A Fast and Frugal Finance

Bridging Contemporary Behavioral Finance and Ecological Rationality

William P. Forbes, Aloysius Obinna Igboekwu, Shabnam Mousavi

• Publisher: Academic Press
• Year: 2019
• Print ISBN: 9780128124956
• eBook ISBN: 9780128124963
• Topic: Business
• Subtopic: Finance
• Index: Business

A Fast and Frugal Finance: Bridging Contemporary Behavioural Finance and Ecological Rationality adds psychological reality to classical financial reasoning. It shows how financial professionals can reach better and quicker decisions using the 'fast and frugal' framework for decision-making, adding dramatically to time and outcome efficiency, while also retaining accuracy. The book provides the reader with an adaptive toolbox of heuristic tools and classification systems to aid real-world decisions. Throughout, financial applications are presented alongside real-world examples to help readers solve established problems in finance, including stock buying and selling decisions, when faced with not only risk but fundamental uncertainty.The book concludes by describing potential solutions to financial problems in the forefront of contemporary debates, and calls for taking psychological insights seriously.- Demonstrates how well-constructed 'fast and frugal' models can outperform standard models in time and outcome efficiency- Focuses on how financial decisions are made in reality, using heuristics, rather than how such decisions should be made- Discusses how cognition and the decision-making context interact in producing 'fast and frugal' choices that follow ecological rationality- Explores the development of decision-making trees in finance to aid in decision-making

Part 1

A fast-and-frugal approach to finance

Outline

Chapter 1. Introduction

Chapter 2. Fast-and-frugal heuristics

Chapter 3. Adaptive or efficient financial markets?

Chapter 4. Financial regulations and heuristics

Chapter 5. When fast-and-frugal works best

Chapter 1

Introduction

Abstract

This first chapter introduces some of the potential of a fast-and-frugal finance and why such a recasting of standard finance is needed. It does this partly by returning to some of the precursors of Gigerenzer's fast-and-frugal approach, but also by pointing to some of the key themes that motivate Gigerenzer's own work with the ABC Group. These include the distinction between risk and uncertainty, the distinction between classical and ecological rationality, and the less-is-more principle.

Keywords

Fast-and-frugal; Risk/uncertain outcomes; Classical/ecological rationality; Less-is-more

A fast-and-frugal finance might at last allow an understanding of how context and cognition shape the bewildering array of evidence we observe regarding the operation and efficiency of financial markets and their funding of corporations. This has not been a great decade for Finance as an academic subject or profession. Following the global financial crisis, the ensuing Eurozone and LIBOR rigging crises finance academics are commonly portrayed as either knaves or fools. Shiller and Shiller (2011) ask if finance as a subject is hindered by its excessive interest in the techniques of optimising an individual's self-interest. One aspect of expanding finance theory's focus is adopting a fast-and-frugal approach.

Fast-and-frugal reasoning offers a way out of this decline in finance theory's influence and credibility. It does this by reducing one of the central planks of current finance theory, this is the expected utility model of decision-making under uncertainty, or what Gigerenzer calls classical rationality (Gigerenzer and Goldstein (1996)), to a special case of a more general theory of financial decision-making.

In the classical view the decision-maker's calculative powers are infinite and costless. Information, if it is relevant, should always be used in making a decision. Such heroic assumptions fit in with a broader modelling strategy that we can model investors'/agents' behaviour “as if” they had unlimited calculative powers, were aware of all possible outcomes, etc. The justification for such heroism being that what matters is a model's predictions not its assumptions (Freidman (1953)).

But often researchers confuse statistical tests that explain data, used to estimate the model well, and true predictive performance based on out–of–sample performance Gigerenzer (2004). Indeed the very function of statistical inference in establishing a maintained hypothesis is often hopelessly confused in social science, as argued below by Gigerenzer and Murray (1987).

1.0.1 A fast-and-frugal alternative to behavioural finance

Fast-and-frugal reasoning, developed by Gerd Gigerenzer of the Max-Planck Institute for Human Development in Berlin and his colleagues in the Center for Adaptive Behavior and Cognition, reject the classical view of rational choice in favour of boundedly rational perspective.

Boundedly rational investors satisfice, rather than optimise, in their decision-making. Satisficing is a word of Northumbrian origin suggesting a choice algorithm which required limited information and calculative skill to achieve an acceptable, if not optimal, outcome. The focus is upon finding choices that yield an acceptable, rather than the best, outcome. This is in part because, following Herbert Simon, fast-and-frugal reasoning casts doubt on the notion of a global, or market-wide, best outcome.

