Stock Price Behavior Research Articles

The Behaviour of Stock Prices of Sri Lankan Hotel and Travel Sector Companies: A comparison of Civil War and Easter Attack 2019

The Behaviour of Stock Prices of Sri Lankan Hotel and Travel Sector Companies: A comparison of Civil War and Easter Attack 2019

Forests for Fama

In this article, the author addresses Eugene Fama’s skepticism regarding the predictability of stock market bubbles. To do so, he applies two ensemble learning methods, the random cut forest and random forest algorithms, to build a model that predicts large near-term drawdowns based on patterns in stock price behavior. The model includes three predictive variables. The first factor is an anomaly score produced by random cut forest, an algorithm specifically designed to detect outliers in streaming data. The second and third factors are the standard deviation of price returns and the return convexity over specified time horizons, with return convexity defined as the difference between one-year price returns and six-month price returns. The author’s predictions are based on random forest regressions. He applies the model to a large universe of equity sectors and factors. Blocked time-series cross-validation is used to evaluate the predictive efficacy of the model. The author shows that across the sectors and factors considered, the model presented produces predictive scores that are strongly positive. Although bubble prediction is surely a multidimensional endeavor that requires input from a variety of tools and sources, the author demonstrates that a framework built upon ensemble methods can be informationally additive to the detection of bubblelike behavior across a wide array of stocks.

Price Reversals After Extreme Price Shocks: Impact of Earnings Information With Time Series Evidence From Emerging Market

The purpose of the article is to analyze the relevance of earnings fundamentals in predicting extreme price reversals of an emerging stock market. We collect monthly price data on six sector indices from Bombay Stock Exchange (BSE) of India for the period 2004–2019. The research decomposes industry stock returns into Potential Maximum Gains (PMG) and Potential Maximum Losses (PML) with price extremes at first and then tests price reversal behavior using Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) and vector autoregressive (VAR) models. The study finds symmetry between PMG and PML in the banking, realty, and oil sectors, while the asymmetric reversal behavior is noted in the automobiles and capital goods industries. The presence of industry fundamentals in the models estimating the reversal behavior of share prices enhances their predictive power, which suggests the significance of value strategies in making gains from extreme price variations. The price reversal behavior is sector specific and found inconsistent in emerging market. Hence, the investors cannot overlook the relevance of the industry characteristics and earnings fundamentals while predicting the stock price behavior in emerging markets.

Predicting Stock Indices Trends using Neuro-fuzzy systems in COVID-19

Predicting the ebb and flow of stock markets is a complex and challenging exercise owing to the disruptive and uncertain behavior of stock prices. The COVID-19 pandemic is an example of an event that, had a drastic impact on global stock markets, due to business activities and trading being severely affected. It is important, therefore, to be able to predict how stock markets behave in a crisis period. We find that stock markets obtain the worst returns in countries where there are higher reported positive cases of coronavirus. This study employs adaptive neuro-fuzzy inference systems (ANFIS), comprising of a controller and the stock market process, to predict the behavior of selected stock indices. After training ANFIS and evaluating the resultant data, we estimate statistical errors and found that 100 training epochs provide marginally better results. To test the accuracy of our results, we used hit rate success and report that the neuro-fuzzy system predicts stock market trends with an average accuracy of 65.84%, an improvement over earlier techniques reported in the literature. Finally, we compute the rate of return using a buy-and-hold strategy and a neuro-fuzzy system, and identify that market indices outperform by employing the proposed method

Technical Analysis as a Tool to Predict Stock Behaviour - A Study of BSE FMCG Stocks

Stock market is a place which decides the liquidity of a security by offering trading facilities. The price behaviour of stocks reflects the movements in their respective sector or company. The technical analysis, as a tool, offers insight into the price movements of these securities and tries to forecast the future price trends by using various tool like RSI, MACD, EMA etc. These trends can be for short or long term. The paper attempts to analyze and forecast the price movements of certain FMCG sector companies which are a part of Sensex with the help of various technical analysis tools. The paper also attempts to take an insight into the power of these tools to predict the stock prices under various conditions.

