The price-to-book effect on the JSE : valuation disparities and subsequent performance

CITATION: Du Toit, S. G. & Krige, J. D. 2014. The price-to-book effect on the JSE : valuation disparities and subsequent performance. South African Journal of Business Management, 45(4): a143, doi:10.4102/sajbm.v45i4.143.The original publication is available at https://sajbm.orgThe purpose of this study was to determine whether the relative out- or underperformance of a value portfolio versus a growth portfolio can be anticipated in advance by comparing a valuation difference multiple with the subsequent fiveyear relative performance of the value and growth portfolios. The valuation difference multiple was calculated as the median price-to-book value (P/B) ratio of the growth portfolio divided by the median P/B ratio of the value portfolio. Using monthly data for the period 1991 to 2011, this study found that in most instances the higher the valuation difference multiple, the higher the outperformance of the value portfolio over the subsequent five-year period, as compared to the growth portfolio.https://sajbm.org/index.php/sajbm/article/view/143Publisher's versio

The price-to-book effect on the JSE: Valuation disparities and subsequent performance

The purpose of this study was to determine whether the relative out- or underperformance of a value portfolio versus a growth portfolio can be anticipated in advance by comparing a valuation difference multiple with the subsequent fiveyear relative performance of the value and growth portfolios. The valuation difference multiple was calculated as the median price-to-book value (P/B) ratio of the growth portfolio divided by the median P/B ratio of the value portfolio. Using monthly data for the period 1991 to 2011, this study found that in most instances the higher the valuation difference multiple, the higher the outperformance of the value portfolio over the subsequent five-year period, as compared to the growth portfolio

Constraint Handling in Efficient Global Optimization

This is the author accepted manuscript. The final version is available from ACM via the DOI in this record.Real-world optimization problems are often subject to several constraints which are expensive to evaluate in terms of cost or time. Although a lot of effort is devoted to make use of surrogate models for expensive optimization tasks, not many strong surrogate-assisted algorithms can address the challenging constrained problems. Efficient Global Optimization (EGO) is a Kriging-based surrogate-assisted algorithm. It was originally proposed to address unconstrained problems and later was modified to solve constrained problems. However, these type of algorithms still suffer from several issues, mainly: (1) early stagnation, (2) problems with multiple active constraints and (3) frequent crashes. In this work, we introduce a new EGO-based algorithm which tries to overcome these common issues with Kriging optimization algorithms. We apply the proposed algorithm on problems with dimension d ≤ 4 from the G-function suite [16] and on an airfoil shape example.This research was partly funded by Tekes, the Finnish Funding Agency for Innovation (the DeCoMo project), and by the Engineering and Physical Sciences Research Council [grant numbers EP/N017195/1, EP/N017846/1]

The synergistic integration of computational fluid dynamics and experimental fluid dynamics for ground effect aerodynamics studies

This article highlights the ‘synergistic’ use of experimental fluid dynamics (EFD) and computational fluid dynamics (CFD), where the two sets of simulations are performed concurrently and by the same researcher. In particular, examples from the area of ground effect aerodynamics are discussed, where the major facility used was also designed through a combination of CFD and EFD. Three examples are than outlined, to demonstrate the insight that can be obtained from the integration of CFD and EFD studies. The case studies are the study of dimple flow (to enhance aerodynamic performance), the analysis of a Formula-style front wing and wheel, and the study of compressible flow ground effect aerodynamics. In many instances, CFD has been used to not only provide complementary information to an experimental study, but to design the experiments. Laser-based, non-intrusive experimental techniques were used to provide an excellent complement to CFD. The large datasets found from both experimental and numerical simulations have required a new methodology to correlate the information; a new post-processing method has been developed, making use of the kriging and co-kriging estimators, to develop correlations between the often disparate data types

Effect of early vasopressin vs norepinephrine on kidney failure in patients with septic shock. The VANISH Randomized Clinical Trial

