Pessimistic Investors Research Articles

Implied volatility curve: an empirical study on Chinese SSE 50 ETF options

ABSTRACT This paper studies the determinants shaping the implied volatility curve of Chinese SSE 50 ETF options and the predictability of its patterns. Initially, the research examines the economic variables influencing the curve’s curvature. Our analysis reveals a pronounced disparity in how investor sentiment and risk appetite impact the curvature of call versus put options’ volatility curves. Subsequently, the study distinguishes four patterns of the volatility curve, exploring the causes of their formation. We observe that shifts in market conditions precipitate alterations in the curve’s patterns, with pessimistic investor sentiment and diminished risk appetite correlating with a more pronounced skewness in the curve. In the final phase, the research employs machine learning techniques to prognosticate the patterns of the volatility curve. The chosen model demonstrates robust proficiency in forecasting Smile and Smirk patterns, yet it notably struggles to predict the Frown pattern, which typically emerges alongside major market emergencies.

Probability distortion and non-participation

This paper explores the impact of probability distortion on investors’ participation decisions and market performance. Specifically, we construct a one-period investment model with two types of investors—probabilistic pessimistic and neutral investors. The former assign more weight to bad outcomes and tail events when making decisions. Our findings suggest that non-participation arises from these probabilistic pessimistic investors. In equilibrium, their participation decisions affect the asset premium, which can be decomposed into the risk premium and probabilistic premium.

Optimists, Pessimists, and Stock Prices

We review the academic findings from psychology and economics on disagreement—specifically, the effect of disagreement on asset prices. We discuss measurement of disagreement and how disagreement, coupled with constraints on short selling, can sideline pessimistic investors and result in overpricing. We review the literature on short selling in financial markets, paying particular attention to how and why some issues become hard-to-borrow and what factors go into the determination of borrowing costs, and we discuss the evolution of borrowing costs over the last several decades. We show how an examination of the prices and borrowing costs for constrained stocks can lead to an improved understanding of how disagreement in financial markets arises and is resolved, and we discuss directions for future research.

Do auditors care about what retail investors say? Evidence from China

Using China's stock forum postings from 2008 to 2020, we document that when client firms exhibit pessimistic investor sentiment, auditors exert greater effort and charge higher fees from those clients. This increase in audit fees is attributed to the downward pressure on stock prices and the elevated litigation risk of the clients. We also reveal that the impact of pessimism on audit fees is stronger for firms with more widespread pessimism, those audited by larger audit firms and auditors with higher rankings, and those with a greater retail investor influence.

Disclosure paternalism

This study presents a model in which behavioral investors shape their current expectations based on statistical analysis of historical non-disclosure events. Investors may hold overly optimistic expectations following a non-disclosure event, thereby disrupting unraveling toward forthcoming disclosures. While a regulator can mandate disclosure, this protective intervention has its drawbacks. Overprotection prevents investors from learning from losses and leads to cycles of high compliance followed by high mispricing when innovations in transactions render current regulations ineffective. An unregulated market, on the other hand, tends toward high transparency over time. The model further explains negative market reactions to regulation, an association between price drift and transparency, reversals in market confidence, and that regulators should favor laissez-faire in times of investor pessimism. Further implications are explored for regulations that facilitate learning and prevent cycles.

The impact of ambiguity on dynamic portfolio selection in the epsilon-contaminated binomial market model

We consider dynamic portfolio selection under ambiguity in the classical multi-period binomial market model. Ambiguity is incorporated in the real-world probability measure through an epsilon-contamination, that gives rise to a completely monotone capacity conveying a pessimistic investor’s ambiguous beliefs. The dynamic portfolio selection problem is formulated as a Choquet expected utility maximization problem on the final wealth. Then, the optimal final wealth is proved to be a function of the final stock price: this allows a dimension reduction of the problem, switching from an exponential to a linear size with respect to the number of periods. Finally, an explicit characterization of the optimal final wealth is given in the case of a constant relative risk aversion utility function and the interaction between the ambiguity and the relative risk aversion parameters is investigated.

