Stock Price Behavior Research Articles

10‐K complexity, analysts' forecasts, and price discovery in capital markets

AbstractThis paper examines the behaviour of stock prices and analysts' earnings forecasts following firms' increasingly complex 10‐K filings. Our evidence suggests that greater information uncertainty outweighs greater analyst ability, and the net effect drives greater underreaction in analysts' earnings forecasts following the release of more complex 10‐Ks. Further analysis suggests that analyst underreaction mitigates market overreaction to the information in low‐complexity 10‐K filings, and overwhelms market overreaction to the information in high‐complexity 10‐K filings thus compromising price discovery of the earnings implications of more complex 10‐Ks.

Testing of Semi Strong form of Market Efficiency of Indian Capital Market with Reference to Annual Dividend Announcement

The investment decision is influenced by many factors, of which one such factor is the return. The shareholders may get the return in the form of dividends which affects the share prices. The behavior of stock prices is unpredictable as price movement for different activities will move in different ways. The stock price influence activities can be divided into economic and corporate activities. The impact of economic activities will be more or less the same on all the stock prices, while the impact of corporate action varies from one stock to the other. Dividend payment is one of the essential corporate actions that will impact the behavior of stock prices. This research highlights the impact of dividend payment one the behavior of stock prices and their abnormal returns.

Using dynamic semantic structure of news flow to enhance financial forecasting: a twelve-year study on twitter news channels

AbstractThis research holds significance for advancing financial forecasting methodologies by shifting the focus from traditional sentiment analysis of individual tweets to exploring intricate semantic relationships within news tweets from top-followed news channels on Twitter. Addressing a notable research gap in financial forecasting, often dominated by sentiment analysis, our study endeavors to fill the void left by the underexplored intricate relationships within news entities and their dynamic semantic evolution. Motivated by the inherent challenges in predicting the random walk behavior of stock prices, we contend that incorporating longitudinal data derived from the semantic relationships between news entities can enhance the accuracy of stock market forecasts. The study pioneers a twelve-year exploration, encompassing data from 55 leading news channels on Twitter, boasting a collective following of 714 million users. The approach employs natural language processing (NLP) to extract two million unique entities, whose semantics are analyzed through complex network analysis, laying the foundation for the forecasting model. Finally, this research introduces a model linked to the dynamic semantic structure of news flow. The predictive model considers the impact of exogenous variables influenced by the evolving relationships among news entities. The results offer a proof of concept, highlighting the potential of utilizing dynamic semantic relationships among news entities for financial prediction. On average, the model demonstrates an improvement in accuracy of 40.3% across ten different stock price predictions. These findings are expounded through relevant theories, offering a theoretical foundation for observed patterns and indicating a promising direction for future research in this domain.

Corporate earnings, dividend payments, and stock price behavior of manufacturing firms listed in Nigeria

The purpose of the study is to investigate the effects of corporate earnings and dividend payments on the stock price behaviour of manufacturing firms in Nigeria between 2013 and 2022. The study adopted an ex-post facto research design, and deployed Generalised Method of Moments (sys-GMM) and Granger causality tests for the data analysis. The two-step system GMM is used to control for omitted variables such as bias, unobserved panel heterogeneity or heteroscedasticity, autocorrelation within panels or groups, endogeneity issues, and measurement errors in the data. Findings show that share price (SP) and its predictors yield valuable insights into the factors influencing stock valuation. Dividend per share (DPS) emerges as a significant positive predictor, suggesting a notable impact on share price. Firm size (FSZ) proves to be a statistically significant predictor, indicating that the size of a firm plays a role in influencing stock prices, with larger firms associated with a decrease in share price. Conversely, Earnings per Share (EPS) and Dividend Yield (DY3) do not exhibit statistical significance, suggesting limited predictive power in the model. By way of conclusion, the study provides an understanding of the factors influencing share price behavior within the Nigerian manufacturing sector and the positive impact of past performance, particularly lagged share prices, confirming the persistence of stock market trends. The practical implications of this study are that it underscores the understanding of financial dynamics in emerging economies, particularly in the context of manufacturing firms in Nigeria.

What drives the price behavior of US sustainable stocks?

