Distributionally Robust Mean-Variance Portfolio Selection with Wasserstein Distances

We revisit Markowitz’s mean-variance portfolio selection model by considering a distributionally robust version, in which the region of distributional uncertainty is around the empirical measure and the discrepancy between probability measures is dictated by the Wasserstein distance. We reduce this problem into an empirical variance minimization problem with an additional regularization term. Moreover, we extend the recently developed inference methodology to our setting in order to select the size of the distributional uncertainty as well as the associated robust target return rate in a data-driven way. Finally, we report extensive back-testing results on S&P 500 that compare the performance of our model with those of several well-known models including the Fama–French and Black–Litterman models. This paper was accepted by David Simchi-Levi, finance.

EXPERIMENTAL INVESTIGATION INTO FACTORS AFFECTING THE TRANSIENT FLOW OF FLUID THROUGH AN ORIFICE IN REALISTIC CONDITIONS

During operations, damage can occur with a resulting ingress or egress of fluid. The incoming water affects the reserve buoyancy and it can also change stability and hull girder loading. During a flooding event it is vital that the flow through the damaged orifice and the movement of floodwater around the structure can be predicted quickly to avoid further damage and ensure environmental safety. The empirical measure coefficient of discharge is used as a simplified method to quantify the flooding rate. In many internal flow applications the coefficient of discharge is estimated to be 0.6 but recent research shows that it can vary considerably when applied to transient flooding flows. This paper uses an experimental setup to investigate how changes to the orifice edges and position within the structure affect the flow. It is then used to investigate the coefficient in a more realistic scenario, a static compartment in waves.

ChromeBat: A Bio-Inspired Approach to 3D Genome Reconstruction

With the advent of Next Generation Sequencing and the Hi-C experiment, high quality genome-wide contact data are becoming increasingly available. These data represents an empirical measure of how a genome interacts inside the nucleus. Genome conformation is of particular interest as it has been experimentally shown to be a driving force for many genomic functions from regulation to transcription. Thus, the Three Dimensional-Genome Reconstruction Problem (3D-GRP) seeks to take Hi-C data and produces a complete physical genome structure as it appears in the nucleus for genomic analysis. We propose and develop a novel method to solve the Chromosome and Genome Reconstruction problem based on the Bat Algorithm (BA) which we called ChromeBat. We demonstrate on real Hi-C data that ChromeBat is capable of state-of-the-art performance. Additionally, the domain of Genome Reconstruction has been criticized for lacking algorithmic diversity, and the bio-inspired nature of ChromeBat contributes algorithmic diversity to the problem domain. ChromeBat is an effective approach for solving the Genome Reconstruction Problem.

Concentration Inequalities on the Multislice and for Sampling Without Replacement

We present concentration inequalities on the multislice which are based on (modified) log-Sobolev inequalities. This includes bounds for convex functions and multilinear polynomials. As an application, we show concentration results for the triangle count in the G(n, M) Erdős–Rényi model resembling known bounds in the G(n, p) case. Moreover, we give a proof of Talagrand’s convex distance inequality for the multislice. Interpreting the multislice in a sampling without replacement context, we furthermore present concentration results for n out of N sampling without replacement. Based on a bounded difference inequality involving the finite-sampling correction factor $$1 - (n / N)$$ 1 - ( n / N ) , we present an easy proof of Serfling’s inequality with a slightly worse factor in the exponent, as well as a sub-Gaussian right tail for the Kolmogorov distance between the empirical measure and the true distribution of the sample.

Measuring Accounting Asset Informativeness

We develop and validate an empirical measure of the informativeness of accounting assets in measuring firm-specific economic capital, an important determinant of both cash flows and intrinsic values. Our validation tests show that the asset informativeness measure is sensitive to differences in both accounting methods and implementation decisions at the firm level, and corresponds to the way equity investors use the information in accounting assets. We find that accounting assets contain substantial information about firms' productive capacity (economic capital) and the information is not summarized in several earnings attributes often associated with earnings quality.

Large Deviations and Gradient Flows for the Brownian One-Dimensional Hard-Rod System

We study a system of hard rods of finite size in one space dimension, which move by Brownian noise while avoiding overlap. We consider a scaling in which the number of particles tends to infinity while the volume fraction of the rods remains constant; in this limit the empirical measure of the rod positions converges almost surely to a deterministic limit evolution. We prove a large-deviation principle on path space for the empirical measure, by exploiting a one-to-one mapping between the hard-rod system and a system of non-interacting particles on a contracted domain. The large-deviation principle naturally identifies a gradient-flow structure for the limit evolution, with clear interpretations for both the driving functional (an ‘entropy’) and the dissipation, which in this case is the Wasserstein dissipation. This study is inspired by recent developments in the continuum modelling of multiple-species interacting particle systems with finite-size effects; for such systems many different modelling choices appear in the literature, raising the question how one can understand such choices in terms of more microscopic models. The results of this paper give a clear answer to this question, albeit for the simpler one-dimensional hard-rod system. For this specific system this result provides a clear understanding of the value and interpretation of different modelling choices, while giving hints for more general systems.