Behavioural explanations of Expectile VaR forecasting and dynamic hedging strategies for downside risk during the COVID‐19 pandemic: Insights from financial markets

AbstractIn this paper, we investigate the influence of confirmation bias on Expectile Value at Risk (EVaR) forecasting among fundamentalist, optimistic, and pessimistic investors in cryptocurrency, commodity, and stock markets before and during the COVID‐19 pandemic. Utilizing the DCC‐range GARCH model, we also explore the conditional minimum downside risk hedge ratios. Our findings demonstrate that confirmation bias leads to excessive EVaR for financial market returns, regardless of the period being before or during COVID‐19. Moreover, fundamentalists' expectations in all markets remain constant, while without confirmation bias, optimists' and pessimists' expectations tend to converge to zero over time but diverge significantly during turbulent periods. When confirmation bias is present, the average distance between these expectations widens. Analysing the hedge ratio results, with or without confirmation bias, also unveils the conditional minimum downside risk hedge ratios. These ratios indicate the optimal proportions for hedging downside risk in each financial market during different periods. We find that the conditional minimum downside risk hedge ratios are generally lower (higher) during the pre‐COVID‐19 (COVID‐19) period, implying that hedging costs are higher during the COVID‐19 period. These insightful findings offer valuable insights for traders and regulators in identifying and understanding the risk conditions of cryptocurrency, commodity, and stock markets. Additionally, the analysis of conditional minimum downside risk hedge ratios provides investors with essential information on how to strategically position their portfolios to mitigate and manage risk during both tranquil and turbulent market conditions, with and without confirmation bias.

International stock return predictability: The role of U.S. uncertainty spillover

This paper explores the implications of U.S. uncertainty for cross-country asset pricing. We propose a global common spillover index of U.S. uncertainty (GSIU) based on the Partial Least Square method, and show that this index is a powerful predictor of aggregate stock returns around the globe, both in- and out-of-sample. Additionally, we find that GSIU affects stock returns through both cash flow and discount rate channels. Furthermore, our index is linked to well-known pricing factors, namely that an increase in GSIU is generally associated with a deteriorating macroeconomic condition, higher levels of stock market volatility and economic policy uncertainty, increased disaster risk, as well as pessimistic investor sentiment, both domestically and internationally.

Forecasting volatility with investor pessimism index: Exploring the predictive power of search queries

AbstractThis paper discusses whether investor sentiment contains useful information to predict volatility by constructing investor pessimism index based on Baidu, which is the largest search engine in China. We discover that the investor pessimism index is significantly negatively correlated with contemporaneous returns but predicts following days' returns reversals. Furthermore, the heterogeneous autoregressive model of realized volatility (HAR‐RV) with investor pessimism index performs better than other HAR‐RV‐type benchmark models. We perform a series of robustness tests, which include alternative construction of investor pessimism index, crisis subsample, alternative predictive model, alternative measures of volatility, and considering the role of institutional investor. Our results remain robust. Taken together, investor pessimism index constructed in this paper seem to be a suitable proxy to measure investor sentiment in China and can enhance the accuracy of volatility forecasting in the Chinese stock market.

Oil price shocks and stock–bond correlation

This paper investigates the role of oil as a determinant of the US stock–bond correlation. The analysis uses monthly data over the period from February 1990 to July 2021. We examine the impact of oil shocks, using the Ready (2018) method, alongside a range of macroeconomic variables on the nature of stock–bond dynamic correlation. Our main findings demonstrate that during recessionary periods, the stock–bond correlation is adversely and statistically explained by oil supply shocks, and that correlation tends to statistically diverge from that of it is counterpart during expansionary periods. In addition, demand shocks are more pronounced in periods of pessimistic investor sentiment, whereas supply and risk shocks appear during optimistic periods. From a risk management standpoint, a backtesting exercise shows that the incorporation of supply and demand shocks generally improves the forecast of portfolio volatility under various portfolio weighting schemes and market conditions. A time-varying hedging exercise also reveals that accounting for both demand and supply shocks reduces the cost of hedging, mainly following major crisis periods. The contribution of shocks appears most with a long position in the bond market after the 2014 oil crisis period. Our main results remain the same after performing a set of the robustness checks.