PurposeThis paper aims to identify the key drivers of US sustainable stock price movements in both the short and long term, deploying a rich collection of variables corresponding to green finance, investor attention and sentiment, market fear and uncertainty, macroeconomic variables, common market risk factors, commodity markets and the carbon emission market.Design/methodology/approachThe empirical analysis is based on two main methodologies. First, the elastic net penalized regression is utilized to select the factors most influential on the price formation of sustainable stocks. Second, short- and long-run dynamics of the chosen factors are examined using the dynamic simulations of the autoregressive distributed lag (DYNARDL) model.FindingsOf 32 candidate variables, the elastic net chooses US renewable energy, European sustainable stock market, EU ETS emission allowances, public attention to sustainable finance, gold and European renewable energy as the most contributing factors to the price behavior of sustainable stocks. The DYNARDL estimation results reveal that US renewable energy, European sustainable stock market and EU ETS emission allowances are important determinants in the short and long term, while public attention (European renewable energy) tends to affect sustainable stock prices only in the short (long) run.Practical implicationsThe corresponding short- and long-run effects of US renewable energy, EU ETS emission allowances and European sustainable stocks on US sustainable stock prices should induce policymakers to keep the price behavior of these factors under systematic review. The formulation of policy measures could serve to safeguard the sustainable stock market from the price vagaries in these influential markets.Originality/valueRelevant literature often focuses on the reaction of sustainable stocks to mainstream assets and risk proxies, limiting analysis to a few factors and providing an incomplete understanding of the drivers behind sustainable stock prices. More comprehensive research is needed due to the lack of studies on the determinants of sustainable stock prices and the growing global demand for these investments. This paper aims to address this gap by examining the potential explanatory power of 32 candidate factors representing key players in the global economic and financial landscape.

Dilemma of the investment strategy: dividend cut-offs (retrospective analysis)

The author considers the results of a statistical analysis of changes in stock prices for 10 largest Russian companies in various industries over 12 years in connection with dividend payments. Statistical patterns of stock price behavior before and after the dividend cut-offs are revealed, which allows investors to build their dividend strategies and answer the “eternal” question of question of whether to sell or to buy shares before the dividend cut-off. A retrospective statistical analysis is carried out on 120 dividend payments.

Factors affecting Stock Price Behaviour of Commercial Banks in Nepal Stock Exchange

This research investigates how factors such as Dividends Per Share (DPS), Earnings Per Share (EPS), Price-Earnings Ratio (PER), and Net Worth Per Share influence the Market Price Per Share of commercial banks in Nepal. The study analyzed the panel data from four commercial banks, consisting of 40 observations, to evaluate the correlation and influence of factors affecting stock price movements. In this study, secondary panel data covering ten years (2070/2071-2079/2080) has been used. Based on the results of this investigation, there is a significant positive correlation between the Market Price Per Share and the Dividends Per Share, Price-Earnings Ratio, and Net Worth Per Share but a negligible effect of Market Price Per Share and Earnings Per Share of commercial banks.

Corporate Governance and Stock Return Volatility

This research highlights the importance of corporate governance and helps investors and financial economists understand stock price behaviour after COVID-19. The study aims to find out the influence of the independent board of commissioners, and audit quality on the volatility of share returns in companies operating in the body care product sector and listed in the EIB in 2021-2022. In addition, this study also employs four control variables to avoid the model specification error. The population in this study is a company of body care products listed on the Indonesian Stock Exchange for the year 2021-202. The number of samples used is as many as eight companies in 2021-2022 based on purposive sampling methods that meet the sample selection criteria. The data analytical techniques used are descriptive statistical testing, classical assumption testing, and hypothetical testing using SPSS software version 25 and GRETL. The research results show that the independent board of commissioners and the audit quality influence share return volatility. As a sector directly related to daily consumer needs, the primary consumer goods sector tends to be highly responsive to changing trends and consumer preferences. It makes it interesting research subject to understand how the volatility of share returns in this sector reacts to external and internal factors.