Evidence-based Stock Price Behaviour Around Cash Dividend Announcements in an Emerging Market Set-up

This article examines how stock prices behave following cash dividend announcements in the Indian equity market considering 3,671 cash dividend announcements during the period 2012–2019, using both aggregate-level and firm-level analysis. Further, the study investigates the factors that lead to the generation of abnormal gain or loss following dividend announcements. We found that cash dividend announcements generate abnormal stock returns in the Indian equity market immediately after the dividend announcement. However, this impact persists for a very short spell, and after that, the stock prices normalize gradually. Results also show that firms with a higher promoter shareholding gain less on dividend announcements. The empirical evidence of this study thinly supports the information signalling theory and dividend clientele theory in the Indian market.

January anomalies on CEE stock markets

Numerous studies show that stock markets are often impacted by various calendar anomalies that disrupt the “random walk” behavior of stock prices. These anomalies contradict the Efficient markets theory and can be exploited to generate abnormal returns. This paper investigates the presence of two of them, namely the January effect and the January barometer, on the stock markets of 12 Central and Eastern European (CEE) countries. The paper examines the statistical significance of differences in returns recorded over the month of January and returns recorded over the other months (the January effect), as well as the statistical significance of differences between returns recorded during the remainder of year after a positive January return and after a negative January return (the January barometer). The results show, among other things, that the statistically significant January effect affects the Estonian, Lithuanian, Czech, Romanian, and Latvian stock markets. On the Romanian and Lithuanian stock markets, statistically significantly higher January returns are accompanied by statistically significantly higher January price volatility. On the other hand, we can speak of a statistically significant January barometer only in the case of the Latvian, Lithuanian, and Ukrainian stock markets. The presence of these anomalies is contrary to the Efficient market theory. It can be assumed that proper investment strategies based on these calendar anomalies should be able to generate abnormal returns. AcknowledgmentThis paper is an outcome of the science projects VEGA (1/0613/18) and VEGA (1/0221/21).

Evolutionary Framework with Bidirectional Long Short-Term Memory Network for Stock Price Prediction

As an important part of the social economy, stock market plays an important role in economic development, and accurate prediction of stock price is important as it can lower the risk of investment decision-making. However, the task of predicting future stock price is very difficult. This difficulty arises from stocks with nonstationary behavior and without any explicit form. In this paper, we propose a novel bidirectional Long Short-Term Memory Network (BiLSTM) framework called evolutionary BiLSTM (EBiLSTM) for the prediction of stock price. In the framework, three independent BiLSTMs correspond to different objective functions and act as mutation individuals, then their respective losses for evolution are calculated, and finally, the optimal objective function is identified by the minimum of loss. Since BiLSTM is effective in the prediction of time series and the evolutionary framework can get an optimal solution for multiple objectives, their combination well adapts to the nonstationary behavior of stock prices. Experiments on several stock market indexes demonstrate that EBiLSTM can achieve better prediction performance than others without the evolutionary operator.

Power Distance, Political Uncertainty, and Stock Price Crash Risk: International Evidence

This study tests the effect of unbalanced power distance (PD) (i.e., Hofstede’s cultural dimensions PD index) and individual stock price crash risk. We examine the stock price behavior of listed firms in 37 countries from 2004 to 2016 and use multivariate analyses to document that societal PD is important in explaining firms’ propensity to release accounting information. This propensity suggests a psychological tendency regarding timing management, particularly for bad news. As countries with large PD prefer to keep things under control, the result is fewer unexpected stock price crashes during the long windows between election events. However, because large-PD countries focus their markets on maintaining temporary peace before and during periods of political events (i.e., national elections), crash risk increases after the political event window. Consistent with these predictions, we find that in large-PD countries, companies generally have less incentive to hide negative information and thus generate stock price crashes. This situation is substantially changed during the postpolitical windows, when firms and ways of spreading information are more controlled by the government. Our findings suggest that formal mechanisms alone are insufficient to explain the behaviors of corporate disclosure that are entangled with informal instruments.

