Stock Price Behavior Research Articles

Stock Markets’ Reactions to the Announcement of the Hosts. An Event Study in the Analysis of Large Sporting Events in the Years 1976–2032

This study attempts to estimate the impact of the announcements of hosts of large sporting events on domestic stock markets. The research problem is to establish a connection between the uniqueness of a sporting event and investors’ beliefs through stock price behavior. Using appropriate estimation windows, 13 different sporting events classified as large, including mega and major events, were tested. The obtained results show that, in principle, one day after the announcement of the host of a large sporting event, an average positive reaction of 0.22% is observed on national stock exchanges. The analyzed events were also classified as being neutral for capital markets or generating positive or negative reactions when the host country is announced to the public.

The US, Economic News, and the Global Financial Cycle

We provide evidence for a causal link between the US economy and the global financial cycle. Using intraday data, we show that US macroeconomic news releases have large and significant effects on global risky asset prices. Stock price indexes of 27 countries, the VIX, and commodity prices all jump instantaneously upon news releases. The responses of stock indexes co-move across countries and are large - often comparable in size to the response of the S&P 500. Further, US macroeconomic news explains on average 23 percent of the quarterly variation in foreign stock markets. The joint behavior of stock prices, bond yields, and risk premia suggests that systematic US monetary policy reactions to news do not drive the estimated effects. Instead, the evidence points to a direct effect on investor’ risk-taking capacity. Our findings show that a byproduct of the United States' central position in the global financial system is that news about its business cycle has large effects on global financial conditions.

Understanding the Price Behaviour of selected banks from banking sector in Bombay Stock Exchange

Over the past two decades, the globalisation trend has led to a significant increase in financial markets, particularly the stock market. Stock markets play an important role as an indicator of the economy's overall performance. Analyzing past price movements is how Technical Analysis predicts future price movements. It encourages the mobilization of funds, provides equity capital to the banking sector, and offers adequate returns for investors. The Indian banking sector has undergone significant changes in recent years, with banks diversifying from traditional deposit-taking and lending activities to more complex activities such as investment banking, insurance, and wealth management. Therefore, understanding the price behavior of banks listed on the Bombay Stock Exchange (BSE) is crucial for investors, policymakers, and researchers. By using stock market indicators, investors can observe significant market movements. The main objective of this paper is to analyze a top 5 bank within the banking sector and forecast its stock price behavior. Additionally, the paper aims to test the effectiveness of technical indicators such as RSI and MACD in predicting the stock's behavior. The study focuses on analyzing the stock price trends of selected banks over a period of five years (2017-2022).

Short- and Long-Run Determinants of the Price Behavior of Us Clean Energy Stocks: A Dynamic Ardl Simulations Approach

Short- and Long-Run Determinants of the Price Behavior of Us Clean Energy Stocks: A Dynamic Ardl Simulations Approach

Oil Price and Stock Market Nexus in Nigeria: An Asymmetric Cointegration Based on Non-Linear Ardl Approach

This paper examined the asymmetric nexus between oil price and stock prices in Nigeria using nonlinear autoregressive distributed lag model (NARDL) to capture both long-run and short-run asymmetric relations. To achieve this, monthly time series data from 2014m1 to 2023m6 were gathered from the Central Bank of Nigeria (CBN) and the Nigeria Exchange Group (NGX). The estimation results confirmed the existence of both long-run and short-run asymmetry behaviour of stock prices in Nigeria. Precisely, in the long-run, interest rate, exchange rate and oil price increase tend to increase stock price levels in Nigeria. Similarity, in the short-run, only oil price increase seems to increase stock prices while oil price decrease seems to decrease stock prices in Nigeria. Furthermore, the empirical findings from asymmetric effect show that negative changes in exchange rate fell sharply due to decrease in oil price which further translate to stock price, suggesting the existence of feed backs from oil to stock markets. The paper recommends that investors should invest in Nigerian stock market in that a hike in oil price leads to appreciation in domestic currency, which translate to appreciation in stock price in Nigeria and vice versa. This suggests that investment in stock market can be used as a tool to ease the domestic currency pressure to a sustainable level. Hence, the need to maintain stable oil prices cannot be overstressed.

