Stock Price Changes Research Articles

The Stock Price Changes and Performance of JD after Second Listing in Hong Kong

In recent years, the trend of Chinese concept stocks returning to Hong Kong stocks market has been continuously strengthening due to multiple factors. This article analyzes the changes of JD's stock price after its second listing, as well as its financial performance after its second listing. According to the analysis, the changes in costs and benefits of JD after its second listing can to some extent explain the changes in its stock price after its second listing. In addition, this study investigates the macro market environment of JD after its second listing, and finds that bearish factors such as the power game between the Chinese and American governments and the Chinese government's antitrust policies for Internet industry also affect JD's stock price. Overall, this study to some extent supplements the current research gap and provides suggestions for other Chinese concept stocks planning to return to the Hong Kong stock market in the future.

COMPARATIVE ANALYSIS OF GEOMETRIC BROWNIAN MOTION, ARTIFICIAL NEURAL NETWORK AND NAIVE BAYESIAN TECHNIQUES USING NIGERIA STOCK EXCHANGE DATA

This paper presents a comparative analysis of three market price forecast models, namely Geometric Brownian Motion, Artificial Neural Network, and Naive Bayesian techniques, using data from the Nigeria Stock Exchange. The exploratory data analysis results indicate slight variations in the mean and median of the log stock price over a 5-year period. The data shows a relatively small spread from the mean and approximately symmetric distribution, as indicated by a skewness value close to zero. The normality test confirms that the log stock price data follows a normal distribution. The forecast using Artificial Neural Network (ANN) shows a minimal change in future stock price, suggesting low returns and moderate risk in the Nigerian stock market. The graphical representation of the ANN model demonstrates a constant path with little variation. Similarly, the Naive Bayesian technique provides a similar forecast to the ANN model, indicating limited profit potential. The Geometric Brownian Motion model also forecasts little variation in future stock prices, with 2023 showing slightly higher values. The accuracy of the forecast models is evaluated using the Mean Absolute Percentage Error (MAPE). The results indicate that the ANN model has an error of 4.60%, the Naive Bayesian model has an error of 9.29%, and the Geometric Brownian Motion model has an error of 12.67%. These findings suggest that the ANN model performs better in terms of accuracy compared to the other two models.

Prediction and investigation of stock price related to Chinas new energy vehicles after the opening of the pandemic based on the LSTM model

This research examines the repercussions of the COVID-19 pandemic on China's new energy vehicle market through the utilization of machine learning models for stock price prediction. Specifically, the Long Short-Term Memory (LSTM) and Artificial Neural Network (ANN) are employed to forecast stock prices and price changes for BYD, Changan Automobile, and Guangzhou Automobile Group. While the LSTM model successfully captures the patterns in the stock price data, it exhibits a lag of one day in its predicted outcomes, indicating its reliance on the previous day's price. However, both models encounter challenges in accurately predicting stock price changes, displaying notable disparities from actual values. The classification task of forecasting whether prices will rise or fall also yields unsatisfactory accuracy scores, highlighting the models' limitations in comprehending the dynamics of the stock market. This study reveals that understanding the impact of the pandemic on the NEV market holds significant importance for informed decision-making and effective navigation of China's automotive industry in the aftermath of the pandemic. Furthermore, further modification for the model is also required to enhance the precision and dependability of stock price forecasts.

Comparative Analysis of Stock Prices, Abnormal Return, Cumulative Abnormal Return, and Trading Volume Activity in the Indonesia Stock Exchange: An Event Study of the Russia-Ukraine War

The Russia-Ukraine war has captured international attention and raised concerns about its potential implications on global financial markets. This study aimed to investigate the interplay between geopolitical events, market reactions within the Indonesia Stock Exchange (IDX), and the market efficiency of the IDX. The study employed event study methodology and analyzed changes in stock prices, abnormal returns, cumulative abnormal returns, and trading volume activity. The sample comprised 53 companies in the energy sector and 57 companies in the food and beverage sector listed on the IDX. The analysis focused on data from 10 days before and after three Russia-Ukraine conflict-related events, namely (1) the announcement of Russia’s invasion of Ukraine on the 24th of February 2022, (2) the announcement of an oil import embargo on Russia by the European Union on the 31st of May 2022, and (3) the announcement of the first wheat export ship’s departure from the port of Odesa on the 1st of August 2022. Both paired sample t-tests and paired sample Wilcoxon signed rank tests were conducted to assess the statistical significance of differences in the means of paired samples. The findings revealed significant differences in average stock prices before and after all three events in the energy sector. However, only events 2 and 3 displayed significant differences in average abnormal returns and cumulative abnormal returns. Moreover, events 1 and 3 exhibited significant differences in average trading volume activity. In the food and beverage sector, a significant difference in average stock prices was observed before and after event 2, while all three events presented significant differences in average abnormal returns and cumulative abnormal returns. Furthermore, event 3 showed a significant difference in average trading volume activity. These findings indicated that the IDX displayed varying reactions to different Russia-Ukraine conflict-related events. Notably, events involving multiple countries or entities exerted a greater impact on the energy and food and beverage sectors within the IDX, leading to more pronounced market reactions. Additionally, the findings suggested that the IDX exhibited a semi-strong form of market efficiency.