Mathematical Analyses of the Upper and Lower Possibilistic Mean – Variance Models and Their Extensions to Multiple Scenarios

Possibility theory is the one of the most important and widely used uncertainty theories because it is closely related to the imprecise probability and expert knowledge. The possibilistic mean - variance (MV) model is the counterpart of the Markowitz’s MV model in the possibility theory. There are variants of the possibilistic MV model, which are called as the upper and lower possibilistic MV models. However, to the best of our knowledge, analytical solutions and exact efficient frontiers of these variants are not presented in the literature when the possibil-ity distributions are given with trapezoidal fuzzy numbers. In this study, under this assumption, we make mathemat-ical analyses of the upper and lower possibilistic MV models and derive their analytical solutions and exact efficient frontiers. Based on the max-min optimization framework, we also propose their extensions where there are multiple upper (lower) possibilistic mean scenarios. We show that the proposed extensions have the ease of use as the upper and lower possibilistic MV models. We also illustrate and compare the upper and lower possibilistic mean - variance models and their proposed extensions with an explanatory example. As we expect, we see that these extensions can be effectively used in portfolio selection by conservative investors.

코로나19 방역정책이 글로벌 주식시장에 미치는 경제적인 영향 분석

This paper investigates the impact of COVID-19 global fear index and government response index on the returns an volatiles of stock markets. Applying the panel data regression, this paper utilizes the COVID-19 confirmed cases and deaths, and the government response indices. This paper finds several empirical results. First, the government response index has a negative impact on the returns of stock markets. This finding indicates that pessimistic investors lead to fall the stock returns down. Second, COVID-19 fear index intensifies the uncertainty of stock markets with a increase in volatilities. Third, stringency index and government response index have a negative impact on investors’ sentiments and increase high volatility. Finally, containment and health index stimulates optimistic sentiments for investors and stabilizes the volatility of stock markets. As a result, COVID-19 pandemic aggravates investors’ sentiments and enlarges the volatility of stock markets. In addition, the government response policy affects the returns and volatility of stock markets. This finding indicate that the government response policy of COVID-19 pandemic stimulates the investor sentiment of stock markets.

The directional spillover effects and time-frequency nexus between stock markets, cryptocurrency, and investor sentiment during the COVID-19 pandemic

PurposeThis paper aims to analyze the connectedness between Gulf Cooperation Council (GCC) stock market index and cryptocurrencies. It investigates the relevant impact of RavenPack COVID sentiment on the dynamic of stock market indices and conventional cryptocurrencies as well as their Islamic counterparts during the onset of the COVID-19 crisis.Design/methodology/approachThe authors rely on the methodology of Diebold and Yilmaz (2012, 2014) to construct network-associated measures. Then, the wavelet coherence model was applied to explore co-movements between GCC stock markets, cryptocurrencies and RavenPack COVID sentiment. As a robustness check, the authors used the time-frequency connectedness developed by Barunik and Krehlik (2018) to verify the direction and scale connectedness among these markets.FindingsThe results illustrate the effect of COVID-19 on all cryptocurrency markets. The time variations of stock returns display stylized fact tails and volatility clustering for all return series. This stressful period increased investor pessimism and fears and generated negative emotions. The findings also highlight a high spillover of shocks between RavenPack COVID sentiment, Islamic and conventional stock return indices and cryptocurrencies. In addition, we find that RavenPack COVID sentiment is the main net transmitter of shocks for all conventional market indices and that most Islamic indices and cryptocurrencies are net receivers.Practical implicationsThis study provides two main types of implications: On the one hand, it helps fund managers adjust the risk exposure of their portfolio by including stocks that significantly respond to COVID-19 sentiment and those that do not. On the other hand, the volatility mechanism and investor sentiment can be interesting for investors as it allows them to consider the dynamics of each market and thus optimize the asset portfolio allocation.Originality/valueThis finding suggests that the RavenPack COVID sentiment is a net transmitter of shocks. It is considered a prominent channel of shock spillovers during the health crisis, which confirms the behavioral contagion. This study also identifies the contribution of particular interest to fund managers and investors. In fact, it helps them design their portfolio strategy accordingly.

