Pay for Performance from Future Fund Flows: The Case of Private Equity

Lifetime incomes of private equity general partners are affected by their current funds’ performance through both carried interest profit sharing provisions, and also by the effect of the current fund’s performance on general partners’ abilities to raise capital for future funds. We present a learning-based framework for estimating the market-based pay for performance arising from future fundraising. For the typical first-time private equity fund, we estimate that implicit pay for performance from expected future fundraising is approximately the same order of magnitude as the explicit pay for performance general partners receive from carried interest in their current fund, implying that the performance-sensitive component of general partner revenue is about twice as large as commonly discussed. Consistent with the learning framework, we find that implicit pay for performance is stronger when managerial abilities are more scalable and weaker when current performance contains less new information about ability. Specifically, implicit pay for performance is stronger for buyout funds compared to venture capital funds, and declines in the sequence of a partnership’s funds. Our framework can be adapted to estimate implicit pay for performance in other asset management settings in which future fund flows and compensation depend on current performance.Private equity; Venture capital; Fundraising; Compensation; Incentives

Reasoning with Streamed Uncertain Information from Unreliable Sources

Humans or intelligent software agents are increasingly faced with the challenge of making decisions based on large volumes of streaming information from diverse sources. Decision makers must process the observed information by inferring additional information, estimating its reliability, and orienting it for decision making. Processing streaming trust framework, when fact is getting created and inferred is a process in online mode and our paper works effciently in online mode. In online mode, someone initiates a query and gets an output based on the query. In this paper we have mainly shown that unstructured reports from unreliable and heterogeneous sources are processed to generate structured information in Controlled English. Uncertainty in the information is modelled using Subjective Logic that allows statistical inference over uncertain information. Trustworthiness of information is modelled and conflicts are resolved before fusion. This process is totally undertaken on streaming information resulting in new facts being inferred from incoming information which immediately goes through trust assessment framework and trust is propagated to the inferred fact. In this paper, we propose a comprehensive framework where unstructured reports are streamed from heterogeneous and potentially untrustworthy information sources. These reports are processed to extract valuable uncertain information, which is represented using Controlled Natural Language and Subjective Logic. Additional information is inferred using deduction and abduction operations over subjective opinions derived from the reports. Before fusing extracted and inferred opinions, the framework estimates trustworthiness of these opinions, detects conflicts between them, and resolve these conflicts by analysing evidence about the reliability of their sources. Lastly, we describe an implementation of the framework using International Technology Alliance (ITA) assets (Information Fabric Services and Controlled English Fact Store) and present an experimental evaluation that quantifies the efficiency with respect to accuracy and overhead of the proposed framework

Club Deals in Leveraged Buyouts

We analyze the pricing and characteristics of club deal leveraged buyouts (LBOs)—those in which two or more private equity partnerships jointly conduct an LBO. Using a comprehensive sample of completed LBOs of U.S. publicly traded targets conducted by prominent private equity firms, we find that target shareholders receive approximately 10% less of pre-bid firm equity value, or roughly 40% lower premiums, in club deals compared to sole-sponsored LBOs. This result is concentrated before 2006 and in target firms with low institutional ownership. These results are robust to controls for target and deal characteristics, including size, Q, measures of risk, and time and industry fixed effects. We find little support for benign motivations for club deals based on capital constraints, diversification motives, or the ability of clubs to obtain favorable debt amounts or prices, but it is possible that the lower pricing of club deals is an inadvertent byproduct of an unobserved benign motivation for club formation

Effective transfer entropy approach to information flow between exchange rates and stock markets

We investigate the strength and direction of information flow between exchange rates and stock prices in several emerging countries by the novel concept of effective transfer entropy (an alternative non-linear causality measure) with symbolic encoding methodology. Analysis shows that before the 2008 crisis, only low level interaction exists between these two variables and exchange rates dominate stock prices in general. During crisis, strong bidirectional interaction arises. In the post-crisis period, the strong interaction continues to exist and in general stock prices dominate exchange rates. © 2014 Elsevier Ltd. All rights reserved

Location attestation and access control for mobile devices using GeoXACML

Access control has been applied in various scenarios in the past for negotiating the best policy. Solutions with XACML for access control has been very well explored by research and have resulted in significant contributions to various sectors including healthcare. In controlling access to the sensitive data such as medical records, it is important to guarantee that the data is accessed by the right person for the right reason. Location of access requestor can be a good indication for his/her eligibility and reasons for accessing the data. To reason with geospatial information for access control, Geospatial XACML (eXtensible Access Control Markup Language) is proposed as a standard. However, there is no available implementation and architecture for reasoning with Geospatial XACML policies. This paper proposes to extend XACML with geohashing to implement geospatial policies. It also proposes an architecture for checking reliability of the geospatial information provided by clients. With a case study, we demonstrate how our framework can be used to control the privacy and data access of health service data in handheld devices

