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PurposeThe 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.Design/methodology/approachThe 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.FindingsThe 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.Originality/valueThe 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.

This study revisits an important issue in financial theory: the instability of market beta. To this end, we demonstrate that the linear constant risk model is misleading and does not reproduce changes in beta correctly. We develop a new nonlinear market model to capture beta instability over time for three main states: bear, normal, and bull markets. Our model endogenously identifies these states and their thresholds. We then apply this econometric specification to four major sustainable stock indexes in the US, Europe, Asia, and the World for 2004–2015. The results provide three main findings. First, the market beta is time-varying and changes asymmetrically and nonlinearly, suggesting that the systematic risk statistically differs between market regimes for the US, Europe, and the World. Second, the positive sign of beta in the bull market for these three regions suggests that systematic risk increases as economic conditions improve. Third, the lowest level of beta in the bear market indicates the usefulness of the sustainable stock index to hedge and cover investors’ portfolios against risk, particularly in a bear market.

Academic research on trend-following investing has almost exclusively focused on testing various trading rules’ profitability. However, all existing trend-following rules are essentially ad hoc, lacking a solid theoretical justification for their optimality. This paper aims to address this gap in the literature. Specifically, we examine the optimal trend-following when the returns follow a two-state process, randomly switching between bull and bear markets. We show that if a Markov model governs the return process, it is optimal to follow the trend using the Exponential Moving Average rule. However, the Markov model is unrealistic because it does not represent the bull and bear market duration times correctly. It is more sensible to model the return process by a semi-Markov model where the state termination probability increases with age. Under this framework, the optimal trend-following rule resembles the Moving Average Convergence/Divergence rule. We confirm the validity of the semi-Markov model with an empirical study demonstrating that the theoretically optimal trading rule outperforms the popular 10-month Simple Moving Average and 12-month Momentum rules across a universe of international markets.

PurposeIn this paper, we provide new evidence to strengthen the stock market overreaction hypothesis by examining a new context that has not been explored before. Our research is inspired by the widely held belief that investor sentiment experiences abrupt changes from optimism to pessimism as the market switches between bull and bear states.Design/methodology/approachIf the stock market overreaction hypothesis is correct, it implies that investors are inclined to become excessively optimistic during bull markets and overly pessimistic during bear markets, resulting in overreaction and subsequent market correction. Consequently, the study first develops two testable hypotheses that can be used to uncover the presence of stock market overreaction with subsequent correction. These hypotheses are then tested using long-term data from the US market.FindingsThe study's findings support the hypothesis while also revealing a significant asymmetry in investor overreaction between bull and bear markets. Specifically, our results indicate that investors tend to overreact towards the end of a bear market, and the subsequent bull market starts with a prompt and robust correction. Conversely, investors appear to overreact only towards the end of a prolonged bull market. The correction during a bear market is not confined to its initial phase but extends across its entire duration.Research limitations/implicationsOur study has some limitations related to its focus on investigating stock market overreaction in the US market and analyzing the pattern of mean returns during bull and bear market states. Expanding our study to different global markets would be necessary to understand whether the same stock market overreaction effect exists universally. Furthermore, exploring the relationship between volatility and overreaction during different market phases would be an exciting direction for future research, as it could provide a more complete picture of market dynamics.Practical implicationsOur study confirms the presence of the stock market overreaction effect, which contradicts the efficient market hypothesis. We have observed specific price patterns during bull and bear markets that investors can potentially exploit. However, successfully capitalizing on these patterns depends on accurately predicting the turning points between bull and bear market states.Social implicationsThe results of our study have significant implications for market regulators. Stock market overreactions resulting in market corrections can severely disrupt the market, leading to significant financial losses for investors and undermining investor confidence in the overall market. Further, the existence of overreactions suggests that the stock market may not always be efficient, raising regulatory concerns. Policymakers and regulators may need to implement policies and regulations to mitigate the effects of overreactions and subsequent market corrections.Originality/valueThis paper aims to provide additional support for the stock market overreaction hypothesis using a new setting in which this hypothesis has not been previously investigated.

PurposeThis research examines how macroeconomic variables can precisely predict bull/bear stock markets in China and Taiwan.Design/methodology/approachThis paper adopts a two-state Markov switching model to characterize the bull and bear markets spanning from 1994 to 2019 and then conduct a bear stock market predictability test by running regressions between the filtered probabilities of bear markets and a series of macroeconomic variables in turn at different horizons of 1, 3, 6, 12 and 24 months.FindingsThis paper shows that inflation rates, changes in real exchange rates, and foreign currency reserve growth are key predictors of bear markets in China, while term spreads, unemployment rates and foreign reserve growth are major factors that can predict bear markets in Taiwan. Remarkably, industrial production growth does not have predictive power for bear markets, which may suggest emerging markets are driven by fund flows rather than real economic activities. Besides, the impact directions of foreign currency reserve growth are opposite, which may be due to different proportions of the financial accounts in their balance of payments.Practical implicationsIn practical respect, this paper provides market participants the usefulness, impact direction and implications of bear market predictors when building their market-timing strategies in China and Taiwan stock markets. The government institutions may also thereby make appropriate policies to prevent huge stock market downturns and serious drawbacks.Originality/valueIt highlights the “fund-driven market hypothesis” and “foreign currency reserve effects” that commonly dominate Taiwan and China stock markets since both are highly affected by international funds.