Stock Price Correlation Analysis with Twitter Sentiment Analysis Using The CNN-LSTM Method

The intricate interplay between stock prices, reflecting a company's intrinsic value, and multifaceted factors like economic conditions, corporate performance, and market sentiment, constitutes a vital research domain. Grounded in sentiment analysis, our study deciphers public opinions from vast textual data to gauge sentiment, leveraging Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models. We focus on Bank Central Asia (BBCA), a prominent Indonesian banking institution, aiming to forecast stock price fluctuations by analyzing sentiment trends extracted from social media, especially Twitter. Meticulous experimentation, encompassing data segmentation, feature extraction, augmentation, and model refinement, yields significant enhancements in prediction accuracy. The CNN-LSTM model's performance improves from 73.41% to a robust 77.75% accuracy, with F1-scores rising from 73.00% to 75.42%. Importantly, strong correlations emerge between sentiment predictions and actual stock price movements, validated by Spearman correlation coefficients. Positive sentiment exhibits a substantial correlation of 0.745 with stock price changes, while negative sentiment exerts notable influence with a correlation coefficient of 0.691. In summary, our study advances the field of sentiment-driven stock price prediction, showcasing deep learning's effectiveness in extracting sentiment from social media narratives. The implications extend to understanding market dynamics and potentially integrating sentiment-aware strategies into financial decision-making. Future research directions could explore model transferability across financial contexts, real-time sentiment data integration, and interpretability techniques for enhanced practicality in sentiment-driven predictions.

From Outbreak to Vaccination: An Analysis of the Commercial Property Market Reaction to COVID-19 in Malaysia

This paper examines the impact of the COVID-19 pandemic on the performance of the commercial property market in Malaysia. Differing from previous studies that focus on the first wave of COVID-19 that erupted in March 2020, we examine how the commercial property market reacted to the resurgence of COVID-19 cases and the subsequent response (lockdowns, fiscal support, and vaccination program) of the government in dealing with the threat of the COVID-19 pandemic. We find that abnormal returns of listed property companies, REITs, and hotel operators decrease following the implementation of the first nationwide lockdown. Subsequent nationwide lockdowns, however, do not exert a significant impact on abnormal returns. Moreover, we find that 1-day window abnormal returns during the first and third nationwide lockdowns increase with increases in COVID-19 cases. This suggests that investors believe in the ability of these nationwide lockdowns to slow down the spread of the COVID-19 virus. In addition, we document a positive impact of blanket loan moratorium announcements and change in vaccination rate on abnormal returns. The economic magnitude of the stock price changes in response to these announcements could be used as a reference point by policymakers and investors when faced with future pandemics of a similar scale.

Financial Metrics Impact on LQ-45 Stock Price Changes: Evidence from IDX

This quantitative research investigates the influence of Return On Assets (ROA), Net Profit Margin (NPM), Earnings Per Share (EPS), and Debt to Equity Ratio (DER) on stock price fluctuations within the LQ-45 index companies listed on the Indonesia Stock Exchange (IDX) from 2017 to 2019. Employing SPSS tests, including multicollinearity and heteroscedasticity assessments, this study reveals that ROA, NPM, EPS, and DER collectively exert a significant impact on stock price changes. These findings underscore the relevance of financial performance metrics in gauging stock price dynamics, offering valuable insights for investors and market analysts.
 Highlights:
 
 
 Financial Metrics Significance: This research emphasizes the crucial role of financial metrics in understanding stock price fluctuations.
 
 
 LQ-45 Companies Impact: It assesses the impact of financial metrics on stock prices within the specific context of LQ-45 companies.
 
 
 Empirical Evidence: The study presents empirical evidence from the Indonesia Stock Exchange, enhancing its practical relevance for investors and analysts.
 
