Bear Market Periods Research Articles

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.

The Impact of ESG Risks on the Performance of Infrastructure Mutual Funds

Prior studies find mixed evidence on the impact of environmental, social, and governance (ESG) risk ratings on mutual fund performance. These studies implicitly assume that ESG risks are equally important for all sectors. In this article, we argue that ESG risk ratings are more likely to matter for securities related to a sector with a highly visible footprint. We provide complementary evidence to the existing literature by focusing on the performance of mutual funds investing in the infrastructure sector (IMFs). Using a sample of US equity IMFs, we find that lower ESG commitment is associated with higher performance (Carhart alpha) during normal periods, whereas it is associated with lower performance during recessions or bear market periods. Interestingly, our results also show that ESG risk scores do not affect the performance of comparable mutual funds not investing in the infrastructure sector (even in bear market periods).

Efficient Asset Allocation: Application of Game Theory-Based Model for Superior Performance

In this paper, we compared the models for selecting the optimal portfolio based on different risk measures to identify the periods in which some of the risk measures dominated over others. For decades, the best known return-risk model has been Markowitz’s mean-variance model. Based on the criticism of the classical Markowitz model, a whole series of risk measures and models for selecting the optimal portfolio have been developed, which are divided into two groups: symmetrical and downside risk measures. Based on the tools provided by game theory, we presented a minimax model for selecting the optimal portfolio based on the maximum loss as a measure of risk. Recent research has shown the adequacy of the application of this risk measure and its dominance concerning variance in certain circumstances. Theoretically, the model based on maximum loss as a measure of risk relies on a much smaller number of assumptions that must be satisfied. In the empirical part of the paper, we analyzed the real return performance, structure, correlation, stability, and predictive efficiency of the model based on maximum loss return as a measure of risk and compared it with the other famous models to determine whether the maximum loss-based risk measure model is more suitable for use in certain circumstances than conventional return-risk models. We compared portfolios created based on different models over the period of 2000–2020 from a selected sample of stocks that are components of the STOXX Europe 600 index, which covers 90% of the free market capitalization in the European capital market. The observed period included 3 bear market periods, including the period of market decline during the COVID-19 crisis. Our analysis showed that there was no significant difference in portfolio returns depending on the selected model using the “buy-and-hold” strategy, but there were crisis periods. The results showed a significantly higher stability of portfolios selected on the criterion of minimizing the maximum loss than others. In periods of market decline, this portfolio achieved the best performance and had a shorter recovery period than others. This allowed superior use of the minimax model at least for investors with a pronounced risk aversion.

Liquidity Risk and Asset Pricing in Pakistan Stock Exchange

This paper empirically investigates the impact of liquidity risk on stock returns in Pakistan and determines investors’ attitude under bull and bear market conditions. Specifically, the liquidity adjusted capital asset pricing model(CAPM) is modified by including the interaction between the liquidity risk and the indicators of bull- and bear-market periods to investigate whether the pricing of liquidity risk differs in both upward and downward market trends. The analysis is carried out for a large panel of Pakistani manufacturing firms listed at the Pakistan Stock Exchange for the period January 2000 – December 2015. We use alternative liquidity risk measures to check the robustness of the liquidity risk effect. We observe that higher liquidity risk yields higher excess stock returns, implying pricing of liquidity risk during the examined period. The results also reveal that the liquidity risk is positively and significantly related to excess returns in the high-liquidity-risk beta portfolios, whereas it is negatively or insignificantly related to excess returns of low-liquidity-risk beta portfolios. The results also provide evidence that stocks affected by liquidity risk yield positive expected returns in both bull and bear market conditions. However, we find significant differences in the pricing of liquidity risk under upward and downward market trends. The robustness check confirms that the findings on the pricing of liquidity risk are not driven by any specific measure of liquidity.