In this article, we aim to explain what causes the depth of a stock market drawdown using the discretionary global macro approach. Our key finding is that the increase in credit risk to high/very high level after the beginning of a drawdown significantly explains the depth of the drawdown. An expected aggressive monetary policy tightening can trigger a correction, especially if accompanied with a high recession probability. Further, an expected aggressive monetary policy easing, as a sign of an imminent recession, can deepen the total drawdown. However, the depth of the total drawdown depends of whether the drawdown transitions to the ultimate credit crunch stage.

PurposeThis 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.Design/methodology/approachThe study uses two quantitative methods, one-way ANOVA and hierarchical regression model (HLM) to analyze individual investors' financial risk tolerance (FRT) in India.FindingsThe 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.Practical implicationsThe 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.Originality/valueInteraction effect of bull and bear market phases on relationship between age and PWE and FRT has been scantly studied.

This study investigates the sentiment effect in the Chinese agricultural futures market by constructing market-level and future-level futures sentiment indices. First, our findings reveal that sentiment in futures and the domestic stock market has a significant and positive impact on future-level sentiment, especially during bull markets, whereas sentiment in the global stock market has minimal influence. Second, an analysis of the relationship between sentiment and futures returns indicates that sentiment, both at the market and future levels, strongly and favorably influences the movement of futures returns. Third, after decomposing the future-level sentiment into contagious sentiment and idiosyncratic sentiment, this paper concludes that contagious sentiment and idiosyncratic sentiment positively affect futures returns in both bull and bear markets. Overall, this research provides clear evidence that sentiment plays a crucial role in determining agricultural futures returns and offers guidance to regulators to identify potential market bubbles and implement “sentiment regulation.” JEL Classification: G12, G14. Plain language summary We construct market-level and future-level futures sentiment to examine the sentiment effect in the Chinese agricultural futures market. First, this study finds that sentiment in futures and the domestic stock market has a large and favorable impact on future-level sentiment, particularly during bull markets. However, sentiment in the global stock market has little bearing on futures sentiment. Second, an analysis of the correlation between sentiment and futures returns indicates that sentiment, both at the market and future levels, strongly and favorably influences the movement of futures returns. Third, after decomposing the future-level sentiment into contagious sentiment and idiosyncratic sentiment, this paper concludes that contagious sentiment and idiosyncratic sentiment positively affect futures returns in both bull and bear markets. This paper provides clear evidence that sentiment affects agricultural futures returns, and offers guidance to regulators to identify potential market bubbles, and implement “sentiment regulation”.

This study?s aim is to investigate systemic risk in the Chinese stock market. To this end, we analyze risk contributions to the Chinese stock market from 2007 to 2018 at the sector level using the Conditional Value at Risk (CoVaR) approach proposed by Adrian and Brunnermeier (2016). For the full sample period, we find that the information technology sector is the top contributor to systemic risk in the Chinese stock market. To distinguish the risk contribution of each sector under different market regimes, we propose an adjusted Bry-Boschan program to identify turning points in the stock market, which captures regime shifting between bull and bear markets. We find that the risk contribution of each sector in a bear market is significantly higher than that in the following bull market. We also find that the top contributor to systemic risk in the Chinese stock market changes across market regimes. Our findings have important policy implications. First, policymakers may use the early identification of systemically risky sectors of the stock market to improve the pertinence of economic policy-making. Second, it may allow security regulators to foster an environment in which incentives for risk taking by financial practitioners are reduced. This study?s aim is to investigate systemic risk in the Chinese stock market. To this end, we analyze risk contributions to the Chinese stock market from 2007 to 2018 at the sector level using the Conditional Value at Risk (CoVaR) approach proposed by Adrian and Brunnermeier (2016). For the full sample period, we find that the information technology sector is the top contributor to systemic risk in the Chinese stock market. To distinguish the risk contribution of each sector under different market regimes, we propose an adjusted Bry-Boschan program to identify turning points in the stock market, which captures regime shifting between bull and bear markets. We find that the risk contribution of each sector in a bear market is significantly higher than that in the following bull market. We also find that the top contributor to systemic risk in the Chinese stock market changes across market regimes. Our findings have important policy implications. First, policymakers may use the early identification of systemically risky sectors of the stock market to improve the pertinence of economic policy-making. Second, it may allow security regulators to foster an environment in which incentives for risk taking by financial practitioners are reduced.