Bear Market Research Articles

How Does Climate Change Risks Affect the Connectedness Between the Stocks of Clean Energy, Electric Vehicles, and Rare Earths? Analysis for Bear, Bull, and Normal Market Conditions

How Does Climate Change Risks Affect the Connectedness Between the Stocks of Clean Energy, Electric Vehicles, and Rare Earths? Analysis for Bear, Bull, and Normal Market Conditions

The Performance of Pakistani Equity Mutual Funds During Bull and Bear Market

This study examines equity mutual funds in Pakistan within bull and bear market contexts.It traces the evolution and significance of mutual funds, particularly their adoption by investors for retirement and financial goals. Despite a substantial presence of open-ended and closed-ended mutual funds, Pakistan's mutual fund industry remains comparatively small on a global scale. Equity mutual funds, focused on stocks, appeal to young investors seeking higher returns with moderate risk. These funds enable risk diversification and effective portfolio management by experienced professionals. While small investors benefit from equity funds, their performance is subject to public scrutiny due to robust regulatory oversight. This research sheds light on equity mutual funds' role in Pakistan's financial landscape, emphasizing their importance in providing accessible and diversified investment options for a range of investors.

An Empirical Investigation of Herd Behaviour in Surplus Economy: Evidences from Muscat Stock Market

This study investigates herd behaviour in the surplus economy of the Sultanate of Oman under Bear and Bull market conditions. The first aim of the study is to determine if the investors at Muscat Stock Market (MSM) exhibit herd behavior or not. The second aim of the study is to compare the different stochastic time-series models commonly used to test and forecast herd behavior. Finally, the study aims to determine whether herd behavior in the MSM is predominantly risk-driven as measured by Cross-Sectional Standard Deviation (CSSD) or return-driven as measured by Cross-Sectional Absolute Deviation (CSAD) of stock returns from the overall market return. Our analyses are based on daily Muscat Stock Market’s (MSM) index returns for the period starting 1st of January 2010 and ending 31^st of December 2019, the pre Covide-19 pandemic. Our findings disclose: (1) that MSM exhibits herd behavior, (2) that there are statistically significant among the different stochastic time-series models used to test and forecast herd behavior in MSM, where ARIMA (1,0,0) model exhibits the highest predictive power of herd behavior in MSM (3), that MSM’s herd behavior is driven more by CSSD than CSAD in the bearish and bullish market conditions.

A Statistical Method for the Prediction of the Bear and Bull Stock Markets Based on the Zeroes of Riemann’s Zeta Function

We define a method for predicting the stochastic behavior of the Bull and Bear periods of the stock market. In this paper, initially, we carry on a comprehensive evaluation of more frequently used statistical methods for evaluating Stock markets. Our work is based on collecting 40 years of data from the Italian stock market. The proposed solution is defined using the statistical analysis of the Bear and Bull Stock markets. We defined a new system to predict the trend of a stock market price, where the trend of the succession of Bull and Bear markets can be described by a probability density function given by a Gaussian distribution. Furthermore, we consider the inverses of the relative time intervals as a measure of the speed with which the phenomenon of the Bear market (or, equivalently, the Bull market) develops in that interval of time. Therefore, this factor can ultimately represent the first statistical weight of the single percentage variation. Again, the time intervals of the individual Bear and Bull market periods are considered, calculated from 01/01/1973. This allows us to consider the hypothesis that a secondary factor of probability is the temporal distance of the event that has already occurred. This work includes a criterion for statistically generating the most probable values of the next Bear and Bull markets and the length of the time intervals corresponding to these market situations. This criterion is based on the following hypothesis: 
 To obtain the distribution of the predictive points of max and min in the succession of Bull and Bear markets, it is assumed that, in the long period, the random distribution of the successive max and min takes the trend of the distribution of the distance fluctuations between the zeroes of the Riemann's function which, in turn, is approximated by a Unitary Gaussian Distribution (GUE). Our results show that: 
 
 The linear interpolation of the Variations of the market (positive and negative) relative to different and successive sampling sets for future trends do not show high percentage variations between them, 
 Above all, the lengths of the single time intervals of the future variations, relative to different and successive sampling sets, are quite similar to each other. Hence, the method appears to be basically stable and promising. 

