arXiv subjects

J. E. Beasley

Publications and source records attributed to J. E. Beasley.

7 recordsLinked to original sources

arXiv2026

Asset pre-selection for a cardinality constrained index tracking portfolio with optional enhancement

Index trackers are important passive investments offering the return and risk of the market encapsulated by the index, the largest US index tracker was valued at $900 billion in early 2026. Using a two-stage approach of asset selection followed by estimation on S&P 500 data, we explore the role of cardinality constraints in determining the effectiveness of the tracker's reproduction of market return and risk. We compare eight pre-selection procedures: forward selection or backward elimination; implemented using ordinary least squares or least absolute deviation regression; with or without a regression constant. We show experimentally that out-of-sample tracking errors decrease according to the inverse of the square root of cardinality and out-of-sample tracking error, transaction volume and return-risk ratios all improve as the cardinality constraint is relaxed. By contrast for enhanced returns, cardinalities of the order 10 to 20 are most effective.

q-fin.PM↗

arXiv2023

An algorithm for the optimal solution of variable knockout problems

We consider a class of problems related to variable knockout, where knockout means set a variable to zero. Given an optimisation problem formulated as a zero-one integer program the question we consider in this paper is what might be an appropriate set of variables to knockout of the problem, in order that the optimal solution to the problem that remains after variable knockout has a desired property. This property might be related to the optimal solution value after knockout, or require the problem after knockout to be infeasible. We present an algorithm for the optimal solution of this knockout problem. Computational results are given for an illustrative example based upon shortest path interdiction using publicly available shortest path test problems.

math.OC↗

arXiv2021

A discrete optimisation approach for target path planning whilst evading sensors

In this paper we deal with a practical problem that arises in military mission planning. The problem is to plan a path for one, or more, agents to reach a target without being detected by enemy sensors. Agents are not passive, rather they can initiate actions which aid evasion. They can knockout sensors. Here to knockout a sensor means to completely disable the sensor. They can also confuse sensors. Here to confuse a sensor means to reduce the probability that the sensor can detect an agent. Agent actions are path dependent and time limited. By path dependent we mean that an agent needs to be sufficiently close to a sensor to knock it out. By time limited we mean that a limit is imposed on how long a sensor is knocked out or confused before it reverts back to its original operating state. The approach adopted breaks the continuous space in which agents move into a discrete space. This enables the problem to be formulated as a zero-one integer program with linear constraints. The advantage of representing the problem in this manner is that powerful commercial software optimisation packages exist to solve the problem to proven global optimality. A heuristic for the problem based on successive shortest paths is also presented. Computational results are presented for a number of randomly generated test problems that are made publicly available.

math.OC↗

arXiv2020

Quantitative portfolio selection: using density forecasting to find consistent portfolios

In the knowledge that the ex-post performance of Markowitz efficient portfolios is inferior to that implied ex-ante, we make two contributions to the portfolio selection literature. Firstly, we propose a methodology to identify the region of risk-expected return space where ex-post performance matches ex-ante estimates. Secondly, we extend ex-post efficient set mathematics to overcome the biases in the estimation of the ex-ante efficient frontier. A density forecasting approach is used to measure the accuracy of ex-ante estimates using the Berkowitz statistic, we develop this statistic to increase its sensitivity to changes in the data generating process. The area of risk-expected return space where the density forecasts are accurate, where ex-post performance matches ex-ante estimates, is termed the consistency region. Under the 'laboratory' conditions of a simulated multivariate normal data set, we compute the consistency region and the estimated ex-post frontier. Over different sample sizes used for estimation, the behaviour of the consistency region is shown to be both intuitively reasonable and to enclose the estimated ex-post frontier. Using actual data from the constituents of the US Dow Jones 30 index, we show that the size of the consistency region is time dependent and, in volatile conditions, may disappear. Using our development of the Berkowitz statistic, we demonstrate the superior performance of an investment strategy based on consistent rather than efficient portfolios.

q-fin.PM↗

arXiv2019

A nonlinear optimisation model for constructing minimal drawdown portfolios

In this paper we consider the problem of minimising drawdown in a portfolio of financial assets. Here drawdown represents the relative opportunity cost of the single best missed trading opportunity over a specified time period. We formulate the problem (minimising average drawdown, maximum drawdown, or a weighted combination of the two) as a nonlinear program and show how it can be partially linearised by replacing one of the nonlinear constraints by equivalent linear constraints. Computational results are presented (generated using the nonlinear solver SCIP) for three test instances drawn from the EURO STOXX 50, the FTSE 100 and the S&P 500 with daily price data over the period 2010-2016. We present results for long-only drawdown portfolios as well as results for portfolios with both long and short positions. These indicate that (on average) our minimal drawdown portfolios dominate the market indices in terms of return, Sharpe ratio, maximum drawdown and average drawdown over the (approximately 1800 trading day) out-of-sample period.

q-fin.RM↗

arXiv2018

Packing a fixed number of identical circles in a circular container with circular prohibited areas

In this paper we consider the problem of packing a fixed number of identical circles inside the unit circle container, where the packing is complicated by the presence of fixed size circular prohibited areas. Here the objective is to maximise the radius of the identical circles. We present a heuristic for the problem based upon formulation space search. Computational results are given for six test problems involving the packing of up to 100 circles. One test problem has a single prohibited area made up from the union of circles of different sizes. Four test problems are annular containers, which have a single inner circular prohibited area. One test problem has circular prohibited areas that are disconnected.

math.OC↗

arXiv2018

Packing unequal rectangles and squares in a fixed size circular container using formulation space search

In this paper we formulate the problem of packing unequal rectangles/squares into a fixed size circular container as a mixed-integer nonlinear program. Here we pack rectangles so as to maximise some objective (e.g. maximise the number of rectangles packed or maximise the total area of the rectangles packed). We show how we can eliminate a nonlinear maximisation term that arises in one of the constraints in our formulation. We indicate the amendments that can be made to the formulation for the special case where we are maximising the number of squares packed. A formulation space search heuristic is presented and computational results given for publicly available test problems involving up to 30 rectangles/squares. Our heuristic deals with the case where the rectangles are of fixed orientation (so cannot be rotated) and with the case where the rectangles can be rotated through ninety degrees.

math.OC↗