Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Bruce J Vanstone"'
Publikováno v:
Journal of the Operational Research Society. 74:374-402
Publikováno v:
Pacific-Basin Finance Journal. 79:102011
Publikováno v:
Accounting Research Journal. 34:106-112
Purpose The pitching research template (PRT) is designed to help pitchers identify the core elements that form the framework of any research project. This paper aims to provide a brief commentary on an application of the PRT to pitch an environmental
Publikováno v:
Accounting & Finance. 61:4007-4024
Autor:
Geoffrey Harris, Colette Southam, Thomas Aspinall, Simone Kelly, Bruce J Vanstone, Adrian Gepp
Publikováno v:
Accounting & Finance. 61:3797-3819
This study models the term structure of the European Union Emissions Trading Scheme. The one‐factor geometric Brownian motion model of Abadie and Chamorro is replicated using the data now available and then compared with a two‐factor short‐term
Publikováno v:
Computational Economics. 58:943-964
Statistical arbitrage refers to a suite of quantitative investment strategies employed chiefly by hedge funds and proprietary trading firms. The arbitrageur can draw on a number of different approaches to identify and exploit an arbitrage opportunity
Publikováno v:
Pacific-Basin Finance Journal. 77:101906
Publikováno v:
Accounting & Finance. 60:4361-4386
Consumption behaviour and financial literacy are primary factors in determining the financial well‐being of retirees. This paper uses an existing financial literacy index to examine how financial literacy directly, and via an interaction with consu
Financial applications of semidefinite programming: a review and call for interdisciplinary research
Publikováno v:
Accounting & Finance. 60:3527-3555
Optimisation problems in finance commonly have non‐linear constraints for which previous solutions have required unrealistic assumptions. However, many of these can be efficiently solved as semidefinite programming (SDP) problems, which have less r
Publikováno v:
Applied Intelligence. 49:3815-3820
Despite continuous improvement in the range and quality of machine learning techniques, accurately predicting stock prices still remains as elusive as ever. We approach this problem using a modern autoregressive neural network architecture and incorp