Does Financial Information Influence The Behavior of Stock Price? Evidences From Nepali Commercial Banks

Financial information is the document that presents the overall financial health of the organization. This study examines the influence of financial information on stock price behavior of Nepalese commercial banks. Twelve commercial banks are selected as a sample out of total twenty-six commercial banks of Nepal based on operation more than ten years with availability of necessary information. This study utilizes quantitative research approach with causal-comparative research design. Adopting descriptive statistics and multiple regression analysis for data analysis, study found that financial information like net interest margin, net profit margin, dividend payout ratio and capital adequacy ratio have significant positive influence on determining stock price behavior of Nepalese commercial banks. In addition, evidence indicates that financial information is the major influencing factor while determining stock price and investment decision so investors should properly analyze financial information for superior investment decision.

Factors Influencing Stock Price Variability of Commercial Banks in Nepal

This study investigates the impact of variables such as Dividends Per Share (DPS), Earnings Per Share (EPS), Price-Earnings Ratio (PER), and Net Worth Per Share on the Market Price Per Share of Nepalese commercial banks. The panel data (40 observations) of four commercial banks have been used to assess the association and impact of determinants of stock price behavior. In this study, secondary panel data covering ten years (2011/2012-2020/2021) has been used. According to the findings of this study, Earnings per share has a significant positive relationship with the Dividends Per Share and Price-Earnings Ratio but a negligible positive relationship with Market Price Per Share, Earnings Per Share, and Net Worth Per Share of commercial banks.

Abnormal trading volume, news and market efficiency: Evidence from the Jamaica Stock Exchange

This paper investigates market efficiency of the Jamaica Stock Exchange (JSE). Together, weak and semi-strong form efficiency claim that historical and newly released public information do not predict future stock price movement. We test both forms of market efficiency by analyzing stock price behavior during times of abnormal trading volume and around the release dates of earnings information. Abnormal trading volume may be driven by liquidity demand or reflect new or private information flow to the market. Using JSE data over the period 2000 to 2021, we find price dynamics consistent with price pressure as firms experience negative abnormal returns on the day of abnormal trading activity but offsetting positive abnormal stock returns on the following day. Further findings show post earnings announcement drift on the JSE. Taken as a whole, the evidence suggests violations of market efficiency and has implications for capital allocation in this emerging market.

Interpretable stock price forecasting model using genetic algorithm-machine learning regressions and best feature subset selection

Recent stock market studies adopting machine learning and deep learning techniques have achieved remarkable performances with convenient accessibility. However, machine learning and deep learning models are notorious for their black-box structure. To build human-friendly interpretability in stock price prediction, many studies focus on the relationship between input features and the outcome by measuring the feature importance. However, the feature-importance-based interpretability methods have such drawbacks as relative feature importance, vague importance of correlated features, and impractical interpretability. Furthermore, they overlook two principal characteristics of time series stock price data: time-dependency and collective behavior of features. As a solution to catch the collective behavior of features over a whole data period, we propose the best feature subset selection. Additionally, for the solution to reflect the time-dependent characteristic of stock price data over a short data period, we propose piecewise best feature subset selection. The proposed algorithm uses two separate input feature sets: internal technical indicators and external market prices. This bilateral forecasting scheme goes through a two-stage feature selection process composed of feature set expansion, hybridized genetic algorithm-machine learning regressions to select important features, and importance score filtering to select optimal features. Finally, the best feature subset is selected for forecasting and interpretation. The proposed method achieves the best feature subset of parsimoniously fewer features for interpretability and improves average forecasting Root Mean Squared Error by 10.42% for the optimal feature set and 13.47% for the best feature subset of the internal technical indicators. For enhanced local interpretability in this study, we use Savitzky–Golay smoothing as part of piecewise optimal curve fitting to examine each potential grouping of external features. The proposed local interpretability technique using piecewise optimal curve fitting and piecewise best feature subset provides a more timely-flexible interpretation of stock price behavior using a few best features for piecewise data segments. Compared with other feature-importance interpretability techniques that only rely on either a single data point or a whole data period, the proposed interpretability technique overcomes their limitations, and reflects the main characteristics of time series data.

