Abstract

     An essential issue of listed firms is adjusting their capital structure as stock market volatility increases. Our study examines this concern by using panel data of the Shanghai Stock Exchange for the period 2008–2018. We find that stock market volatility has immediate positive effects on both total market leverage and short-term market leverage but a negative influence on the long-term market leverage of Chinese listed firms. In this scenario, Chinese listed firms adjust their debt structure by using high bank debts and cutting trade credit due to lower debt costs. Further analyses confirm that the proportion of bank debts to total debts visibly increases while that of trade credit to total debts distinctly decreases. Furthermore, we implement robust tests regarding potential issues, such as sample selection, model selection, endogenous factors, and quantile regression to strengthen the robustness of the main findings. This study provides the first framework for investigating a link between the stock market volatility and capital structure decisions in a typical emerging market.

Introduction

     To date, determinants of capital structure have remained an incomprehensible subject in corporate finance. When firms are allowed to issue stocks on the equity market, equity issuance plays a vital role in the surging corporate capital. In terms of volatility, Welch (2004) documents that a critical matter of corporate finance is the volatility of stock price as a driver of notable changes in the behaviors and financial decisions of corporate managers. Figlewski and Wang (2000) demonstrate that the increase of the volatilities of stock market returns and individual stock returns leads to the decline in market values at the firm level. Following market timing theory, listed companies tend to issue equity when their market values are comparatively high relative to their book and past market values, and then repurchase their issued equity when their market values are low, which exerts a persistent effect on corporate capital structure (Baker & Wurgler, 2002). Chen et al. (2014) and Ahmed and Hla (2019) show that the volatility of individual stock returns significantly influences the capital structure of listed firms in the United States (US) and Pakistan markets, respectively. Most of their findings support the prediction of Tradeoff Theory (TOT) that companies diminish debts to balance and deal with the increasing prospect of default owing to high volatility risk; still, the practical results of Ahmed and Hla (2019) from the Pakistan market are pretty complicated. Similar to Pakistan, China is known as a developing equity market that is unstable, sensitive, and enormously volatile with unexpected shocks from the global market. In addition, the unique capital structure and debt structure of Chinese enterprises are inherently an exciting topic and thus widely explored in research (Fan et al., 2021; Zhang et al., 2015). To our best knowledge, the nexus between stock market volatility and corporate capital structure has not been explored. Surviving literature and evidence have powerfully prompted us to investigate the effect of stock market volatility on the capital structure choices of listed firms in the concrete context of the China stock market rather than imitating previous studies which focus on the volatility effect of individual stock returns on the corporate leverage.

Conclusion

     Choosing the capital structure as the increased stock market volatility is a vital concern of listed firms. This matter becomes more critical for listed firms activating in emerging markets, especially in China’s equity market, due to the following problems. First, emerging equity markets are immature and volatile. Simultaneously, listed firms in developing markets are quite sensitive to the equity market’s volatility. Second, thus far, corporate financing in China market essentially proceeds from internal funding and bank debts. In addition, Chinese firms universally have a high proportion of mature debts. Third, trade credit is an unofficial debt channel popularly used by Chinese firms when facing strict procedures from banks and credit institutions.

     Our study examines how the Chinese stock market volatility immediately affects the capital structure choices of Chinese listed firms from 2008 to 2018. The unique point of this research is the approach based on the capital use routines of Chinese firms instead of using two traditional capital structure theories to explain Chinese corporate capital structure, as in excellent previous literature (Chen, 2004; Huang & Song, 2006; Ni & Yu, 2008; Tong & Green, 2005; Zou & Xiao, 2006). We find that Chinese listed firms use the total market leverage and short-term market leverage at high levels, but long-term market leverage is used at low levels when the China stock market volatility increases and the lending interest simultaneously decreases. Empirical results point out that the positive correlation between corporate market leverage and stock market volatility is derived from the decline in the listed firm’s market value and the increase in bank debts. Trade credit significantly decreases while the bank debts tend to strongly increase in the debt structure of Chinese listed firms as the China stock market increases in volatility. Furthermore, we detect that short-term debts and bank debts are preferred in the capital structure of Chinese listed firms in response to the growing uncertainty of the China stock market and the change in lending interest rate.

Abstract

     Our study finds that momentum is a persistent phenomenon that exhibits great variability in its strength in the UK stock market. Inspired by psychological evidence that cognitive biases can shift overtime, we conjecture that there may be two stock market states, namely, the calm and the turbulent market state, and that the switch between these two market states is governed by market volatility. Using Bayesian estimation methods, our results confirm the role of market volatility as the critical switching variable, which is also found to have additional predictive power for momentum returns in the turbulent market state. Somewhat contradictory to the findings in cross-sectional studies, we find that past returns have a negative impact on momentum profits. We also find that both winners and losers tend to perform better in the turbulent market state than in the calm market state and that losers’ outperformance is responsible for large momentum losses in the turbulent market state. Investment strategies that take advantage of the predictability of momentum dynamics outperform momentum strategies. Our findings are not readily reconciled with risk-based explanations but can be loosely explained in a behavioural framework.

