Closing Stock Prices Research Articles

Multiobjective Automated Type-2 Parsimonious Learning Machine to Forecast Time-Varying Stock Indices Online

Real-time forecasting of the financial time-series data is challenging for many machine learning (ML) algorithms. First, many ML models operate offline, where they need a batch of data, which may not be available during training. Besides, due to a fixed architecture of the majority of the offline-based ML models, they suffer to deal with the uncertain nature of financial time-series data. In contrast, online learning mode evolving-structured ML models could be promising for financial time-series forecasting. For real-time deployment of such models, low memory demand is a must. Besides, the model’s explainability plays a crucial role in forecasting financial time-series. Considering all the requirements, a rule-based autonomous neuro-fuzzy learning algorithm called the parsimonious learning machine (PALM) is proposed here to forecast time-varying stock indices. To provide efficient automation of the proposed algorithm by maintaining the model explainability in terms of limited number linguistic IF-THEN rules, two popular multiobjective evolutionary algorithms (MEAs), such as a real-coded genetic algorithm (GA) and a self-adaptive differential evolution (DE) algorithm are utilized here. In addition, fuzzy type-2 variants of PALMs’ are considered here due to better uncertainty handling capacity than their type-1 counterparts. To evaluate the proposed algorithm’s performance, the closing stock price of fifteen (15) different stock market indices are predicted here. From the results, it is observed that the MEA-based PALMs are performing better than the state-of-the-art benchmark online ML models and providing a rule-based explainable model to the end-user.

A new attention-based LSTM model for closing stock price prediction

Financial time-series prediction has been a demanding and popular subject in many fields. Latest progress in the deep learning technique, especially the deep neural network, shows great potentials in accomplishing this difficult task. This study explores the possible neural networks to improve the accuracy of the financial time-series prediction, while the main focus is to predict the closing price for next trading day. In this paper, we propose a new attention-based LSTM model (AT-LSTM) by combining the Long Short-Term Memory (LSTM) networks with the attention mechanism. Six stock markets indices with four features were used as the input to the model. We evaluate the model performance in terms of MSE, RMSE and MAE. The results for these three metrics are 0.4537, 0.6736 and 0.4858, respectively. The results suggest that our model is skillful in capturing financial time series, and the predictions are robust and stable. Furthermore, we compared our results with the previous work. As a result, our proposed AT-LSTM exhibits a significant performance improvement and outperforms other methods.

AEI-DNET: A Novel DenseNet Model with an Autoencoder for the Stock Market Predictions Using Stock Technical Indicators

Predicting stock market prices is an important and interesting task in academic and financial research. The volatile nature of the stock market means that predicting stock market prices is a challenging task. However, recent advancements in machine learning, especially in deep learning techniques, have made it possible for researchers to use such techniques to predict future stock trends based on historical financial data, social media news, financial news, and stock technical indicators (STIs). This work focused on the prediction of closing stock prices based on using ten years of Yahoo Finance data of ten renowned stocks and STIs by using 1D DenseNet and an autoencoder. The calculated STIs were first used as the input for the autoencoder for dimensionality reduction, resulting in less correlation between the STIs. These STIs, along with the Yahoo finance data, were then fed into the 1D DenseNet. The resultant features obtained from the 1D DenseNet were then used as input for the softmax layer residing inside the 1D DenseNet framework for the prediction of closing stock prices for short-, medium-, and long-term perspectives. Based on the predicted trends of the stock prices, our model presented the user with one of three suggested signals, i.e., buy, sell, or hold. The experimental results showed that the proposed approach outperformed the state-of-the-art techniques by obtaining a minimum MAPE value of 0.41.

