Empirical Analysis and Trading Strategies for Defaulted Debt Securities with Models for Risk and Investment Management

This study empirically analyzes the historical performance of defaulted debt from Moody’s Ultimate Recovery Database (1987-2010). Motivated by a stylized structural model of credit risk with systematic recovery risk, we argue and find evidence that returns on defaulted debt co-vary with determinants of the market risk premium, firm specific and structural factors. Defaulted debt returns in our sample are observed to be increasing in collateral quality or debt cushion of the issue. Returns are also increasing for issuers having superior ratings at origination, more leverage at default, higher cumulative abnormal returns on equity prior to default, or greater market implied loss severity at default. Considering systematic factors, returns on defaulted debt are positively related to equity market indices and industry default rates. On the other hand, defaulted debt returns decrease with short-term interest rates. In a rolling out-of-time and out-of-sample resampling experiment we show that our leading model exhibits superior performance. We also document the economic significance of these results through excess abnormal returns, implementing a hypothetical trading strategy, of around 5-6% (2-3%) assuming zero (1bp per month) round-trip transaction costs. These results are of practical relevance to investors and risk managers in this segment of the fixed income market.Distressed Debt; Recoveries; Default; Credit Risk

A Freudian <i>Dream</i>: Interpretations of <i>A Midsummer Night’s Dream</i> by Psychoanalysts and Psychoanalytically Informed Literary Critics

The thesis analyses interpretations of A Midsummer Night’s Dream by Freudian and post-Freudian clinicians, and by literary critics influenced by psychoanalytic theory. The primary material is principally taken from the Psychoanalytic Electronic Publishing database, and includes 37 papers or chapters by psychoanalysts and some by psychoanalytically informed literary critics, addressing aspects of the Dream. This considerable body of critical analysis of the play has largely been ignored by literary critics. Certain themes in this substantial body of criticism are identified and analysed: how dreams in the play have been variously interpreted psychoanalytically; the clinical interest in dreams within a dream and the relevance of this to the play and to the device of the play within the play; the dark side of the Dream including the function of comedy to disguise the play’s nightmare quality; and the dominance of oedipal interpretations to the neglect of other aspects of Freud’s writing about love. The thesis considers how far psychoanalytic criticism of the play reflects changes in psychoanalytic theory and phases of literary criticism. The thesis highlights the absence of meaningful interaction between Freudian clinicians and literary critics who examine the Dream during the same sixty year period from the 1950s, missing opportunities for productive intellectual dialogue. The thesis observes that literary critics refer more than clinicians to more recent psychoanalytic thinking; and that there are places where the clinicians could have enhanced their interpretations by reference to Freud’s writing on humour, on love and object choices, on illusion and transference-love. The thesis concludes that psychoanalytic critics of the play make a complementary contribution to literary criticism, and that the papers merit greater prominence in the reception history of the play

Personalized anticoagulant management using reinforcement learning.

Introduction: There are many problems with current state-of-the-art protocols for maintenance dosing of the oral anticoagulant agent warfarin used in clinical practice. The two key challenges include lack of personalized dose adjustment and the high cost of monitoring the efficacy of the therapy in the form of International Normalized Ratio (INR) measurements. A new dosing algorithm based on the principles of Reinforcement Learning (RL), specifically Q-Learning with functional policy approximation, was created to personalize maintenance dosing of warfarin based on observed INR and to optimize the length of time between INR measurements. This new method will help improve patient’s INR time in therapeutic range (TTR) as well as minimize cost associated with monitoring INR when compared to the current standard of care. Procedure: Using the principles of Reinforcement Learning, an algorithm to control warfarin dosing was created. The algorithm uses 9 different controllers which correspond to 9 different levels of warfarin sensitivity. The algorithm switches between controllers until it selects the controller that most closely resembles the individual patient’s response, and thus the optimal dose change (?Dose) and time between INR measurements (?Time) are personalized for each patient, based on INR observed in the patient. Three simulations were performed using data from 100 artificial patients, generated based on data from real patients, each. The first simulation that was performed was an ideal case scenario (clean simulation where the coefficient of variance (CV) of noise added to the model output = 0) using only the warfarin RL algorithm to prove efficacy. The second simulation was performed using the current standard of care and a CV = 25% to simulate intra-patient variability. The third simulation was performed using the warfarin RL algorithm with a CV = 25%. 180 days were simulated for each patient in each simulation and the measurements that were used to benchmark the efficacy of the therapy were INR time in therapeutic range (TTR) and the number of INR measurements that were taken during simulation. Results: The first simulation yielded a mean TTR = 92.1% with a standard deviation of 4.2%, and had a mean number of INR measurements = 7.94 measurements/patient. The second simulation yielded a mean TTR = 45.3% with a standard deviation of 16.4%, and had a mean number of INR measurements = 12.3 measurements/patient. The third simulation yielded a mean TTR = 51.8% with a standard deviation of 10.8%, and had a mean number of INR measurements = 8.05 measurements/patient. A p-value \u3c .001 suggests that there is a statistically significant difference between the 2 algorithms. Conclusion: Results from the simulations indicate that the warfarin RL algorithm performed better than the standard of care at keeping the patient’s INR in therapeutic range and also reduced the number of INR measurements that were necessary. This algorithm could help improve patient safety by increasing the patient’s INR TTR in the presence of intra-patient variability, and also help reduce the heavy cost associated with the therapy by minimizing the number of INR measurements that are necessary

Thatcherite mythology: eight Tory leadership candidates in search of an economic policy

Michael Jacobs discusses the alternative versions of Thatcherite economics being offered by the Conservative leadership candidates, which do not reflect the actual economic needs of the country today