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This paper aims to investigate Chinese bull and bear markets. The Chinese stock market has experienced a long period of bear cycle from early 2000 until 2006, and then it fluctuated greatly until 2010. However, the cyclical behaviour of stock markets during this period is less well established. This paper aims to answer the question why the Chinese stock market experienced a long duration of bear market and what factors would have impacted this cyclical behaviour.

By comparing the intervals of bull and bear markets between stocks and indices based on a Markov switching model, this paper examines whether different industries or A- and B-share markets could lead to different stock market cyclical behaviour and whether firm size can determine the relationship between the firm stock cycles on the market cycles.

This paper finds a high degree of overlapping of bear cycles between stocks and indices and a high level of overlapping between the bear market and a fraction of stock with increasing stock prices. This leads to the conclusion that the stock performance and trading behaviour are widely diversified. Furthermore, the paper finds that the same industry may have different overlapping intervals of bull or bear cycles in the Shanghai and Shenzhen stock markets. Firms with different sizes could have different overlapping intervals with bull or bear cycles.

This paper fills the literature gap by establishing the cyclical behaviour of stock markets.

The purpose of this paper is to examine semi-strong market efficiency by observing the behavioral finance implications of Jim Cramer’s recommendations in bull vs bear markets. The authors extend the literature by analyzing investor reaction through the lenses of prospect theory, overreaction, and herding.

The authors test for abnormal returns in response to Mad Money buy and sell recommendations. The authors use a sample of buy and sell recommendations from MadMoneyRecap.com from July 28, 2005 through February 9, 2009. The 3.5-year time period is the most recent and comprehensive set of Mad Money recommendations that has been tested to date.

The results indicate market inefficiency at the semi-strong level. Furthermore, the findings highlight the loss aversion tendencies of investors in regards to prospect theory of Kahneman and Tversky (1979) as well as the disposition effect of Shefrin and Statman (1985). Evidence also exists consistent with the herding and overreaction hypotheses.

The evidence suggests contrarian behavior in which investors respond positively to good news in bad times – perhaps, in effort to stay the course and at least break even. This behavior may suggest that losers tend to hold on to losses in hopes of recouping them. Thus, positive information in bad times could further persuade market participants to hang on to or buy more of losers, while also persuading non-shareholders to buy in as well.

Though other studies including Kenny and Johnson (2010) have estimated abnormal returns in response to analyst recommendations, to the knowledge, none has examined behavioral implications of investor reaction to buy and sell recommendations in both bull and bear markets. Furthermore, the study captures a longer bull and bear market and covers two definitions of such markets.

The purpose of this paper is to investigate the role of volatility risk on stock return predictability specified on two global financial crises: the dot-com bubble and recent financial crisis.

Using a broad sample of stock options traded on the American Stock Exchange and the Chicago Board Options Exchange from January 2001 to December 2010, the effect of different idiosyncratic volatility forecasting measures are examined on future stock returns in four different periods (Bear and Bull markets).

First, the authors find clear and robust empirical evidence that the implied idiosyncratic volatility is the best stock return predictor for every sub-period both in Bear and Bull markets. Second, the cross-section firm-specific characteristics are important when it comes to stock returns forecasts, as the latter have mixed positive and negative effects on Bear and Bull markets. Third, the authors provide evidence that short selling constraints impact negatively on stock returns for only a Bull market and that liquidity is meaningless for both Bear and Bull markets after the recent financial crisis.

These results would be helpful to disclose more information on the best idiosyncratic volatility measure to be implemented in global financial crises.

This study empirically analyses the effect of different idiosyncratic volatility measures for a period that involves both the dotcom bubble and the recent financial crisis in four different periods (Bear and Bull markets) and contributes the existing literature on volatility measures, volatility risk and stock return predictability in global financial crises.

This study aims to analyze how risk tolerance is influenced by bull and bear market phases, age and professional work experience (PWE) of investors in emerging economies. The authors also analyze how different market phases (bull and bear) influence risk tolerance of investors in emerging economies for different age groups and with varying PWE.

The study uses two quantitative methods, one-way ANOVA and hierarchical regression model (HLM) to analyze individual investors' financial risk tolerance (FRT) in India.

The authors find that age and PWE have positive relationship with FRT behavior. However, interactions of these variables with market phase variable indicate that risk tolerance has nonlinear increasing relationship with investor's age and PWE. The risk tolerance of older investors is consistently high in both bull and bear market conditions, while young investors display a nonlinear risk behavior in different market conditions.

The study suggests that financial planners should include a longitudinal risk profiling of investors based on age groups, PWE and the current market phase to better understand investors' FRT and also to prefer more context-specific advice to investors in emerging economies, which, consequently, result in increasing the retail investors' interest in otherwise sparsely participated equity market.

Interaction effect of bull and bear market phases on relationship between age and PWE and FRT has been scantly studied.

The purpose of this paper is to explore the relationship between Dow Jones Islamic Market World Index, Islamic gold-backed cryptocurrencies and halal chain in the presence of state (regime) dynamics.

The authors have used the Markov-switching model to identify bull and bear market regimes. Moreover, the dynamic conditional correlation, the Baba, Engle, Kraft and Kroner- generalized autoregressive conditional heteroskedasticity and the wavelet coherence models are applied to detect the presence of spillover and contagion effects.

The findings indicate various patterns of spillover between halal chain, Dow Jones Islamic Market World Index and Islamic gold-backed cryptocurrencies in high and low volatility regimes, especially during the COVID-19 pandemic. Indeed, the contagion dynamics depend on the bull or bear periods of markets.

These present empirical findings are important for current and potential traders in gold-backed cryptocurrencies in that they facilitate a better understanding of this new type of assets. Indeed, halal chain is a safe haven asset that should be combined with Islamic gold-backed cryptocurrencies for better performance in portfolio optimization and hedging, mainly during the COVID-19 period.

