Jan Smolarski

The use of financial information by private equity funds in evaluating new investments

Purpose - The purpose of this paper is to examine the use of financial information and valuation methods among private equity funds in Europe and India. The authors analyze differences in the choice of valuation methods and how the use of financial information differs among funds in the UK, Pan Europe and India. Design/methodology/approach - A survey approach was utilized in collecting proprietary data from European and Indian private equity funds. The data were classified according to fund type, country grouping, size, risk profile, labor cost and industry structure and analyzed using MANOVA and ANOVA. Findings - The results show that the use of valuation models is relatively homogeneous across countries and that the use of financial information appears to be driven to a large extent by fund type and fund focus. The use of audited financial statements appears to increase as firms mature. Significant differences were found in standard financial adjustments between the two fund types and between the country groupings. Results based on labor cost are weakly significant whereas industry structure does not appear to have an impact on how fund managers evaluate investments. Research limitations/implications - The results indicate that fund managers adapt their decision-making behavior according to investment type and risk. The authors argue that understanding asymmetrical and structural issues may potentially improve investment decision-making processes. The main conclusion for researchers is that buy-out and venture capital funds should not be combined as one asset class. Since a survey approach was used, the study is subject to the belief that fund managers do not internalize decisions well, which could reduce the effectiveness of the research design. Originality/value - There are few studies in the areas covered by this paper due to the proprietary nature of the private equity industry. The results are important because they help in understanding how fund managers use decision aids such as financial statements and valuation techniques. A better understanding of current practices will help fund managers and fund sponsors in devising improved decision aids and processes, which ultimately may lead to fewer non-performing investments. This is especially important in private equity since investment decisions are often irreversible and binary.

Extreme events: a study of small oil and gas firms

The oil and gas industry is subject to different types of risks, many of which have the potential to generate extreme results. Classifying extreme events as global, industry specific and firm specific, we use a Bayesian probability model and the Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) model to evaluate the impact of disclosure of extreme events on returns and return volatilities. The results suggest political events have more of a pronounced effect compared to those classified as economic events. The overall effects are more pronounced at the global and firm‐level classifications. At the firm level, extreme economic events have a more significant impact than political extreme events.

Perception of Risk by Venture Capital and Private Equity Firms: A European Perspective

We introduce and test an integrated model that links venture capitalist risk perceptions with the choice of governance measures. The sample consists of 104 European private equity funds. The results provide support for our proposed model and we identify several links between pre-contractual screening and post-contractual control mechanisms. We find that diligent pre-investment screening enhances the perceived effectiveness of post-investment control mechanism suggesting a number of behavioral implications for the venture capital fund manager and the entrepreneur

Possibilistic risk management and counterfactual probabilities

Possibility theory is applied to assessing the relative risk associated with very rare, high-consequence hazards. The probability of rare negative events has to be estimated from a few past occurrences that are spread over long exposure periods, with countermeasures added in response to each event to attempt to guard against recurrence. Traditional risk assessments based on conditional probability and statistical expected value are very sensitive to the uncertainty associated with rare events. A new measure of possibility for events whose probability is not well measurably different from zero is proposed and illustrated in the context of possible release of hazardous material from a high containment research laboratory and in the context of large insurance company failures. Strategies for managing and reducing risk that do not depend on well-measured probabilities are discussed.

For Whom Hurdle Rate and High-Watermark Exist?

In this study, we first conduct multinomial logistic regression analysis to see how hedge fund attributes affect hedge fund managers’ decision of whether to offer a hurdle rate and/or high-watermark. Hedge funds taking more risky position and collecting high performance fee are more likely to offer hurdle rate and/or high-watermark. Second, we conduct cross-sectional regression analysis to see how hedge fund attributes affect hedge fund performance. Our results indicate that hurdle rate and high-watermark are restrictions for hedge fund managers on collecting fee and that hurdle rate and high-watermark cannot be considered to be incentives. We also find that hedge funds collecting high performance fee and having large amount of funds are more likely to outperform those collecting low performance fee and having small amount of funds. While conducting cross-sectional regression analysis, we use three different measures of hedge fund performance: alpha, palpha and Sharpe ratio. Alpha and palpha are obtained from the optimal model by investment strategy controlling for hedge fund risk associated with risk factors different by its investment strategy. In addition, we control for survivorship and instant history biases. So, our results from alpha and palpha are more credible than those of Soydemir et al. (2012) which employs only Sharpe ratio.

Dynamic Exposure of Hedge Funds to the Changes in the Risk Factors

This paper models exposure of hedge fund to risk factors and examines time-varying performance of hedge funds. From existing models such as ABS-factor model, SAC-factor model, and four-factor model, we extract the best six factors for each hedge fund portfolio by investment strategy. Then, we find combinations of risk factors that most explain variance in performance of each hedge fund portfolio by investment strategy. The results show instability of coefficients in the performance attribution regression. Incorporating time-varying factor exposure feature would be the best way to appropriately measure hedge fund performance. Furthermore, the optimal models with fewer factors exhibit greater explanatory power than existing models. Time-varying model customized by investment strategy of hedge funds would clearly show how sensitive to risk factors managements of hedge funds are according to market conditions.

Audit client composition and predicting large bank failures: A possibilistic approach

We develop a possibility based model with the aim of helping firms to deal with difficult-to- predict catastrophic failures. Our study is motivated by the recent economic turmoil and recent large financial firm failures. Our results suggest that specialization is an alternative to portfolio diversification in predicting catastrophic events.

Improved Performance and Cost in Cyclic Production Systems

Abstract Increased competition has forced companies to focus more attention on producing at a globally competitive cost. To cope, firms focus on flexible manufacturing, integration, and automation to help ensure that firm-specific manufacturing environments remain competitive. Firms also focus on cost efficiencies, which enable them to be competitive over specific production runs and product life cycles. A common way of reducing cost at this level is to reduce set-up times. Previous research has shown that reduction of average machine setup time virtually guarantees lower production costs. The same is also true of the variance of machine setup time. However, recent research has found that reducing setup time, without any change in variance, can increase waiting time and work-in-process (WIP) inventory levels potentially reducing benefits from continuous improvement techniques. On the other hand, adding fixed idle time while holding the variance constant may reduce waiting time. The optimal fixed idle time depends only on the means and variances of setup, service, and arrival times. We show that an even greater reduction is achievable when the distribution of setup time is known by adding variable idle time, which is a non-increasing function of setup time, thereby reducing the combined setup time variance. We present procedures for finding the optimal variable idle time as a function of setup time. We also show how to implement our results.