Limits of Disclosure

One big focus of attention, criticism, and proposals for reform in the aftermath of the 2008 financial crisis has been securities disclosure. Many commentators have emphasized the complexity of the securities being sold, arguing that no one could understand the disclosure. Some observers have noted that disclosures were sometimes false or incomplete. What follows these issues, to some commentators, is that, whatever other lessons we may learn from the crisis, we need to improve disclosure. How should it be improved? Commentators often lament the frailties of human understanding, notably including those of everyday retail investors—people who do not understand or even read disclosure. This leads, naturally and unsurprisingly, to prescriptions for yet more disclosure, simpler disclosure, and financial literacy education. We believe that improvements in disclosure will not do much to prevent or minimize the effects of future crises. Indeed, the role of disclosure in investment decisions is far more limited, and far less straightforward, than is typically assumed. To caricature a bit for ease of exposition, the straightforward story is as follows: Read carefully, understand what you read, conduct any additional inquiry you deem appropriate, and then decide—if the security seems good, buy it; if not, don’t. Also, consider that whoever is selling you the security knows more than you do about it and has an incentive to present it more favorably than it warrants. But many investors, even sophisticated investors, do not start with cautious or neutral presumptions about a security and do not carefully read the disclosure to appraise the security on its merits before deciding whether to invest. As the literature extensively discusses, investors may be eager to buy “the hot new thing” that their peers are buying. Why do the peers buy it? One part of the story may be the old and often-told explanation: some investors tired of “boring” returns, saw an opportunity to supercharge their yields, and believed the perennial pitch made for new financial instruments—that they offered more return than risk. But our aim is not to explain what motivated investor behavior; our aim is to point out what did not sufficiently motivate investor behavior. Our argument is not just about the present crisis. Indeed, the complex role that disclosure plays in an investor’s decision as to whether to buy a security is just one example of disclosure’s limits. Those limits reflect the complexity of human decisionmaking. Why should disclosure work? The obvious answers are that better information should make for better decisions and that the specter of disclosure should constrain behavior. But these answers are importantly incomplete. Better information should, in principle, lead to better decisions, but other factors may be far more important. This was the case with disclosure regarding the securities at issue in the financial crisis. We discuss another example as well: executive compensation disclosures

Limits of Disclosure

One big focus of attention, criticism, and proposals for reform in the aftermath of the 2008 financial crisis has been securities disclosure. Many commentators have emphasized the complexity of the securities being sold, arguing that no one could understand the disclosure. Some observers have noted that disclosures were sometimes false or incomplete. What follows these issues, to some commentators, is that, whatever other lessons we may learn from the crisis, we need to improve disclosure. How should it be improved? Commentators often lament the frailties of human understanding, notably including those of everyday retail investors—people who do not understand or even read disclosure. This leads, naturally and unsurprisingly, to prescriptions for yet more disclosure, simpler disclosure, and financial literacy education. We believe that improvements in disclosure will not do much to prevent or minimize the effects of future crises. Indeed, the role of disclosure in investment decisions is far more limited, and far less straightforward, than is typically assumed. To caricature a bit for ease of exposition, the straightforward story is as follows: Read carefully, understand what you read, conduct any additional inquiry you deem appropriate, and then decide—if the security seems good, buy it; if not, don’t. Also, consider that whoever is selling you the security knows more than you do about it and has an incentive to present it more favorably than it warrants. But many investors, even sophisticated investors, do not start with cautious or neutral presumptions about a security and do not carefully read the disclosure to appraise the security on its merits before deciding whether to invest. As the literature extensively discusses, investors may be eager to buy “the hot new thing” that their peers are buying. Why do the peers buy it? One part of the story may be the old and often-told explanation: some investors tired of “boring” returns, saw an opportunity to supercharge their yields, and believed the perennial pitch made for new financial instruments—that they offered more return than risk. But our aim is not to explain what motivated investor behavior; our aim is to point out what did not sufficiently motivate investor behavior. Our argument is not just about the present crisis. Indeed, the complex role that disclosure plays in an investor’s decision as to whether to buy a security is just one example of disclosure’s limits. Those limits reflect the complexity of human decisionmaking. Why should disclosure work? The obvious answers are that better information should make for better decisions and that the specter of disclosure should constrain behavior. But these answers are importantly incomplete. Better information should, in principle, lead to better decisions, but other factors may be far more important. This was the case with disclosure regarding the securities at issue in the financial crisis. We discuss another example as well: executive compensation disclosures

Exact shering box solutions of MHD flows with resistivity, viscosity and cooling

Axisymmetric incompressible modes of the magneto-rotational instability (MRI) with a vertical wavenumber are exact solutions of the non-linear local equations of motion for a disk (shearing box). They are referred to as "channel solutions". Here, we generalize a class of these solutions to include energy losses, viscous, and resistive effects. In the limit of zero shear, we recover the result that torsional Alfv\'en waves are exact solutions of the non-linear equations. Our method allows the extension of these solutions into the dissipative regime. These new solutions serve as benchmarks for simulations including dissipation and energy loss, and to calibrate numerical viscosity and resistivity in the Zeus3D code. We quantify the anisotropy of numerical dissipation and compute its scaling with time and space resolution. We find a strong dependence of the dissipation on the mean magnetic field that may affect the saturation state of the MRI as computed with Zeus3D. It is also shown that elongated grid cells generally preclude isotropic dissipation and that a Courant time step smaller than that which is commonly used should be taken to avoid spurious anti-diffusion of magnetic field.Comment: 17 pages, 18 figures, MNRAS in pres

