Private equity returns and disclosure around the world

We study the returns the venture capital and private equity investment from 221 venture capital and private equity funds that are part of 72 venture capital and private equity firms, 5040 entrepreneurial firms (3826 venture capital and 1214 private equity), and spanning 32 years (1971 - 2003) and 39 countries from North and South America, Europe and Asia. We make use of four main categories of variables to proxy for value-added activities and risks that explain venture capital and private equity returns: market and legal environment, VC characteristics, entrepreneurial firm characteristics, and the characteristics and structure of the investment. We show Heckman sample selection issues in regards to both unrealized and partially realized investments are important to consider for analysing the determinants of realized returns. We further compare the actual unrealized returns, as reported to investment managers, to the predicted unrealized returns based on the estimates of realized returns from the sample selection models. We show there exists significant systematic biases in the reporting of unrealized investments to institutional investors depending on the level of the earnings aggressiveness and disclosure indices in a country, as well as proxies for the degree of information asymmetry between investment managers and venture capital and private equity fund managers. Klassifikation: G24, G28, G31, G32, G3

Data Quality Problems Troubling Business and Financial Researchers: A Literature Review and Synthetic Analysis

The data quality of commercial business and financial databases greatly affects research quality and reliability. The presence of data quality problems can not only distort research results, destroy a research effort but also seriously damage management decisions based upon such research. Although library literature rarely discusses data quality problems, business literature reports a wide range of data quality issues, many of which have been systematically tested with statistical methods. This article reviews a collection of the business literature that provides a critical analysis on the data quality of the most frequently used business and finance databases including the Center for Research in Security Prices (CRSP), Compustat, S&P Capital IQ, I/B/E/S, Datastream, Worldscope, Securities Data Company (SDC) Platinum, and Bureau Van Dijk (BvD) Orbis and identifies 11 categories of common data quality problems, including missing values, data errors, discrepancies, biases, inconsistencies, static header data, standardization, changes in historic data, lack of transparency, reporting time issues and misuse of data. Finally, the article provides some practical advice for librarians to facilitate their scholarly communications with researchers on data quality problems

Dopamine modulates the neural representation of subjective value of food in hungry subjects.

Although there is a rich literature on the role of dopamine in value learning, much less is known about its role in using established value estimations to shape decision-making. Here we investigated the effect of dopaminergic modulation on value-based decision-making for food items in fasted healthy human participants. The Becker-deGroot-Marschak auction, which assesses subjective value, was examined in conjunction with pharmacological fMRI using a dopaminergic agonist and an antagonist. We found that dopamine enhanced the neural response to value in the inferior parietal gyrus/intraparietal sulcus, and that this effect predominated toward the end of the valuation process when an action was needed to record the value. Our results suggest that dopamine is involved in acting upon the decision, providing additional insight to the mechanisms underlying impaired decision-making in healthy individuals and clinical populations with reduced dopamine levels.This is the author's accepted manuscript. The final version is available from the Society for Neuroscience in the Journal of Neuroscience at http://www.jneurosci.org/content/34/50/16856.abstract

When Moneyball Meets the Beautiful Game: A Predictive Analytics Approach to Exploring Key Drivers for Soccer Player Valuation

