERS: THE ROLE OF ULTIMATE CONTROLLERS

Adopting the event study method, this paper examines how the stock market reacts to the public release of environmental performance rankings of heavy polluters. Specifically, we explore the impact of the public release of the 100 best and 100 worst companies in terms of environmental performance on the stock prices of companies involved, and the potential moderation of ultimate controllers. We find that the stock prices of the companies on the lists were negatively affected by the event. However, stock price changes are not significantly related to the relative rankings on the list. Furthermore, companies whose ultimate controllers possess a larger control–ownership wedge experienced a less severe fall in stock prices than did their counterparts. This study sheds light on the nuances of the financial implications of negative environmental publicity.

The Effect Of Working Capital Turnover, Profitability, Liquidity And Solvency On Stock Prices

This study aims to determine the effect of working capital turnover, profitability, liquidity, solvency on the stock price of telecommunication companies for the period 2013 - 2022, both partially and simultaneously. The financial ratios studied are: Working Capital Turnover (WCTO), Net Profit Margin (NPM) Current Ratio (CR) and Debt Equity Ratio (TATO) to stock prices. The population in this study is the telecommunications subsector listed on the Indonesia Stock Exchange (IDX) from 2013 to 2022. The research sample was 7 telecommunications sector companies obtained using purposive sampling technique. The data collection technique used is documentation. Data analysis was carried out using panel data analysis techniques. The results shown that partially the Working Capital Turnover, Profitability, and Solvency variables had a significant positive effect on stock prices. While the Liquidity variable has no significant effect on stock prices. However, the results of the study simultaneously variables Working Capital Turnover, Profitability, Liquidity, Solvency affect stock prices. Keywords: Working Capital Turnover, Profitability, Liquidity, Solvency on Stock Price

Predicting stock price of construction companies using weighted ensemble learning

Modeling the behavior of stock price data has always been one of the challenging applications of Artificial Intelligence (AI) and Machine Learning (ML) due to its high complexity and dependence on various conditions. Recent studies show that this will be difficult to do with just one learning model. The problem can be more complex for companies in the construction sector, due to the dependency of their behavior on more conditions. This study aims to provide a hybrid model for improving the accuracy of prediction for the stock price index of companies in the construction section. The contribution of this paper can be considered as follows: First, a combination of several prediction models is used to predict stock prices so that learning models can cover each other's errors. In this research, an ensemble model based on Artificial Neural Network (ANN), Gaussian Process Regression (GPR), and Classification and Regression Tree (CART) is presented for predicting the stock price index. Second, the optimization technique is used to determine the effect of each learning model on the prediction result. For this purpose, first, all three mentioned algorithms process the data simultaneously and perform the prediction operation. Then, using the Cuckoo Search (CS) algorithm, the output weight of each algorithm is determined as a coefficient. Finally, using the ensemble technique, these results are combined and the final output is generated through weighted averaging on optimal coefficients. The proposed system was implemented, and its efficiency was evaluated by real stock data of construction companies. The results showed that using CS optimization in the proposed ensemble system is highly effective in reducing prediction error. According to the results, the proposed system can predict the price index with an average accuracy of 96.6 %, which shows a reduction of at least 2.4 % in prediction error compared to the previous methods. Comparing the evaluation results of the proposed system with similar algorithms indicates that our model is more accurate and can be useful for predicting the stock price index in real-world scenarios.

Economic policy uncertainty and company stock prices: Empirical evidence from blockchain companies

Blockchain (BC) is a sought-after technology, and the investment in it has garnered significant attention. We utilize a wavelet-based quantile-on-quantile approach to investigate the correlation between economic policy uncertainty (EPU) and BC stock market prices across various periods and market circumstances. The findings suggest an increase in EPU has the potential to depress the stock price of the BC company. It is evident from the outcomes across various time scales that EPU exerts a broad inhibitory influence on the stock price of the BC company in the immediate term, with the extent of this effect potentially diminishing as time passes.