Introduction

     Momentum, first documented by Jegadeesh and Titman (1993), is a well-established phenomenon in the stock market. In the rational framework, momentum profits reflect variations in expected returns and are compensations for risks (Johnson 2002; Sagi and Seasholes 2007; Vayanos and Woolley 2013; Albuquerque and Miao 2014); whereas in the behavioural framework, momentum profits are the outcome of exploiting mispriced stocks, thanks to cognitive biases or bounded rationality (Barberis, Shleifer, and Vishny 1998; Daniel, Hirshleifer, and Subrahmanyam 1998; Hong and Stein 1999). Despite more than two decades of extensive studies, empirical findings on the sources of momentum profits are mixed and the question ‘what drives momentum?’ remains.

     Recent research work, including Barroso and Santa-Clara (2015), Daniel and Moskowitz (2016), and Dobrynskaya (2019), finds that momentum strategies that are profitable on average over time suffer occasional substantial losses and that these losses are predictable to some extent.1 These empirical findings on momentum dynamics and its predictability are intriguing and offer a new perspective for us to examine the causes of momentum and its time-varying characteristics. Daniel and Moskowitz (2016) conclude that none of the crash risk, volatility risk, and Fama and French (1993) factors, can fully explain their findings. They also point out that the existence of the same phenomena and option-like features for momentum strategies in other financial asset markets challenges explanations such as Merton (1974) story. Dobrynskaya (2019) investigates the explanatory power of 13 risk factors for the profitability of investment strategies based on the predictability of momentum crashes, and all factor betas except one are found to be close to zero and statistically insignificant.

Conclusion

     This study extends the investigation of momentum dynamics conditional on market state defined by market volatility. We investigate the momentum effect in the UK stock market and find that it is a significant phenomenon with great variability over time. In line with the recent studies on momentum effect in the US stock, the most striking features of its dynamics are the occasional sharp reversal and its association with market volatility. Informed by the patterns in momentum time series data and psychological evidence of time-varying cognitive biases and heuristics, we construct a two-regime switching model with lagged market volatility as the switching variable to capture the dominance of momentum effect and occasional reversals in the stock market in the short run and show that it performs well in explaining the facts both within and out of sample.

     We find a significant negative nonlinear relationship between the lagged market volatility and the momentum return, and a significant negative relationship between the past return and the momentum return in the calm market state. These relationships are robust across momentum strategies and over time. Momentum dynamics, especially the occasional sharp reversals, are highly predictable. Investment strategies that follow the prediction based on the two-regime switching model significantly outperform momentum strategies. The superior performance of the model-guided investment strategy in combination with the more favourable-to-investors distributional characteristics of its monthly holding period returns, particularly, the lower standard deviation and positive skewness, represents an anomaly which challenges the efficient market hypothesis.

Abstract

     This paper studies the US and global economic fundamentals that exacerbate emerging stock markets volatility and can be considered as systemic risk factors increasing financial stability vulnerabilities. We apply the bivariate HEAVY system of daily and intra-daily volatility equations enriched with powers, leverage, and macro-effects that improve its forecasting accuracy significantly. Our macro-augmented asymmetric power HEAVY model estimates the inflammatory effect of US uncertainty and infectious disease news impact on equities alongside global credit and commodity factors on emerging stock index realized volatility. Our study further demonstrates the power of the economic uncertainty channel, showing that higher US policy uncertainty levels increase the leverage effects and the impact from the common macro-financial proxies on emerging markets’ financial volatility. Lastly, we provide evidence on the crucial role of both financial and health crisis events (the 2008 global financial turmoil and the recent Covid-19 pandemic) in raising markets’ turbulence and amplifying the volatility macro-drivers impact, as well.

Introduction

     A common stylized fact about emerging economies is the high volatility of their stock markets (De Santis 1997; Aggarwal et al. 1999; Xu 1999; Cano-Berlanga and Giménez-Gómez 2018). The liberalization of the emerging world’s financial markets, which attracted a significant amount of capital flows by foreign institutional investors, has been the first step of an integration process with significant economies’ interdependence and asset markets’ synchronization. Given that emerging economies are characterized by critical vulnerabilities to external shocks, they exhibit higher equity market fluctuations than the developed markets and it is worth investigating how US and global common economic forces affect their intra-daily volatility. In general, modeling the volatility of financial returns has crucial implications for asset allocation, risk management practices, and financial stability oversight. Robust modeling and reliable forecasting of the volatility trajectory of financial instruments has been the main task and objective of financial economics applications for business operations, given that volatility constitutes one of the fundamental input variables in estimations and decision processes of any corporation on investing and funding choices. Financial volatility is also closely inspected by policymakers since it is perceived to constitute an early warning crisis signal, entailing critical destabilizing threats for the financial system (see, for example, Kürüm et al. 2018).

