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Student Managed Funds (SMFs) are extremely popular investment programs at many colleges and universities that provide their students with experiential learning opportunities to manage real money. However, the size, scope and specific features of these SMFs differ substantially. The purpose of this paper is to deliberate about a panel discussion on several important SMF issues that took place at the Southern Finance Association conference in November, 2016.

The panel includes one moderator and four panelists, all of whom serve as SMF faculty directors at their respective schools.

The panelists’ answers show that almost no two SMFs are created the same, supervised the same way by different faculty directors or managed the same way by their respective students.

The panelists provide insight about their respective SMFs and offer advice on how to create SMFs and how to supervise students managing SMFs in a more effective manner.

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.

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.

The authors use factor analysis and regression analysis to identify relevant factors for measuring risk tolerance.

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.

Uses for future research streams devoted to risk tolerance and risk management.

The results also have practical applications for the financial services industry, particularly risk management, portfolio management and financial planning.

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.

This study aims to look into how volatility significantly impacts the tracking error for daily-rebalanced leveraged bull and bear ETFs.

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.

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.

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 may be time-period specific and may not translate to other time periods.

The implication is that market timing may be feasible for enhancing daily returns for both leveraged bull and bear ETFs. However, any specific timing strategies go beyond the scope of this paper.

In this study, the paper examined the effects of expected market volatility on the daily tracking error of leveraged bull and bear ETFs. Specifically, the paper performed multiple linear regression analysis using Morningstar return data for the ETFs and their underlying benchmark and CBOE VIX data. The findings suggest that market timing could be beneficial for increasing daily yields for leveraged and inverse ETFs.