Behavioral Factors Affecting the Home Bias Phenomenon: Experimental Tests

ABSTRACTAlthough international portfolio theory states that an optimal portfolio should be well diversified, in practice, investors tend to invest excessively in domestic assets. This tendency, which is commonly referred to in the finance literature as “home bias” (HB), has puzzled economists for many decades. This research develops and presents experiments designed to test the behavioral factors related to HB discussed in the literature. In particular, these experiments test whether the factors of “familiarity” and “fluency” (ease of pronunciation) affect HB. In addition, using a method of controlled experiments, we examine the willingness to take risk as a basic source of HB. We also examine HB under three different market conditions (normal, bear and bull markets). Results indicate that subjects tended to take less risk with foreign, unfamiliar and nonfluent (difficult to pronounce) assets compared with local, familiar and fluent assets. This tendency increased significantly when the three factors were present together (foreign, unfamiliar and nonfluent assets) compared with when only one of the three factors was present. The results also revealed that HB increased during bear market periods.

Bear market periods during the 2007–2009 financial crisis: Direct evidence from the Visegrad countries

The main goal of this paper is a quantitative identification of bear market periods during the 2007–2009 global financial crisis in the case of the Visegrad Group stock markets. We analyse four countries, namely Poland, the Czech Republic, Hungary, and Slovakia and, for comparison, the US stock market. The sample period begins on May1, 2004, and ends on April 30, 2013, i.e. it includes the 2007 US subprime crisis. We use the statistical method of dividing market states into bullish and bearish markets. Our results reveal October 2007–February 2009 as the common downmarket period of the recent global financial crisis, except for Slovakia. It is instructive to formally identify crises, as it enables sensitivity analyses of various relationships and linkages among international stock markets using econometric and statistical tools, with respect to the pre-, post- and crisis periods. Moreover, we investigate the effect of increasing cross-market correlations in the crisis compared to the pre-crisis period, applying both standard contemporaneous correlations and volatility-adjusted correlation coefficients. The results confirm that accommodating heteroskedasticity is critical for detecting contagion across economies. A number of studies document that crossmarket correlations vary over time, thereby making the benefits of international portfolio choice and diversification questionable.

An intelligent short term stock trading fuzzy system for assisting investors in portfolio management

Financial markets are complex systems influenced by many interrelated economic, political and psychological factors and characterised by inherent nonlinearities. Recently, there have been many efforts towards stock market prediction, applying various fuzzy logic techniques and using technical analysis methods.This paper presents a short term trading fuzzy system using a novel trading strategy and an “amalgam” between altered commonly used technical indicators and rarely used ones, in order to assist investors in their portfolio management. The sample consists of daily data from the general index of the Athens Stock Exchange over a period of more than 15 years (15/11/1996 to 5/6/2012), which was also divided into distinctive groups of bull and bear market periods.The results suggest that, with or without taking into consideration transaction costs, the return of the proposed fuzzy model is superior to the returns of the buy and hold strategy. Τhe proposed system can be characterised as conservative, since it produces smaller losses during bear market periods and smaller gains during bull market periods compared with the buy and hold strategy.

The asymmetric predictability of high-yield bonds

This study examines the relationship between the high-yield bonds market and the stock market and indicates that stock returns lead high-yield bond returns. Specifically, this study further shows that this lead–lag relationship is more solid during bear market periods since a downward trend in the stock market implies a high likelihood of the exercise of the equity put in short position embedded in a high-yield bond at maturity. We also conducted out-of-sample forecast using a VAR model, an AR model and naïve estimation during bear market and non-bear market periods. Our results demonstrate that high-yield bond returns are better predicted by a VAR model that includes past stock returns than by an AR model or naive estimation during bear market periods, but such is not the case during non-bear market periods.

Asymmetric effects of monetary policy on an emerging stock market

This paper examines the relationship between monetary policy and the stock market based on data from two stock indices in China - the Shanghai Composite Index and Shenzhen Composite Index. The result shows only unanticipated monetary policy can affect the stock return significantly. Based on anticipated effects, we examine whether monetary policy has asymmetric effects on the stock market by using a linear model with a dummy variable and a modified Markov–switching model respectively. Our empirical results support existing evidence for developed markets, suggesting that monetary policy has a significant impact on stock returns in bear–market periods, whereas the impact is weak and insignificant in bull–market periods. Given this, we surmise that investor sentiments and borrowing constraints are the main causes of the asymmetric effects found.

