Closing Stock Prices Research Articles

Market Response to Disclosure of Corporate Social Responsibility (CSR) during the COVID-19 Pandemic

This article aims to disclose the CSR on stock price and trading and examine the profitability as the reinforcement variable between CSR disclosure on stock prices and the trading volume during the COVID-19 Pandemic. The object of the study was the LQ45 companies in the Indonesia stock exchange between 2019-2020. However, the study only involved 38 companies. We used the 2019 and 2020 data accessed from the official site of the Indonesia Stock Exchange (IDX) www.idx.com. The closing stock price and trading volume data were obtained from www.yahoofinance.com. Data were tested and analyzed using Partial Least Square (PLS) technique. The study found that the disclosure of CSR was positively but not significantly influential on the stock price. The disclosure of CSR is negatively but not significantly influential on the trading volume. The profitability does not strengthen the influence of CSR disclosure on stock price, and the profitability does not strengthen the negative influence of CSR disclosure on trading volume. The research findings suggest the company management continues to run CSR programs despite the unstable economic conditions due to the COVID-19 Pandemic

Dynamic of the States of Three Different Stock Markets from Correlation and Partial Correlation Changes

The core focus of the study is to examine financial states using index effect on stock to stock correlations of developed, developing and emerging market. The three markets such as S&P 500, KOSPI 200 and DSE are declared as developed, developing and emerging market respectively. To study the similarity between stock price changes, we calculate the time series of the daily log return. Closing stock prices of the targeted markets have been used to measure the daily return of the stocks. To analyze the market mobility, Pearson correlation coefficient, partial correlation, and index effect on stock to stock correlation techniques have been applied. The study found that the companies of developed and emerging market are more strongly correlated than those of developing market during big crash. On the other hand, developing market shows less index effect on stock correlations during crisis. Moreover, insignificant index effect has been found in emerging market during calm state. No significant effect of DSE index on stock to stock correlations in the period of global financial crisis has been observed, implying that global financial crisis did not hit to the DSE in this period. Before the market crash, the interactions between stocks became low enough which corresponds to lower value of average correlation for all types of market. Finally, the change of correlation and partial correlation can be a good indicator to identify and predict the financial states of all the markets which will further helps the stakeholders to make proper economic decisions.

Stock price prediction using machine learning on least-squares linear regression basis

Predicting the future of a stock price is a difficult task due to the high level of randomness in the movement of prices. This research aims to use a machine-learning algorithm to estimate the closing stock price of a dataset to help aid in the prediction of stock prices leading to higher accuracy in prediction. The intention of the model is for it to be used as a day trading guide. The algorithm being used is called the least-squares linear regression model. It takes in a dependent variable, in this case, would be our closing price of the stock and an independent variable, which is the day each stock price was recorded.

Application of Vector Autoregressive with Exogenous Variable: Case Study of Closing Stock Price of PT INDF.Tbk and PT ICBP.Tbk

Multivariate time series are widely used in various fields such as finance, economics, and the stock market. One analysis model that is widely used for multivariate time series data is the VAR model. Vector autoregressive (VAR) is a model used to describe the relationship between several variables. The VAR model provides an alternative approach that is very suitable for forecasting purposes and is very suitable for solving economic data problems. The variables used in this study consisted of endogenous variables with closing prices of ICBP and INDF shares and exogenous variables with exchange rates collected from January 2017 to July 2020. In this study, the best model, VARX (1,0), was obtained. also the relationship between variables through the impulse response function and granger causality. Furthermore, forecasting is also carried out for the next 30 days using the best model, VARX (1,0).

Modeling for Stock Price Forecasting in Colombo Stock Exchange: An Historical Analysis of Stock Prices

Stock prediction with data mining techniques is one of the interesting areas being investigated in recent research. Weighted Moving Average (WMA) technique is one such widely used technique in stock forecasting, in which each historical data term can have its own weightage. One of the main drawbacks of WMA is that there is no exact base to determine those weighting factors. Because of this drawback, the investors can assign arbitrary weightages on periodical data though it is misleading the investment decisions. The present study addresses the limitations in Weighted Moving Average technique and tries to generate more reliable and statistically proven weighting factors for stock price prediction. We develop a more reliable stock predictive model by using a panel data set of quarterly closing stock prices of 41 companies for a period of ten years, approximating a sample size of 1680 observations. The Auto Regressive Moving Average model analysed in our study provides strong evidence for statistically significant impact of past stock prices on current stock prices. The study further found statistically more reliable weight factors for past four quarters which can be used for forecasting future stock prices. The findings of the present study confirm that the weight factor drops as the data become older in a linear pattern.

