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Firm managers make ethical decisions regarding the form and quality of disclosure. Disclosure can have long-term implications for performance, earnings manipulation, and even fraud. We investigate the impact of venture capital (VC) backing on the quality and informativeness of disclosure controls and procedures for newly public companies. We find that these controls and procedures are stronger, as evidenced by fewer material weaknesses in internal control under Section 302 of the Sarbanes–Oxley Act, when companies are VC-backed. Moreover, these disclosures are informative and are more likely to be followed by subsequent financial statement restatements than are disclosures made by non-VC-backed IPO companies.

Do compensation consultants drive up CEO pay for the benefit of managers, or do they design pay packages to benefit firm owners? Using a large sample of UK firms from the FTSE All‐Share Index over the 2003–2011 period, we show a positive correlation between the presence of compensation consultants and CEO pay. Importantly, isolating this effect is somewhat dependent on the endogenous selection of consultants and the statistical modelling strategy deployed. We find evidence that compensation consultants improve CEO compensation design when their expertise is of greater importance (e.g. during the post‐financial crisis period, or for firms that have particularly weak compensation policies). In addition, our findings show that compensation consultants increase CEO pay–performance sensitivity. The balance of evidence supports optimal contracting theory more than managerial power theory, but the authors caution the limits to this verification. We are careful to note that the more compelling evidence for the positive effect of pay consultants on CEOs is based on advanced methods (such as propensity score matching and difference‐in‐differences), and that more standard approaches (such as OLS and fixed effects) are unlikely to reveal the same level of causality of consultants on CEO pay.

Awakening of zygotic transcription in animal embryos relies on maternal pioneer transcription factors. The interplay of global and specific functions of these proteins remains poorly understood. Here, we analyze chromatin accessibility and time-resolved transcription in single and double mutant zebrafish embryos lacking pluripotency factors Pou5f3 and Sox19b. We show that two factors modify chromatin in a largely independent manner. We distinguish four types of direct enhancers by differential requirements for Pou5f3 or Sox19b. We demonstrate that changes in chromatin accessibility of enhancers underlie the changes in zygotic expression repertoire in the double mutants. Pou5f3 or Sox19b promote chromatin accessibility of enhancers linked to the genes involved in gastrulation and ventral fate specification. The genes regulating mesendodermal and dorsal fates are primed for activation independently of Pou5f3 and Sox19b. Strikingly, simultaneous loss of Pou5f3 and Sox19b leads to premature expression of genes, involved in regulation of organogenesis and differentiation.

Targeted therapies have shown significant patient benefit in about 5–10% of solid tumors that are addicted to a single oncogene. Here, we explore the idea of ligand addiction as a driver of tumor growth. High ligand levels in tumors have been shown to be associated with impaired patient survival, but targeted therapies have not yet shown great benefit in unselected patient populations. Using an approach of applying Bagged Decision Trees (BDT) to high-dimensional signaling features derived from a computational model, we can predict ligand dependent proliferation across a set of 58 cell lines. This mechanistic, multi-pathway model that features receptor heterodimerization, was trained on seven cancer cell lines and can predict signaling across two independent cell lines by adjusting only the receptor expression levels for each cell line. Interestingly, for patient samples the predicted tumor growth response correlates with high growth factor expression in the tumor microenvironment, which argues for a co-evolution of both factors in vivo., npj Systems Biology and Applications, 3, ISSN:2056-7189

This paper investigates the relationship between a CEO's foreign experience and CEO compensation. Our analysis is based on the constituent firms of the UK FTSE 350 index from 1999 to 2015. We find that foreign CEOs and national CEOs with foreign working experience receive significantly higher levels of total compensation compared to similar UK CEOs without such characteristics. The results are robust to the endogenous CEO selection using propensity score matching methods, as well as other modelling approaches. Our results show that pay premiums are attributable to the specialized foreign expertise and foreign networks of CEOs, which stem from foreign experience rather than broader general managerial skills.