 
 Keywords: Financial metrics, stock price changes, LQ-45 companies, Indonesia Stock Exchange, SPSS analysis.

Pengaruh Kinerja Keuangan Terhadap Harga Saham Pada Perusahaan Propertydan Real Estate Yang Terdaftar Di Bursa Efek Indonesia

Property and real estate is a sector that has an important role in the economy in Indonesia. The purpose of this study is to determine the effect of the variables on profitability, leverage, and market ratio on stock prices in property and real estate companies listed on the Indonesia Stock Exchange (IDX). The population of this study are property and real estate companies listed on the Indonesia Stock Exchange (IDX), totaling 80 companies. The sampling technique chosen was by using a purposive sampling technique so that a sample of 50 companies was obtained. The data analysis method used is multiple linear regression analysis with the Statistical Package for the Social Sciences (SPSS) program. The results of the study show that a) Profitability has a significant positive effect on stock prices b) Leverage has no significant or negative effect on stock prices c) Market Ratio has a significant positive effect on changes in stock prices.
 Keywords: Stock Price, Profitability, Leverage, and Market Ratio

Application Analysis of Financial Time Series in Stock Market

Time series analysis is a basic tool in number investing. The time-series sequence is a sequence of variables measured in a fixed section sequence over some time. The fluctuation of stock price affects all aspects of social and economic life, so effectively predicting the trend of stock price changes has great economic and social value. Based on the existing research results on time series, this paper takes the stock market as an entry point, and sorts and summarizes the literature from the application fields and application methods of time series. The research results show that the application fields of time series involve the stock market, enterprises, macro economy, etc., among which the application of the stock market is the most commonly used and the most extensive. The research based on time series mainly innovates and corrects the research methods. The research in this paper can provide a theoretical reference value for further research on time series.

The Impact of Short-selling Institutions on Chinese Stock Companies and MarketFrom the Case of Luckin Coffee

As a major engine of the capital market, the stock market is favored by investors. But the huge profit space and operability are also easy to breed the soil of corruption and fraud. At present, due to the increased participation efficiency of short-selling institutions, the financial risks faced by enterprises have further increased. Through the case of Luckin coffee, we observe the stock price changes after it encountered short selling, combined with the model of the arbitrage pricing principle to find the role of short-selling institutions in the market. By disclosing corporate financial fraud, short-sellers can improve the effectiveness of market supervision to a certain extent, reduce the occurrence of stock market premiums, and bring stock prices back to their true levels. On the premise of ensuring the stability of the stock market and introducing a reasonable system, the Chinese market can also consider developing a similar mechanism to inject fresh blood into economic development.

The Impact of Three Red Lines Policy on Chinas Real Estate Industry

This paper seeks to comprehensively investigate the extent to which the Three Red Lines Policy has affected the real estate industry. The Three Red Lines policy are financial regulatory guidelines in China introduced in August 2020To figure out the impact of the Three Red Lines Policy, this paper studies the intentions of the policy, a case study on the comparative stock price changes of Vanke and Evergrande and predicting the future stock changes of Vanke through python, and an analysis of how property companies change. The findings suggest that the policy has cooled down the overheated property market, and because it limits the debts to the real estate sector, it has also changed the development trends of the industry. The Three Red Lines Policy would have a profound impact on the future development of the real estate industry.

Investigation on the Factors Influencing Stock Prices

This article focuses on the impact of financial data of some companies that can be found from financial websites n the company's stock price. This is because investors always lack trust in financial analysts because they are suspicious of their motives. Therefore, this article discusses the impact of some financial data on stock prices in addition to the traditional models such as dividends, ROE and other factors. For example, profit, solvency, forward P/E ratio, etc. The intention is to help external investors to judge the change of stock price more accurately by using basic financial data. This will ensure their interest as much as possible.

Risk and Return Analysis of Banking Sectors With Reference To Nifty

This study conducts a focused analysis of the banking sector's performance within the context of the Nifty index, a key benchmark in the Indian stock market. It investigates the return and risk characteristics of investments in the banking sector by examining historical data. The study evaluates returns through stock price changes and dividend yields, while also assessing risk factors like volatility and beta. Findings from this analysis provide essential insights into the banking sector's relative performance, aiding investors and financial professionals in making informed decisions about portfolio allocation and risk management within the Indian financial market.