Simon's concept of bounded rationality stresses the evolutionary adaption of our minds to the environment in which they operate. As Arrow (2004) has argued we have to be careful that such a definition does not become so broad as to be meaningless for

“there is no general criterion for determining which limit on rationality holds in any given context and the building of a complete theory of the economy is a project for the future.”

For this reason our application of statistical reasoning must constrain itself to problems of limited domain, what Leonard Savage calls “small worlds” (Savage (1954)), within which we can adequately specify the bounds on reasonable behaviour that do apply.

Gigerenzer (2004) reminds us that the process of optimisation does not ensure an optimal, or best, outcome since in most applications it simply identifies the maxima/minima of some continuous, twice–differentiable, performance function. This is fine if the performance criteria studied can bear the weight of these assumptions; but simply “blackboard economics” if they cannot.

Gigerenzer and Selten (2001a) see the main characteristics of the adaptive-toolbox for decision-making as

“First it refers to a collection of rules or heuristics rather than a general purpose decision-making algorithm... Second these are fast, frugal and computationally cheap... Third, these heuristics are adapted to particular environments... This “ecological rationality... allows for the possibility that heuristics can be fast, frugal, and accurate all at the same time.”

(Gigerenzer and Selten (2001a), p. 9)

Thus we can see Gerd Gigerenzer and the fast-and-frugal reasoning tradition as inheriting the baton of Simon's bounded rationality project. As such we agree with van der Sar (2004) (p. 442) that what is needed by finance scholars and practitioners is a credible alternative to the standard model, that embeds a coherent, credible, view of investor psychology. Professor Gigerenzer's fast-and-frugal reasoning may yet offer such an alternative.

1.1 Context and cognition

Many of us will recall being chastised by our parents for how calculators and spreadsheets have made us lazy in conducting the necessary mental arithmetic to do our shopping or budget for the month. In truth our minds have adapted to an environment in which we have access to a calculating machine in our pocket, mobile phone, or the cloud. In Simon's famous phrase

“Human rational behaviour is shaped by scissors whose two blades are the structure of the task environments and the computational capabilities of the actor.”

(Simon (1990), p. 7)

While this may all seem rather banal and unworthy of comment it stands in marked opposition to the vast majority of finance theory we might think of. Simon (1959) points out nearly all economic models, including those in standard finance, assume agents are rational in the classical sense. Further these agents/investors inhabit a competitive world that swiftly culls the irrational from their midst. Thus

“the classical economic theory of markets with perfect competition and rational agents is a deductive theory that requires almost no contact with empirical data once its assumptions are accepted.”

(Simon (1959), p. 254.)

Simon points out that there is a wide variety of problems where the assumptions of classical economics might seem a reasonable assumption, for example in heavily traded foreign exchange markets. But, importantly, there are many legitimate areas of inquiry in financial decision-making and investment practice where the assumptions of classical rationality seem honoured only in their breach.

Simon's early work, summarised in Simon (1959), focused upon areas where classical rationality seemed least likely to apply. Two of these were where perfect foresight regarding future outcomes was impossible and so the agent/investor was forced to predict outcomes and where decision-makers faced goal conflicts and perhaps uncertainty, rather than risk, regarding possible outcomes.

Both these deviations from classical rationality are likely to occur in financial markets. The relatively random evolution of asset prices is a central finding of empirical research in finance (Fama (1970)), while the multiple conflicting motives of investors are well documented Statman (2011).

Simon points out that while in experiments with toy gambles between known pay-offs expected utility theory/classical rationality appears to perform adequately as a description of decision-making under uncertainty problems emerge as soon as it is deployed into more realistic contexts of practical application. Simon (1959) points out that while economists took comfort in the affirmation of experimental results behavioural researchers felt inspired to look for altern...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Foreword
• Acknowledgements
• Part 1: A fast-and-frugal approach to finance
• Chapter 1: Introduction
• Chapter 2: Fast-and-frugal heuristics
• Chapter 3: Adaptive or efficient financial markets?
• Chapter 4: Financial regulations and heuristics
• Chapter 5: When fast-and-frugal works best
• Part 2: Applications of fast-and-frugal finance
• Chapter 6: Fast-and-frugal asset pricing
• Chapter 7: Fast-and-frugal portfolio theory
• Chapter 8: Fast-and-frugal financial analysis
• Chapter 9: Inference under the law of small numbers: Earnings streaks rather than earnings numbers
• Chapter 10: A fast-and-frugal finance
• References
• Index