The impact of terrorism on stock price behavior: An event study of a sample of tourism companies listed on the Iraq Stock Exchange

تهدف هذه الدراسة الى بيان تأثير الإعلان عن توزيع الأرباح في ضل لا تماثل المعلومات وفي ضل ظاهرة الإرهاب لغرض التنبؤ بأسعار الأسهم للشركات المدرجة في سوق العراق للأوراق المالية، تَّم تطبيق الدراسة على عينة من مختلف قطاعات السوق مع مراعاة التنوع في القطاعات شملت (1) شركة سد الموصل استوفت شروط الدراسة والتي حَددت الشركات التي وزعت أرباح لسنتين متتاليين (2014 -2015)، وتَّم استعمال أُسلوب دراسة الحدث بنافذة حدث (40) يوم بمدة (20) قبل وبعد الحدث لقياس لا تماثل المعلومات، إذ تم اعتماد أسلوب التنبؤ للتّعرف على اّلاثار المستقبلية لقرار مقسوم الارباح على قرارات المستثمرين في ضل ظروف عدم الاستقرار ، إضافة الى استعمال أسلوبين احصائيين لاختبار فرضيات الدراسة هما أسلوب تحليل الانحدار وأسلوب السيناريوهات وتوصلت الدراسة الى مجموعة من الاستنتاجات أهمها امكانية تحقيق عوائد غير عادية بالاعتماد على المحتوى المعلوماتي لمقسوم الارباح كما أن هنالك تأثيرًا ذي دلالة احصائية لقرار مقسوم الأرباح في لا تماثل المعلومات ويساهم أسلوب السيناريو في التنبؤ بأسعار الأسهم بشكل افضل من الأسلوب التقليدي، ومن اهم التوصيات التي توصلت اليها الدراسة ضرورة اعتماد الأساليب العلمية لقياس تأثير الأحداث الإرهابية المؤثرة الخاصة بالسوق على دقّة النتائج المالية ولاسيمّا استخدام نماذج رياضية لقياس تأثير الأحداث الخاصة بالسوق على أسعار الأسهم كذلك يٌفضل اعتماد أُسلوب السيناريو في التنبؤ بأسعار الأسهم والأداء المالي واعتماده كنموذج يقدم خيارات متعددة أمام القرارات المالية فضلاً عن عدم الاكتفاء بالعائد غير العادي كأداة واحدة فقط لاتخاذ قرارات الاستثمار بل يجب أن يؤخذ بنظر الاعتبار العوامل الأخرى مثل المخاطرة.

Renewable Energy Stocks’ Performance and Climate Risk: An Empirical Analysis

This article studies the relationship between renewable energy stocks’ performance and climate risk. It shows that publicly held renewable energy stocks underperform as a reaction to climate policy information releases, modeled by feed-in tariff (FIT) legislation announcements. The study examined stock price behaviors 2 days before and 30 days after FIT policy announcements. The stock sample used in the study has 3702 firm-day combinations, which included 180 cleantech firms and 32 events from 2007 to 2017. Based on the residual analysis of the sample’s abnormal return, it indicated that the FIT announcements are associated with significant declines in returns. The cumulative abnormal return until Day 18 was a significant −0.83%, while the average abnormal return on the day was −0.16% at normal levels. The study partially excluded the likelihood of a transitory result by varying the measurement horizon. It also adopted both the market model and the Fama–French three-factor models to rule out model misspecification when estimating abnormal returns and thus increased the robustness. In fact, the results were stable to changes in estimating the model’s specifications. In addition, the study compared the portfolio’s performance with mimicking portfolios in terms of size, book-to-market equity (BE/ME), and the firms’ geographic location. It demonstrated that the documented anomaly of the portfolio of renewable energy companies is robust.

Effects of Inflation, Ten-Year Bond Yield Rate, and VIX Index on the Stock Prices of Banks Across All Three Market Capitalizations in India

This study investigates the impact of critical economic factors—namely, inflation, the 10-year bond yield rate, and the VIX index—on the stock prices of banks operating across different market capitalization segments in India. Through a comprehensive regression analysis framework, this research quantifies the relationships between these economic factors and bank stock prices while accounting for potential variances across large-cap, mid-cap, and small-cap banks. Utilizing data from the past five years, this analysis not only provides a nuanced understanding of how these macroeconomic indicators influence bank stock prices but also explores the specific effects on banks of varying market capitalizations. The findings reveal that small-cap companies are predominantly influenced by internal management decisions and capital allocation, whereas the consumer price index significantly predicts and reflects stock price behavior. Conversely, the bond yield rate and VIX index show minimal impact on stock prices. This study offers valuable insights for investors, policymakers, and financial institutions, aiding in the development of informed investment strategies and risk management practices.