External macroeconomic shocks and stock price behavior in Nigeria

This research work investigates the relationship between external macroeconomic shocks and stock price behavior in Nigeria. Variables such as exchange rate (EXR), US real interest rate (USRINTR), and world oil price (WOP) are adopted to capture external macroeconomic shocks while all share price index is used to proxy stock price. The research work uses Johansen cointegration and structural vector autoregressive model as the estimation method. Findings from the study confirm that no long-term co-movement exists between the stock price and the selected external shocks. Findings from the study equally show that both US real interest rate (USRINTR) and world oil price (WOP) are the major external shock predictors of the stock price in Nigeria.

Borsa İstanbul 30 Endeksinde Yer Alan Hisse Senetlerinin Kümelenmesi: COVID-19 Öncesi ve COVID-19 Dönemi İncelemesi (Clustering of Stocks Included in Borsa Istanbul 30 Index: A Pre-COVID-19 and COVID-19 Period Analysis)

Purpose – This study is based on the price behavior of stocks included in the Borsa Istanbul 30 (BIST 30) index over many years. With the help of the variables used in the research, it is aimed to determine in which cluster the stocks in the Borsa Istanbul 30 index are included with the Canopy Clustering Algorithm. Design/Methodology/Approach – In this study, clustering was performed with the Canopy Algorithm, and then it was examined whether there was a significant difference on the basis of variables with the Wilcoxon and Paired Sample t test. The stocks included in the study consist of stocks included in the BIST 30 index as of 07.12.2020. The data set used in the study covers the period of 2012-2020 and each stock's daily; closing, beta coefficient, volatility, volume, market value/book value (PD/DD) variables. In this context, the data set was included in the analysis in two groups, pre-COVID-19 pandemic and COVID-19 period. Findings – It was seen that the Canopy Algorithm revealed significant results in the clustering analysis and when the clustering results were examined, 19 of the 30 stocks were in the same cluster both before and after the pandemic. The effects of variables on the formation of clusters were examined and it was concluded that the variables were the right choices for cluster analysis. Discussion – Investment instruments have different risk-return relationships. Investors who want to invest in stocks should have information about the behavior of stocks while diversifying their portfolio. In other words, it is not thought that it would be beneficial to include stocks that move together in decreases and increases in the same portfolio in terms of reducing the total risk of the portfolio. At this point, getting to know the stocks will provide various benefits to the investors.

Behavior of stock prices due to the lock-up period expiration in IPOs and follow-ons

ABSTRACT The objective of this study was to verify the effects of the lock-up expiration on the behavior of prices and volumes in IPOs and follow-ons in the Brazilian market and to identify factors that may explain the existence and magnitude of abnormal returns. Few studies were found to investigate this phenomenon in Brazil, which were limited to the analysis of IPOs without examining the effect on follow-ons and the construction of abnormal accumulated returns compared to the Ibovespa, instead of benchmarks appropriate to each stock's risk. Lock-up clauses exist to mitigate the problem of information asymmetry in public offers but expose investors to the risk of a price drop after its expiration. Understanding the magnitude of this impact is essential for investors in the stock market. Through this article's analysis, investors will be able to estimate the magnitude of the price variation around the lock-up expiration, what factors explain the returns, and whether there are indications of short selling limitations. The event study method was applied, comparing returns to the Ibovespa and an individual reference portfolio composed of similar companies. Database: 313 offers that occurred on the Brazilian stock market between 2004 and 2019. Evidence of volume increase was found around the expiry of lock-up in IPOs, but the price drop was verified only in companies with private equity funds as shareholders. In follow-ons, in which the asymmetry of information about the issuer is less pronounced, the opposite situation was verified. There are several extensions and lock-up formats worldwide, which provide different impacts on volume and price. This article contributes to the literature when analyzing this event in Brazil and extending the analysis to follow-ons. A possible interpretation for the phenomenon is the restrictions on short-selling in the Brazilian market.

