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«ISSN 1836-8123 Ethics and Quantitative Finance Jason West No. 2012-04 Series Editor: Dr. Alexandr Akimov Copyright © 2012 by author(s). No part of ...»

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ISSN 1836-8123

Ethics and Quantitative Finance

Jason West

No. 2012-04

Series Editor: Dr. Alexandr Akimov

Copyright © 2012 by author(s). No part of this paper may be reproduced in any form, or stored in a retrieval

system, without prior permission of the author(s).

Ethics and Quantitative Finance

Jason West

Department of Accounting, Finance and Economics

Griffith Business School

Griffith University

Nathan, QLD, Australia, 4111,

+61 7 37354272 (w)

j.west@griffith.edu.au

Abstract The field of quantitative analysis is often mistaken to be a discipline free from ethical burdens. The quantitative financial analyst or ‘quant’ profession holds a position of significant responsibility as the keeper of mathematical models used in complex derivative security pricing and risk management. Despite this responsibility very few postgraduate programs address the teaching of ethics and professional standards in their curriculum, and the credibility of the profession has suffered as a result of several high-profile financial losses. Some of these failures could have been avoided and their impacts diminished if ethical considerations were integrated with quantitative method. Appropriate development in ethics education for quants is needed to identify points in the decision-making process where ethical questions can arise, and to explain how quants can protect stakeholders from the costs of unethical behaviour. An approach to ethics education needs to flexible and allow for different methods to infuse ethical coverage into the course. Such an approach will go some way towards aligning the profession with other specialisations in banking and avoid the need for complex and unnecessary regulation.

Key words: Quantitative finance, ethics education, professional standards, mathematical models.

JEL Codes: C58, C02, I22, A20

1. Introduction The expansion of the financial markets along with the individuals, corporations, and financial intermediaries participating in them has led to a number of consequences. First, the volume of people employed in the finance profession has grown substantially. A second consequence is the decline of traditional barriers between segmented markets and the development of new financial instruments driven by increasing competition. From this a need has been created to establish specialised competence and service as a means of differentiating skills and expertise among financial institutions. Lastly finance professionals are employed by firms competing for assets and profits in a more organised and systematic manner. This has resulted in greater task specialisation which has encouraged the hiring of individuals equipped with intimate knowledge of asset pricing.

In the 35 years since the publication of the Black-Scholes option pricing model the development of financial instruments has grown more sophisticated partly driven by the enormous volume of derivative contracts traded. The increased complexity of financial instrument stimulated the need for a mathematical approach to security pricing. Specialised master's degrees have grown in many fields such as health care and the sciences, and finance is no exception. The master's degree in quantitative finance, which combines maths, computer science and business strategy, has grown in both stature and recognition since the mid-1990s. The role of a quantitative financial analyst or ‘quant’ is to use mathematical techniques, computing technology and data manipulation to solve complex problems associated with asset pricing, trading and risk control in financial services. Quants work in such diverse fields as constructing stock portfolios, designing statistical arbitrage trading strategies and analysing data to define consumer shopping habits. Investment banks, mutual funds and trading companies, as well as other firms such as resource houses and insurers, are often heavily endowed with specialised individuals who possess a deep knowledge of applied financial theory and the mathematical approach to security pricing.

A little regarded fact is that quants typically hold positions of responsibility that are greater than what their job title suggests. Specialisation in financial mathematics is required for the valuation and risk management of complex derivatives and a deep understanding of the mathematics involved is often beyond the grasp of company executives. Financiers and traders who are subject to ethics oversight and professional codes of conduct, rely heavily on the quants to create and maintain complex mathematical models, while the quants themselves are left to operate in a relative ethics vacuum. The practice of quantitative finance rarely strays from mathematical principles or the search for computational efficiency and an appreciation for the ethical responsibilities of their role, beyond very basic internal bank compliance training, usually goes unchecked.

This study examines the evolution of the roles and responsibilities of quants in the financial service sector and the key ethical considerations of their role. We examine the motivations sustaining the growth of the quantitative finance education market and conduct a broad assessment of the differentials in business ethics education between mathematical finance and more general finance programs, including the MBA. This will highlight that the absence of ethics education in mathematical finance is a significant contributor to deficiencies in financial risk management and asset valuation, which imposes heretofore unaccounted risks.





Ethical considerations for a selection of typical scenarios confronting the quant profession are discussed. The terms quantitative finance, mathematical finance and financial engineering are used interchangeably through this article.

2. A Brief History of Mathematical Finance and Quantitative Finance Education The history of mathematical finance starts with Théorie de la Spéculation published 1900 by Louis Bachelier (Bachelier, 1900). This analysis, revolutionary at the turn of the century, used a stochastic process known as Brownian motion to model stock prices and then price stock options however it gained little attention in academia and even less appreciation from the banking sector. The first influential work of mathematical finance was the theory of portfolio optimisation by Harry Markowitz who used mean-variance estimates of portfolios to quantify investment strategies (Markowitz, 1952). This created the first real shift away from the concept of trying to identify the best single stock as an investment. Using linear regression to quantify the risk (variance) and return (mean) of a portfolio of stocks and bonds, an optimisation strategy was used to identify the portfolio with highest mean return relative to a given variance of returns. Almost simultaneously William Sharpe adopted a mathematical approach to estimate the correlation between stocks and the market itself (Sharpe, 1963), which has guided much of portfolio theory since. The portfolio-selection work of Markowitz and Sharpe introduced mathematics to the so-called ‘black art’ of investment management. The work of Samuelson and Merton (1974) allowed one-period discrete-time models to be replaced by continuous time, Brownian-motion models while the quadratic utility function implicit in mean-variance optimisation was replaced by more general increasing, concave utility functions. With time however, financial analysis has become much more sophisticated.