Analisis Harga Saham Perbankan Sebelum dan Setelah Pengumuman Covid-19 di Indonesia

This study aims to analyze banking stock prices before and after the announcement of Covid-19 in Indonesia. The stock price in this study uses the daily closing stock price. The sample was selected using purposive sampling technique. This study collected 5 national private foreign exchange banking companies listed in the ranking with the largest number of assets, namely Bank Central Asia Tbk, Bank Cimb Niaga Tbk, Bank Pan Indonesia Tbk, Bank OCBC NISP Tbk, and Bank Danamon Indonesia Tbk. Data were analyzed using the Wilcoxon Signed Rank Test. The analysis results show that there are significant differences in the stock prices of banking companies before and after the announcement of Covid-19 in Indonesia.

Perbedaan Harga Saham Dan Volume Transaksi Sebelum Dan Sesudah Pengumuman Kasus Covid-19 (Studi Pada Indeks Saham LQ-45)

The purpose of this research is to find out whether there are differences in stock prices and transaction volumes due to the announcement of the Covid-19 case. This research includes quantitative research. The population of companies in this study are companies listed on the LQ-45 stock index. Data in the form of daily closing stock prices and trading volume activity data, namely 5 days before and after the first case of Covid-19 was announced. For the data analysis method, this study applies a nonparametric difference test, namely the Wilcoxon Signed Ranks Test with the SPSS 25 program. Based on the results of this study, it shows that the significance value is 0.000 < 0.05, so there is a difference between stock prices before and after the Covid-19 case was announced. Stock prices tend to decline. Meanwhile, the results of the transaction volume research show that if the significance value is 0.488 > 0.05, it means that there is an insignificant difference between before and after the Covid-19 case was announced. The volume of stock transactions has increased more than decreased.

ANALISIS POTENSI ABNORMAL RETURN POSITIF TERBESAR SAHAM PT.KALBE FARMA SELAMA PANDEMI COVID-19

This study aims to analyze the largest potential positive abnormal return present in PT. Kalbe Farma during the Covid-19 pandemic. The event study method was used in this study to study indonesia's capital market reactions during the observation period. The observation period was carried out when covid-19 was detected in Indonesia from March 2020 to February 2021 or one month after the implementation of the first covid-19 vaccination in Indonesia. Secondary data used in this study is in the form of daily stock closing stock price data, and daily JCI. The results of the study found that there was an abnormal positive return with the largest value on March 31, 2020 with an abnormal positive return value of 0.12013 and January 11, 2021 with a value of 0.15726

Quantile regression with time-series data and applications in modeling of some big bank stock markets

In the OLS regression model, the mean of the dependent variable is estimated based on the mean of the independent variables. The relationship between the independent variable and the dependent variable needs to be considered in many values instead of just through the mean of the dependent variable, then the quantile regression model is the optimal choice. In this paper, we study a quantile regression model in which the percentiles of the dependent variable are spread from 10% to 90%, with step by 10%, of the time-series data through autoregression model, to compare the fit of the models as well as the errors of the model. To demonstrate the results, we applied it to the time-series data on the closing stock prices of the four largest bank codes (on August 2, 2021) namely VCB, VPB, TCB, and BID. In which, percentile regression models have a very high fit, corresponding to about 80% or more. Besides, the estimated parameters are all statistically significant, different from the OLS regression model where some estimated parameters are not statistically significant. Furthermore, the OLS regression model is based on the mean, so it is susceptible to the outlier values, while the quantile is not affected by the outliers. In other words, the quantile regression model overcomes this weakness, so the quantile regression model is not affected by the outliers.