Pengaruh Return on Equity, Return on Asset, dan Firm Size Terhadap Tingkat Underpricing Saham Perusahaan yang Melakukan Go-Public di Bursa Efek Indonesia Periode 2016 - 2019

The purpose of this study was to analyze the effect of financial and non-financial ratios in the form of return on equity, return on assets, and firm size on the level of underpricing. The hypothesis is tested using multiple linear regression analysis methods. The data used in this research are annual financial report data, opening stock price on the first trading day, and closing stock price on the first trading day. Eviews 10 is an analytical tool used in research. Sampling in this study used a purposive sampling method and obtained data from 75 companies from 160 companies that carried out the Go-public process on the Indonesian Stock Exchange for the period 2016-2019. The analytical method used is quantitative methods. The results of this study indicate that the return on equity (ROE) and return on assets (ROA) variables do not have a significant effect on the level of underpricing of the shares of companies that go public on the Indonesian Stock Exchange in the 2016-2019 period, while the firm size variable has a significant effect. on the level of underpricing of shares of companies that went public on the Indonesian stock exchange for the 2016-2019 period

Market Efficiency of Indian Capital Market: An Event Study Around the Announcement of Results of Lok Sabha Election 2019

Market efficiency categorizes a stock market into three sections based on the reaction of share prices to private and public information. This paper mainly deals with reactions of stock market dynamics to information in political events considering the impact of result announcement of the Lok Sabha Elections 2019 on the Indian Stock market as reflected in the behaviour of share prices. Taking BSE 100 as the proxy market, daily closing stock prices of the 30 companies listed in BSE SENSEX was used. An estimation window of 120 trading days was taken prior to the event window. The standard Market model was applied to calculate the AAR and CAAR during the event window of 21 days. Further the Augmented Dickey Fuller (ADF) Test for unit root is applied to measure the stationary of the variables and the presence of ARCH/GARCH effect is tested to understand the volatility during the study period. The Runs Test was used to test the randomness of AAR and the paired sample t test was applied to check the impact of the event on the volume of trading. Consistent negative returns were observed following the event. But the absence of volatility and the insignificant results indicated that market efficiency Indian Stock Market is in a semi strong form.

REAKSI PASAR MODAL TERHADAP PENGUMUMAN PAKET KEBIJAKAN EKONOMI XVI (Studi Empiris Pada Saham Perusahaan Yang Masuk Dalam Indeks LQ 45)

This study aims to analyze whether there are significant differences in abnormal returns before and after the announcement of economic policy XVI and trading volume activity before and after the announcement of XVI economic policy on November 16, 2018. This study uses event study, where observations of the average abnormal return are carried out. and the average trading volume activityduring the 11 day observation period. In this study data was obtained from the Indonesia Stock Exchange. The data used in this study include daily closing stock prices (closing price), daily stock trading volume, and the number of shares outstanding. The sample used amounted to 45 LQ45 index companies. The results of this study indicate (1) there is no significant difference in abnormal returns before and after the announcement of economic policy XVI (2) on the trading volume activity indicator there are significant differences before and after the announcement of XVI economic policy.

Dividends Announcement and Semi Strong Form of Efficiency at The Nairobi Securities Exchange in Kenya

The objective of this study was to establish the effects of dividend announcement to current market prices at the Nairobi Securities Exchange, with four specific objectives; to determine the information content of dividend announcements, to determine the extent to which prices converge to new values after dividend announcements on a sector by sector basis, to establish the market reaction to announced information and also to establish whether investors can secure excess returns by acting on announced information. A 66 days event timeline was employed from 2005 to 2015 on daily closing stock prices. A sample of 179 dividend announcements from 22 listed companies in 8 sectors were drawn and analysed using an OLS Market Model. Findings of the research conclude that; dividend announcements do have an impact on stock prices for the Agricultural, Banking, Commercial, Construction, Manufacturing and Telecomm Sectors and not for the Energy Sector, Insurance Sector and the Nairobi Securities Exchange Market. It takes more than five days for prices to adjust to their correct values and this makes it possible for market players to profit from the inefficiency by earning abnormal returns. We conclude that the Nairobi Securities Exchange is not semi-strong form efficient.

The Effects of Financial Performance on Stock Returns: Evidence of Machine and Heavy Equipment Companies in Indonesia

This study was made to determine the effect of Current Ratio (CR), Total Assets Turnover (TAT), Return on Assers (ROA), Debt to Equity Ratio (DER) on stock returns in the machinery and heavy equipment sub-sector companies listed on the Stock Exchange Indonesia (IDX) in the period 2014 - 2018. Stock returns are calculated based on changes in closing stock prices and estimated influence of financial performance factors using the panel data regression method. Based on empirical findings show that financial performance that has been proxy using; CR, TAT, ROA, and DER do not affect stock returns. The results of the study have implications that the company's financial performance is not an important consideration for investors in the decision to buy shares in the Machinery and Heavy Equipment sub-sector company. In many studies, stock prices are much influenced by macroeconomic variables, such as; exchange rates, inflation, interest rates, and oil prices.