Asian stock markets, a decade after global recession, coupled or decoupled?

AbstractThis paper examines the dynamics of price integration among Asian financial markets during the postfinancial crisis period. We employ the autoregressive distributed lag (ARDL) bounds test to analyze the long‐run relationships, and the nonlinear ARDL (NARDL) test to explore asymmetries in the stock price behavior. The cointegration results could not produce any conclusive evidence of long‐run relations between stock markets. The estimated NARDL model affirms the presence of asymmetries in the stock price behavior. Namely, in the long run, we find a significant relation among stock price decreases of some markets while the long‐run relations among price increases are almost absent. Furthermore, in the short run, only negative changes in the stock price exert significant influences on the price performance of peers. This study improves the previous research in the area by testing the asymmetric effects of stock price integration on portfolio optimizations for global investors.

Monetary policy surprises, stock returns, and financial and liquidity constraints, in an exchange rate monetary policy system

This study examines the impact of monetary policy surprises on the stock price behaviour of a small developed economy, whose monetary policy is based on the exchange rate. We find that monetary policy surprises associated with all contractionary policy levers and a neutral policy lever, have a consistently significant and negative impact on stock returns. In comparison, only monetary policy surprises associated with a downward re-centering policy lever, has a significantly positive effect on stock returns. Using a recalibrated classification system, we also find that monetary policy surprises differ across sectors of the economy. Our results show how monetary policy surprises can have a significant impact on the stock market by having a disproportionate effect on sectors that face financial and liquidity constraints.

Impact of Technical Parameters for Short- and Long-term Analysis of Stock Behavior

Stock price forecasting is a type of time series problem that forecasts the future price or status of a company on the basis of analysis of time respective values. As the price of stock or company varies with respect to time, its behavior can be analyzed by different machine learning approaches. In this work, methodology is proposed to evaluate the stock position with variation in time using deep learning approach such as recurrent neural network (RNN). This methodology used the technical parameters to evaluate the long term and short-term analysis of any stock or share. This approach also evaluates and gives suggestions to investors either to buy or sell any stock for long term and gives return at very low risk. In this paper work, hybridization of co-relation analysis and deep learning approach for stock price and long-term behavior analysis. The proposed work is termed as time lagged weight optimized RNN (TL-WO-RNN) that is adopted in this work and effectively predict the technical parameters and on the basis of that stock behavior is also predicted. The result analysis was performed on data from different sectors and such as Telecom, Powers, Manufacturing, Finance, Software sectors, etc. The result analysis shows the effectiveness of the TL-WO-RNN algorithm as compared to existing work.

Online discriminative graph learning from multi-class smooth signals

Graph signal processing (GSP) is a key tool for satisfying the growing demand for information processing over networks. However, the success of GSP in downstream learning and inference tasks such as supervised classification is heavily dependent on the prior identification of the relational structures. Graphs are natural descriptors of the relationships between entities of complex environments. The underlying graph is not readily detectable in many cases and one has to infer the topology from the observed signals which admit certain regularity over the sought representation. Assuming that signals are smooth over the latent class-specific graph is the notion we build upon. Firstly, we address the problem of graph signal classification by proposing a novel framework for discriminative graph learning. To learn discriminative graphs, we invoke the assumption that signals belonging to each class are smooth with respect to the corresponding graph while maintaining non-smoothness with respect to the graphs corresponding to other classes. Discriminative features are extracted via graph Fourier transform (GFT) of the learned representations and used in downstream learning tasks. Secondly, we extend our work to tackle increasingly dynamic environments and real-time topology inference. To this end, we adopt recent advances of GSP and time-varying convex optimization. We develop a proximal gradient (PG) method which can be adapted to situations where the data are acquired on-the-fly. Beyond discrimination, this is the first work that addresses the problem of dynamic graph learning from smooth signals where the sought network alters slowly. The introduced online framework is guaranteed to track the optimal time-varying batch solution under mild technical conditions. The validation of the proposed frameworks is comprehensively investigated using both synthetic and real data. The proposed classification pipeline outperforms the-state-of-the-art methods when applied to the problem of emotion classification based on electroencephalogram (EEG) data. We also perform network-based analysis of epileptic seizures using electrocorticography (ECoG) records. Moreover, by applying our method to financial data, our approach infers the relationships between the stock-price behavior of leading US companies and the recent events including the COVID-19 pandemic.

