Market ambiguity and individual investor information demand

We examine whether ambiguity in the market leads to an increase in information demand by individual investors. Drawing on the asset-pricing model proposed by Mele and Sangiorgi (2015), which incorporates market ambiguity, we measure individual information demand using daily Google searches and measure market ambiguity using a metric based on the market trades of institutional investors. We find that individual investors increase their information demand during periods of greater market ambiguity. We also provide evidence that information demand from individual investors spikes around earnings announcement days primarily when market uncertainty is driven by net-selling activity. Overall, these results suggest that the disagreement among institutional investors either represents uncertainty or contributes to the uncertainty related to a stock, leading to increased demand for information from individual investors.     

Improving Volatility Forecasts Using Market‐Elicited Ambiguity Aversion Information

Distinguishing between risk and uncertainty, this paper proposes a volatility forecasting framework that incorporates asymmetric ambiguity shocks in the (exponential) generalized autoregressive conditional heteroskedasticity‐in‐mean conditional volatility process. Spanning 25 years of daily data and considering the differential role of investors' ambiguity attitudes in the gain and loss domains, our models capture a rich set of information and provide more accurate volatility forecasts both in‐sample and out‐of‐sample when compared to ambiguity‐free or risk‐based counterparts. Ambiguity‐based volatility‐timing trading strategies confirm the economic significance of our proposed framework and indicate that an annualized excess return of 3.2% over the benchmark could be earned from 1995 to 2014.     

Uncertainty avoidance,loss aversion and stock market participation

Stock market participation differs a lot across countries. Cultural dimensions could be a potential factor for that. We show that indeed uncertainty avoidance (UAI) is linked to rates of stock market participation across countries. We can show even more that uncertainty avoidance has an indirect effect through loss aversion on stock market participation. The country level effects are confirmed on the individual level using data from a recent large-scale international survey, but on individual level there is also a strong effect of UAI on stock market participation after controlling for loss aversion. These results are robust after controlling for ambiguity aversion, and economic and demographic variables. Finally, we find that UAI is related to negative attitudes about stock markets in general.     

Robust portfolio choice with stochastic interest rates

We determine the optimal investment strategy for an ambiguity-averse investor in a setting with stochastic interest rates. The investor has access to stocks, bonds, and a bank account and he is ambiguous about the expected rate of return of both bonds and stocks. The investor can have different levels of ambiguity aversion about the two types of risky assets. We find that it is more important to take model uncertainty about the stock dynamics than model uncertainty about the bond dynamics into account. Furthermore, the investor’s ambiguity increases his hedging demand. Consequently, the bond/stock ratio increases with his ambiguity and implies less extreme positions in the bank account. Altogether, our model yields portfolio allocations which are more in line with what is implementable in practice. Finally, we demonstrate that neglecting model uncertainty implies significant losses for the investor.     

Accounting Treatment of Inherent versus Incentive Uncertainties and the Capital Structure of the Firm

This paper studies the accounting treatment of uncertainty and how it affects a firm's capital structure. We distinguish two sources of uncertainty that raise reliability concerns: inherent uncertainty and incentive uncertainty. By inherent uncertainty, we refer to uncertainty about the quality of raw information regarding future cash flows. By incentive uncertainty, we refer to uncertainty about the quality of accounting numbers conveying the raw information. We explore features of accounting that can effectively deal with these two types of uncertainties in order to aid in the debt‐equity decision of the firm. To handle inherent uncertainty, preferable accounting involves flexible revenue/expense recognition rules that recognize more profit when the uncertainty level is low. To deal with incentive uncertainty, a stringent revenue/expense recognition rule may be desirable to fend off management's opportunistic reporting behavior. Inflexible accounting rules cause a firm's financing choices to deviate from what would hold with complete information. Given any accounting rule, an information environment with a lower (higher) uncertainty regarding future cash inflows leads to higher (lower) expected debt financing. This is because assessed default risk is increasing in the uncertainty of future cash inflows, holding the uncertainty of the outflows constant.     