Stock Market Reaction to COVID-19: Evidence in Customer Goods Sector with the Implication for Open Innovation

The coronavirus pandemic has spread all over the world, affecting both the health and economic sectors. The aim of this research was to observe stock prices of customer goods before and after the COVID-19 pandemic using event study and the comparison test. The sample included data of daily closing stock prices and volume of stock trade during the three months before (−90 days) and after (+90 days) the occurrence of the COVID-19 pandemic ongoing, totaling 2670 observation data both before and after the COVID-19 pandemic, for a total of 5340. The research findings indicate a significant difference between the daily closing stock price and volume of stock trade before and after the COVID-19 pandemic. The current research has both theoretical and practical implications: the findings strengthen the efficient market hypothesis, which states that the more complete the provided information, the more efficient the market. The practical implication is that investors should be careful when choosing to invest. Investors should choose customer goods sector companies that provide products that are much needed by customers, for example, pharmacy, food, beverages, etc. Future research is needed to investigate the long-term impact of the pandemic on the economy.

Dampak Covid-19 Terhadap Harga Saham dan Kinerja Keuangan Perusahaan

The purpose of this study to determine the impact of Covid-19 on stock prices and financial performance of companies on LQ-45 issuers listed on the IDX. This study examines the impact of Covid-19 on stock prices and financial performance before being exposed to Covid-19 for Q3 2019 and after being exposed to Covid-19 for Q1 2020. Stock prices are measured using closing stock prices and financial performance is measured using a ratio formula that is Return on Assets (ROA), Operating Profit Margin (OPM), and Net Profit Margin (NPM). This study uses a sample of LQ-45 companies that have published their financial statements on the Indonesia Stock Exchange (IDX). This research uses multiple linear regression methods using SPSS 25. The results of this study indicate that (a) Pandemic Covid-19 has an impact on stock prices and (b) Pandemic Covid-19 has an impact on financial performance.

Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic

As the 2019 novel coronavirus disease (COVID-19) pandemic rages globally, its impact has been felt in the stock markets around the world. Amidst the gloomy economic outlook, certain sectors seem to have survived better than others. This paper aims to investigate the sectors that have performed better even as market sentiment is affected by the pandemic. The daily closing stock prices of a total usable sample of 1,567 firms from 37 sectors are first analyzed using a combination of hierarchical clustering and shape-based distance (SBD) measures. Market sentiment is modeled from Google Trends on the COVID-19 pandemic. This is then analyzed against the time series of daily closing stock prices using augmented vector autoregression (VAR). The empirical results indicate that market sentiment towards the pandemic has significant effects on the stock prices of the sectors. Particularly, the stock price performance across sectors is differentiated by the level of the digital transformation of sectors, with those that are most digitally transformed, showing resilience towards negative market sentiment on the pandemic. This study contributes to the existing literature by incorporating search trends to analyze market sentiment, and by showing that digital transformation moderated the stock market resilience of firms against concern over the COVID-19 outbreak.

Prediction of Banking Sectors Financial Data of Dhaka Stock Exchange Using Autoregressive Integrated Moving Average (ARIMA) Approach

This paper investigates the predictability with the banking sector data of the Dhaka Stock Exchange (DSE) by using the Autoregressive Integrated Moving Average (ARIMA) process. Through different formal tests on the data set, the best-fitted model selected was ARIMA (0,2,1) for the data series. This study was select five banks from DSE such as Al-Arafah bank limited, EXIM bank limited, Islami bank limited, National bank limited, and one bank limited and use these data to train the model and checks the predictive power of the model. Only analyzed results of Al-Arafah bank limited are presented in this paper because the same results have been produced for other remaining companies. The obtained results show that all the companies closing stock prices are non-stationary. It is also found that the original value curve and the predicted value curve are very much identical. So, the fitted model is performed better. For the validity of the model, the root means squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were checked.

The impact of earnings management on market liquidity

This article investigates the impact of earnings management on market liquidity measured by the depth of the market. Managers have desired to provide amazing performance of companies, manage their earnings through non-discretionary accruals. Consequently, investors have trouble evaluating the stock value and misunderstanding of the market liquidity because of manipulated information.To this aim, the fixed-effect model (FEM) is implemented to analyze the financial information of 170 listed firms on the Vietnam Stock Exchange over the period 2013–2016. The empirical results emphasized that market liquidity is influenced by earnings management that means the higher level of earnings management, the better equity liquidity. The findings provide additional insight into the determinants of stock liquidity such as earnings management, firm size, daily trading dollar volume of stock, average daily trading dollar volume of the firm, daily returns of stock, daily stock returns, average closing stock price of the firm.