The relation between gold and stocks: an analysis of severe bear markets

No prior research has (1) studied the relation between gold and stocks for the four severe bear markets since 1960s, (2) used different segments of stock markets simultaneously for analysis and (3) implemented a system of equations to control for exogenous and endogenous variables to investigate the role of gold for investments hedge in these severe bear market periods, and compare the results with its role in nonbear market periods. Results show that gold was a good instrument for hedging stock market risk for only two of the four severe bear market periods analysed. For nonbear market periods, except for small-cap stocks, gold also did not offer good risk hedging. The findings are of interest, as it coincides with the fact that small-cap stocks are the riskiest and most volatile investment even during economic good times, and gold is found to offer a risk-hedging power for this segment of the stock market.

An Analysis of Herding Behaviour during Market Cycles in South Africa

Herding behaviour can be captured by the relationship between share price movements with the market, typified by beta. We examine herding behaviour for the period 1995 to 2011 and find that it is absent overall, yet present during bear market periods only. When examined alongside the market cycle, herding appears to dramatically fluctuate before a market contraction. Conceptually, herding can be seen as an explanatory factor for the existence of a nonlinear market model. Our findings infer that a negative market reaction (contraction) is preceded by an increase in herding. The evidence of herding in during a South African market contraction can thus impact financial forecasts and volatility estimates of the market. Further, it could possibly indicate the level of confidence of market participants – both experienced and inexperienced individuals tend to follow the group consensus in times of a market downturn, yet deviate from the group consensus in times of a market upturn.

The Influence of Macroeconomic Factors over the Firm’s Diversification Strategy: Empirical Evidence from US Publicly Trading Firms from 1979 to 2006

The objective of this research is to establish whether a relationship between firm motives for diversification and macroeconomic conditions exists. To measure the influence of the eco nomic factors on the firm diversification decision, if any, several multi-factor models are employed. As a measurement of diversification level, the change in the number of segments per firm by year (SegGrowth) is estimated on some economic indexes. The study period is divided on sub-periods based on the bull and bear markets as defined by Dow Theory and the Standard & Poor’s Equity Research. Results from the regression models suggest that the firm diversification decision is influenced by poor macroeconomic conditions. The average number of segments per year during bear market periods is significantly greater than that for the bull market periods. These results indicate that firms have a propensity to diversify dur ing poor economic conditions. One possible explanation is that when the firm faces eco nomic and financial risks, the diversification could be a solution to lower the risk or to exploit new productive opportunities.

La influencia de los factores macroeconómicos sobre la estrategia de diversificación corporativa: datos empíricos de empresas de comercio público de EE.UU. pertenecientes al período comprendido entre 1979 y 2006

The objective of this research is to establish whether a relationship between firm motives for diversification and macroeconomic conditions exists. To measure the influence of the economic factors on the firm diversification decision, if any, several multi-factor models are employed. As a measurement of diversification level, the change in the number of segments per firm by year (SegGrowth) is estimated on some economic indexes. The study period is divided on sub-periods based on the bull and bear markets as defined by Dow Theory and the Standard & Poor’s Equity Research. Results from the regression models suggest that the firm diversification decision is influenced by poor macroeconomic conditions. The average number of segments per year during bear market periods is significantly greater than that for the bull market periods. These results indicate that firms have a propensity to diversify during poor economic conditions. One possible explanation is that when the firm faces economic and financial risks, the diversification could be a solution to lower the risk or to exploit new productive opportunities. Key words: Macroeconomic factors; diversification decision.

Zur Rendite-Risiko-Beziehung am deutschen Aktienmarkt – Eine empirische Analyse der Beziehung zwischen dem Deutschen Aktienindex DAX und dem Volatilitätsindex VDAX

Summary/Zusammenfassung Zur Rendite-Risiko-Beziehung am deutschen Aktienmarkt Eine empirische Analyse der Beziehung zwischen dem Deutschen Aktienindex DAX und dem Volatilitatsindex VDAX Diese Studie untersucht den empirischen Zusammenhang zwischen den Volatilitatsindizes VDAX bzw. VDAX-New und dem Aktienmarktindex DAX. Der im internationalen Umfeld dokumentierte negative Zusammenhang zwischen impliziten Volatilitatsindex- und Aktienmarktindexrenditen kann auch am deutschen Aktienmarkt nachgewiesen werden, wobei der negative Zusammenhang im Falle negativer DAX-Renditen betragsmasig starker ausgepragt ist als bei positiven DAX-Renditen. Neben dieser Asymmetrie konnen zusatzliche quadratische Effekte, die sich als Angst bzw. Uberschwang interpretieren lassen, nicht beobachtet werden. Die empirischen Ergebnisse sind robust hinsichtlich des gewahlten Volatilitatsindexes, der Datenperiodizitat sowie des Untersuchungszeitraums und lassen sich mit Erkenntnissen aus der Behavioral-Finance-Forschung erklaren. Es er...