Prediction of BRIC Stock Price Using ARIMA, SutteARIMA, and Holt-Winters

The novel coronavirus has played a disastrous role in many countries worldwide. The outbreak became a major epidemic, engulfing the entire world in lockdown and it is now speculated that its economic impact might be worse than economic deceleration and decline. This paper identifies two different models to capture the trend of closing stock prices in Brazil (BVSP), Russia (IMOEX.ME), India (BSESN), and China (SSE), i.e., (BRIC) countries. We predict the stock prices for three daily time periods, so appropriate preparations can be undertaken to solve these issues. First, we compared the ARIMA, SutteARIMA and Holt-Winters (H-W) methods to determine the most effective model for predicting data. The stock closing price of BRIC country data was obtained from Yahoo Finance. That data dates from 01 November 2019 to 11 December 2020, then divided into two categories--training data and test data. Training data covers 01 November 2019 to 02 December 2020. Seven days (03 December 2020 to 11 December 2020) of data was tested to determine the accuracy of the models using training data as a reference. To measure the accuracy of the models, we obtained the means absolute percentage error (MAPE) and mean square error (MSE). Prediction model Holt-Winters was found to be the most suitable for forecasting the Brazil stock price (BVSP) while MAPE (0.50) and MSE (579272.65) with Holt-Winters (smaller than ARIMA and SutteARIMA), model SutteARIMA was found most appropriate to predict the stock prices of Russia (IMOEX.ME), India (BSESN), and China (SSE) when compared to ARIMA and Holt-Winters. MAPE and MSE with SutteARIMA: Russia (MAPE:0.7; MSE:940.20), India (MAPE:0.90; MSE:207271.16), and China (MAPE: 0.72; MSE: 786.28). Finally, Holt-Winters predicted the daily forecast values for the Brazil stock price (BVSP) (12 December to 14 December 2020 i.e., 115757.6, 116150.9 and 116544.1), while SutteARIMA predicted the daily forecast values of Russia stock prices (IMOEX.ME) (12 December to 14 December 2020 i.e., 3238.06, 3241.54 and 3245.01), India stock price (BSESN) (12 December to 14 December 2020 i.e.,. 45709.38, 45828.71 and 45948.05), and China stock price (SSE) (11 December to 13 December 2020 i.e., 3397.56, 3390.59 and 3383.61) for the three time periods.

IMPLEMENTASI PAKET SHINY PADA PEMODELAN MULTISCALE AUTOREGRESSIVE UNTUK DATA HARGA SAHAM BBRI

Stocks are an investment that attract people because they can earn large profits by having claim rights to the company's income and assets so investors have to observe stock price movements in the future to achieve investment goals. One of the statistical methods for time series data modeling is ARIMA. However, modeling assumptions must be fulfilled to use that method so an alternative model is proposed, namely nonparametric regression model, which has no modeling assumptions requirement. In this study, the nonparametric regression multiscale autoregressive (MAR) with two different filter and decomposition level J are compared to choose the best model and forecast it. The data are closing stock price, high stock price and low stock price of BBRI’s stocks that divided into 2 parts, namely in sample data from March 19, 2020 to February 4, 2021 to form a model and out sample data from February 5, 2021 to March 23, 2021 used for evaluation of model performance based on MAPE values. The chosen best model for each stock price are the MAR model with wavelet haar filter and decomposition level 5 for the closing stock price which produces a MAPE value of 1.194%, the MAR model with wavelet haar filter and decomposition level 5 for the high stock price which produces a MAPE value of 1.283%, and the MAR model with a wavelet haar filter and decomposition level 5 for the low stock price which produces a MAPE value of 1.141%, indicating that the models have excellent forecasting capability. In this study, Graphical User Interface (GUI) using R software with the help of shiny package is also built, making data analyzing easier and generating more interactive display output.

Pengaruh Likuiditas dan Solvabilitas Melalui Profitabilitas terhadap Nilai Perusahaan

The purpose of this research is to know and analyze the influence of liquidity and solvency through profitability on the corporate value at pharmaceutical sub-sector companies in the Indonesia stock exchange. That research is using data of secondary from annual financial statements and closing stock prices. sample in the research are pharmaceutical sub-sector companies in the Indonesia stock exchange period 2015-2019. With the purposive sampling method used there are nine companies that are sampled in this research. Data analysis in this research uses path analysis with SPSS software. The resulting research informs :1) simultaneously the current ratio and debt to asset ratio variables have significant effect on the return on asset variable, 2) the current ratio variable partially has positive and significant effect on the return on asset variable, 3) the debt to asset ratio variable partially has negative and insignificant effect on the return on asset variable, 4) the current ratio and debt to asset ratio variables simultaneously have significant effect on the stock price variable, 5) partially current ratio and debt to asset ratio variable have positive and significant effect on the stock price variable, 6) return on asset variable has positive and significant effect on the stock price variable, 7) indirectly current ratio and debt to ratio variables return on asset through variable have insignificant effect on the stock price variable.