This paper explores the effect of borrower and lender state-ownership on the consequences of corporate fraud in the debt market. Fraud revelations can increase a firm’s information and credit risk, and are therefore expected to significantly affect future bank loan conditions. The Chinese economy provides a unique setting from which to study the influence of state-ownership on debt contracting because it is dominated by state-owned banks (SBs) and firms. Using a sample of bank loans and enforcement actions announced between 2001 and 2012, we find that, after fraud announcements, the cost of private debt increases significantly, but not for loans issued by SBs to state-owned enterprises (SOEs). Moreover, we find evidence that SBs grant, and SOEs receive, lower interest rates. Additional tests show that SOEs that received a more favorable interest rate after the announcement of fraud from a SB perform worse than other firms. These results indicate that despite the bank reforms SBs continue to favor SOEs and this could lead to sub-optimal lending.

We document that analysts cater to short-term investors by issuing optimistic target prices.Catering dominates among analysts at brokers without an investment banking arm as they face lower reputational cost. The market does not see through the analyst catering activity and theirforecasts lead to temporary stock overpricing that short-term institutional investors exploit to offload their holdings to retail traders. We also report evidence consistent with catering brokers being rewarded with more future trades channelled through them. Our study identifies a new sourceof conflicts of interest in analyst research originating from the ownership composition of a stock.

Financial intermediaries such as venture capitalists (VCs) not only provide financing, they also play an active role in firm governance and in financial practices before a firm goes public. Venture capitalists are actively engaged in monitoring and advising their portfolio firms. Thus, one also expects them to exert significant influence over the development of financial reporting practices. This chapter reviews recent literature and empirical evidence on VCs and financial reporting quality in newly public firms. It surveys the role of VCs in such activities as earnings management. In particular, it discusses how their monitoring activities and reputation can impact how their portfolio firms establish financial reporting practices. Subsequently, it also reviews the consequences of misreporting, and whether they affect VC behavior ex ante. Finally, the chapter uses recent data to provide empirical evidence on the effect of VCs on accrual and real earnings management.

PURPOSE Parameter identifiability and confidence intervals were determined using a profile likelihood (PL) analysis method in a quantification model of the cerebral metabolic rate of oxygen consumption (CMRO2 ) with direct 17 O MRI. METHODS Three-dimensional dynamic 17 O MRI datasets of the human brain were acquired after inhalation of 17 O2 gas with the help of a rebreathing system, and CMRO2 was quantified with a pharmacokinetic model. To analyze the influence of the different model parameters on the identifiability of CMRO2 , PLs were calculated for different settings of the model parameters. In particular, the 17 O enrichment fraction of the inhaled 17 O2 gas, α, was investigated assuming a constant and a linearly varying model. Identifiability was analyzed for white and gray matter, and the dependency on different priors was studied. RESULTS Prior knowledge about only one α-related parameter was sufficient to resolve the CMRO2 nonidentifiability, and CMRO2 rates (0.72-0.99 µmol/gtissue /min in white matter, 1.02-1.78 µmol/gtissue /min in gray matter) are in a good agreement with the results of 15 O positron emission tomography studies. Nonconstant α values significantly improved model fitting. CONCLUSION The profile likelihood analysis shows that CMRO2 can be measured reliably in 17 O gas MRI experiment if the 17 O enrichment fraction is used as prior information for the model calculations. Magn Reson Med 78:1157-1167, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Motivation Dynamic models are used in systems biology to study and understand cellular processes like gene regulation or signal transduction. Frequently, ordinary differential equation (ODE) models are used to model the time and dose dependency of the abundances of molecular compounds as well as interactions and translocations. A multitude of computational approaches, e.g. for parameter estimation or uncertainty analysis have been developed within recent years. However, many of these approaches lack proper testing in application settings because a comprehensive set of benchmark problems is yet missing. Results We present a collection of 20 benchmark problems in order to evaluate new and existing methodologies, where an ODE model with corresponding experimental data is referred to as problem. In addition to the equations of the dynamical system, the benchmark collection provides observation functions as well as assumptions about measurement noise distributions and parameters. The presented benchmark models comprise problems of different size, complexity and numerical demands. Important characteristics of the models and methodological requirements are summarized, estimated parameters are provided, and some example studies were performed for illustrating the capabilities of the presented benchmark collection. Availability and implementation The models are provided in several standardized formats, including an easy-to-use human readable form and machine-readable SBML files. The data is provided as Excel sheets. All files are available at https://github.com/Benchmarking-Initiative/Benchmark-Models, including step-by-step explanations and MATLAB code to process and simulate the models. Supplementary information Supplementary data are available at Bioinformatics online.