Conclusions

     Our study has examined the HEAVY model and its extension with leverage, power transformations, and macro-characteristics. For the realized measure, our empirical results favor the macro-augmented cross asymmetric power specification, where the lags of both powered variables—squared negative returns, and realized variance—move the dynamics of the power transformed conditional variance of the latter. Similarly, modeling the returns with a double asymmetric power process, we found that not only the powered realized measure asymmetry but the power transformed squared negative returns, as well, help to forecast the conditional variance of the latter. The macro-augmentation of the asymmetric power model ensures the superiority of our contribution, which can be implemented in several investment and risk management practices. We further demonstrated the forecasting dominance of the extended specifications over the benchmark HEAVY and standard volatility models through the out-of-sample forecasting across multiple short- and long-term horizons.

     Moreover, we demarcate our study from previous literature by estimating the significant US uncertainty effect on the power of leverage (Heavy and Arch), and the macro-determinants of emerging markets realized variance. The US-led uncertainty spillovers shed light on new evidence for volatility modeling and macro-financial linkages literature. Our findings’ novelty is twofold: Given higher (lower) daily US policy uncertainty levels, mostly associated with economic downturns (upturns), (i) heavy and leverage effects become more (less) acute in realized variance modeling, and (ii) US EPU volatility, financial uncertainty, credit conditions, commodity market benchmarks, and disease news impact on emerging financial volatility increases (decreases). Similarly, financial and health crisis events magnify further the Heavy, Arch, and Macro parameters of the bivariate system and the EPU indirect impact on the volatility drivers, as well.

Abstract

     This paper explores the effectiveness of predictors, including nine economic policy uncertainty indicators, four market sentiment indicators and two financial stress indices, in predicting the realized volatility of the S&P 500 index. We employ the MIDAS-RV framework and construct the MIDAS-LASSO model and its regime switching extension (namely, MS-MIDAS-LASSO). First, among all considered predictors, the economic policy uncertainty indices (especially the equity market volatility index) and the CBOE volatility index are the most noteworthy predictors. Although the CBOE volatility index has the best predictive ability for stock market volatility, its predictive ability has weakened during the COVID-19 epidemic, and the equity market volatility index is best during this period. Second, the MS-MIDAS-LASSO model has the best predictive performance compared to other competing models. The superior forecasting performance of this model is robust, even when distinguishing between high- and low-volatility periods. Finally, the prediction accuracy of the MS-MIDAS-LASSO model even outperforms the traditional LASSO strategy and its regime switching extension. Furthermore, the superior predictive performance of this model has not changed with the outbreak of the COVID-19 epidemic.

Introduction

     Financial market volatility is not only a key factor in assessing the risk of financial assets (see, e.g., Wang et al., 2015; Bee et al., 2016; Clements & Liao, 2017; Li & Wei, 2018; Ji et al., 2021) but also an important parameter in the pricing of financial derivatives and asset allocation (Graham & Harvey, 1996; Zhu & Ling, 2015), as well as an important factor in portfolio management (Ayub et al., 2015; Cederburg et al., 2020). Therefore, accurately predicting the volatility of the stock market is particularly important for investors when deciding on the sizes and timing of their investments.

     Despite the many benefits of accurately predicting stock market volatility, improving predictive accuracy has proven challenging. First, the diversity of potential predictors poses a great challenge to the selection of predictors. For example, studies have determined the predictive abilities of macroeconomic and financial variables for stock market volatility (Nonejad, 2017; Paye, 2012). In recent years, with the introduction of economic policy uncertainty indices, some studies have also determined the predictive power of some economic policy uncertainty indicators (e.g., the economic policy uncertainty index, trade policy uncertainty index, monetary policy uncertainty index, equity market volatility index and geopolitical risk index) relative to stock market volatility (Alqahtani et al., 2020a; Gupta & Wohar, 2019; Li et al., 2020a; Olasehinde-Williams, 2021; Paye, 2012; Yu et al., 2018). In addition, studies have found evidence that the stock market can be driven by investors’ psychology (Daniel et al., 2002; Tseng, 2006) and determined the predictive power of some market sentiment indicators (see, e.g., Gupta et al., 2014; Perez-Liston et al., 2014; Oliveira et al., 2017; Jin et al., 2020; Liang et al., 2020a; Wang et al., 2020a). Other indicators, such as financial stress indices, have also been proven to have potential forecasting ability (Gupta et al., 2014; Singh, 2016; Sum, 2014). The predictive abilities of these potential predictors tend to change with changes in various external factors, making it more difficult to find stable predictors.