The Influence of Fundamental and Macroeconomic Analysis on Stock Price

Investments that provide quite promising returns are stock investments. To see whether stocks can be profitable or not, simply investors often look in terms of changes in stock prices. The purpose of this study is to analyze the effect of fundamental and macroeconomic analysis on stock prices. Fundamental analysis is measured by return on assets (ROA), current ratio (CR), and debt to equity ratio (DER), while macroeconomic analysis is measured by inflation and interest rates. The data used in this study is secondary data in the form of company financial reports indexed by LQ45 for the 2018-2022 period. This study uses multiple linear regression analysis. The results showed that from the fundamental factors, the ROA variable had a positive and significant effect on the stock price of the LQ45 index. Meanwhile, the CR and DER variables don't affect the stock price of the LQ45 index. From macroeconomic factors, namely inflation and interest rates, they don't affect the stock price of the LQ45 index. Simultaneously the variables ROA, CR, DER, Inflation, and Interest Rates don't affect the stock price of the LQ45 index.

Accounting information and stock price changes in Nepalese hydropower companies

The study aimed to examine the relationship between accounting information (earnings per share, dividend per share and book value per share) on stock price changes (volatility) in Nepalese hydro power companies. It examined the impact of dividend per share, earnings per share and book value per share on stock price changes. Out of the 51 hydropower companies listed in Nepal Stock Exchange, Butwal Power Company Limited (BPC) and Chilime Hydro Power Company Limited (CHPC) were randomly selected for the study. The study period was covered 2073/2074 to 2078/2079 and data collected through the annual reports as well as data based on the Nepal Stock Exchange. Market value per share (MVPS) was taken as dependent variable and earnings per share (EPS), dividend per share (DPS) and book value per share (BVPS) as independent variables. Multiple regression analyses was used to identify the impact of accounting information on share price changes. A positive relation was found between EPS and MVPS in both companies. However DPS and BVPS showed mixed relationship (negative and positive) with MVPS of BPC and CHPC. The EPS of BPC found positive impact on MVPS. However DPS revealed negative impact on MVPS of both companies.

Reporting comprehensive income and firms' earnings management

This study investigates the impact of reporting comprehensive income and other income through performance or equity statements on firms' earnings management through the selective sale of available-for-sale (AFS) securities. The objective is to explore how Canadian firms can improve transparency by altering managers' accounting behavior by switching from equity statements to income statements. The study used a difference-in-differences method in a quasi-experimental framework. The results showed that negative net income and post-period net income in performance statements can reduce realized gains and losses. Available-for-sale securities also affect these gains and losses in performance and equity statements. Single statement adopters only smooth earnings when net income is positive or when available-for-sale gains are large enough to offset negative earnings. Income statement (IS) adopters reduced earnings smoothing more than other treatment firms. Managers with less job security were found to be more likely to engage in earnings smoothing, and earnings smoothing decreased more during the pre-to-post period. Equity incentives reduced the tendency to smooth earnings, particularly in treatment firms with CEOs whose pay is more sensitive to stock price changes. Treatment firms outperform control firms in predicting future earnings using realized gains and losses on AFS securities per share. The findings suggest that firms manage earnings less effectively by selectively selling AFS securities when comprehensive income (CI) or other comprehensive income (OCI) is presented in performance statements. Firms win when earnings management is reduced because it reduces the informativeness of realized gains and losses on AFS securities. Managers will use more earnings management strategies as long as they have incentives.

Prediction of NEPSE Index Movement Using Technical Analysis

Due to the nature of the stock and index, predicting index or price movements is a challenging task. The nature of index volatility makes it impossible for investors to profit from stocks. To address this problem, investors need to make accurate predictions about future index or stock price changes. In this study, data has collected for 6 months (December 2022 to May 2023) and technical trading tools such as candlesticks and Google Form Questionnaire Surveys were used to determine the movement of the NEPSE index. A questionnaire survey is completed with 62 answers. Given the Nepalese context and due to the global economic crisis and uncertain political environment, as a result, in a bearish market, the candlestick stick pattern produces mixed results while the trader can benefit from it in the very short term, but sometimes does not provide the right and appropriate movement.