This paper identifies a new incentive for managers to engage in corporate fraud stemming from the relative performance evaluation feature of CEO promotion tournaments. We document higher propensities to engage in fraud for firms with strong tournament incentives (as proxied for by the CEO pay gap). We posit that the relative performance evaluation feature of CEO promotion tournaments creates incentives to manipulate performance, while the option-like character can motivate managers to engage in risky activities. We thereby extend previous corporate fraud literature that focuses mainly on equity-based incentives and reports mixed findings. Our results are robust to using different fraud samples, and controlling for other known determinants of fraud as well as manager skills.

This paper explores how managers' and supervisors' equity incentives impact the likelihood of committing corporate fraud in Chinese-listed firms. Previous research has shown that corporate fraud in China is a widespread phenomenon and has severe consequences for affected firms and executives. However, our understanding of the reasons that fraud is committed in a Chinese setting has been very limited thus far. This is an increasingly important topic, because corporate governance is rapidly changing in China, and it is unclear whether adopting the executive compensation practices of the West is appropriate for Chinese firms. We show that managers' equity incentives increase their propensity to commit corporate fraud. We also find that this effect is more pronounced for state-owned firms. However, we find a negative but not significant relationship between the equity incentives of the supervisory board and the incidence of fraud.

This paper examines the relationship between performance persistence and corporate governance (as proxied for by board characteristics and shareholder structure). We document systematic differences in performance persistence across listed companies in China during 2001–2011, and empirically demonstrate that firms with better corporate governance show higher performance persistence. The results are robust over both the short and long terms. We also find that performance persistence is an important factor in refinancing, and it can lower companies’ costs of borrowing. Overall, our findings offer important implications for business ethics, as we demonstrate how corporate governance can lower companies’ costs of debt.

Lung cancer, with its most prevalent form non-small-cell lung carcinoma (NSCLC), is one of the leading causes of cancer-related deaths worldwide, and is commonly treated with chemotherapeutic drugs such as cisplatin. Lung cancer patients frequently suffer from chemotherapy-induced anemia, which can be treated with erythropoietin (EPO). However, studies have indicated that EPO not only promotes erythropoiesis in hematopoietic cells, but may also enhance survival of NSCLC cells. Here, we verified that the NSCLC cell line H838 expresses functional erythropoietin receptors (EPOR) and that treatment with EPO reduces cisplatin-induced apoptosis. To pinpoint differences in EPO-induced survival signaling in erythroid progenitor cells (CFU-E, colony forming unit-erythroid) and H838 cells, we combined mathematical modeling with a method for feature selection, the L1 regularization. Utilizing an example model and simulated data, we demonstrated that this approach enables the accurate identification and quantification of cell type-specific parameters. We applied our strategy to quantitative time-resolved data of EPO-induced JAK/STAT signaling generated by quantitative immunoblotting, mass spectrometry and quantitative real-time PCR (qRT-PCR) in CFU-E and H838 cells as well as H838 cells overexpressing human EPOR (H838-HA-hEPOR). The established parsimonious mathematical model was able to simultaneously describe the data sets of CFU-E, H838 and H838-HA-hEPOR cells. Seven cell type-specific parameters were identified that included for example parameters for nuclear translocation of STAT5 and target gene induction. Cell type-specific differences in target gene induction were experimentally validated by qRT-PCR experiments. The systematic identification of pathway differences and sensitivities of EPOR signaling in CFU-E and H838 cells revealed potential targets for intervention to selectively inhibit EPO-induced signaling in the tumor cells but leave the responses in erythroid progenitor cells unaffected. Thus, the proposed modeling strategy can be employed as a general procedure to identify cell type-specific parameters and to recommend treatment strategies for the selective targeting of specific cell types., Author Summary A major challenge in the development of therapeutic interventions is the selective inhibition of a signal transduction pathway in one cell type such as a cancer cell leaving the other cell type such as a healthy cell as unaffected as possible. Here, we propose a new approach that combines mathematical modeling based on quantitative experimental data with statistical methods. We demonstrate based on simulated data that our approach can determine which parameters are the same and which parameters differ in two exemplary cell types. We compare a lung cancer cell line to the precursor cells of red blood cells. We show that the same signal transduction network induced by erythropoietin (EPO), a hormone that is frequently employed to treat anemia in cancer patients, regulates survival of both cell types. Based on our experimental data in combination with our computational approach, we identify seven cell type-specific differences in this signaling pathway. Our strategy allows predicting therapeutic targets that could be inhibited to interfere with survival of lung cancer cells while leaving production of red blood cells unaffected.