Stock Market Behavior after Large Price Changes and Winner-Loser Effect

This study examines the behavior of stock prices following large price changes. It also examines the presence of disposition prone investors and effect of size on stock returns. The study uses daily prices data from Pakistan Stock Exchange (Formerly, Karachi Stock Exchange (KSE)) for the period of January 2001 to July 2012. Our findings suggest that winners perform better than losers after experiencing large price shocks thus showing a momentum behavior. This also suggests that most of the investors in KSE behave rationally and we do not find any evidence of disposition effect in KSE. Further, pooled regression estimates show that size is positively related to post-event abnormal returns. The fixed effect model shows the presence of unobservable firm-specific and time specific effects that account for price continuation.

Corporate Green Bond and Stock Price Reaction

The aim of this paper is to investigate the investors’ reaction to environmental actions taken by companies such as the issues of “green bond”. We conduct an event study around the announcement of green bond issuances for all publicly traded companies in the World in the period 2013-2019 (the largest period in literature on this field). Using CARs, we investigate the stock price behavior to green bond issues for 414 listed companies and we demonstrated significant stock price increases around the announcement date of first-time green bond issues. For second issues, the positive stock price reaction to eco-friendly initiatives decreases while it completely disappears for the subsequent issues. From the management perspective, green bond issue seems an eco-friendly action with decreasing marginal benefits, because after the first issue, the market is already aware about the firm’s commitment to green projects.

Impact of Mergers and Acquisitions on the stock price behavior of Merger and Acquirer Companies in Oil and Gas Industry

Impact of Mergers and Acquisitions on the stock price behavior of Merger and Acquirer Companies in Oil and Gas Industry

Simulating Stochastic Diffusion Processes and Processes with “Market” Time

The paper focuses on modelling, simulation techniques and numerical methods concerned stochastic processes in subject such as financial mathematics and financial engineering. The main result of this work is simulation of a stochastic process with new market active time using Monte Carlo techniques.The processes with market time is a new vision of how stock price behavior can be modeled so that the nature of the process is more real. The iterative scheme for computer modelling of this process was proposed.It includes the modeling of diffusion processes with a given marginal inverse gamma distribution. Graphs of simulation of the Ornstein-Uhlenbeck random walk for different parameters, a simulation of the diffusion process with a gamma-inverse distribution and simulation of the process with market active time are presented.To simulate stochastic processes, an iterative scheme was used:
 xk+1 = xk + a(xk, tk) ∆t + b(xk, tk) √ (∆t) εk,,
 where εk each time a new generation with a normal random number distribution.Next, the tools of programming languages for generating random numbers (evenly distributed, normally distributed) are investigated. Simulation (simulation) of stochastic diffusion processes is carried out; calculation errors and acceleration of convergence are calculated, Euler and Milstein schemes. At the next stage, diffusion processes with a given distribution function, namely with an inverse gamma distribution, were modelled. The final stage was the modelling of stock prices with a new "market" time, the growth of which is a diffusion process with inverse gamma distribution. In the proposed iterative scheme of stock prices, we use the modelling of market time gains as diffusion processes with a given marginal gamma-inverse distribution.The errors of calculations are evaluated using the Milstein scheme. The programmed model can be used to predict future values of time series and for option pricing.