IMPORTANCE: Norepinephrine is currently recommended as the first-line vasopressor in septic shock; however, early vasopressin use has been proposed as an alternative. OBJECTIVE: To compare the effect of early vasopressin vs norepinephrine on kidney failure in patients with septic shock. DESIGN, SETTING, AND PARTICIPANTS: A factorial (2×2), double-blind, randomized clinical trial conducted in 18 general adult intensive care units in the United Kingdom between February 2013 and May 2015, enrolling adult patients who had septic shock requiring vasopressors despite fluid resuscitation within a maximum of 6 hours after the onset of shock. INTERVENTIONS: Patients were randomly allocated to vasopressin (titrated up to 0.06 U/min) and hydrocortisone (n = 101), vasopressin and placebo (n = 104), norepinephrine and hydrocortisone (n = 101), or norepinephrine and placebo (n = 103). MAIN OUTCOMES AND MEASURES: The primary outcome was kidney failure-free days during the 28-day period after randomization, measured as (1) the proportion of patients who never developed kidney failure and (2) median number of days alive and free of kidney failure for patients who did not survive, who experienced kidney failure, or both. Rates of renal replacement therapy, mortality, and serious adverse events were secondary outcomes. RESULTS: A total of 409 patients (median age, 66 years; men, 58.2%) were included in the study, with a median time to study drug administration of 3.5 hours after diagnosis of shock. The number of survivors who never developed kidney failure was 94 of 165 patients (57.0%) in the vasopressin group and 93 of 157 patients (59.2%) in the norepinephrine group (difference, -2.3% [95% CI, -13.0% to 8.5%]). The median number of kidney failure-free days for patients who did not survive, who experienced kidney failure, or both was 9 days (interquartile range [IQR], 1 to -24) in the vasopressin group and 13 days (IQR, 1 to -25) in the norepinephrine group (difference, -4 days [95% CI, -11 to 5]). There was less use of renal replacement therapy in the vasopressin group than in the norepinephrine group (25.4% for vasopressin vs 35.3% for norepinephrine; difference, -9.9% [95% CI, -19.3% to -0.6%]). There was no significant difference in mortality rates between groups. In total, 22 of 205 patients (10.7%) had a serious adverse event in the vasopressin group vs 17 of 204 patients (8.3%) in the norepinephrine group (difference, 2.5% [95% CI, -3.3% to 8.2%]). CONCLUSIONS AND RELEVANCE: Among adults with septic shock, the early use of vasopressin compared with norepinephrine did not improve the number of kidney failure-free days. Although these findings do not support the use of vasopressin to replace norepinephrine as initial treatment in this situation, the confidence interval included a potential clinically important benefit for vasopressin, and larger trials may be warranted to assess this further. TRIAL REGISTRATION: clinicaltrials.gov Identifier: ISRCTN 20769191

Applications of Polynomial Chaos-Based Cokriging to Aerodynamic Design Optimization Benchmark Problems

In this work, the polynomial chaos-based Cokriging (PC-Cokriging) is applied to a benchmark aerodynamic design optimization problem. The aim is to perform fast design optimization using this multifidelity metamodel. Multifidelity metamodels use information at multiple levels of fidelity to make accurate and fast predictions. Higher amount of lower fidelity data can provide important information on the trends to a limited amount of high-fidelity (HF) data. The PC-Cokriging metamodel is a multivariate version of the polynomial chaos-based Kriging (PC-Kriging) metamodel and its construction is similar to Cokriging. It combines the advantages of the interpolation-based Kriging metamodel and the regression-based polynomial chaos expansions (PCE). In the work the PC-Cokriging model is compared to other metamodels namely PCE, Kriging, PC-Kriging and Cokriging. These metamodel are first compared in terms of global accuracy, measured by root mean squared error (RMSE) and normalized RMSE (NRMSE) for different sample sets, each with an increasing number of HF samples. These metamodels are then used to find the optimum. Once the optimum design is found computational fluid dynamics (CFD) simulations are rerun and the results are compared to each other. In this study a drag reduction of 73.1 counts was achieved. The multifidelity metamodels required 19 HF samples along with 1,055 low-fidelity to converge to the optimum drag value of 129 counts, while the single fidelity models required 155 HF samples to do the same

Surrogate Models and Mixtures of Experts in Aerodynamic Performance Prediction for Mission Analysis

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140436/1/6.2014-2301.pd

An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection

Validation of Agent-Based Models in Economics and Finance

Since the survey by Windrum et al. (Journal of Artificial Societies and Social Simulation 10:8, 2007), research on empirical validation of agent-based models in economics has made substantial advances, thanks to a constant flow of high-quality contributions. This Chapter attempts to take stock of such recent literature to offer an updated critical review of the existing validation techniques. We sketch a simple theoretical framework that conceptualizes existing validation approaches, which we examine along three different dimensions: (i) comparison between artificial and real-world data; (ii) calibration and estimation of model parameters; and (iii) parameter space exploration. Finally, we discuss open issues in the field of ABM validation and estimation. In particular, we argue that more research efforts should be devoted toward advancing hypothesis testing in ABM, with specific emphasis on model stationarity and ergodicity