To measure the market value of a professional soccer (i.e., association football) player is of great interest to soccer clubs. Several gaps emerge from the existing soccer transfer market research. Economics literature only tests the underlying hypotheses between a player’s market value or wage and a few economic factors. Finance literature provides very theoretical pricing frameworks. Sports science literature uncovers numerous pertinent attributes and skills but gives limited insights into valuation practice. The overarching research question of this work is: what are the key drivers of player valuation in the soccer transfer market? To lay the theoretical foundations of player valuation, this work synthesizes the literature in market efficiency and equilibrium conditions, pricing theories and risk premium, and sports science. Predictive analytics is the primary methodology in conjunction with open-source data and exploratory analysis. Several machine learning algorithms are evaluated based on the trade-offs between predictive accuracy and model interpretability. XGBoost, the best model for player valuation, yields the lowest RMSE and the highest adjusted R2. SHAP values identify the most important features in the best model both at a collective level and at an individual level. This work shows a handful of fundamental economic and risk factors have more substantial effect on player valuation than a large number of sports science factors. Within sports science factors, general physiological and psychological attributes appear to be more important than soccer-specific skills. Theoretically, this work proposes a conceptual framework for soccer player valuation that unifies sports business research and sports science research. Empirically, the predictive analytics methodology deepens our understanding of the value drivers of soccer players. Practically, this work enhances transparency and interpretability in the valuation process and could be extended into a player recommender framework for talent scouting. In summary, this work has demonstrated that the application of analytics can improve decision-making efficiency in player acquisition and profitability of soccer clubs

Developing a Machine Learning based Systematic Investment Startegy: A case study for the Construction Industry

In this research work, an end-to-end systematic investment strategy based on machine learning models and leveraging the construction industry operational and management practices knowledge, is implemented. First, a literature research in the field of behavioral finance is done, presenting the current state of the knowledge and trends in the industry. A suitable investment opportunity exploiting prevailing market inefficiencies around earnings announcements is identified. Second, an extensive literature research is performed identifying the most relevant characteristics of construction companies’ operations and major risk factors they are exposed to. These insights are used to engineer a set of relevant variables. Third, advanced statistical techniques are used to select the most relevant subset of features, which includes market and analysts’ expectation data, macroeconomic indicators, the delay in reporting earnings, and the most important financial dimensions for construction firms. Fourth, the earnings’ surprise classification problem is characterized by a class imbalance and asymmetric misclassification costs. These issues are a consequence of the desired business application, and are addressed by selecting an appropriate evaluation metric. Additionally, considerations on the temporal dimension and generative process of the data are made to select an appropriate validation scheme. Five different state-of-the-art machine learning algorithms are considered: a multinomial logistic regression, a bagging classifier, a random forest, an XGBoost and a linear Support Vector Machine. The multinomial logistic regression is found to be the most suitable model, exhibiting a bias towards predicting positive earnings’ surprises over the rest of classes. The firm size, and the profitability and valuation measures, portrayed by the Return on Assets and Enterprise Value multiples, are found to be the most important variables when predicting earnings surprises. To conclude, the systematic investment strategy based on the investment signals produced by the selected machine learning model is back-tested, being the performance of the long-short portfolio driven by the positive surprise one as a consequence of the selected model bias. Keywords: Quantitative Investing, Machine Learning, Behavioral Financ

Income Smoothing over the Business Cycle: Changes in Banks’ Coordinated Management of Provisions for Loan Losses and Loan Charge-offs from the Pre-1990 Bust to the 1990s Boom

We provide evidence that banks smooth income by managing provisions for loan losses and loan charge-offs in a coordinated fashion that varies across the bust and boom phases of the business cycle and across homogeneous and heterogeneous loan types. In particular, during the 1990s boom, we predict and find that banks accelerated provisioning for loan losses and made this less obvious by accelerating loan charge-offs, especially for homogenous loans for which charge-offs are determined using number-of-days-past-due rules. We also provide evidence that the valuation implications of banks’ provisions for loan losses and loan charge-offs vary across the phases of the business cycle and loan types reflecting the effect of these factors on banks’ income smoothing. In particular, during the 1990s boom, we predict and find that charge-offs of homogenous loans have a positive association with current returns and future cash flows, because these charge-offs are recorded primarily by healthy banks with good future prospects reducing over-stated allowances for loan losses. We also predict and find that these charge-offs have a positive association with future returns that is explained by their positive association with future net income and recoveries. Our results are consistent with the market only partially appreciating healthy banks’ overstatement of charge-offs of homogeneous loans based on number-of-days-past-due rules during the 1990s boom, because of the perceived non-discretionary nature of these charge-offs