Research on the Influence of Monetary Policy on Stock Market Return

These changes in China's stock market are influenced by factors such as the share-trading reform and the international financial crisis. During this period, a series of monetary policies implemented in China also had some profound effects on China's stock market. Capital market has always been the main transmission channel of monetary policy. In recent years, China's interest rate system, share-trading system and other reforms have been gradually implemented, and the reform of interest rate marketization has achieved certain results, but it has also brought more uncertainty to the transmission of stock market and monetary policy. With the continuous expansion of the stock market, it has played an important role in the transmission process of monetary policy. At the same time, monetary policy can affect the price of financial assets, thus changing people's behavioral decisions and achieving monetary policy goals. When the actual monetary policy changes beyond or below expectations, it will cause the change of stock price. The existence of expectation effect makes the relationship between monetary policy and stock price change more complicated: taking loose monetary policy as an example, when the actual degree of easing is greater than investors' expectations, stock prices will be positively stimulated by easing; When the degree of easing is less than investors' expectations, the stock price will turn downward. Therefore, it is worth studying to analyze the influence mechanism among monetary policy, stock market and real economy.

The Influence of Earning Per Share, Turnover of Total Asset, Return on Equity and Debt Asset Ratio on LQ45 Index Share Price Period 2017 – 2021 listed on the Indonesia Stock Exchange

Due to the inflation factor, for that we need to invest in an investment asset, the current value of money will decrease. The investment instrument studied in this study were shares, using financial ratios to establish whether financial ratios are correlated with stock prices so that we can search for companies that can provide benefits in the form of capital gains, the object of this research uses ratios, among others: earnings per share, total asset turnover, return on equity, and debt/asset ratio. This research, using the help of the Eviews version 10 program as an application to process the data used, was carried out on companies listed on the lq45 index. In this study, the purposive sampling method was used as the sampling method. The number of samples used were 23 companies with research data carried out from 2017 to 2021, so 115 observation data were obtained. From the results of this study it is known that: (1) Earnings per share has a significant positive effect on share prices, this is evidenced with a significance of 0.0000 and a t value of 11.35574, so H1 is accepted; (2) The Turnover of Total Assets has a significant positive effect on stock prices, this is evidenced by the significance of 0.0156 and the value of the arithmetic t (2.456378) for which H2 is accepted; (3) Return on Equity has a significant positive effect on share prices, this is evidenced by a significance of 0.0010 and a t-count value of 3.368250, so H3 is accepted; (4) The debt to asset ratio has a significant negative effect on share prices, this is evidenced by a significant value of 0.1872 and a t-count value of (1.327267) so H4 is rejected; (5) Obtained Simultaneous Test Results Arithmetic results F of 33.50263 with probability 0.000000 can be concluded if simultaneously all variables have a significant influence on stock prices. The results of the regression analysis show the adjusted value (r^2) of 0.532807, which means that variations in Earnings per Share, Total Asset Turnover, Return on Equity, and Debt to Assets Ratio can explain the 53.28 % of changes in stock price. While the remaining 46.72% is explained by other variables not examined in this study.

New drugs and stock market: a machine learning framework for predicting pharma market reaction to clinical trial announcements

Pharmaceutical companies operate in a strictly regulated and highly risky environment in which a single slip can lead to serious financial implications. Accordingly, the announcements of clinical trial results tend to determine the future course of events, hence being closely monitored by the public. Most works focus on retrospective analysis of announcement impact on company stock prices, bypassing the consideration of the problem in the predictive paradigm. In this work, we aim to close this gap by proposing a framework that allows predicting the numerical values of announcement-induced changes in stock prices. In fact, it is a problem of the impact prediction of the specific event on the corresponding time series. Our framework includes a BERT model for extracting the sentiment polarity of announcements, a Temporal Fusion Transformer for forecasting the expected return, a graph convolution network for capturing event relationships, and gradient boosting for predicting the price change. We operate with one of the biggest FDA (the Food and Drug Administration) datasets, consisting of 5436 clinical trial announcements from 681 companies for the years 2018–2022. During the study, we get several significant outcomes and domain-specific insights. Firstly, we obtain statistical evidence for the clinical result promulgation influence on the public pharma market value. Secondly, we witness inherently different patterns of responses to positive and negative announcements, reflected in a stronger and more pronounced reaction to negative clinical news. Thirdly, we discover two factors that play a crucial role in a predictive framework: (1) the drug portfolio size of the company, indicating the greater susceptibility to an announcement in the case of low diversification among drug products and (2) the announcement network effect, manifesting through an increase in predictive power when exploiting interdependencies of events belonging to the same company or nosology. Finally, we prove the viability of the forecast setting by getting ROC AUC scores predominantly greater than 0.7 for the classification of price change on historical data. We emphasize the transferability and generalizability of the developed framework on other datasets and domains but on the condition of the presence of two key entities: events and the associated time series.