XPENG Equity Structure Analysis and Stock Price Forecast

The market competition for new energy vehicles is becoming increasingly fierce. Under the Internet operation mode, the automobile manufacturing industry is divided into new forces and traditional automobile enterprises, which have a profound impact on the profit model of the automobile industry, and the driving force of enterprise operation is working capital. It is necessary the manage enterprise working capital to improve the comprehensive competitiveness of enterprises. Xiaopeng (also called XPENG) is a listed company of automobile brands. It adopts a dual-class share structure to allocate the company's shares. The structure can also be used to analyze how this will affect XPENG's stock price and predict the future trend of XPENG's stock price. The paper aims to analyze how this will affect the stock price of XPENG Company through the dual-class share structure, and predict the future direction of the stock price of XPENG Company. At the same time, the paper also hopes to forecast the stock price situation of XPENG Company through a series of reports and comparisons with other tram industries.

Is There A Conflict Of Interest In Indonesia’s Soe Investment Decision?

In financial management, the Director of State-Owned Enterprises (SOEs) in Indonesia synergizes with the Ministry of SOE to make investment decisions. One of the most common types of investment in SOEs is the purchase of shares in private companies. Through buying shares, SOEs can expand their business lines and project bigger profits. However, it is often found that the decision to purchase private company shares is made by SOEs due to a conflict of interest. This paper aims to determine the level of involvement of conflicts of interest in the decisions of SOEs in Indonesia to invest in the purchase of shares of private companies. Due to data limitations, the authors used a qualitative technique with an explanatory case study to meet the research aims. The analysis tested of 14 Indonesian SOEs' investment decisions when investing in the purchase of shares in private companies during the 2013–2022 period. The findings show allegations of conflicts of interest in several share investment decisions made by SOEs in Indonesia. This conflict of interest can occur because of a family relationship between the Board of Directors of SOEs and the management of private companies. In addition, there is also a political-party relationship between the parties involved. These findings can help several parties, including SOE and the Ministry of SOE, better understand the limits of investment that can be made. In the long run, the Ministry of SOE, as the supervisor of SOEs, can establish an early warning system to determine whether or not the investments made are appropriate. Most of the previous research only examined the effects of a company's investment and its impacts. It explores the company's stock price valuation process. No research has been found that specifically discusses SOE investment in Indonesia in private company shares.

A survey on cryptocurrency price prediction

A type of digital currency known as a cryptocurrency allows all transactions to be completed online. There is no hard cash version of this soft currency. We highlight that a decentralized currency differs from a centralized currency in the any user of a virtual currency can purchase services without the need for third parties to get involved. Due to its extreme price volatility, using these cryptocurrencies has an impact on trade and international relations. Moreover, the constantly fluctuating oscillations indicate the urgent need for a more precise method of predicting this price. Deep learning techniques that use effective learning models for training data, including the LSTM, GRU, and Feedback Neural Network, can be used to do this. Benchmark datasets are used to test the suggested strategy. That brings us to the neural network, one of the clever data mining technologies that researchers in many domains have been using for the past ten years. In the current economy, stock market data is essential. There are two types of forecasting methodologies: nonlinear models (ARCH, GARCH, Neural Network) and linear models (AR, MA, ARIMA, ARMA). To forecast a company's stock price based on past prices, we employed the Box Jenkins Model also known as ARIMA, and Long Short-Term Memory (LSTM), and Feedback Neural Network also known as RNN.

Fundamental Analysis and Trading Volume on Stock Prices with Company Size as a Control Variable

This research aims to determine the influence of fundamental variables and trading volume on stock prices in energy sector companies. This research uses the variables Debt Equity Ratio, Return on Assets, Trading Volume and Share Price. The population used in this research is energy sector companies in 2019 - 2022. The sampling method in this research is purposive sampling method so that 120 company data is obtained in 4 years. The analysis method uses multiple linear regression. Conclusions from the results of this research in the energy sector on the Indonesia Stock Exchange (BEI) in the 2019-2022 period the debt equity ratio variable has a negative and significant influence on shares, the return on assets variable has a negative and significant influence on share prices, the trading volume variable does not has an influence on share prices. Then the control variable, namely company size, has no effect on share prices.