The perceived fairness of performance evaluation: The role of uncertainty

This paper investigates the relationship between performance evaluation procedures and managerial perceptions of procedural justice. We examine two metric and two process characteristics. Metric characteristics are the diversity of metrics used by superiors and their reliance on outcome vs. effort metrics. Regarding process characteristics, we analyse the amount of subordinate's voice in the performance evaluation process, and the extent of formalization of the process. Using uncertainty management theory, we argue that justice effects of performance evaluation procedures may not be direct but are instead conditional on the amount of task uncertainty managers face in the their job context as well as on managers’ tolerance for ambiguity. Using a sample of 178 managers from the banking industry, we find that all four performance evaluation characteristics are related to justice perceptions, yet their effect depends on the level of task uncertainty and tolerance for ambiguity. These findings explain some inconsistencies in extant studies on fairness of performance evaluation procedures.     

Economic uncertainty: Mispricing and ambiguity premium

We uncover two channels of effect in the financial market when investors face macroeconomic uncertainty. Conditional on a common mispricing index, we find that economic uncertainty exposure (EUE) induces disagreement, which amplifies mispricing. The highest EUE quintile produces an annualised mispricing alpha of 9.96%, more than double the unconditional mispricing effect. An ambiguity premium of 3.84% alpha is documented in the “non-mispricing” quintile. The EUE-induced mispricing effect is different from the existing limits of arbitrage explanations. The ambiguity premium is predictably observed during the unfolding of shocks of COVID-19 to the market.     

Home-field advantage or a matter of ambiguity aversion? Local bias among German individual investors

This paper investigates whether familiarity induced by ambiguity aversion can help explaining the local bias phenomenon among individual investors. Using geographic closeness as a proxy for investor familiarity, we find that investors pull out of (unfamiliar) remote stocks and pour into (familiar) local stocks during times of increased market uncertainty. Moreover, the magnitude of this ‘flight to familiarity’ increases in the spread of an investor's ambiguity (about expected returns) between local and remote stocks. Our results prove robust to a number of alternative explanations of local bias. Specifically, we rule out a ‘home-field advantage’, where investors are able to translate information advantages about nearby companies into excess returns on their local stockholdings. We conclude that individual investors’ local bias is induced by ambiguity aversion in the portfolio selection process rather than a trading strategy based on superior information about local companies.     

Information Uncertainty and Expected Returns

This study examines the role of information uncertainty (IU) in predicting cross-sectional stock returns. We define IU in terms of “value ambiguity,” or the precision with which firm value can be estimated by knowledgeable investors at reasonable cost. Using several different proxies for IU, we show that (1) on average, high-IU firms earn lower future returns (the “mean” effect), and (2) price and earnings momentum effects are much stronger among high-IU firms (the “interaction” effect). These findings are consistent with analytical models in which high IU exacerbates investor overconfidence and limits rational arbitrage.This revised version was published online in August 2005 with a corrected cover date.     

Cost Uncertainty and Taxpayer Compliance

The existence of a private cost borne by audited taxpayers affects the tax enforcement policy. This is so because tax auditors will face now two sources of uncertainty, namely, the typical one associated with taxpayers’ income and that associated with the taxpayers’ idiosyncratic attitude towards tax compliance. Moreover, the inspection policy can be exposed to some randomness from the taxpayers’ viewpoint due to the uncertainty about the audit cost borne by the tax authority. In this paper we provide an unified framework to analyze the effects of all these sources of uncertainty in a model of tax compliance with strategic interaction between auditors and taxpayers. We show that more variance in the distribution of the taxpayers’ private cost of evading raises both tax compliance and the ex-ante welfare of taxpayers. The effects of the uncertainty about the audit cost faced by the tax authority are generally ambiguous. We also discuss the implications of our model for the regressive (or progressive) bias of the effective tax system.     