Stock Price Reaction to the Stock Splits Announcement of Listed Companies in the Colombo Stock Exchange

A stock split is a corporate event that directly impacts the number of a company’s shares and indirectly on stock prices. This study tests the effect of the stock splits on the share price of companies listed in the Colombo Stock Exchange during the periods of pre and post stock split announcement in accordance with the Efficient Market Hypothesis. The main objective of this paper is to identify the overall impact of a stock split announcement on stock prices. This study analyses 88 annual stock splits during the ten (10) year period from 2009 to 2019 by taking the listed companies in the Colombo Stock Exchange into consideration. It uses the event study methodology to test the market efficiency of the Colombo Stock Exchange, and the market model is run with the aid of abnormal returns, which are calculated based on daily closing stock prices and the All-Share Price Index. For analysing the results, the graphical analysis and t statistics have been utilized. According to the event day average abnormal return, the majority of stock splits were more negative than positive with a significant t value at 5% by indicating that investors were taking the stock split announcement as bad news just after the split announcement was released. Each day with a significant Average Abnormal Return shows more positives than negatives. Graphical results have shown both Average Abnormal Return, and Cumulative Average Abnormal Return has remained continuously negative up to 18 and 25 days, respectively, by implicating that stock splits have made a deleterious impact on stock return. This study finally concludes that the information regarding the stock splits has not been absorbed efficiently by the market because the market reactions before and after the date of the split announcement were significant at 5%, although the Average Abnormal Return got a quick reaction to the announcement. Furthermore, results had not provided evidence for Semi-Strong Form efficiency of the Colombo Stock Exchange since the significant stock price adjustments before and after the event day was noticed. By this study, the policymakers and investors are convinced that all information has not been incorporated into stock prices in making their decisions.

Investors' reactions to COVID-19 related announcements: evidence from the cargo shipping industry

PurposeThe purpose of this study is to examine the impact of the corona virus (COVID-19) pandemic on stock returns of listed cargo shipping companies.Design/methodology/approachThe author employs the events study methodology to examine this phenomenon. A sample of 49 listed cargo shipping companies in the container, dry bulk and tanker sub-sectors from Asia, North America, and Europe was selected and their daily closing stock prices from 1st January 2020 to 31st December 2020 were utilized.FindingsThe results reveal that there was an overall negative overreaction to the announcement by World Health Organization (WHO) that declared COVID-19 a pandemic. The approvals of USD 857 billion stimulus package by the European Union (EU) and Pfizer vaccine by Food and Drug Administration (FDA) in USA received slight positive reactions. The Greek, Singaporean and Taiwanese shipping stocks were the least affected stocks as their respective shipping industries remained resilient during 2020.Research limitations/implicationsThis study provides evidence to confirm the fact that COVID-19 has affected stock markets; however the impact is un parallel among cargo shipping stocks of different countries.Originality/valueThe majority of studies have conducted country level analyses of the COVID-19 and stock market performance phenomenon. However, there have been sectoral disparities in terms of their susceptibility to economic shocks from COVID-19. This study's focal point is on the cargo shipping sector which synonymous with other sectors has not been immune to the current pandemic. The study also extends the timeline of events to incorporate those from June to December 2020.