National team intervention and price efficiency differentiation under the impact of COVID-19

ABSTRACT This paper examines how the ‘national team’, a direct government investment policy, influences the price efficiency of listed companies in the Shanghai Stock Index (SSE). We find that stocks directly invested in by the national team show greater price efficiency than stocks without direct bailout. We also analyse the impact of the COVID-19 pandemic on this price efficiency differentiation. The data period is divided into two sub-periods, and the results show that the price efficiency of stocks decreased after COVID-19. The reduction in price efficiency is more notable for stocks with direct government investment; the difference observed under national team investment in price efficiency is clearly reduced. Increased pessimistic investor expectations due to the pandemic could explain these changes. Our study provides novel evidence for the policy evaluation of national team intervention and a more detailed explanation about the decrease in financial market efficiency in the post-COVID-19 era.

INVESTOR SENTIMENTS AND STOCK RETURNS IN EMERGING MARKET: EVIDENCE FROM PAKISTAN STOCK EXCHANGE (PSX)

Research is being done to determine whether investor mood has a substantial influence on Pakistan's diverse industry returns. Investor sentiment research mostly focuses on the stock market, with little emphasis given to how investor sentiment affects returns in particular industries. Using Principle Component Analysis, our research work used a repetitive variable to investigate the association between investor sentiment and the equity market returns of a certain sector. Study discovers a correlation between market investor sentiment and selected industrial returns. According to the intended research, investor sentiment can be divided into two categories: optimistic and pessimistic. The findings reveal that optimistic investor sentiment positively affects equity returns in the cement, textile, and auto industries, while pessimistic investor sentiment has no impact on stock returns in the food, fertilizer, power, pharmaceutical, and oil industries. For the time period spanning 2017–2021, we used the most recent statistical method, the Markov Switching Model, to examine the robustness of long-term correlations among the research variables. Additionally, the consequence of the optimistic and pessimistic states on the profits of various Pakistani businesses is also looked at. The findings showed a substantial relationship between industry performance and investor sentiment. Keywords: Investor sentiments, behavioral finance, stock returns, leverage effect, Markov Switching Model.

Investor trading behavior and asset prices: Evidence from quantile regression analysis

AbstractConsidering the behavior anomaly under both rising and falling market conditions, this paper aims to address whether the investor trading behavior is sensitive to a different quantile of stock return dispersions by using quantile regression model. Results show that investor trading behavior has significant impacts on different quantiles of stock return dispersions, and reveal the smile slope of investor trading behavior effect which is stronger at the extreme quantile distributions than the median distribution. Moreover, results evidence that the investor trading behavior effect with optimistic investor sentiment should be stronger than the investor trading behavior effect with pessimistic investor sentiment. Finally, this paper sheds light on the anchoring effect of investor trading behavior, and demonstrates that the anchor of investor trading behavior has a positive and significant impact on stock returns. These patterns hold when accounting for stock specific characteristics, various factors and market conditions.

Does Risk Propensity of Investors Affect the Heuristic Choice of SRI Portfolio Selection?—A Comparison between a Market Portfolio and SRI Portfolio

From the last few years, awareness of socially responsible investments (SRI) investment is increasing in India. Study in SRI portfolio is gaining importance in finance research. To select an optimum portfolio (SRI or non-SRI), the investor should have basic knowledge about the weights. It is found that equal weight does not always give the desired result to the investors. An investor can also define the weight function heuristically based on their risk propensity to avoid the criticality in statistical calculation. The present article tries to get a heuristic optimum portfolio by comparing the SRI and non-SRI portfolios based on the risk-taking ability of the investors. Two types of investors are considered–optimistic and pessimistic. SENSEX and GREENEX are considered as non-SRI and SRI portfolios respectively. The main aim of the study is to see what type of investors get near optimum solutions if they go for SRI investment. After the analysis, it is found that the pessimistic investors’ decision is nearer to the optimum solution as compared to the optimistic investors’ decision if they go for SRI investment. So, it can be said that the pessimistic investors’ investment decision is better when compared to the optimistic investors in SRI investment.