Conclusions

     In this paper, we explore the effectiveness of 9 economic policy uncertainty indices, 4 market sentiment indicators and 2 financial stress indices in predicting the realized volatility of the S&P 500 index. We use several MIDAS-RV-X models to determine the predictive power of individual predictors and further construct PCA-, PLS- and LASSO-based MIDAS-RV extensions, a MIDAS-LASSO model with regime switching and various combination forecasts to find the optimal forecasting strategy. The out-of-sample forecasting performance of our forecasting models of interest is evaluated mainly by the MCS test and out-of-sample R2 statistics.

     The empirical results first show that the economic policy uncertainty indicators (especially the EMV) and the VIX are more likely to produce better forecasting accuracy than the three market sentiment indicators and two financial stress indices. Among these predictors, the VIX has the strongest predictive power, followed by the EMV. Second, the combination forecasts and MIDAS-LASSO models usually have statistically significant relative forecasting performance. Across all forecasting models of interest, the MIDAS-LASSO model with regime switching has the best forecasting performance, followed by the MIDAS-LASSO model, indicating that LASSO technology can capture more valuable information from a large set of predictors than other information integration methods. The forecasting accuracy of MIDAS-LASSO with regime switching is also superior to that of the traditional HAR-LASSO method and its regime switching extension. When distinguishing between high- and low-volatility regimes, we find that the VIX is the best predictor under both scenarios. In addition, the economic policy uncertainty indicators other than GEPU either help predict high stock market volatility or low stock market volatility, but the market sentiment indices other than the VIX and the financial stress index (OFRFSI) exhibit statistically significant relative predictive performance only during low-volatility regimes. Across all considered forecasting methods, the MS-MIDAS-LASSO model still exhibits the best predictive performance during both high- and low-volatility regimes, but the MIDAS-LASSO model can outperform other forecasting methods except for MS-MIDAS-LASSO only during high-volatility regimes. We finally consider the impact of the COVID-19 epidemic. During the COVID-19 epidemic, the predictive power of the VIX diminished, and it was no longer the best out-of-sample predictor, while the predictive power of the EMV increased, making it the best predictor during this special period. However, the MIDAS-LASSO model with regime switching still exhibited the best forecasting performance across all considered forecasting models during the COVID-19 epidemic.

Abstract

     This study examines the impact of the economic policy uncertainty index (EPU) and macroeconomic variables on the volatility of the Pakistan stock market using the GARCH-MIDAS (mixed data sampling) model. The model allows us to observe whether those variables contain valuable information to forecast stock market volatility. Our empirical findings show several outcomes. First, our out-of-sample results show economic policy uncertainty index has predictive power to forecast Pakistan stock market volatility. Second, among all variables, oil prices are the most powerful predictor of volatility with a higher out of sample R square value. Third, all macroeconomic variables including exchange rate, short-term interest rate, money supply M2, foreign direct investment, gold prices, inward remittances, industrial production, and consumer price index (proxy for inflation) contain useful information for stock market volatility forecasting. However, the long-run interest rate is an ineffective indicator of volatility during the sample period study. Finally, we find that the combination forecast information is also useful for volatility forecasting.

Introduction

     Volatility in financial markets is an issue of great concern. As a risk measure, volatility considers an important state variable for asset pricing and investment. Additionally, the volatility dynamics are associated with portfolio allocation, risk management, hedging, and options pricing (Christoffersen & Diebold, 2000; Chun et al., 2020; Mei et al., 2017; Morales and Callaghan, 2011; Naeem et al., 2019; Wang and Nishiyama, 2015; Zhang et al., 2020). The higher volatility creates widespread panic and results in disorderly market situations that reduce investors’ confidence and that lead to declines in business investments and economic growth. Therefore, understanding the mechanism of economic dynamics and accurately estimating future volatility is essential.

     However, it is prudently observed by financial analysts, policymakers, and market practitioners. Nonetheless, before estimation, it is hard to find the main economic drivers of volatility. The seminal work of SCHWERT and WILLIAM (1989), who posits a time-varying link between stock market uncertainty and macroeconomic variables. Moreover, Paye (2012) examines the strong linear connection between macroeconomic indicators and stock market volatility. Both theoretical and empirical studies confirm that institutional and macroeconomic determinants are potential drivers of financial markets volatility (Engle et al., 2013; Bahloul et al., 2017; Fang et al., 2018; Hsu et al., 2019). The literature shows that financial market volatility and macroeconomic indicators are intrinsically associated (Chen et al., 1986; Mele, 2007; Christiansen et al., 2012).