Short-Sale Constraints and Stock Prices: Evidence from Implementation of Securities Refinancing Mechanism in Chinese Stock Markets

Qualified Securities for Short-sale Refinancing (QSSR) is a unique trading mechanism that has exogenously increased the supply of loanable securities in Chinese stock markets. Using difference-in-differences (DID) methodology, this paper is the first to investigate whether and to what extent additions to the QSSR eligibility list affect short selling activities and stock price behaviors. The paper finds that stocks added to the QSSR list exhibit better liquidity and less negative skewness in returns than non-QSSR stocks. However, QSSR stocks are more volatile and display a higher frequency of extreme negative returns. In addition, on average, QSSR stocks experience larger negative abnormal returns (ARs) and cumulative abnormal returns (CARs) relative to non-QSSR stocks, and the difference in CARs is positively related to investor heterogeneity. The results indicate that short selling has mixed effects on stock prices. Removing short-sale constraints can improve liquidity and reduce price bubbles, but can also increase return volatility and amplify market crashes.

Integrating Navier-Stokes equation and neoteric iForest-BorutaShap-Facebook’s prophet framework for stock market prediction: An application in Indian context

The paper presents a framework for forecasting stock prices for normal time and pandemic period and tests its forecasting efficacy. It is based on the specification of dependent and independent variables drawn from the Navier-Stokes equation, which defines the relationship between velocity and kinematic viscosity, density, pressure, and external force field. We define stock market proxies for the variables used in the equation in modeling stock price behavior and extend our framework to build in aspects of behavioral finance like hindsight bias, representative bias, and anchoring bias. The predictive exercise has been carried out on stock prices of Indian companies belonging to different industrial verticals to test whether our framework can handle diversity. AI framework comprising Isolation Forest (iForest), BorutaShap, and Facebook's Prophet Algorithm has been used for automatic anomaly elimination, dedicated feature screening, and forecasting future stock price movements. The results reveal interesting insights into the varied response of stock prices of different companies to macroeconomic shocks and would be quite useful for fund managers, regulators, and policymakers. Several numerical and statistical checks indicate the efficiency of our proposed framework in yielding superior quality forecasts, both in normal and challenging times.

Stock Market Prediction Using LSTM Technique

Abstract: One of the most intricate machine learning problems is the share value prediction. Stock market prediction is an activity in which investors need fast and accurate information to make effective decisions. Moreover, the behavior of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. Prices of stocks are depicted by time series data and neural networks are trained to learn the patterns from trends in the existing data. This system employed algorithm using LSTM to improve the accuracy of stock price prediction.

Resilience of Environmental and Social Stocks under Stress: Lessons from the COVID‐19 Pandemic

This paper examines whether environmental and social (ES) activities affect the resiliency of firms during the COVID‐19 crisis. We study a sample of 330 firms operating in five developed countries: Canada, France, Japan, the UK and the US. Our analysis shows that US firms with a high ES ranking experienced a significantly lower stock price range volatility during the Covid stock market rundown of February‐March 2020. Such findings also hold for Japanese firms but only later on after the introduction of government support. In terms of returns, compared to their peers with a low ES ranking, Japanese and UK stock prices with a high ES ranking suffered more during and after the market rundown. For other countries, we do not find significant differences in stock price behavior based on ES ratings. Our findings suggest that engaging with ES activities is not associated with a better or worse performance during crisis times, which has important implications for investors and managers.

Cost of Debt and Corporate Opacity

This paper expands upon the study of Bethani et. al (2019) regarding the association between firms’ cost of debt and their ownership structure. It is investigated whether corporate information opacity is associated with the level of cost of debt for family- owned firms. We examined a sample of companies listed to the Athens Stock Market at the period of 2017-2019. In line with the findings of Bethani et al. (2019) for the period 2009-2016 we found no significant association between corporate opacity and family ownership. Τhe cost of debt of the sample firms is negatively associated with their liquidity. In addition, we found that sample firms with certain stock price behavior and trading volume tend to finance their activities through the stock market than rather than using bank loans.

Stock Price Behaviour around the Announcement Date of Cash Dividend on the Indonesia Stock Exchange

This study aims to explain the stock prices behavior around the announcement date of cash dividends. The study compares the stock prices a week before the cumulative date, cumulative date, record date, a week after the record date, and two weeks after the record date. The stock prices are also compared to the cash dividends nominals in 2017 and cash dividends in 2018. The researcher obtains the research data from LQ45 companies in 2018. The data is tested using the Wilcoxon Signed-Ranks Test. The results show that the date of announcement and recording of dividends do not have an impact on the increase in stock prices, but the result shows a decline in stock prices. The results of this study imply that the announcement of cash dividends does not trigger stock buying action around the announcement date for the companies who distribute cash dividends.