Understanding Parents\u27 Experiences with Children with Type 1 Diabetes: A Qualitative Inquiry

Parenting is often described as a stress-inducing experience, which can be further complicated or made more stressful and anxiety-inducing when parenting children with chronic conditions such as type 1 diabetes (T1D). The incidence of T1D among children has risen and continues to rise globally, resulting in a need to understand the experiences of parenting children with T1D. The purpose of this qualitative inquiry was to explore the lived experiences, and the meaning ascribed to those experiences, of being a parent of a child with T1D. This qualitative study was conducted through an interpretivist paradigm and includes the experiences of 29 parents (19 mothers and 10 fathers) of 24 children (aged 6 to 15 years) with T1D. Parents, and parent dyads, completed demographic questionnaires and written prompts, and participated in focus group interviews. Three themes were developed from the data, namely, (a) the costs of T1D, (b) the ultimate helicopter mom, and (c) dealing with “being different”. Generally, the participants reported on the direct (e.g., financial and time) and indirect (e.g., family planning) costs associated with parenting children with T1D, their role as a primary provider and anxieties with relinquishing control and dealing with the stigma surrounding a diabetes diagnosis. Unique findings from this study included the impact a T1D diagnosis had on future family planning as parents navigated the fear and uncertainty of having additional children with T1D, as well as the internal conflict parents had with entrusting others to care for their child, especially if they deemed them to be unqualified or unnecessarily stigmatizing or ostracizing their child

Mood Disorders and Trauma: What are the Associations?

Objectives: Mood dysregulation in traumatized children may be misdiagnosed as bipolar disorder (BD) and conversely, the diagnosis of BD overlooked. Our aim is to characterize the relationship between trauma and mood dysregulation and pediatric BD. Methods: We are assessing youths ages 8-18 who present with mood symptoms and past trauma divided into two groups: 1. Trauma+Unmodified DSM-IV-TR BD (T+BD) and 2. Trauma+Mood Disorder NOS (T+MD). Differences in clinical variables between groups are analyzed using t-tests for continuous and chi-square tests for categorical variables (α= 0.05). Results: Age at onset of trauma for youth with T+BD (n=10) compared with T+MD (n=10) was similar (2.6±1.8 versus 3.3±1.9 years; p=0.4) as were types of trauma and number of incidents, and age at onset of mood symptoms (T+BD 7±2.5 versus T+MD 7.8±1.8 p=0.4). The T+BD group had higher scores on the sexual abuse subscale of the Childhood Trauma Questionnaire (p=0.04) and BPRS mania subscale (p=0.02), and higher total number of major depressive episodes (p=0.04) and manic episodes (p=0.03) per the KSCID. Youth with T+BD reported a trend toward higher rates of ideation to self-harm compared to youth with T+MD (p=0.08). Both groups had similar PTSD and ADHD symptoms, and similar number of psychotrophic medications (BD 3.6±2.9 MD 2.7±2.1 p=0.4). Finally, family history findings suggest a trend towards higher rates of any Axis I disorder in the T+BD families (p=0.07), and significantly higher rates of anxiety disorders (p=0.05), BD (p=0.04), and schizophrenia (p=0.02). Conclusions: Results suggest differences in clinical presentation and higher rates of BD and schizophrenia in the T+BD families. Taken together, these preliminary results suggest potential biological and genetic vulnerabilities which may predispose children to develop specific mood disorders under certain circumstances; the ability to identify these children early on could change their prognostic trajectory

Dual positive and negative regulation of GPCR signaling by GTP hydrolysis

G protein-coupled receptors (GPCRs) regulate a variety of intracellular pathways through their ability to promote the binding of GTP to heterotrimeric G proteins. Regulator of G protein signaling (RGS) proteins increase the intrinsic GTPase activity of G-subunits and are widely regarded as negative regulators of G protein signaling. Using yeast we demonstrate that GTP hydrolysis is not only required for desensitization, but is essential for achieving a high maximal (saturated level) response. Thus RGS-mediated GTP hydrolysis acts as both a negative (low stimulation) and positive (high stimulation) regulator of signaling. To account for this we generated a new kinetic model of the G protein cycle where GGTP enters an inactive GTP-bound state following effector activation. Furthermore, in vivo and in silico experimentation demonstrates that maximum signaling output first increases and then decreases with RGS concentration. This unimodal, non-monotone dependence on RGS concentration is novel. Analysis of the kinetic model has revealed a dynamic network motif that shows precisely how inclusion of the inactive GTP-bound state for the G produces this unimodal relationship

Causal Learning via Manifold Regularization.

This paper frames causal structure estimation as a machine learning task. The idea is to treat indicators of causal relationships between variables as 'labels' and to exploit available data on the variables of interest to provide features for the labelling task. Background scientific knowledge or any available interventional data provide labels on some causal relationships and the remainder are treated as unlabelled. To illustrate the key ideas, we develop a distance-based approach (based on bivariate histograms) within a manifold regularization framework. We present empirical results on three different biological data sets (including examples where causal effects can be verified by experimental intervention), that together demonstrate the efficacy and general nature of the approach as well as its simplicity from a user's point of view