Conclusion

     This research examines the forecasting ability of macroeconomic variables and economic policy uncertainty index (EPU) for Pakistan stock market volatility. The macroeconomic variables include exchange rate, long-run interest rate, money supply (M2), short-term interest rate, remittances, consumer prices index (proxy for inflation), foreign direct investment, industrial production, global oil prices, and gold prices. We use the GARCH-MIDAS model for forecasting short-and long-run volatility. In our empirical analysis, we find some notable results. First, we find economic policy uncertainty index information is useful for Pakistan stock market volatility prediction. Second, we find global indicators oil prices are more powerful predictor of Pakistan market volatility. Moreover, all macroeconomic variables are effective indicators of Pakistan stock market volatility except long run interest rate. In addition, the combination information for all macroeconomic variables is also useful for forecasting volatility. These findings are in line with the literature (Asgharian et al., 2013; Liu & Pan, 2020). However, the abovementioned studies are not directly involved in forecasting Pakistan’s stock market volatility. In addition, we did not find consistent findings in a longer period, such as two months and three months. Overall, results indicate that macroeconomic variables are significant predictors of stock market volatility in Pakistan. Therefore, the government and policymakers should consider these factors in policymaking and stock market volatility estimation.

1- Introduction

2- Empirical strategy

3- Stylized facts

4- Results

5- Illustrating the results with a “Keynesian” model

6- Conclusions

References

Abstract

Economic theory offers several explanations as to why shifting expectations about future economic activity affect current demand. Abstracting from whether changes in expectations originate from swings in beliefs or fundamentals, we test empirically whether optimism and pessimism about the economy trigger short-term fluctuations in private consumption and investment. Under the assumption that cyclical movements in private consumption and investment growth are exogenous to potential output growth forecasts far into the future, our results are consistent with the idea of private economic agents learning about future potential output growth and adjusting their current demand accordingly. We also propose a simple Keynesian model to illustrate that revisions in expected future income can affect short-term equilibria, in line with the results of the empirical analysis.

Introduction

The notion that changes in expectations about future economic activity affect current aggregate demand has a long tradition in macroeconomics. The vast literature about the impact of confidence on economic activity includes: (i) the view arguing that “animal spirits” (and “sunspots”) eventually lead to busts as they are not supported by fundamentals (Cass and Shell, 1983; Akerlof and Shiller, 2010; De Grauwe and Ji, 2016);1 (ii) the view for which the same waves of optimism and pessimism associated with “animal spirits” do not necessarily lead to busts, rather they lead to self-fulfilling changes in fundamentals (Acharya et al., 2017; Benhabib et al., 2016), as changes in expectations of some agents trigger the actions of rational agents exhibiting strategic complementarities (Weil, 1989; Cooper and John, 1988); and (iii) a view generally referred to as “news-driven business cycles”, which posits that agents become optimistic or pessimistic based on the imperfect (or noisy) information they gather about future developments. If the information is correct the boom lasts, but if it is incorrect or agents are overly optimistic, a bust would occur (Cochrane, 1994; Beaudry and Portier, 2004; 2006; Lorenzoni, 2009; Jaimovich and Rebelo, 2009; Beaudry et al., 2011; Schmitt-Grohé and Uribe, 2012; Blanchard et al., 2013; Forni et al., 2017).

Abstract

The stock market is constantly changing with uncertainties. Rapid dissemination of information and fast capital flow will lead to fluctuations of stock price, and the undulating price will affect the market in return. This is a process of mutual influence and mutual conduction. China’s stock market, which pertains to an emerging market, has been acutely volatile since the very beginning, and often appear radical ups and downs. This paper selects the SSE Composite Index as research object, through the application of GARCH type models to conduct empirical analysis, carving the features of this index from an econometric perspective. And on basis of the status quo of the volatility of SSE Composite Index, it offers some suggestions.

The result shows that from the time series point of view, the SSE Composite Index possesses significant properties of time-varying and clustering. Series distribution of it presents leptokurtosis with significant ARCH and GARCH effects. Moreover, by comparing the fitting and forecast performance of GARCH (1, 1) (symmetric) and TARCH (1, 1) and EGARCH (1, 1) (asymmetric), it can be concluded that EGARCH (1, 1) outperforms the others. Besides, China’s securities market should strengthen its system construction, reduce excessive government intervention and advocate rational investment philosophy.

Introduction

Background

Since the Shanghai Stock Exchange was formally established in 1990, China’s stock market has gone through a development journey of 27 years. Whereas it is now still of no high level of normalization in terms of supervision or institution, and with a strong volatility. The unstable stock market, unscientific investments and occasional event of malignant investment make the whole market full of highrisk, and propose sever challenges to institutions and individuals (Zhang, Luo and Tang, 2012). Many foreign institutional investors approved by the CSRC (China Securities Regulatory Commission) have joined the ranks of investors. Owing to the uncertainty and high-speed international capital flows, the environment of financing and investments has become increasingly complicated. China’s securities market is experiencing a period of both opportunities and risks (Fang, 2010). In this case, an accurate description of how the stock price fluctuates and how to determine the future rate of return of the stock market has become a hot issue in the academia and the investment community. Therefore, the study of volatility has significant theoretical significance and applicable value.