To the best of the authors’ knowledge, this paper is the first research on the impact of the halal chain on the Dow Jones Islamic Market World Index return, Islamic gold-backed cryptocurrencies returns in the bear and bull markets around the global crisis caused by the COVID-19 pandemic.

Academic research has shown that diversification today may not only include stocks and bonds but also alternative investments like hedge funds. However, practical and effective methods to identify the hedge fund styles that really enhance the risk return characteristics of a traditional portfolio as well as optimal allocation sizes are not available. The aim of the paper is to try to close this gap by proposing a portfolio optimization approach based upon the traditional market exposures of the different hedge fund strategies.

For this purpose, the paper first measures the bull and bear market betas of the main hedge fund strategies (equity market neutral, event driven, global macro, relative value, and managed futures). Based on the strategy characteristics, the authors then develop a systematic framework that calculates what percentage of each basic asset should be substituted for by hedge fund strategies to achieve the maximum results. The paper uses hedge fund index data from Hedge Fund Research and Barclay Hedge for the January 1999‐April 2011 sample period.

The empirical results show that this approach leads to an improvement in the annualized return of the optimized portfolio.

The paper adds to the existing literature by showing that it is possible to substitute traditional assets with hedge fund indices based on their exposures (beta) in varying market environments as a way to optimize the overall portfolio.

The authors examine cryptocurrency market behavior using a hidden Markov model (HMM). Under the assumption that the cryptocurrency market has unobserved heterogeneity, an HMM allows us to study (1) the extent to which cryptocurrency markets shift due to interactions with social sentiment during a bull or bear market and (2) the heterogeneous pattern of cryptocurrency market behavior under these two market conditions.

The authors advance the HMM model based on two six-month datasets (from November 2017 to April 2018 for a bull market and from December 2018 to May 2019 for a bear market) collected from Google, Twitter, the stock market and cryptocurrency trading platforms in South Korea. Social sentiment data were collected by crawling Bitcoin-related posts on Twitter.

The authors highlight the reaction of the cryptocurrency market to social sentiment under a bull and a bear market and in two hidden states (an upward and a downward trend). They find: (1) social sentiment is relatively relevant during a bull compared to a bear market. (2) The cryptocurrency market in a downward state, that is, with a local decreasing trend, tends to be more responsive to positive social sentiment. (3) The market in an upward state, that is, with a local increasing trend, tends to better interact with negative social sentiment.

The proposed HMM model contributes to a theoretically grounded understanding of how cryptocurrency markets respond to social sentiment in bull and bear markets through varied sequences adjusted for cryptocurrency market heterogeneity.

The purpose of this paper is to explore the motivation behind mutual funds’ risk shifting behavior by examining its impact on fund performance, while jointly considering fund managers’ compensation incentives and career concerns.

The study uses a sample of US actively managed equity funds over the period 1980-2010. A fund’s risk shifting is estimated as the difference between the fund’s intended portfolio risk in the second half of the year and the realized portfolio risk in the first half of the year. Using the state of the market to identify the dominating type of incentive that fund managers face, we examine the relationship between performance and risk shifting in a cross-sectional regression setting, using the Fama and MacBeth (1973) methodology.

The authors find that poorly performing (well performing) funds are likely to increase (decrease) their risk level in bull markets, while reducing (increasing) it during bear markets. Furthermore, we find that funds that increase risk underperform, while those that decrease their portfolio risk do not. In addition, we find that poorly performing funds that increase (or decrease) their risk underperform across bull and bear markets, while well performing funds that reduce risk during bull markets subsequently outperform.

The paper contributes to the literature on mutual fund risk shifting by providing evidence that the performance consequence of such behavior is dependent on the state of the market and on the funds’ past performance. The results suggest that loser funds tend to be agency prone or be managed by managers with inferior investment skill, and that winner funds exhibit superior investment ability during bull markets. The authors argue that both the agency and investment ability hypotheses are driving fund managers’ risk shifting behavior.

Stirred by scant regard for market phases in portfolio performance assessments, the current paper investigates the active versus passive investment strategies under the bull and bear market conditions in emerging markets focusing on South Africa as a case study.

Methodologically, the measures of Jensen's alpha and Treynor index are applied to the monthly returns of 20 funds from January 2010 to June 2022.

The results are enlightening; though they contradict developed market evidence, they are consistent with emerging market trends. The findings show that actively managed funds outperform the market benchmark and passive investing style under bear and normal market conditions. Passive investment strategy outperforms both market benchmark and actively investing style under bull market conditions.

In the face of improved market efficiency, increased liquidity and recent technological impact, the findings of this study have practical application. The study outcomes should inform and update global investors, especially asset managers interested in emerging markets; however, the limitations of the study should also be considered.

While limited studies consider market conditions when comparing and contrasting the performance of passive versus active investing, such consideration is lacking in emerging markets. The current study corrects this literature imbalance.

This paper aims at analyzing the asymmetries created by the Great Recession in the US real estate sector.

This paper uses a Markov-switching dynamic regression model in which parameters change when the housing market moves from one regime to the other.

The results show that the effect of real estate loans, interest rate, quantitative easing and working age population are asymmetric across bull and bear regimes. It is also found that the estimated parameters are larger in bull regime than bear regime, indicating a tendency to create house price bubbles in bull market.

Since three of those asymmetric variables (real estate loans, interest rate and quantitative easing) are related to monetary policy, the Fed can mitigate their impact on an interest-sensitive sector such as housing by engaging in a countercyclical monetary policy.

The estimated intercept and the variance parameter both vary from one regime to the other, thus justifying the use of a regime-dependent model.