The measurement of aggregate market risk

Preface This volume contains papers produced for the Euro-currency Standing Committee in a joint effort by researchers at several central banks. The papers address measurement of market risk, market dynamics, market liquidity, and the role that information plays in determining market outcomes in unsettled circumstances. The Committee believes that the research undertaken will be of interest to a wider audience, including market participants and the academic community. In publishing the papers, the Committee hopes to stimulate further research in these areas. The papers represent the views of the authors and not necessarily those of the central banks with which they are affiliated nor that of the Euro-currency Standing Committee. In July 1996, the BIS published a report of a working group of the Committee, chaired by Shinichi Yoshikuni of the Bank of Japan, which recommended the establishment of a reporting system on activities in global derivatives markets. That reporting system is to be implemented in 1998. The Report recognised that data on derivatives positions, while indispensable for tracking changes in the size and structure of derivatives markets over time, would shed limited light on how overall portfolio values and market conditions might change in the face of price shocks. The behaviour of markets in the face of shocks has long been an area of fundamental central bank interest and responsibility. When the papers in this volume were discussed by the Committee in May 1997, the Committee accepted the researchers' conclusion that this research did not establish an adequate technical basis or adequate justification for collecting aggregate market risk data. However, the Committee decided to encourage continuing work on other aspects of market behaviour addressed in these papers. In particular, in line with its mandate to monitor sources of potential instability in financial markets, the Committee will continue to encourage and review research on market functioning and price dynamics under stress. Toshihiko Fukui, Chairman, Euro-currency Standing Committee Senior Deputy Governor, Bank of Japan

A Conceptual Model of Investor Behavior

Based on a survey of behavioral finance literature, this paper presents a descriptive model of individual investor behavior in which investment decisions are seen as an iterative process of interactions between the investor and the investment environment. This investment process is influenced by a number of interdependent variables and driven by dual mental systems, the interplay of which contributes to boundedly rational behavior where investors use various heuristics and may exhibit behavioral biases. In the modeling tradition of cognitive science and intelligent systems, the investor is seen as a learning, adapting, and evolving entity that perceives the environment, processes information, acts upon it, and updates his or her internal states. This conceptual model can be used to build stylized representations of (classes of) individual investors, and further studied using the paradigm of agent-based artificial financial markets. By allowing us to implement individual investor behavior, to choose various market mechanisms, and to analyze the obtained asset prices, agent-based models can bridge the gap between the micro level of individual investor behavior and the macro level of aggregate market phenomena. It has been recognized, yet not fully explored, that these models could be used as a tool to generate or test various behavioral hypothesis.behavioral finance;financial decision making;agent-based artificial financial markets;cognitive modeling;investor behavior

Which heuristics can aid financial-decision-making?

© 2015 Elsevier Inc. We evaluate the contribution of Nobel Prize-winner Daniel Kahneman, often in association with his late co-author Amos Tversky, to the development of our understanding of financial decision-making and the evolution of behavioural finance as a school of thought within Finance. Whilst a general evaluation of the work of Kahneman would be a massive task, we constrain ourselves to a more narrow discussion of his vision of financial-decision making compared to a possible alternative advanced by Gerd Gigerenzer along with numerous co-authors. Both Kahneman and Gigerenzer agree on the centrality of heuristics in decision making. However, for Kahneman heuristics often appear as a fall back when the standard von-Neumann-Morgenstern axioms of rational decision-making do not describe investors' choices. In contrast, for Gigerenzer heuristics are simply a more effective way of evaluating choices in the rich and changing decision making environment investors must face. Gigerenzer challenges Kahneman to move beyond substantiating the presence of heuristics towards a more tangible, testable, description of their use and disposal within the ever changing decision-making environment financial agents inhabit. Here we see the emphasis placed by Gigerenzer on how context and cognition interact to form new schemata for fast and frugal reasoning as offering a productive vein of new research. We illustrate how the interaction between cognition and context already characterises much empirical research and it appears the fast and frugal reasoning perspective of Gigerenzer can provide a framework to enhance our understanding of how financial decisions are made

Bayesian Networks for Asset Management and Financial Risk

This thesis explores the use of Bayesian networks to develop “views” for a Black-Litterman asset allocation model, and determines whether they can help in the creation of better investment portfolios. Views represent an investor’s expectations of the future performance of a company’s shares: an estimate of expected return, and a measure of the uncertainty of this estimate. This thesis aims to automate the creation of views and to pioneer intelligent portfolio construction as part of an algorithmic asset management process

Computational intelligence for evolving trading rules

Copyright © 2008 IEEEThis paper describes an adaptive computational intelligence system for learning trading rules. The trading rules are represented using a fuzzy logic rule base, and using an artificial evolutionary process the system learns to form rules that can perform well in dynamic market conditions. A comprehensive analysis of the results of applying the system for portfolio construction using portfolio evaluation tools widely accepted by both the financial industry and academia is provided.Adam Ghandar, Zbigniew Michalewicz, Martin Schmidt, Thuy-Duong Tô, and Ralf Zurbrug

WARNING: Physics Envy May Be Hazardous To Your Wealth!

The quantitative aspirations of economists and financial analysts have for many years been based on the belief that it should be possible to build models of economic systems - and financial markets in particular - that are as predictive as those in physics. While this perspective has led to a number of important breakthroughs in economics, "physics envy" has also created a false sense of mathematical precision in some cases. We speculate on the origins of physics envy, and then describe an alternate perspective of economic behavior based on a new taxonomy of uncertainty. We illustrate the relevance of this taxonomy with two concrete examples: the classical harmonic oscillator with some new twists that make physics look more like economics, and a quantitative equity market-neutral strategy. We conclude by offering a new interpretation of tail events, proposing an "uncertainty checklist" with which our taxonomy can be implemented, and considering the role that quants played in the current financial crisis.Comment: v3 adds 2 reference