We develop a simple agent-based financial market model in which speculators’ market entry decisions are subject to herding behavior and market risk. In addition, speculators’ orders depend on price trends, market misalignments and fundamental news. Using a mix of analytical and numerical tools, we show that a herding-induced market entry wave may amplify excess demand, triggering lasting volatility outbursts. Eventually, however, higher stock market risk reduces stock market participation and volatility decreases again. Simulations furthermore reveal that our approach is also able to produce bubbles and crashes, excess volatility, fat-tailed return distributions and serially uncorrelated price changes. Moreover, trading volume is persistent and correlated with volatility.

Introduction

The goal of our paper is to develop a simple agent-based financial market model to explain a number of important stylized facts of stock markets. In particular, we analytically and numerically demonstrate that speculators’ market entry and exit behavior may give rise to volatility clustering. Our model’s key features and its main implications may be summarized as follows. We assume that there is a market maker who adjusts stock prices with respect to speculators’ orders, which, in turn, use technical and fundamental trading rules to determine their trading behavior. Speculators’ market entry decisions depend on two socio-economic principles. First, speculators are subject to herding behavior and increasingly enter the stock market as the number of active speculators increases. Second, speculators react to stock market risk. The higher the past volatility of the stock market, the lower the probability that a speculator will enter the stock market. As it turns out, the stock market is relatively stable if the number of active speculators is low. Since stock market risk is then perceived as negligible, more and more speculators become active. Consequently, excess demand increases, the market maker adjusts stock prices more strongly and volatility picks up. Due to the increase in stock market risk, stock market participation eventually decreases again. Confronted with a lower excess demand, the market maker needs to adjust stock prices less strongly. We also show that the repeated inflow and outflow of speculators along with their heterogeneous trading behavior may produce bubbles and crashes, excess volatility, serially uncorrelated returns and a fat-tailed return distribution. Moreover, trading volume displays significant memory effects and is strongly correlated with volatility. Keeping track of the individual speculators’ wealth dynamics reveals that heterogeneity among speculators may be a persistent phenomenon of financial markets, i.e. neither do a few speculators accumulate all the wealth and dominate the market nor does a substantial fraction of speculators go bankrupt and vanish from the market. Our paper adds to the burgeoning stream of literature on agent-based financial market models (see Chiarella et al., 2009a; Hommes and Wagener, 2009; Lux, 2009 for surveys). Within these models, speculators apply technical and fundamental trading rules to determine their orders. Technical trading rules (Murphy, 1999) are usually based on trend extrapolation and tend to destabilize the dynamics of financial markets. In contrast, fundamental trading rules (Graham and Dodd, 1951) bet on mean reversion, exercising a stabilizing impact on the dynamics of financial markets. Models by Day and Huang (1990), De Grauwe et al. (1993), Brock and Hommes (1998), LeBaron et al. (1999), Farmer and Joshi (2002), Chiarella et al. (2007), Franke and Westerhoff (2012) and Jacob Leal and Napoletano (2017), for example, show that (non-linear) interactions between speculators relying on technical and fundamental trading rules can produce dynamics which resembles the dynamics of actual financial markets quite closely. Without question, this line of research helps us to improve our understanding of the functioning of financial markets. For instance, agent-based financial market models reveal that a bubble may emerge if speculators forcefully rely on technical analysis while a crash can be set in motion if speculators put more weight on fundamental analysis. Such a time-varying impact of technical and fundamental trading rules can also produce volatility clustering. Financial markets tend to be relatively stable when speculators prefer fundamental analysis but turn wilder when speculators opt for technical analysis.

Purpose – The purpose of this paper is to predict real gross domestic product (GDP) growth and business cycles by using information from both liquidity and volatility measures. Design/methodology/approach – The paper estimates liquidity and volatility measures from over 5,000 NYSE rms and extracts a common factor, which the paper calls uncertainty. In-sample and out-ofsample forecasting tests are used to determine the ability of the uncertainty factor to predict growth in real GDP, industrial production, consumer price index, real consumption and changes in real investment. Findings – The paper finds that on average, positive shocks to the uncertainty factor occur in the quarters preceding and at the beginning of a recession. During the quarters toward the end of recessions, there are negative shocks to uncertainty on average. Originality/value – Previous research has explored using either liquidity or volatility to forecast economic activity. The paper bridges the two branches of research and finds a link to real GDP growth and business cycles.