The development of Bitcoin futures : exploring the interactions between cryptocurrency derivatives

We utilise a high-frequency analysis to investigate the period surrounding the establishment of two new futures contracts based on the performance of Bitcoin. Our analysis shows that there have been significant pricing effects sourced from both fraudulent and regulatory unease within the industry. While analysing breakpoints in efficiency, we verify the view that Bitcoin futures dominate price discovery relative to spot markets. However, we add to this research by finding that CBOE futures are found to be the leading source of informational flow when compared directly to their CME equivalent

Misclassification Risk and Uncertainty Quantification in Deep Classifiers

In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a classifier’s predictions and reduce the likelihood of acting on erroneous predictions. The second is a novel way to train the classifier such that erroneous classifications are biased towards less risky categories. We combine these two approaches in a principled way. While doing this, we extend evidential deep learning with pignistic probabilities, which are used to quantify uncertainty of classification predictions and model rational decision making under uncertainty.We evaluate the performance of our approach on several image classification tasks. We demonstrate that our approach allows to (i) incorporate misclassification cost while training deep classifiers, (ii) accurately quantify the uncertainty of classification predictions, and (iii) simultaneously learn how to make classification decisions to minimize expected cost of classification errors

Analysis of cross-correlations between financial markets after the 2008 crisis

We analyze the cross-correlation matrix C of the index returns of the main financial markets after the 2008 crisis using methods of random matrix theory. We test the eigenvalues of C for universal properties of random matrices and find that the majority of the cross-correlation coefficients arise from randomness. We show that the eigenvector of the largest deviating eigenvalue of C represents a global market itself. We reveal that high volatility of financial markets is observed at the same times with high correlations between them which lowers the risk diversification potential even if one constructs a widely internationally diversified portfolio of stocks. We identify and compare the connection and cluster structure of markets before and after the crisis using minimal spanning and ultrametric hierarchical trees. We find that after the crisis, the co-movement degree of the markets increases. We also highlight the key financial markets of pre and post crisis using main centrality measures and analyze the changes. We repeat the study using rank correlation and compare the differences. Further implications are discussed. © 2013 Elsevier B.V. All rights reserved

Pay for Performance from Future Fund Flows: The Case of Private Equity

Lifetime incomes of private equity general partners are affected by their current funds’ performance through both carried interest profit sharing provisions, and also by the effect of the current fund’s performance on general partners’ abilities to raise capital for future funds. We present a learning-based framework for estimating the market-based pay for performance arising from future fundraising. For the typical first-time private equity fund, we estimate that implicit pay for performance from expected future fundraising is approximately the same order of magnitude as the explicit pay for performance general partners receive from carried interest in their current fund, implying that the performance-sensitive component of general partner revenue is about twice as large as commonly discussed. Consistent with the learning framework, we find that implicit pay for performance is stronger when managerial abilities are more scalable and weaker when current performance contains less new information about ability. Specifically, implicit pay for performance is stronger for buyout funds compared to venture capital funds, and declines in the sequence of a partnership’s funds. Our framework can be adapted to estimate implicit pay for performance in other asset management settings in which future fund flows and compensation depend on current performance.

Dynamic risk spillovers between gold, oil prices and conventional, sustainability and Islamic equity aggregates and sectors with portfolio implications

This paper investigates the time-varying equicorrelations and risk spillovers between crude oil, gold and the Dow Jones conventional, sustainability and Islamic stock index aggregates and 10 associated disaggregated Islamic sector stock indexes (basic materials, consumer services, consumer goods, energy, financials, health care, technology, industrials, telecommunications and utilities), using the multivariate DECO-FIAPARCH model and the spillover index of Diebold and Yilmaz (2012). We also conduct a risk management analysis at the sector level for commodity-Islamic stock sector index portfolios, using different risk exposure measures. For comparison purposes, we add the aggregate conventional Dow Jones global index and the Dow Jones sustainability world index. The results show evidence of time-varying risk spillovers between these markets. Moreover, there are increases in the correlations among the markets in the aftermath of the 2008–2009 GFC. Further, the oil, gold, energy, financial, technology and telecommunications sectors are net receivers of risk spillovers, while the sustainability and conventional aggregate DJIM indexes as well as the remaining Islamic stock sectors are net contributors of risk spillovers. Finally, we provide evidence that gold offers better portfolio diversification benefits and downside risk reductions than oil. © 2017 Elsevier B.V