We posit that when a subject firm has greater accounting comparability with industry peer firms it facilitates that firm’s learning from peer firm investments leading to better investments by the subject firm. Therefore, we expect firms with greater accounting comparability with industry peers to generate more and higher quality innovations, which are key determinants of corporate innovative efficiency. In the cross-section, we expect that the benefits from learning are likely greater (smaller) when peer-firms are successful (unsuccessful) innovators. Moreover, we expect the benefits of greater accounting comparability to be greater in highly competitive business environments. We find that accounting comparability with peer firms is positively associated with innovative efficiency of subject firms. Moreover, this association is stronger when peers have successful innovations and for firms in industries with high product similarity and strong product market competition.

This paper investigates the relation between foreign experience and CEO compensation. Using a sample of large UK firms from the FTSE 350 index from 2003 to 2011, we find that foreign-born CEOs and national CEOs with foreign working experience receive significantly higher levels of total compensation compared to those without these characteristics. We document that this foreign-CEO pay premium is stronger in firms that are more globalized. The results are robust to controlling for firm-specific economic and corporate governance characteristics as well as the endogenous CEO selection using propensity score matching. Our results show that the foreign-CEO pay premium is attributed to specialized foreign expertise rather than broader general managerial skills.

Receptor tyrosine kinases (RTKs) are high-affinity cell surface receptors for growth factors that are frequently deregulated in cancer. Signaling through these receptors has been associated with increased cancer cell proliferation and resistance to cytotoxic therapies. To block this detrimental signaling, many companies are developing inhibitory antibodies against various RTKs. A key challenge in clinical studies is the optimal stratification of patients who may benefit from these therapies. For an RTK targeted antibody, the detection of the respective growth factor in the tumor microenvironment may be an important bio-marker. Beyond the physical presence of the growth factor, the decision whether a cancer cell will respond to growth factor-induced signals is governed by complex intra-cellular signaling networks. We compared different approaches to predict cellular responses and will highlight a hybrid approach that combines mechanistic modeling based on ordinary differential equations with a machine learning algorithm. The models are trained on in vitro drug response screens and then applied to predict response in patient samples. The mechanistic models are trained on quantitative data from signal transduction studies as well as RNAseq data for cellular characterization. Using the hybrid approach, a correlation between growth factor expression in the tumor microenvironment and its predicted response was identified. This supports the hypothesis of addiction of tumors to growth factors abundant in the tumor microenvironment, and might enable more robust patient stratification in the future. Citation Format: Helge Hass, Kristina Masson, Sibylle Wohlgemuth, Violette Paragas, John E. Allen, Mark Sevecka, Emily Pace, Jens Timmer, Joerg Stelling, Gavin MacBeath, Birgit Schoeberl, Andreas Raue. Predicting ligand-dependent tumors from multi-dimensional signaling features [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1312.