Introduction

Recently, there has been a large branch of literature that examines market liquidity. Pástor and Stambaugh (2003), Acharya and Pedersen (2005), Chen (2005) and Sadka (2006), all look into systematic liquidity risk. Additionally, as there are several different measures of liquidity, many studies have focused on identifying a common systematic liquidity factor (Chordia et al., 2000; Hasbrouck and Seppi, 2001; Eckbo and Norli, 2002; Korajczyk and Sadka, 2008). Amihud, Hameed, Kang and Zhang (2015) examine 45 countries and find evidence that the illiquidity premium exists in a number of international markets. With the most recent financial crisis, there has been an interest in the apparent causal link between a reduction in liquidity and an economic slowdown. Czauderna et al. (2015) find a bidirectional link between liquidity (as measured using exchange traded funds) and market returns in the German stock market between July 2006 and June 2010. Wagner and Winter (2013) include liquidity risk in models to evaluate daily mutual fund performance for funds focused on the European market. When using a six-factor model including liquidity and idiosyncratic risk, they find that a large number of funds have significant loadings on both the liquidity and idiosyncratic risk factors. In measuring exposure to the liquidity risk factor, they find that on average, fund managers prefer liquid stocks. In a paper by Næs et al. (2011), they show that this link between liquidity and recessions is not a recent phenomenon but has existed in past recessions as well. They find that, on average, there is an increase in illiquidity before a recession followed by an increase in liquidity during the tail end of the recession. Furthermore, measures of liquidity help in forecasting future real gross domestic product (GDP) growth. Switzer and Picard (2016) also examine measures of aggregate liquidity and their relation to economic cycles. Rather than using a linear model, as was done by Næs et al. (2011), Switzer and Picard (2016) use nonlinear models and find the relationship between aggregate liquidity measures and the real economy to be significantly less pronounced. In addition to linking liquidity to business cycles, several papers have explored stock market volatility and its relation to real macroeconomic variables and business cycles (Schwert, 1989; 1990; Hamilton and Gang, 1996). Hamilton and Gang (1996) find that stock volatility may be useful in forecasting economic activity. We merge these two trains of thought and check if there is any added benefit from considering liquidity and volatility jointly. In Carlston (2012), multiple daily liquidity and volatility measures are estimated from daily stock prices and returns. Common factors, what we will term as “uncertainty”, are extracted across all of the liquidity and volatility measures. He finds that this common risk factor carries a significant premium and helps explain the cross section of expected returns. In this paper, we follow the methodology of Næs et al. (2011) to explore a possible link between this common liquidity and volatility measure and the real economy and business cycles.

The momentum anomaly was first documented in the U.S. stock markets by Jegadeesh and Titman (1993) who showed that recent winner (loser) stocks continue winning (losing) over the next three to 12 months and that a zero-cost portfolio that buys recent winners and short sells recent losers produced significant abnormal returns. The momentum anomaly has since been documented in several other countries and other asset classes (e.g. Asness, Moskowitz and Pedersen, 2013; Rouwenhorst, 1998). Though the existence of the momentum anomaly is now well accepted, its source and the reason for its persistence are still contentious. Two of the most prominent behavioural explanations of the momentum anomaly include the underreaction story of Barberis, Shleifer and Vishny (1998) and Hong and Stein (1999), and the overconfidence and self-attribution story of Daniel, Hirshleifer and Subrahmanyam (1998). Meanwhile the persistence of these anomalous momentum profits is usually attributed to arbitrage costs. These arbitrage costs come in the form of holding and transaction costs. Holding costs are considered as the primary source of arbitrage costs (Ackert and Tian, 2000; Ben-David and Roulstone, 2005; Pontiff, 2006; Shleifer and Vishny, 1997) while idiosyncratic is risk deemed as the primary holding cost (Pontiff, 2006; Shleifer and Vishny, 1997).

Market volatility, liquidity, and stock returns are all variables of significant interest to financial economists, market regulators, and investors.2 However, why and how these variables are interrelated has not been fully understood. For example, the literature provides little guidance as to why the returns of certain securities are more sensitive to volatility shocks than the returns of other securities. In addition, no previous study explicitly considers the role of liquidity providers in the analysis of the relation between market volatility and stock returns. As a result, prior research attributes the negative relation between market volatility and market returns primarily to greater risk premiums that are associated with higher market volatility.3 In this study, we shed additional light on the relation between market volatility and stock returns by examining the cross-section of stock returns that result from volatility shocks using the Chicago Board Options Exchange Market Volatility Index (VIX). 4 Our study shows that the negative relation between market volatility and stock returns arises not only from greater risk premiums but also greater illiquidity premiums that are associated with higher market volatility. We also provide estimates of the direct effect of volatility shock on stock returns, which is driven by greater risk premiums, and the indirect effect of volatility shock on stock returns, which is driven by greater illiquidity premiums associated with higher market volatility.