Initial Public Offerings, Earnings Management and Venture Capital: Evidence in Bull and Bear Market Conditions

Earnings management at the time of the IPO is an important issue and has captured considerable attention of academic literatures. However, there have been few studies testing earnings management in the context of market condition, and when financial intermediaries such as venture capital (VC) involve in the IPO process. This study investigates how VCs influence on earnings management of IPOs upon market condition. We find evidence that if IPO firms go public in the bear market periods, VCs are more likely to inflate their earnings more aggressively at the IPO, compared to the bull market periods. Specifically, while most of studies have argued that VCs have certification effect and monitoring effect to discourage earnings management, we argue that, with empirical finding in Korea, VCs have incentives to inflate their IPOs’ earnings for harvesting high returns in the IPO year and the inflated earnings are more deepened in bear market periods.

Behavioral Finance: An Analysis of the Performance of Behavioral Finance Funds

This article analyzes 31 mutual funds whose portfolio construction methodologies employ aspects of behavioral finance. The assets of the mutual funds in the study were valued at approximately US$ 16 billion as of August 2009. Major findings of the authors include the following. First, evidence exists of a strong seasonality effect among behavioral funds. Second, behavioral fund managers exhibit an inability to predict equity market reversals due, in large part, to their willingness to attempt to benefit from trend momentum. Third, they have superior performance during bull market periods vis-à-vis bear market periods, notably with small-cap behavioral funds generally outperforming their large-cap peers. When examining the outperformance of behavioral funds against a respective benchmark, there is no conclusive evidence to suggest that these strategies outperform. The most common observations that behavioral funds attempt to exploit include the momentum effect, winner–loser effect, post-earnings-announcement drift, and prospect theory. Insider buying was also observed as a key signal used by behavioral fund managers to anticipate future market trends. <b>TOPICS:</b>In portfolio management, portfolio construction, mutual fund performance

Investor sentiment and the stock market’s reaction to monetary policy

This paper shows that monetary policy decisions have a significant effect on investor sentiment. The effect of monetary news on sentiment depends on market conditions (bull versus bear market). We also find that monetary policy actions in bear market periods have a larger effect on stocks that are more sensitive to changes in investor sentiment and credit market conditions. Overall, the results show that investor sentiment plays a significant role in the effect of monetary policy on the stock market.

Direction‐of‐change forecasting using a volatility‐based recurrent neural network

AbstractThis paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction‐of‐change of the market in the case of the NASDAQ composite index. The sample extends over the period 8 February 1971 to 7 April 1998, while the sub‐period 8 April 1998 to 5 February 2002 has been reserved for out‐of‐sample testing purposes. We demonstrate that the incorporation in the trading rule of estimates of the conditional volatility changes strongly enhances its profitability, after the inclusion of transaction costs, during bear market periods. This improvement is being measured with respect to a nested model that does not include the volatility variable as well as to a buy‐and‐hold strategy. We suggest that our findings can be justified by invoking either the ‘volatility feedback’ theory or the existence of portfolio insurance schemes in the equity markets. Our results are also consistent with the view that volatility dependence produces sign dependence. Copyright © 2008 John Wiley &amp; Sons, Ltd.

A neurofuzzy model for stock market trading

This study investigates the forecasting ability of trading strategies based on neurofuzzy models, recurrent neural networks and linear regression models. The performance of the trading strategies was considered upon the prediction of the direction-of-change of the market in case of Nikkei 255 Index returns. The results demonstrate that the profitability of the trading rule based on the neurofuzzy model is consistently higher to that of the other models as well as of a buy and hold strategy during bear market periods.