Zobrazeno 1 - 10
of 1 115
pro vyhledávání: '"Roberts, Stephen P."'
We introduce a novel approach to options trading strategies using a highly scalable and data-driven machine learning algorithm. In contrast to traditional approaches that often require specifications of underlying market dynamics or assumptions on an
Externí odkaz:
http://arxiv.org/abs/2407.21791
We present an energy/entropy stable and high order accurate finite difference method for solving the nonlinear (rotating) shallow water equations (SWE) in vector invariant form using the newly developed dual-pairing and dispersion-relation preserving
Externí odkaz:
http://arxiv.org/abs/2310.12739
Forecasting models for systematic trading strategies do not adapt quickly when financial market conditions rapidly change, as was seen in the advent of the COVID-19 pandemic in 2020, causing many forecasting models to take loss-making positions. To d
Externí odkaz:
http://arxiv.org/abs/2310.10500
This paper examines the asymptotic convergence properties of Lipschitz interpolation methods within the context of bounded stochastic noise. In the first part of the paper, we establish probabilistic consistency guarantees of the classical approach i
Externí odkaz:
http://arxiv.org/abs/2310.07039
We derive and analyse well-posed boundary conditions for the linear shallow water wave equation. The analysis is based on the energy method and it identifies the number, location and form of the boundary conditions so that the initial boundary value
Externí odkaz:
http://arxiv.org/abs/2309.16116
We consider a conditional factor model for a multivariate portfolio of United States equities in the context of analysing a statistical arbitrage trading strategy. A state space framework underlies the factor model whereby asset returns are assumed t
Externí odkaz:
http://arxiv.org/abs/2309.02205
Network momentum provides a novel type of risk premium, which exploits the interconnections among assets in a financial network to predict future returns. However, the current process of constructing financial networks relies heavily on expensive dat
Externí odkaz:
http://arxiv.org/abs/2308.12212
We investigate the concept of network momentum, a novel trading signal derived from momentum spillover across assets. Initially observed within the confines of pairwise economic and fundamental ties, such as the stock-bond connection of the same comp
Externí odkaz:
http://arxiv.org/abs/2308.11294
Autor:
Wang, Lawrence, Roberts, Stephen
Modern neural networks are undeniably successful. Numerous works study how the curvature of loss landscapes can affect the quality of solutions. In this work we study the loss landscape by considering the Hessian matrix during network training with l
Externí odkaz:
http://arxiv.org/abs/2307.11948
We introduce Deep Inception Networks (DINs), a family of Deep Learning models that provide a general framework for end-to-end systematic trading strategies. DINs extract time series (TS) and cross sectional (CS) features directly from daily price ret
Externí odkaz:
http://arxiv.org/abs/2307.05522