Worst-case analysis of Omega-VaR ratio optimization model

The Omega ratio, a performance measure that separately considers upside and downside deviations from a fixed threshold, improves the Sharpe ratio by incorporating the higher-order moments. In this paper, we analyse the performance of a robust optimization model based on maximizing the worst-case of Omega ratio by taking its threshold point as the robust value of a defined percentile of the underlying loss distribution. To this aim, the threshold point is computed as the worst-case of the value-at-risk at a particular confidence level. We formulate robust model of the proposed strategy under two cases of uncertainty sets, the mixture distribution uncertainty and the box uncertainty. We show that, in the first case, the problem reduces to a second-order cone program (SOCP) and, in the second one, to a semi-definite program (SDP), hence tractable in both the cases. We conduct a comprehensive empirical investigation of the proposed models over six data sets across the globe, namely BSE 100 (India), FTSE 100 (UK), Hang Seng (Hong Kong), S&P Asia 50 (Asia), Dow Jones Industrial Average (USA), and IBEX (Spain). We compare our models with the three variants of the Omega ratio model, one its robust variant taking worst-case conditional value-at-risk as threshold point, and two of its nominal variants using value-at-risk and conditional value-at-risk as threshold points, respectively. We find that the proposed model under mixture distribution uncertainty exhibits a better performance over most of the data sets and scenarios than its CVaR-based robust counterpart. Under the box set, the proposed model performs similar or generates mixed results compared to its CVaR-based robust counterpart model. We also note that both the proposed robust models save investors against the risk of losses over the bearish phase of market in comparison to their nominal counterparts. Finally, on comparing the proposed model under mixture distribution uncertainty with the box uncertainty, the former model is found to be more suitable for optimistic investors, whereas the later strategy is more ideal for pessimistic investors.

The COVID-19 pandemic uncertainty, investor sentiment, and global equity markets: Evidence from the time-frequency co-movements

We use daily data of the Google search engine volume index (GSVI) to capture the pandemic uncertainty and examine its effect on stock market activity (return, volatility, and illiquidity) of major world economies while controlling the effect of the Financial and Economic Attitudes Revealed by Search (FEARS) sentiment index. We use a time–frequency based wavelet approach comprising wavelet coherence and phase difference for our empirical assessment. During the early spread of the COVID-19, our results suggest that pandemic uncertainty, and FEARS sentiment strongly co-move, and increased pandemic uncertainty leads to pessimistic investor sentiment. Furthermore, our partial wavelet analysis results indicate a synchronization relationship between pandemic uncertainty and stock market activities across G7 countries and the world market. Our results are robust to the inclusion of alternative pandemic fear measure in the form of equity market volatility infectious disease tracker. The pandemic uncertainty and associated sentiment implications could be one plausible reason for increased volatility and illiquidity in the market, and hence, policymakers should look upon this issue for the financial market stability perspective.

The influence of investor sentiment on stock prices among industries in the US

Research has shown that company-specific fundamental valuation factors impact stock prices, including diluted earnings (DEPS), book value (BV), and dividends (DIV) on a per-share basis. Free cash flow (FCF) has also been investigated, albeit not as extensively. Recently, investor sentiment, a behavioural factor, has been studied. Using the ordinary least square (OLS) method, this study explores the impact of company-specific fundamental valuation factors on stock prices of firms in significant industries in the US, for which investor sentiment is statistically significant. The results indicate that the industry variable is significant in the stock price. Further, investor sentiment, specifically whether it is optimistic or pessimistic, is significant in two industries, finance and manufacturing. Finally, the significance of the valuation factors differed based on investor optimism, for which all included variables are significant, and investor pessimism, for which only earnings-based factors are significant in the finance sector but not in manufacturing.