Arguably the major revolution in mathematical finance came with the work of Fischer Black and Myron Scholes along with fundamental contributions by Robert C. Merton, who modelled financial markets using stochastic models (Black and Scholes, 1973; Merton, 1973). Even more sophisticated mathematical models have since been derived such as, inter alia, multi-factor market models, parametric copulas, extreme value theory to manage investments in fixed income, foreign exchange, commodities and debt, as well as hybrids among these asset classes.

Prior to the rapid sophistication of the global financial markets quants would have studied humanities at Oxford or Harvard and found a job via the ‘old-boy’ network. During the transformation of the financial markets in the 1970s investment banks hired individuals who weren’t bound by the conventions of a university education which turned naive youths into bold young traders, many of whom possessed great instincts and bravado. The next transformation occurred during the 1990s where only those with PhDs in mathematics or physics were considered suitable to master the growing complexity of a great number of new financial instruments available in the main trading centres. As much as traditional bankers reject the notion, quantitative analysts have greatly altered the financial landscape in terms of new approaches to asset pricing, trading strategies and computational efficiency.

Growth in the number and location of financial mathematics education programs has subsequently paralleled the growth in the financial engineering profession, with its progressive influence across many aspects of financial services. The first formal postgraduate quantitative finance program was offered through the Stuart School of Business at the Illinois Institute of Technology in 1990. A choice of programs was originally offered; the Masters of Science in Quantitative Finance and the Masters of Science in Financial Markets and Trading. Both programs have since been combined. Rival programs were developed in 1994 by the Polytechnic Institute of New York University which offered a financial engineering degree and Carnegie Mellon who offered a computational finance program. The Oregon Graduate Institute (OGI) School of Science and Engineering offered a computational finance program in 1996 (now discontinued) which was the first attempt to teach a program based on the computer science pedagogy. Mathematical finance programs have since emerged from higher profile institutions such as Stanford, Chicago, Columbia, Princeton, Cornell and MIT as well as from prestigious institutions in Europe. Myriad universities in Asia-Pacific also offer quant finance programs highlighting the growth in sophistication of the Asian markets.

Since the pioneering work of these universities in developing a quantitative finance program, the structure of the curriculum has remained virtually unchanged and almost identically replicated by universities across the globe. Universities generally house quantitative finance programs within their relevant business school however some attempts have been made to integrate the program in other related disciplines such as mathematics, computer science or operations research. While several of the so-called top-tier universities offer quantitative finance programs, the majority are offered by technology-focussed vocational institutions (Nygaard, 2005). The curriculum of the programs offered through most schools has been refined over the 2000-2010 period however, notably, the basic structure of quantitative finance courses at most universities is very similar and has not markedly changed. The use of mathematical finance is deeply ingrained in most financial institutions now more than ever before, but quantitative finance programs have generally adopted a one-size fits all approach to program delivery. In the long term, such rigid compliance with the existing suite of mathematical tools used for finance as well as indolence in program development may undermine the need for the profession to evolve with the financial market.

2.1. Homogeneity of mathematical finance programs

Nearly every quantitative finance program focuses on the following core areas: Financial instruments, portfolio analysis, econometrics, financial risk management, credit risk, numerical analysis, computational methods, statistics, derivative security pricing, probability theory, stochastic processes and interest rate modelling. Each subject is taught with respect to the observed behaviour of financial markets, and is generally aimed to equip students with sufficient knowledge to apply mathematical finance at an entry level (Wilmott, 2000).

But quantitative finance courses have adapted physics, mathematics and statistics techniques to the study of finance such that students from non-mathematical backgrounds emerge with a relatively narrow view of mathematics in general. University curricula choose a limited suite of concepts borrowed from mathematics, statistics and computer science largely based on existing popular research approaches to security valuation. This inevitably limits the capability of graduates to confidently develop unorthodox and alternative solutions to common financial problems. While the financial mathematics curriculum has become quite focussed, it still sits between academic chairs and never on any one of them. The course has evolved to the point where students are rarely taught how to construct solutions from first principles and how to tell if a given approach will succeed. For instance Rutledge and Raynes (2010) suggest that it is not possible to claim expertise in numerical analysis if one does not have at least a passing acquaintance with foundational elements such as z-transforms, Nyquist sampling theorem, convergence analysis and error propagation analysis, among others. Very few quant programs employ these concepts.

It is important to remember that before the evolution of postgraduate degrees in financial engineering, financial institutions tended to recruit from physics, mathematics and computer science PhD programs to meet the demand for expertise. To be successful a quant did not necessarily require a background in finance and even today most quant jobs simply require a PhD in a quantitative discipline. Only graduates of the truly elite postgraduate quantitative finance education programs can compete, while many prospective employers generally believe that quant finance postgraduates who do not also boast a PhD cannot match the skills of their PhD counterparts. It is likely that the capability of graduates with only postgraduate quant finance education will continue to be inferior to the capability of graduates from more traditional maths, physics and engineering PhD programs in developing innovative solutions to emerging issues in finance.

2.2. Distinguishing features of general and quantitative finance curricula



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