مقاله سال 2017

Interest rate and foreign exchange rate are two important macroeconomic variables in open economics that significantly affect the stock market. Interest rate represents the stance of the central bank on monetary policy; it affects stock prices through discount rate channels, expected future dividends, and equity premium (Bernanke & Kuttner, 2005). In particular, the worldwide low interest rate environment plays a significant role in improving the global stock market after the subprime crisis. Meanwhile, movements in exchange rates affect stock prices because of their influences on the cash flow and international competitiveness of firms, as well as on capital flows in and out of a country. Numerous studies have investigated the effects of interest rate variation or monetary policy shocks on stock returns (Thorbecke, 1997; Bjørnland & Leitemo, 2009). Other studies have explored the relationship between exchange rate and stock returns (Hau & Rey, 2006; Cho, Choi, Kim & Kim, 2016). However, to the best of our knowledge, few studies have considered the effects of variations in interest and exchange rates on investor behavior at the micro-level. In this study, we address this gap and examine the effects of variations in interest and exchange rates on herding behavior in the stock market. Herding in financial markets refers to a behavioral pattern in which investors suppress their own beliefs and base their investment decisions solely on the collective actions of the market (Christie & Huang, 1995). Existing empirical studies have documented herding behavior in different countries, particularly in emerging markets due to considerable information asymmetry and the lack of maturity in these markets (Chang, Cheng & Khorana, 2000; Chiang & Zheng, 2010). As an important emerging market that is primarily dominated by unsophisticated retail investors, the Chinese stock market provides an interesting setting to analyze herding behavior. Moreover, the recent uncertainty in the economic development of China has resulted in increased fluctuations in interest and exchange rates, as well as in the intensive response of the stock market to these variations. In this context, this study aims to answer the following questions: (1) Do interest and exchange rates induce herding behavior in the Chinese stock market? (2) Under what market conditions will investors respond intensively to variations in interest and exchange rates, and what types of stocks are most affected? (3) Do monetary policy announcements and extreme exchange rate volatility induce herding?

This study distinguishes itself from previous research and contributes to literature in the following aspects. First, this study is the first to examine the effects of variations in interest and exchange rates on herding behavior in the stock market. Our results indicate that interest rate increase and Chinese currency (hereafter CNY) depreciation will induce herding. This phenomenon is mainly manifested in down markets, thereby suggesting that investors respond intensively to bad news. Bikhchandani and Sharma (2000) distinguish between “spurious herding” (fundamental herding), in which investors facing similar decision problems and information sets make similar decisions, and “intentional herding” (non-fundamental herding), which indicates an obvious intent of investors to follow the behavior of others. These researchers suggest that spurious herding may increase the efficiency of financial markets, whereas intentional herding is expected to result in excess volatility and even financial instability. In this study, interest and exchange rates are public information that appears to lead to spurious herding. Nonetheless, the response to these fundamental changes by investors may unnecessarily improve the efficiency of the market, particularly under extreme interest rate or exchange rate volatility, because investors may overreact to unexpected information changes (Bondt & Thaler, 1985). Thus, this study investigates whether variations in interest and exchange rates can induce herding rather than overemphasize the pricing efficiency of fundamental herding.

Second, we propose a method to detect the occurrence of intentional herding in the aggregate market. Macro information, such as interest rate and exchange rate, may induce spurious herding in the stock market; hence, questioning whether intentional herding occurs in this market is natural. With respect to this question, Holmes, Kallinterakis, and Ferreira (2013) analyze herding under different market conditions in Portugal and find that institutional herding is intentional driven by reputational reasons and/or informational cascades. Galariotis, Rong and Spyrou (2015) use the Fama-French three factors and the momentum factor to reflect common risk factors in stock valuation and decompose cross-sectional absolute deviation into fundamental and non-fundamental information parts. Unlike in previous studies, we determine the occurrence of intentional herding by examining whether investors herd on the idiosyncratic risk of stocks. Idiosyncratic volatility (IVOL) measures the idiosyncratic risk of a firm that does not arise from the systematic risk factors. Therefore, the occurrence of significantly varied herding coefficients among different IVOL portfolios comes from idiosyncratic risks rather than from fundamental changes that affect the entire market. Consequently, this phenomenon can be attributed to intentional herding. Consistent with our predictions, we find that the herding level of the highest IVOL quintile portfolio is twice that of the lowest quintile portfolio, which proves the occurrence of intentional herding in the Chinese stock market. After controlling for institutional ownership or number of institutions, the herding levels of the two highest IVOL quintiles weaken, thereby indicating that retail investors play an important role in reinforcing herding with the highest IVOL stocks.