Market truths: theory versus empirical simulations

There have been many debates over the true nature of financial markets and the implications regarding passive and active portfolio management. Much of the literature employ advanced quantitative techniques to produce evidence against, or for, a developed theory. Often times, these studies make stric...

Full description

Saved in:
Bibliographic Details
Published in:Journal of statistical computation and simulation 2006-05, Vol.76 (5), p.385-395
Main Authors: Wojciechowski, William C., Thompson, James R.
Format: Article
Language:English
Subjects:
Applications
Applied sciences
Efficient markets
Exact sciences and technology
Exploratory data analysis
Insurance, economics, finance
Market portfolio
Mathematics
Multivariate analysis
Operational research and scientific management
Operational research. Management science
Portfolio selection
Portfolio theory
Probability and statistics
Risk measures
Sciences and techniques of general use
Simulation
Statistics
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:There have been many debates over the true nature of financial markets and the implications regarding passive and active portfolio management. Much of the literature employ advanced quantitative techniques to produce evidence against, or for, a developed theory. Often times, these studies make strict assumptions and judgment calls that allow one to question the validity of the results. In this paper, we combine computer simulation and exploratory data analysis to gain a better understanding of stock market portfolios and of common risk measures. By taking a computational approach, our study requires a small amount of subjectivity and makes no assumptions about capital markets. This makes our results very difficult to discredit. Our results provide strong evidence against the efficient market hypothesis and display some undesirable properties of several well-known risk measures.
ISSN:0094-9655
1563-5163
DOI:10.1080/10629360500107709