A Historical Data Based Ensemble System for Efficient Stock Price Prediction

Background: Stock price prediction is a challenging task. The social, economic, political, and various other factors cause frequent abrupt changes in the stock price. This article proposes a historical data-based ensemble system to predict the closing stock price with higher accuracy and consistency over the existing stock price prediction systems. Objective: The primary objective of this article is to predict the closing price of a stock for the next trading in more accurate and consistent manner over the existing methods employed for the stock price prediction. Method: The proposed system combines various machine learning-based prediction models employing Least Absolute Shrinkage and Selection Operator (LASSO) regression regularization technique to enhance the accuracy of stock price prediction system as compared to any one of the base prediction models. Results: The analysis of results for all the eleven stocks (listed under Information Technology sector on the Bombay Stock Exchange, India) reveals that the proposed system performs best (on all defined metrics of the proposed system) for training datasets and test datasets comprising of all the stocks considered in the proposed system. Conclusion: The proposed ensemble model consistently predicts stock price with a high degree of accuracy over the existing methods used for the prediction.

Dynamics of Return Linkages and Asymmetric Volatility Spillovers among Asian Emerging Stock Markets

The purpose of this study to investigate the dynamics of return linkages and volatility spillovers between Asian emerging stock markets (China, Hong Kong, Japan, Malaysia, Pakistan, and South Korea). To achieve this task, we used bivariate EGARCH (1) model. We used daily closing stock prices from January 01, 2010, to December 31, 2018. The findings revealed that the own lagged spillovers are statistically significant in all cases at one percent level. Our findings also show that the asymmetric volatility spillovers are significant in all sampled stock markets except China. We find unidirectional volatility spillovers from the markets of China toward Hong Kong, Malaysia toward South Korea, Hong Kong toward South Korea, Pakistan toward Hong Kong, and Japan toward South Korea. Moreover, the volatility spillovers in the majority stock markets are significant and bidirectional. Therefore, these markets are interrelated, and the spillover effect should be taken into consideration by policymakers who are responsible for economic decision making as they can save the financial sector from unexpected financial shockwaves.

Predicting Stock Prices Using Machine Learning Methods and Deep Learning Algorithms: The Sample of the Istanbul Stock Exchange

Stock market prediction in financial and commodity markets is a major challenge for speculators, investors, and companies but also profitable with an accurate prediction. Thus, obtaining accurate prediction results becomes extremely important especially while the stock market is essentially volatile, nonlinear, complicated, adaptive, nonparametric and unpredictable in nature. This study aims to forecast the opening and closing stock prices of 42 firms listed in Istanbul Stock Exchange National 100 Index (ISE-100) using well-known machine learning methods, Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) models and deep learning algorithm, Long Short Term Memory (LSTM) by comparing their forecasting performances. The analysis includes 9 years of data from 01.01.2010 to 01.01.2019. For each firm 2249 data for the opening and 2249 for the closing stock prices were established as daily data sets. Forecasting performance of these methods was evaluated by applying different criteria for each model: root mean squared error (RMSE), mean squared error (MSE) and R-squared (R2). The results of this study show that MLP and LSTM models become advantageous in estimating the opening and closing stock prices comparing to SVM model.

Stock price forecast of macro-economic factor using recurrent neural network

<span id="docs-internal-guid-a29e641b-7fff-1dc7-f2b4-6f5488c7c0a5"><span>The stock market is one of the investment choices that always have traction from time to time. Aside from being a means of corporate funding, investing in the stock market can benefit investors. Investing also has a higher risk because the pattern of stock prices is volatile, which is caused by internal and external factors. One external factor that affects stock prices is the macro-economic, where these factors are events that occur in a country where one of the economic sectors affected is stock prices. Investors often feel confused about the right time in decisions making related to buying or selling stock. One way to look at how the prospect of stock prices is a stock price forecasting activity. For this study, we will be making use of the recurrent neural network (RNN) to forecast stock prices for the next periods. This research involves two variables: the closing stock price and the rupiah exchange rate against the dollar for the daily period. We achieve a MAPE value of 1.546% for RNN model without the variable foreign exchange rate and 1.558% for the RNN model that uses the foreign exchange rate against the dollar.</span></span>