The combinatorial explosion of state spaces is the biggest problem in applying model checking methods to concurrent systems. In this paper we present a new state-space reduction technique that is tailored to system specifications in Rewriting Logic, a unified semantic framework for concurrency which is based on conditional term rewriting modulo equational theories. The idea is to hide “unimportant” details of the system's behavior (such as internal computations) in the equations, and to represent only “interesting” state changes (such as communication operations) by explicit transitions. We show how this optimization can be implemented by transforming the Rewriting Logic specification, avoiding the construction of the full state space. Moreover we establish the correctness of our technique by proving that the original and the reduced system are weakly bisimilar, and demonstrate its usability by applying it to the concurrent functional programming language Erlang.

Motivation: To gain a deeper understanding of biological processes and their relevance in disease, mathematical models are built upon experimental data. Uncertainty in the data leads to uncertainties of the model’s parameters and in turn to uncertainties of predictions. Mechanistic dynamic models of biochemical networks are frequently based on nonlinear differential equation systems and feature a large number of parameters, sparse observations of the model components and lack of information in the available data. Due to the curse of dimensionality, classical and sampling approaches propagating parameter uncertainties to predictions are hardly feasible and insufficient. However, for experimental design and to discriminate between competing models, prediction and confidence bands are essential. To circumvent the hurdles of the former methods, an approach to calculate a profile likelihood on arbitrary observations for a specific time point has been introduced, which provides accurate confidence and prediction intervals for nonlinear models and is computationally feasible for high-dimensional models. Results: In this article, reliable and smooth point-wise prediction and confidence bands to assess the model’s uncertainty on the whole time-course are achieved via explicit integration with elaborate correction mechanisms. The corresponding system of ordinary differential equations is derived and tested on three established models for cellular signalling. An efficiency analysis is performed to illustrate the computational benefit compared with repeated profile likelihood calculations at multiple time points. Availability and implementation: The integration framework and the examples used in this article are provided with the software package Data2Dynamics, which is based on MATLAB and freely available at http://www.data2dynamics.org. Contact: helge.hass@fdm.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.

Summary: Modeling of dynamical systems using ordinary differential equations is a popular approach in the field of systems biology. Two of the most critical steps in this approach are to construct dynamical models of biochemical reaction networks for large datasets and complex experimental conditions and to perform efficient and reliable parameter estimation for model fitting. We present a modeling environment for MATLAB that pioneers these challenges. The numerically expensive parts of the calculations such as the solving of the differential equations and of the associated sensitivity system are parallelized and automatically compiled into efficient C code. A variety of parameter estimation algorithms as well as frequentist and Bayesian methods for uncertainty analysis have been implemented and used on a range of applications that lead to publications. Availability and implementation: The Data2Dynamics modeling environment is MATLAB based, open source and freely available at http://www.data2dynamics.org. Contact: andreas.raue@fdm.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.

This paper identifies a new incentive for managers to engage in corporate fraud stemming from the relative performance evaluation feature of CEO promotion tournaments. We document higher propensities to engage in fraud for firms with strong tournament incentives (as proxied for by the CEO pay gap). We posit that the relative performance evaluation feature of CEO promotion tournaments creates incentives to manipulate performance, while the option-like character can motivate managers to engage in risky activities. We thereby extend previous corporate fraud literature that focuses mainly on equity-based incentives and reports mixed findings. Our results are robust to using different fraud samples, and controlling for other known determinants of fraud as well as manager skills.

This article demonstrates that increased inflows of capital into private equity and venture capital funds can influence the investment behaviour of fund managers by increasing staging intensity, increasing amount per financing round, and accelerating speed of capital allocation. These changes do not appear to be related to expectations of improved investment performance. The study extends the work of Gompers and Lerner who interpret these inflows as demand pressure for venture capital securities to drive up prices during periods of high inflows. Using a unique dataset, the study examines the effects of this pressure on investment behaviour.