A Statistic Method for the Prediction of the Succession of Bear and Bull Stock Market

In this paper, we define an innovative method for predicting the stochastic behavior of the Bull and Bear periods of the stock market. The direct study of the prediction of possible Bull and Bear markets is a different and new approach about the analysis of stock markets. Our work is based on the collection of 40 years of data from the Italian stock market. The proposed solution is defined using the statistical analysis of the Bear and Bull Stock markets and it includes a criterion for statistically generating the most probable values of the next Bear and Bull markets, as well as especially the lengths of the time intervals corresponding to these market situations. We defined a new system for a stock market price trend prediction, where the trend of the succession of Bull and Bear markets can be described by a probability density function given by a Gaussian distribution. Furthermore, we considered the inverses of the relative time intervals as a measure of the speed with which the phenomenon of the Bear market (or, equivalently, the Bull market) develops in that interval of time and therefore, ultimately, it can represent a first statistical weight of the single percentage variation. Again, the time intervals of the individual Bear and Bull market periods are considered, calculated from 01/01/1973. This is based on the hypothesis that a secondary factor of probability is the temporal distance of the event that has already occurred. In this paper, we conduct a comprehensive evaluation of more frequently used statistical methods for evaluating Stock markets, highlighting the novelty of the described method. Simulation results show the ability of the method to define a statistical prediction of the next Bull and Bear markets.

Which Came First, Price or Activity? A Vicious Circle of a Blockchain-based Social Media in the Bear Market

Which Came First, Price or Activity? A Vicious Circle of a Blockchain-based Social Media in the Bear Market

Avoid Equity Bear Markets with a Market Timing Strategy

Avoid Equity Bear Markets with a Market Timing Strategy

A Machine Learning Model for Healthcare Stocks Forecasting in the US Stock Market during COVID-19 Period

This paper study the nowcasting and forecasting for the healthcare stock price in the united states during the Covid-19 period including the google trend data information. The data is collected in monthly data from 2015 to 2020 which are five interested stock price indexes in the healthcare sector. Empirically, the finding reveals that the Bayesian structural time series analysis can be used to investigate the stock price indexes with the google trend data is becoming useful for the prediction in term of current movement. In term of the machine learning algorithms, the unsupervised learning k-Mean algorithm is employed to cluster the cycle regimes of the stock market which provided three regimes such as Bull market, Sideways and Bear market. There are twenty-nine months stand for bull market, thirty-seven months are predictively provided sideways market and five months are referred as the bear market. Additionally, the supervised learning algorithms by using the Linear Discriminant Analysis (LDA), k-Nearest Neighbors (kNN) and Support vector machine (SVM) are used to investigate the cycle regimes of healthcare stock in next five year. The results indicated that LDA is chosen by the highest coefficient validation which represented the the regimes of stock in the healcare sector of the unites states of America will stay on the sideways periods in the next five years. Thus, the finding in this paper can be the useful information for investor to manage their portfolio especially, in healthcare sector during the Covid-19 period.

Finansal Piyasaların Vahşi Doğasına Analojik Yaklaşım

There are many disciplines, many metaphors, many theories, and many studies that attempt to explain the behavior of financial markets and investors. Psychological, neurological and even genetic sciences are generally used in these studies, whose intensity has increased especially in the process of trying to complete the development of behavioral finance. However, the subject that has been used unchanged over the years and that we think has escaped the attention of researchers is the animal metaphors in the markets. It seems that investor behavior and economic human being the subject of research is new, but our imitation of animals goes back to prehistoric times. This shows us that behavioral patterns borrowed from animals existed long before we knew them. In this study, the names of animals used in financial markets have been examined, and it has been tried to determine how many animal names, especially bear and bull markets, turn financial markets into "jungle" and what behaviors of markets and investors are described by these names. Keywords: Behavioral Finance, Stock Market Jargon, Bear Market, Bull Market, Financial Markets JEL Classification: G40, D47

Top 5 Reasons behind the Biopharma Bear Market

Top 5 Reasons behind the Biopharma Bear Market

StockWatch: Vertex Bucks Bear Market with Boston Expansion, CF Growth

StockWatch: Vertex Bucks Bear Market with Boston Expansion, CF Growth

Stock Indices Breakdown during the Pandemic as the Most Dynamic Bear Market in History: Consequences for Individual Investors

The breakdown of stock indices is an obvious part of the financial market cycle. A common question about a bear market is the time and the depth of the downtrend, as well as the speed of the following recovery. As the COVID-19 pandemic spread globally, it induced huge price drops in a very short period, and an uptrend with new historical highs afterwards. The results of this research show that the pandemic breakdown was the fastest bear market in history; however, it does not confirm that future downtrends will be at the same or even greater speed. The consequences for individual investors have forced them to prepare for possible similar market behavior in the future, and to adjust their trading techniques and strategies to these conditions.