TESTING OF THE BLACK SCHOLES AND GARCH MODELS IN LQ45 USING LONG STRADDLE STRATEGY IN 2009-2018

The purpose of this study is to examine the implementation of option contracts using Black Scholes and GARCH on the LQ45 index using the long straddle strategy. This study uses time-series data as a time frame for conducting research, using a sample of closing price data for the LQ 45 daily index for 2009-2018. For the test the model, we used the secondary data of the closing stock price index from February 28, 2009 to March 31, 2019The results of this study are seen by comparing the average percentage value of Average Mean Squared Error (AMSE) of Black Scholes and GARCH with the application of a long straddle strategy, where the smaller the percentage value, the better the model will be. Within one month of option contract due date, Black Scholes is better than GARCH, with an error value on the call option of 2.77% and the put option of 1.56%. Within two months of option contract due date, GARCH is better than Black Scholes, with an error value on the call option of 8.12% and the put option of 4.00%. Within three months of option contract due date, Black Scholes is better than GARCH, with an error value on the call option of 12.38% and on the put option of 5.50%. The long straddle strategy in the LQ45 index only reached a maximum of 60% of possible profits, with an average of around 30% possible profits.

STOCK MARKET PREDICTION: USING ECONOMETRIC MODELS AND NEURAL NETWORKS

Stock market trading involves buying and selling of shares or stocks, which represents ownership of business. This research paper will focus on capturing the algorithmic trading based on historical data and compare present day algorithms to nd the best t model to understand the underlying patterns in stock market trading. A comparative analysis of closing stock price for 12 companies from three different sectors has been considered to understand the efcacy of the models in order to predict the future stock prices with minimal errors. Stock market was earlier predicted using traditional econometric models like the ARIMA and SARIMA, however, in this paper, Machine Learning, a part of Articial Intelligence will be incorporated in the stock data collected from Yahoo Finance to train models and provide predictions/decisions without being explicitly programmed to do so. Models such as OLS, SARIMA, Convolutional Neural Networks and Recursive Neural Networks (LSTM) will also be used to analyze the historical stock data and will be compared for accuracy using testing parameters like Mean Squared Error (MSE).

Does investment in insurance stocks reap diversification benefits? Static and time varying copula modeling

The present article investigates the presence of diversification benefits resulting from the dependence structure in stock returns of Islamic and conventional insurance. Our empirical design is based on the use of daily closing stock prices of 20 Saudi insurance companies and the estimation of a battery of static and time varying copulas. The findings of static copulas estimations show that, at foremost pares, the suitable copulas modeling the relationship between stock returns of “Takaful insurance” and “Conventional life and health insurance” are the Clayton Copula and the Symmetrized Joe Copula (SJC). Moreover, the dependence parameter is practically low revealing an independence among various insurance business lines. The findings of the time varying copulas corroborate those obtained by the static copulas in terms of the weak pairwise correlation between Islamic and Non-Islamic Stock returns.

Government Decision and Its Impact on Market Confidence during COVID-19: Empirical Analysis of Pakistan Stock Exchange

COVID-19 pandemic has had a significant impact on global economies. The economy of Pakistan has been severely influenced by the virus; its industries were directly affected. Consequently, tremendous fluctuations were observed in Pakistan Stock Exchange, leading to unstable share prices which created uncertainty in the stock market. This paper studies the effect of COVID-19 on PSX by observing fluctuations in the Pakistani stock market. Five major industries are selected namely Pharmaceutical, Oil & Gas, Commercial Banking, Food & Personal Care Products and Chemical. Using descriptive statistics quarterly and industrial averages are compared. Secondary data is collected from the Data Portal of Pakistan Stock Exchange (PSX). The findings of the study suggest that in terms of average stock high & average closing stock price Pharmaceutical sector experienced the greatest percentage change from 1st quarter - 3rd quarter whereas in terms of average volume of shares, Food & Personal Care Products recorded the greatest change during the 2nd – 3rd quarter.