Financial Performance Affecting Stock Prices of Food and Beverage Sub-Sector Companies in Indonesia

This research aimed to analyze the effect of return on asset, return on equity, debt to equity ratio and earning per share toward stock price in company food and beverage sub-sector listed on the Indonesia Stock Exchange during the period 2018 to 2022. The population in this research was 118 companies in the consumer non-cyclicals sector. The determination of samples is using purposive sampling method, so that the selected sample is relevant to the research objectives. The company sampled in this research was 16 food and beverage companies. Researchers used data panel regression analysis techniques with data processing using the E-views 10 application to test the research hypothesis. The results shows return on assets and return on equity has no positive effect on stock prices, debt to equity ratio has a negative effect on stock prices and earning per share has a positive effect on the stock price of food and beverage sub-sector companies. These research results are simultaneously shows that the return on asset, return on equity, debt to equity ratio and earning per share variables are capable to explain stock prices on those 16 food and beverages companies listed on the Indonesia Stock Exchange.

Does the Performance of Global Commodities Exhibit Co-movement and Long-run Relationship with the Palm Oil Companies?

While numerous papers investigate on the co-movement of global commodities and stock indices, this paper extends to previous papers by focusing on the individual stock price of palm oil companies namely Sime Darby Plantation, Wilmar International, and Astra Agri Lestari. Weekly data spanning from January 2018 to September 2023 was selected and Autoregressive Distributed Lags (ARDL) model employed to demonstrate the possible co-movement and long-run relationship. Empirical results provide sufficient evidence of co-movement between global commodities with Sime Darby Plantation and Astra Agri Lestari in the long-run. This will allow fund managers, individual investors, and stock traders to make investment decisions, trading strategies, and portfolio management based on the global commodity markets.

IMPACT OF META VERSE ANNOUNCEMENT DATE ON STOCK PRICES

Purpose-The developing metaverse, a virtual world poised to transform online interaction, has raised an important question: how does a company's participation influence its financial standing? This study focuses on the e-commerce behemoth Amazon and its prospective entry into the metaverse. Our major goal is to determine whether a statement by Amazon on their metaverse plans causes a significant impact in their stock price. By examining past stock data with any potential metaverse announcement dates, we hope to determine whether Amazon's excitement about the metaverse translates into an actual financial benefit. This study dives deeper by examining the specifics of the announcement (assuming it occurs). Does the level of commitment and investment mentioned affect the stock price reaction? Finally, this study aims to bridge the gap between the hoopla surrounding the metaverse and its real-world ramifications for corporations such as Amazon. Approach: Event Definition: The event of interest is the particular day on which our selected companies makes a public declaration about entering the metaverse. This date will be critical to our analysis. Event Window: We'll set a timetable around the announcement date. This window will most likely consist of two parts: Estimation Window: The period preceding the announcement, we have selected 110 days, helps set a baseline for Amazon's stock price performance and eliminates the impact of unrelated market movements. Event Window: This time period, we have selected as 10 days before and after following the announcement allows us to monitor any potential changes in stock price due to metaverse news. Findings: Stock Price Change: The investigation may discover a statistically significant boost in companies stock price following a metaverse announcement. This suggests that investors reacted positively to the announcement, maybe seeing the metaverse as a new growth prospect. No Change or Negative Change: Alternatively, the investigation may discover no substantial change, or even a decline in stock price following the announcement. This could suggest investor concern about companies capacity to capitalize on the metaverse, as well as a general market correction during the event window. Impact of Announcement Specificity: The study may find that the level of depth and commitment conveyed in the announcement influences stock price movement. A more thorough and ambitious proposal may elicit a better positive response from investors. Industry-Specific Effects: The research design might be expanded to compare company's stock price movement to those of other companies in different industries following metaverse announcements. This could reveal whether particular industries have a stronger stock market reaction because of their deeper relationship to the metaverse. Limitation: For starters, the metaverse is a very new concept, so any announcement by worldwide companies may focus on future projects. This makes it difficult to estimate the long-term financial impact on the company's stock price. Furthermore, the specifics of the announcement itself (assuming it occurs) are critical. A vague announcement with few specifics may have a lower impact on the stock price than a well-defined plan with realistic investment figures. These constraints underscore the need for additional research as the metaverse and corporate participation in it evolve.