STOCK MARKET PRICE PREDICTION USING MACHINE LEARNING TECHNIQUES

Predicting stock market prices is a challenging task in the financial sector, where the Efficient Market Hypothesis (EMH) posits the impossibility of accurate prediction due to the inherent uncertainty and complexity of stock price behaviour. However, introducing Machine Learning algorithms has shown the feasibility of stock market price forecasting. This study employs advanced Machine Learning models that can predict stock price movements with the right level of accuracy if the correct parameter tuning and appropriate predictor models are developed. In this research work, the LSTM model, which is a type of Recurrent Neural Network (RNN), time series forecasting Facebook Prophet algorithm and Random Forest Regressor model have been implemented on 10 Dhaka Stock Market (DSEbd) listed companies and six international giants for predicting the stock and forecasting the future price. The dataset of domestic companies is extracted from the graphical representation of the DSEbd website, and the international companies' dataset is imported from Yahoo Finance. In this experiment, Facebook Prophet demonstrates a long period of forecasting with reasonable accuracy, capturing daily, weekly, and yearly seasonality, including holiday effects for market trend analysis. Remarkably, the LSTM model exhibits significant accuracy, yielding the best results with evaluation metrics, including RMSE (0.35), MAPE (0.50%), and MAE (0.30). The experimental results underscore the efficiency of LSTM for future stock forecasting, observed over 15 days of upcoming market prices. A comparison of the results shows that the LSTM model efficiently forecasts the next day's closing price.

Exploring the Asymmetric Price Behavior of Health Care and Pharma Sector Stocks of NSE in the Pandemic Period

Exploring the Asymmetric Price Behavior of Health Care and Pharma Sector Stocks of NSE in the Pandemic Period

Stock Prediction Using Machine Learning Methods

The volatility and non-linear nature of the financial stock markets make it incredibly difficult to estimate stock market returns efficiently.. Investors need rapid access to precise information while trading stocks to make intelligent selections. Programable prediction approaches have demonstrated to be increasingly successful in forecasting stock prices with the introduction of artificial intelligence and better computing capacity. However, several variables impact the decision- making process as a stock market trades multiple stocks. Furthermore, it is impossible to forecast the behavior of stock prices. All of these elements make stock price prediction, both vital and tricky. This drives research into the most accurate prediction model that creates the fewest mistakes in its projections. This research studies machine learning approaches and algorithms in an effort to increase the accuracy of stock price prediction. Keywords— Machine Learning, Linear Regression, LSTM, SVM, Decision Tree, Random Forest

Can a firm's hierarchical complexity affect its stock price behavior? Evidence from stock price crash risk

PurposeThis paper examines how a firm's hierarchical complexity, which is determined by the way it organizes its subsidiaries across the hierarchical levels, can impact its stock price crash risk.Design/methodology/approachThe authors employ a measure of hierarchical complexity that captures the depth and breadth of how subsidiaries are organized within a firm. This measure is calculated using information about firms' subsidiaries extracted from the Bureau van Dijk (BvD) database that allows the authors to construct each firm's hierarchical structure. The data sample includes 2,461 USA firms for the period from 2012 to 2017 (11,006 firm-year observations). Univariate tests and panel regression are used for the main analysis. Two-stage-least-squares (2SLS) instrumental variable regression and various other tests are employed for robustness check.FindingsThe results show a positive relationship between hierarchical complexity and stock price crash risk. This relationship is amplified in firms with a greater number of subsidiaries that are hierarchically distanced from the parent company as well as in firms with a greater number of foreign subsidiaries in countries with weaker rule of law.Originality/valueThis paper is the first to investigate the impact hierarchical complexity has on crash risk. The results highlight the role that a firm's organizational structure can have on asset pricing behavior.