Anticipation of Consequences & Sectoral Impact of COVID-19 – An Indian Outlook

The outbreak of the new coronavirus respiratory disease COVID-19 has left the entire world shattered for which no vaccines and advanced treatment is available. WHO has declared this disease as pandemic causing panic and concerns for human health around the world. This virus knocked in India, when India reported its first case on January 30, 2020 in Kerala and till then there is no full stop. Indian economy has been kept on “pause” mode since ever Shri Narendra Modi, has declared nationwide lockdown to prevent the outburst of this pandemic. Beginning from Stock market to other financial markets to various sectors, this has left nothing untouched. The real magnitude of the economic impact would depend on severity of the situation, public health and lifting up of lockdown. So, the researcher has attempted to show the effect of COVID-19 on different sectors of Indian economy and analyze the risk and returns during the pandemic period along measuring volatility. For this purpose, closing stock prices of BSE SENSEX and various other sectoral indices have been taken for the period of 2nd December to 28th April 2020. The comparative sudy uses the basic descriptive, ANOVA and CAPM model in current study.Garch model has been applied to check the volatility.

Pre-trade hedging: Evidence from the issuance of retail structured products

We find evidence consistent with previously unrecognized market manipulation by broker-dealers. Specifically, we show that pre-trade hedging, which is distinct from front-running, alters prices at which derivative trades occur. We show this behavior is intentional by exploiting variation in the design of structured equity products (SEPs). We find positive abnormal returns on SEP pricing dates for which issuers benefit from altering closing stock prices but no such returns on pricing dates of otherwise similar SEPs. We also show that large buy trades near the close of trading are more frequent when SEP issuers have incentives to alter closing stock prices.

Exponential Smoothing Methods for Detection of the Movement of Stock Prices

Business Intelligence is a set of processes, architecture and technologies that convert raw data into meaningful information. BI has a direct impact on an organization’s strategic statistical and operational business decisions. In BI one of the most interesting areas is time series data analysis to predict are stock prices. Prediction and analysis of stock market data has got an important role in today’s economy. The aim of this paper is to predict the daily previous closing stock prices of the major tech giants of NSE (i.e. HCLTECH and TCS), using information from the historical data with the help Exponential Smoothing Methods. The historical stock prices of the stated companies for three years will be used for the training and testing of the methods. It is found that Holt-Winter’s method of exponential smoothing the given the best results out of the other exponential smoothing methods.

The role of AI in capital structure to enhance corporate funding strategies

The purpose of this study was to assess if Artificial Intelligence (AI) could be used in the Capital Asset Pricing Model (CAPM) and whether the use of AI could bring a more accurate estimation of expected returns. Cost of capital defines the minimum return expected from any investment made by a firm. Hence for managers to maximise the value of the corporation, it is essential to have an accurate estimation of the cost of capital. For the purpose of analysing securities, the adjusted closing stock prices of 10 high-tech public companies were studied from January 2013 to January 2019. This research assumed that there is a need to predict returns for the next year. Hence one year of historical data was used to calculate traditional CAPM value and also train the Recurrent Neural Networks (RNN) to predict stock prices of the upcoming year. A generic deep learning network architecture was developed with the use of Long Short Term Memory (LSTM) and dropout layers. After calculating the returns using traditional and AI approaches, two methods for calculation of CAPM were proposed and compared. Following the analysis, it was found that the use of AI improved the accuracy of cost of equity estimations by over 60%. The strong ability of the selected deep learning neural network to predict stock prices, increased the accuracy of estimating returns by at least 18%. This study concluded that AI has significant potentials to replace traditional asset pricing models in the near future.

The reaction of the sharia stock market in the early days of the Covid-19 pandemic in Indonesia

In 2020, Indonesia was affected by the Corona Virus Pandemic outbreak known as Covid-19. This pandemic has spread globally in almost all over the world. As a result, countries in Indonesia and almost all countries around the world reduce their economic activities. With the existence of this pandemic, researchers are trying to test its impact on the capital market in Indonesia, especially on Islamic stocks in Indonesia, namely the Indonesian Sharia Stock Index (ISSI) and the Jakarta Islamic Index (JII). This research is also to contribute to the Market Efficiency Theory which was first presented by Fama (1970) and then further developed in 1991 by Fama, using an event study. Researchers specifically tested Market Efficiency Theory during the Covid-19 pandemic in Indonesia. Researchers took a sample of each Islamic stock index, namely the closing stock price and stock trading volume (liquidity), as many as 21 stock exchange days in March 2020 compared to the 21 exchange days for the sample observation in March of the previous year. Samples and observations were also taken 21 stock exchange days before and after the announcement of the Covid-19 pandemic in Indonesia on March 2, 2020. The method used in the study was the Event Study with Least Square Regression Method using Eviews 9 software. The study aimed to test the impact. the coronavirus pandemic event on the Islamic stock index in Indonesia. The research results are expected to be used as consideration for companies, investors, and stakeholders in anticipating future pandemic events.