Teodora Popescu (Editor). Cognitive approaches to contemporary media. New Castle upon Tyne: Cambridge Scholars Publishing, 2021. Pp. i-xi, 1-210. ISBN: 978-1-5275-6953-9

Teodora Popescu (Editor). Cognitive approaches to contemporary media. New Castle upon Tyne: Cambridge Scholars Publishing, 2021. Pp. i-xi, 1-210. ISBN: 978-1-5275-6953-9

Optimal Goals-Based Investment Strategies for Switching between Bull and Bear Markets

We solve a dynamic, long-horizon, goals-based wealth management problem, given different investment regimes. In a world with a good regime (bull market) and a bad regime (bear market), an investor who is cognizant that regime switching occurs has the potential to do better than an investor who assumes only one regime. However, models with more than one regime incur the additional risk of regime uncertainty. Investors must be able to predict which regime is governing the market with reasonable levels of confidence, or they can be worse off than investors who assume just one regime. Using data from recent history, we develop a framework that determines how accurate regime prediction needs to be to achieve gains from a regime-cognizant goals-based investing approach.

A Case Study in Sequence Risk: A 20-Year Retrospective on the Impact of the 2000–2002 and 2007–2009 Bear Markets on Retirement Nest Egg Sustainability

The first decade of the 2000s saw two prolonged bear markets, both of which produced declines of over 50% in value in the S&P 500 Index from peak to trough. In total, the ten-year period from March 1999 through February 2009 saw the stock market lose more than 30 percent of its value. This period represented the worst decade for the US stock market since the Great Depression. For consumers who had the misfortune of retiring in 1999, this “lost decade” embodied the definition of sequence of returns risk. Researchers at the time could only speculate as to the impact such sharply negative returns might have on the long-term sustainability of retiree spending portfolios.

Investigating the Dynamics of Exchange Traded Funds Across the Bear and Bull Markets: Evidence from Indian Equity ETFs

The present study attempts to examine the tracking ability of Indian equity exchange traded funds (ETFs) across the bearish and bullish market regimes. Also, ETFs’ sensitivity to their respective underlying indices across the two market conditions is examined so as to gain an insight into the differences in risk exposure under the two regimes using DBM. The results found that the tracking error (TE) of ETFs varies across the two market regimes with it higher during the bullish regime. At the same time, ETFs’ responsiveness to their underlying indices is found to be higher during the bearish market regime, which justifies the existence of lower TE during the bearish regime. NIFTYBEES, KOTAKNIFTY and BANKBEES emerged to be the top three performers in terms of tracking efficiency. Further, NIFTYBEES, BANKBEES and JUNIORBEES are reported to provide significantly positive excess returns during the bullish regime. As such, investors considering investment in equity ETFs can opt for the top performing funds where they also stand a chance to earn excess return (in few cases). Also, it is observed the beta coefficients of ETFs varied significantly from unity. It suggests that the ETFs and their respective underlying indices are not subject to similar systematic risk.

Optimal Goals-Based Investment Strategies For Switching Between Bull and Bear Markets

We apply dynamic programming to solve a long-horizon fund choice problem, given that the underlying market can switch between different regimes. The objective function is based on reaching a target level of wealth, following the paradigm of goal-based investing. In a world with a good regime (e.g., a bull market) and a bad regime (e.g., a bear market), we find that an investor who is cognizant of regime switching can potentially do much better over time than an investor who assumes there is only one regime. However, there is a caveat---an investor must be able to predict the regime they are in with reasonable levels of confidence, and if not, they are in fact worse off than an investor who assumes just one regime. Using data from recent history, we find that investors may be better off not switching from existing single-regime models to more complex multiple-regime models.

Bear Market Mutual Funds: Do they Deliver as Promised?

Bear Market Mutual Funds: Do they Deliver as Promised?

Bear Market Mutual Funds: Do they Deliver as Promised?

Bear Market Mutual Funds: Do they Deliver as Promised?

투자자들은 투자자 심리가 위축되는 하락장에서 배당주를 더 선호하는가?

투자자들은 투자자 심리가 위축되는 하락장에서 배당주를 더 선호하는가?