Up: 1:2

Inventory-based versus Prior-based Options Trading Agents

Abraham Othman; Tuomas Sandholm

Algorithmic Finance (2011), 1:2, 95-121
DOI: 10.3233/AF-2011-009

Published: Abstract, PDF.
Archived: SSRN.


Options are a basic, widely-traded form of financial derivative that offer payouts based on the future price of an underlying asset. The finance literature gives us option-trading algorithms that take into consideration information about how prices move over time but do not explicitly involve the trades the agent made in the past. In contrast, the prediction market literature gives us automated market-making agents (like the popular LMSR) that are event-independent and price trades based only on the inventories the agent holds. We simulate the performance of five trading agents inspired by these literatures on a large database of recent historical option prices. We find that a combination of the two approaches produced the best results in our experiments: a trading agent that keeps track of previously-made trades combined with a good prior distribution on how prices move over time. The experimental success of this synthesized trader has implications for agent design in both financial and prediction markets.

Managing Editor

Philip Maymin

University of Bridgeport

Deputy Managing Editor

Jayaram Muthuswamy

Kent State University

Advisory Board

Kenneth J. Arrow

Stanford University

Herman Chernoff

Harvard University

David S. Johnson

AT&T Labs Research

Leonid Levin

Boston University

Myron Scholes

Stanford University

Michael Sipser

Massachusetts Institute of Technology

Richard Thaler

University of Chicago

Stephen Wolfram

Wolfram Research

Editorial Board

Associate Editors

Peter Bossaerts

California Institute of Technology

Emanuel Derman

Columbia University

Ming-Yang Kao

Northwestern University

Pete Kyle

University of Maryland

David Leinweber

Lawrence Berkeley National Laboratory

Richard J. Lipton

Georgia Tech

Avi Silberschatz

Yale University

Robert Webb

University of Virginia

Affiliate Editors

Giovanni Barone-Adesi

University of Lugano

Bruce Lehmann

University of California, San Diego

Unique Features of the Journal

Open access
Online articles are freely available to all.
No submission fees
There is no cost to submit articles for review. There will also be no publication or author fee for at least the first two volumes.
Authors retain copyright
Authors may repost their versions of the papers on preprint archives, or anywhere else, at any time.
Enhanced content
Enhanced, interactive, computable content will accompany papers whenever possible. Possibilities include code, datasets, videos, and live calculations.
Algorithmic Finance is the first journal in the Financial Economics Network of SSRN to allow comments.
The journal is published by IOS Press. In addition, the journal maintains an archive on SSRN.com.
While the journal does reserve the right to change these features at any time without notice, the intent will always be to provide the world's most freely and quickly available research on algorithmic finance.
Online ISSN: 2157-6203
Print ISSN: 2158-5571