arXiv research

Investor-driven information diffusion affects excess comovement in China and the U.S. markets.

Mathematical study of excess growth rate connects info theory with finance.

In statistical learning theory, convex surrogates of the 0-1 loss are highly preferred because of the computational and theoretical virtues that convexity brings in. This is of more importance if we consider smooth surrogates as witnessed by the fact that the smoothness is further beneficial both computationally- by at…

New tool detects 'fleeting modes' causing excess risk in financial markets.

The paper explores the information-theoretic nature of excess risk in machine learning.

Study excess capacity in neural networks using Rademacher complexity.

A new formula reveals symmetries between mean excess and ES functions.

The paper analyzes the excess risk of PCA and provides a precise characterization.

Study excess risk in statistical inference with transformations.

We use a continuous-time random walk (CTRW) to model market fluctuation data from times when traders experience excessive losses or excessive profits. We analytically derive "superstatistics" that accurately model empirical market activity data (supplied by Bogachev, Ludescher, Tsallis, and Bunde)that exhibit transitio…

Proponents of behavioral finance have identified several "puzzles" in the market that are inconsistent with rational finance theory. One such puzzle is the "excess volatility puzzle". Changes in equity prices are too large given changes in the fundamentals that are expected to change equity prices. In this paper, we of…

The paper analyzes competition among fund managers using excess logarithmic returns and constructs games to find optimal allocations.

Currency volatility shocks predict lower excess returns, and buying weak transmitters outperforms selling strong ones.

Optimizes gradual reduction of excess carbon emissions to net-zero.

We derive asset pricing formula for markets with incomplete information and subjective views.

Local SGD proves efficient in overparameterized linear regression.

We study the relation between the trading behavior of agents and volatility in toy markets of adaptive inductively rational agents. We show that excess volatility, in such simplified markets, arises as a consequence of {\em i)} the neglect of market impact implicit in price taking behavior and of {\em ii)} excessive re…

The study classifies cellular pseudomanifolds and their properties.

Sharp stability of Alexandrov's theorem for $C^1$ domains in the small-excess regime

Study on excess mortality in Germany during 2020-21.

New method estimates model risk without knowing function class.

AlphaMLDigger predicts excess returns in fluctuating markets.

News attention to financial intermediaries and crises predicts excess bond premium and macroeconomic movements.

The paper bounds the excess risk of deep neural networks for weakly dependent processes.

Study non-asymptotic bounds for robust estimators under misspecified models.

Paper introduces a novel measure to analyze excess error in classification under covariate shift.

New algorithm achieves optimal privacy and efficiency in non-Euclidean convex optimization.

The paper simplifies arguments for stationary varifolds results.

This paper analyzes multi-pass SGD for least squares, improving generalization bounds.

Excessive leverage, i.e. the abuse of debt financing, is considered one of the primary factors in the default of financial institutions. Systemic risk results from correlations between individual default probabilities that cannot be considered independent. Based on the structural framework by Merton (1974), we discuss …

New method for unbiased regression reduces excess risk.

Paper improves clustering risk bounds for kernel k-means.

We study differentially private (DP) algorithms for stochastic convex optimization (SCO). In this problem the goal is to approximately minimize the population loss given i.i.d. samples from a distribution over convex and Lipschitz loss functions. A long line of existing work on private convex optimization focuses on th…

It is proved by Brendle in [4] that the equatorial disk $D^k$ has least area among $k$-dimensional free boundary minimal surfaces in the Euclidean ball $B^n$. By comparing the excess of free boundary minimal surfaces with the excess of the associated cones over the boundary, we prove the existence of a gap for the area…

This paper concerns integral varifolds of arbitrary dimension in an open subset of Euclidean space satisfying integrability conditions on their first variation. Firstly, the study of pointwise power decay rates almost everywhere of the quadratic tilt-excess is completed by establishing the precise decay rate for two-di…

We prove that a metric measure space $(X,d,m)$ satisfying finite dimensional lower Ricci curvature bounds and whose Sobolev space $W^{1,2}$ is Hilbert is rectifiable. That is, a $RCD^*(K,N)$-space is rectifiable, and in particular for $m$-a.e. point the tangent cone is unique and euclidean of dimension at most $N$. The…

The paper analyzes the generalization performance of spectral clustering algorithms and proposes new methods to improve their effectiveness.

Paper establishes a universal growth rate for smooth surrogate losses in classification.

The study bounds the excess of disjoint nonorientable surfaces in a 4-manifold.

Paper provides estimates for varifolds with critical mean curvature.

Lower bounds show OLS outperforms basis pursuit in overparameterized linear regression.

In this paper, we study an insurer's reinsurance-investment problem under a mean-variance criterion. We show that excess-loss is the unique equilibrium reinsurance strategy under a spectrally negative Lévy insurance model when the reinsurance premium is computed according to the expected value premium principle. Furthe…

New method refines model-free evaluation of complex machine learning models.

Full-batch GD achieves generalization close to any stationary point with fewer assumptions.

Paper improves privacy in SGD with low noise, achieving optimal risk rates.

This research uses reinforcement learning to find optimal emission offsets in greenhouse gas markets.

We study online aggregation of the predictions of experts, and first show new second-order regret bounds in the standard setting, which are obtained via a version of the Prod algorithm (and also a version of the polynomially weighted average algorithm) with multiple learning rates. These bounds are in terms of excess l…

The paper optimizes insurance strategies for two collaborating business lines.