Potential responses by on-campus university students to a university emergency alert

University campuses across Canada and elsewhere are developing and implementing emergency alert systems to warn campus community members about a variety of threats. In this study, focus group discussions were used to examine how undergraduate students living on campus may respond to an emergency alert. A focus group activity used tornado, fire and threatening message alert messages to provide a context for the focus group discussions. After reading the warning message, most students understood the warning message but there was uncertainty about the non-specified threat and how and where to evacuate. Many would believe a message sent by the university as long as it was sent via a phone number that they associated with the university. Personalization of risk varied, and students reported that they would confirm a warning message with a variety of sources including student colleagues, faculty and teaching staff, television and internet sources. Taking protective action by sheltering in place was deemed to be feasible, however evacuation off campus was found to be problematic. We found that the nature of short message service text messages, the characteristics of universities, and the students’ home being in an on-campus residence influenced how the students may respond to an emergency alert message.     

Political uncertainty and firms' information environment: Evidence from China

How political uncertainty affects the supply of value relevant information about a firm is an important but unresolved question. Using an emerging market setting where political leaders are expected to exert significant influence on economic activities, we examine the effect of political uncertainty caused by turnovers of local government leaders on a firm’s information environment. We find that during periods of political uncertainty, the total amount of idiosyncratic information about a firm that is available to the market is reduced. The adverse effect on information supply is manifest in firms that are more politically dependent and stronger when uncertainty is more severe. Further, we provide evidence suggesting that firms react to political uncertainty by reducing the amount and the quality of information provided to investors. We find that information intermediaries such as financial analysts and the media have a moderating effect on the information environment as they increase the production of information during periods of political uncertainty. However, these intermediaries do not negate the net loss of information.     

A decision-theoretic model of asset-price underreaction and overreaction to dividend news

We combine new developments in decision theory with a standard consumption-based asset-pricing framework. In our model the efficient market hypothesis is violated if and only if agents’ beliefs express ambiguity about the stochastic process driving economic fundamentals. Asset price fluctuations result because agents with ambiguous beliefs are prone to a confirmatory bias in the interpretation of new information. We demonstrate that our approach gives rise to price-patterns of “underreaction” and “overreaction” to news about dividend payments. Although these empirical phenomena have received significant attention in the behavioral finance literature, we argue that our decision-theoretic underpinning of psychological attitudes has a less ad hoc flavor than existing approaches.     

Robust multivariate portfolio choice with stochastic covariance in the presence of ambiguity

This paper provides the optimal multivariate intertemporal portfolio for an ambiguity averse investor, who has access to stocks and derivative markets, in closed form. The stock prices follow stochastic covariance processes and the investor can have different levels of uncertainty about the diffusion parts of the stocks and the covariance structure. We find strong evidence that the optimal exposures to stock and covariance risks are significantly affected by ambiguity aversion. Welfare analyses show that investors who ignore model uncertainty incur large losses, larger than those suffered under the embedded one-dimensional cases. We further confirm large welfare losses from not trading in derivatives as well as ignoring intertemporal hedging, we study the impact of ambiguity in that regard and justify the importance of including these factors in the scope of portfolio optimization. Conditions are provided for a well-behaved solution in general, together with verification theorems for the incomplete market case.     

Value at risk methodology of international index portfolio under soft conditions (fuzzy-stochastic approach)

The approach to modelling uncertainty of the international index portfolio by the value at risk (VAR) methodology under soft conditions by fuzzy-stochastic methodology is described in the paper. The generalised term uncertainty is understood to have two aspects: risk modelled by probability (stochastic methodology) and vagueness sometimes called impreciseness, ambiguity, softness is modelled by fuzzy methodology. Thus, hybrid model is called fuzzy-stochastic model. Input data for a stochastic model are unique distribution functions and crisp (real) data. Input data for fuzzy model are fuzzy numbers and crisp (real) data. Input data for hybrid model are fuzzy probability distribution functions, unique distribution functions, and crisp (real) data. Softly defined VAR model is constructed as hybrid model because it is supposed that the input data are difficult to determine as crisp numbers or as some unique distribution functions. Risk is modelled by stochastic methodology on the VAR basis and vagueness is modelled through the fuzzy numbers. The analytical delta normal VAR methodology for international index portfolio under soft conditions is described including illustrative example. It is shown, that methodology described could be considered to be generalised sensitivity analysis.     