Stock Returns Predictability Using Financial Ratios in Malaysia

Stock return predictability is important to maintain confidence and liquidity in a stock market. The conventional theory on stock price behaviour, the Efficient Market Hypothesis, posits that stock return predictability is not possible. This study considers the Adaptive Market Hypothesis to explain the predictability of stock returns in Malaysia using financial ratios as predictor variables. The Adaptive Market Hypothesis is gaining prominence to explain stock behaviour. However, studies have been mixed and limited. This study contributes to the empirical evidence on the relationship of financial ratios to return of stock in emerging markets, specifically Malaysia. Purposive sampling is employed to select the listed companies from the Malaysia Stock Exchange, which are then filtered based on three criteria to arrive at a sample of 392 companies. For predictor variables, seven financial ratios have been identified for their predictive strength to stock returns. This study employs panel data to formulate the multiple predictive regression. The regression model determined is the fixed effects regression with robust standard error. Results show that the relationship to stock returns is significant for the predictor variables of earnings yield, dividend yield, book-to-market ratio, return on assets, current ratio and assets turnover. The relationship between debt-to-equity ratio and stock returns is not significant. The findings show that financial ratios have predictive strength to stock returns in the Malaysia market. To improve on the explanatory strength, it is suggested for future research to incorporate external factors such as GDP, inflation or other economic factors such as interest and exchange rates.

Effectiveness of price limits: Evidence from China's ChiNext market.

Starting from August 24, 2020, the daily stock price limits in China's ChiNext market have been adjusted from 10% to 20%. We use this reform to study the effectiveness of price limits in China's stock market. We test four hypotheses about price limits: delayed price discovery, volatility spillover, trading interference, and magnet effect. Using the event study method, we examine the differences in the behavior of stock price, trading volume, and volatility before and after the reform. We confirm the delayed price discovery, volatility spillover and trading interference hypothesis of price limits, and find that these negative effects of price limits are more serious when lower limits are hit. In addition, we examine the distribution of large price movements before and after the reform and find no evidence of the magnet effect of price limits. The present research has important implications for policymakers and investors in China's stock market.

Short- and long-run determinants of the price behavior of US clean energy stocks: A dynamic ARDL simulations approach

This paper aims to identify factors that drive US clean-energy stock price movements in the short and long term, using a wide range of variables representing the carbon emission market, non-green financial assets, non-renewable commodities, macroeconomic fundamentals, investor attention and sentiment, and global stress and uncertainty. The empirical investigation is carried out in the context of the elastic-net regularization (ENET) approach and dynamic simulations of the autoregressive distributed lag (DYNARDL) model, with proper consideration to the potential presence of structural changes. From among 26 candidate variables, the ENET selects the clean technology market, public attention to clean energy, oil, and gold as the primary factors contributing the most to the behavior of clean-energy stock prices. The estimation results of the DYNARDL model suggest that the clean technology market and oil are vital determinants in the short and long run, while public attention and gold tend to affect clean-energy stock prices only in the short run. Furthermore, the respective magnitudes of influence of the four variables are larger in the short term than in the long term. Our findings offer practical implications for socially responsible investors and policymakers.

The impact of religious announcements on stock prices and investment decisions on the Saudi stock exchange

Although many papers have examined the Saudi stock market, to date none have explored the influence of religiosity on the behavior of stock prices, taking into account the dominance of individual investors. These characteristics distinguish the Saudi stock market from other mature and immature markets. Therefore, we study the reaction of the Saudi stock market to announcements by imams regarding firms' sharia compliance, using a two-step procedure. First, we distribute a questionnaire to gauge individual opinions about imams' announcements. Then, we conduct an appropriate event-study to capture the potential abnormal returns. Our findings indicate that imams’ announcements convey valuable information to the stock market.

A Numerical Approximation on Black-Scholes Equation of Option Pricing

This paper considered the notion of European option which is geared towards solving analytical and numerical solutions. In particular, we examined the Black-Scholes closed form solution and modified Black-Scholes (MBS) partial differential equation using Crank-Nicolson finite difference method. These partial differential equations were approximated to obtain Call and Put option prices. The explicit price of both options is found accordingly. The numerical solutions were compared to the closed form prices of Black-Scholes formula. More so, comparisons of other parameters were discussed for the purpose of investment plans. The computational results shows: increase in stock volatility increases the value of options, when the initial stock price is equal to its strike price the values of call option is higher than the put option. This informs the investor about the behavior of stock prices for the purpose of decision making. Finally, all simulation results presented graphically using MATLAB.