Stock Price Prediction Using Random Forest Method and Twitter Sentiment Analysis

Stock price forecasting could be a vital and thriving topic in financial engineering especially since new techniques and approaches on this matter are gaining ground constantly. Within the contemporary era, the ceaseless use of social media has reached unprecedented levels, which has led to the belief that the expressed public sentiment could be correlated with the behaviour of stock prices. The concept is to acknowledge patterns which confirm this correlation and use them to predict the future behaviour of the assorted stock prices. With little doubt, though uninteresting individually, tweets can provide a satisfactory reflection of public sentiment when taken in aggregate. We develop a system which collects past tweets, processes them further, and examines the effectiveness of varied machine learning techniques like Naive Bayes Bernoulli classification and Support Vector Machine (SVM), for providing a positive or negative sentiment on the tweet corpus. Subsequently, we employ the identical machine learning algorithms to research how tweets correlate with exchange price behaviour. Finally, we examine our prediction's error by comparing our algorithm's outcome with next day's actual close price. Overall, the final word goal of this project is to forecast how the market will behave within the future via sentiment analysis on a collection of tweets over the past few days, also on examine if the idea of contrarian investing is applicable. the ultimate results seem to be promising as we found correlation between sentiment of tweets and stock prices.

Can direct government intervention save the stock market?

With the aim of restoring the investors’ confidence during the 2008 Global Financial Crisis, the Taiwan government bought 47 blue-chip stocks in the Taiwan Stock Exchange. Our study examines the effect of the government intervention across four periods, including the pre-event, event, holding, and selling periods, the first exploring the stock price behavior during the holding period in the literature. During the holding period, the cumulative average daily abnormal returns (CAAR) curves of the intervened stocks sharply rise while its market volatility begins to decrease. Next, the intervention effect does spread to the non-intervened stocks, and the shareholders of the non-intervened stocks do benefit almost as much as those of the intervened stocks. The policy implications of our results are that the government can save the stock market by setting the holding period in its measures and by concentrating on purchasing a limited number of blue-chip stocks.

Availability heuristic and reversals following large stock price changes: evidence from the FTSE 100

<abstract> <p>This paper examines if investors exhibited evidence of the availability heuristic in their investment decisions when significant price changes occurred in the British stock market during the 2010–2018 period. We raise the hypothesis that if a significant stock price move takes place on a day when the stock market index also undergoes a significant change (either positive or negative), then the magnitude of that shock may be increased by the availability of positive investment or negative outcomes. We applied three different proxies for large stock price changes which yielded a robust sample of events for this study. We found no significant evidence of the availability heuristic. In addition, we also found no significant evidence of price overreaction for both price decreases and increases. Inversely, we found robust results that suggest randomness in the behavior of stock prices in this period, thus supporting the efficiency of financial markets and opposing the results from similar studies carried out in the United States.</p> </abstract>

Analysis of Motives and Influences of East Money Convertible Bond Financing

Convertible bonds are fully known as convertible corporate bonds. Since the CSRC issued new financing rules in 2017, convertible bonds have developed rapidly without relevant restrictions. As an important investment and financing tool in the financial market, the orderly development of convertible bonds is very important to maintain financial stability. It is necessary to pay attention to its current situation and study the Countermeasures for its healthy development. On the basis of the existing research, it is found that the motivation of issuance includes easing the conflict of interest, reducing the cost of financing, optimizing the capital structure, etc., and it will have an impact on the company's stock price, equity dilution and predatory behavior of major shareholders; Combined with the case analysis, it is found that there are some problems in the current convertible bond issuance, such as the slow progress of convertible bond investment and education, the inadequate supervision of the issuance market, and the imperfect regulations on the termination of convertible bond transactions. Therefore, this paper puts forward some suggestions for investors, original shareholders and relevant regulatory framework, so as to promote the healthy development of convertible bonds.

Stock Price Behavior of Commercial Banks in Nepal

The purpose of this research is to examine the behavior of stock price in Nepalese commercial banks. This research used MPS as dependent variable and experiment variables as EPS, PE Ratio, DY ratio, Size, ROE, BV per share and ROA. The secondary data has been collected from the annual report published by commercial bank for five years’ period from 2015/16 to 2019/20. Descriptive and analytical research design used to analyze and interpret the data using SPSS version 23. Eighteen commercial banks are taken as sample out of 27 population using convenience sampling technique. Multiple linear regression model has used to show the impact of independent variables on MPS. The result indicates that there is a positive and statistically significant effect of EPS, PE ratio and size of bank son MPS. Other variables have negligible effects.