Who Says there is a High Consensus Among Analysts when Market Uncertainty is High? Some New Evidence from the Commercial Real Estate Market

This paper seeks to determine whether analyst consensus is a function of the level of price informativeness, and therefore attempts to gauge the extent to which analyst consensus is high when market uncertainty is high. There is the view that a small or declining volume of trading implies less information and lower quality information, and that a lower level of price informativeness will lead analysts to put more weight on their private information at the time of forecasting. We add to this view the notion that when uncertainty is high there might be a positive option value of waiting. In this case, it appears reasonable to conclude that analysts may actually lack consensus when uncertainty is high. If this hypothesis is correct, then one would expect the level of price informativeness to override the Keynesian incentive “to fail conventionally than to succeed unconventionally” or at least reduce its magnitude in periods of declining trading volume and low price discovery. Tests of this hypothesis produce favorable results.     

Model Misspecification and Underdiversification

In this paper, we study intertemporal portfolio choice when an investor accounts explicitly for model misspecification. We develop a framework that allows for ambiguity about not just the joint distribution of returns for all stocks in the portfolio, but also for different levels of ambiguity for the marginal distribution of returns for any subset of these stocks. We find that when the overall ambiguity about the joint distribution of returns is high, then small differences in ambiguity for the marginal return distribution will result in a portfolio that is significantly underdiversified relative to the standard mean‐variance portfolio.     

An Extreme Value Analysis Of Advanced Age Mortality Data

Abstract

Extreme value theory describes the behavior of random variables at extremely high or low levels. The application of extreme value theory to statistics allows us to fit models to data from the upper tail of a distribution. This paper presents a statistical analysis of advanced age mortality data, using extreme value models to quantify the upper tail of the distribution of human life spans.

Our analysis focuses on mortality data from two sources. Statistics Canada publishes the annual number of deaths in Canada, broken down by angender and age. We use the deaths data from 1949 to 1997 in our analysis. The Japanese Ministry of Health, Labor, and Welfare also publishes detailed annual mortality data, including the 10 oldest reported ages at death in each year. We analyze the Japanese data over the period from 1980 to 2000.

Using the r-largest and peaks-over-threshold approaches to extreme value modeling, we fit generalized extreme value and generalized Pareto distributions to the life span data. Changes in distribution by birth cohort or over time are modeled through the use of covariates. We then evaluate the appropriateness of the fitted models and discuss reasons for their shortcomings. Finally, we use our findings to address the existence of a finite upper bound on the life span distribution and the behavior of the force of mortality at advanced ages.     

How do investors react under uncertainty?

It has long been accepted that risk plays an important role in determining valuation where risk reflects that investors are unsure of future returns but are able to express their prior expectations by a probability distribution of these returns. Knight (1921) introduced the concept of uncertainty where investors possess incomplete knowledge about this distribution and so are unable to formulate priors over all possible outcomes. One common approach for making uncertainty tractable is to assume that investors faced with uncertainty will base their decisions on the worst case scenario (i.e. follow maxmin expected utility). As a consequence it is postulated that investors will become more pessimistic as uncertainty increases, upgrading bad news and downgrading good news. Using Australian data, we find evidence that investors react to bad news at times of high market uncertainty but largely ignore good news which is consistent with them taking on a pessimistic bias. However, we also find evidence of the reverse when market uncertainty is low with investors taking on an optimistic stance by ignoring bad news but reacting to good news. We also find that the impact that market uncertainty has on the reaction of investors to new information is modified by the prevailing market sentiment at the time of the announcement. Besides throwing light on the question of how uncertainty impacts on investor behaviour, our findings seriously challenge the common assumption made that investors consistently deal with uncertainty by applying maxmin expected utility.