Incentive or performance fees for money managers are frequently accompanied by high-water mark provisions which condition the payment of the performance fee upon exceeding the maximum achieved share value. In this paper, we show that hedge fund performance fees are valuable to money managers, and conversely represent a claim on a significant proportion of investor wealth. The high-water mark provisions in these contracts limit the value of the performance fees. We provide a closed-form solution to the high-water mark

High-Water Marks and Hedge Fund Management Contracts

The Price of Sin: The Effects of Social Norms on Markets

In this paper, we provide a theoretical and empirical framework that allows us to synthesize and assess the burgeoning literature on CEO overconfidence. We also provide novel empirical evidence that overconfidence matters for corporate investment decisions in a framework that explicitly addresses the endogeneity of firms' financing constraints.

/pdf/behavioral-ceos-the-role-of-managerial-overconfidence-21xlpv77re.pdf

Behavioral CEOs: The Role of Managerial Overconfidence

This paper empirically examines whether demographic factors namely gender, age, marital status, income, occupation, and education could be used individually or in combination to differentiate among retail investors in terms of financial risk tolerance (FRT) and risk taking behaviour (FRB), and classify retail investors into FRT and FRB categories. A single cross sectional survey was conducted among 778 retail investors with various levels of investment experience, through a structured questionnaire covering a variety of demographic factors. Four of the six demographic factors were found to be useful in differentiating between levels of investors' FRT and FRB as well as classifying individuals into different FRT and FRB categories.

Retail investors' financial risk tolerance and their risk-taking behaviour: The role of demographics as differentiating and classifying factors

Impact investing is an emerging alternative asset class. In the last few years, the investment in impact investing has grown many folds, however the research has not kept pace with the growing prac...

/pdf/impact-investing-review-and-research-agenda-33z2465orp.pdf

Impact investing: review and research agenda

The growing prevalence of social entrepreneurship has been coupled with an increasing number of so-called “impact investors”. However, much remains to be learned about this nascent class of investors. To address the dearth of scholarly attention to impact investing, this study seeks to answer four questions that are central to understanding the phenomenon. What are the defining characteristics of impact investing? Do impact investors differ from traditional classes of investors and, if so, how? What are the motivations that drive impact investment? And, what criteria do impact investors use when evaluating potential investments?,A partially inductive study based on semi-structured interviews with 31 investors and ethnographic observation was conducted to explore how impact investors differ from other classes of investors in their motivations and unique criteria used to evaluate ventures seeking investment.,This study reveals that impact investors represent a unique class of investors that differs from socially responsible investing, from other types of for-profit investors, such as venture capitalists and angel investors, and from traditional philanthropists. The varied motivations of impact investors and the criteria they use to evaluate investments are identified.,Despite the growing practitioner and media attention to impact investing, several foundational issues remain unaddressed. This study takes the first steps toward shedding light on this new realm of early-stage venture investing and clarifying its role in larger efforts of social responsibility.

Finance or philanthropy? Exploring the motivations and criteria of impact investors

Behavioral finance: A panel discussion

In this article, the authors use KLD Research and Analytics (KLD) rankings from 1991 to 2009 to create three long-only portfolios: high-ranking “Top” stocks, “Shunned” stocks, and “Top and Shunned” stocks. They also construct three 130/30 active-extension portfolios, with 130% exposure in each of the three long-only portfolios and 30% exposure in a short portfolio of low-ranking “Bottom” stocks. The authors find positive, statistically significant abnormal returns for all six portfolios relative to their matched portfolios and the S&P 500. In addition, they find that the three 130/30 active-extension portfolios generally outperform the three long-only portfolios, including producing higher information ratios. As predicted, the highest-performing active-extension portfolio includes stocks for both Top companies and Shunned companies. Moreover, the performance for this 130/30 active-extension portfolio increased as the long and short positions were increased to 140/40 and 150/50. Thus, the results suggest that social responsibility ratings can be actively used to outperform the market. These findings further suggest that employing active-extension strategies that short stocks for companies with low social responsibility ratings may add additional abnormal returns.

Using Social Responsibility Ratings to Outperform the Market: Evidence from Long-Only and Active-Extension Investment Strategies

Purpose – This study aims to look into how volatility significantly impacts the tracking error for daily-rebalanced leveraged bull and bear ETFs. Design/methodology/approach – Using Morningstar return data and Chicago Board Options Exchange (CBOE) volatility index (VIX) data, the paper examines the daily tracking error for leveraged bull and bear ETFs. Tracking error is defined as the difference between the daily returns for a leveraged bull or bear ETF and the multiple of the daily return for that ETF's respective underlying benchmark index. Findings – Changes in the market VIX of the CBOE have a significant and opposite effect on the daily returns for both leveraged bull and bear ETFs. Furthermore, these effects are more pronounced for bear ETFs than similarly leveraged bull ETFs. Research limitations/implications – The sample period (June 19, 2006 to September 22, 2009) contains periods of extraordinarily high volatility. Considering that the VIX reached an all-time high during this period, the results...

Bad news bears

Purpose




Currently, few academics agree on a standard and scientific way to measure risk tolerance. This paper aims to create a unique model for empirically measuring risk tolerance and to make a strong contribution to the growing literature in risk tolerance and risk management.




Design/methodology/approach




The authors use factor analysis and regression analysis to identify relevant factors for measuring risk tolerance.




Findings




The risk tolerance model is based on the acronymed model riskTRACK, which includes the five significant factors this paper identifies for measuring risk tolerance: traditional risk factor, reflective risk factor, allocation risk factor, capacity risk factor and knowledge risk factor.




Research limitations/implications




Uses for future research streams devoted to risk tolerance and risk management.




Practical implications




The results also have practical applications for the financial services industry, particularly risk management, portfolio management and financial planning.




Originality/value




In sum, this research expands previous research in risk tolerance and also adds to the growing literature in risk management. Once again, this paper is unique in that the authors develop a valid and reliable risk tolerance model based on five specific factors for measuring risk tolerance.

Hunter M. Holzhauer

Papers

Finance or philanthropy? Exploring the motivations and criteria of impact investors

Behavioral finance: A panel discussion

Using Social Responsibility Ratings to Outperform the Market: Evidence from Long-Only and Active-Extension Investment Strategies

Bad news bears

RiskTRACK: the five-factor model for measuring risk tolerance