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Output details

11 - Computer Science and Informatics

University of York

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Output 124 of 139 in the submission

Output title

Testing Implications of the Adaptive Market Hypothesis via Computational Intelligence

Type

E - Conference contribution

DOI

10.1109/CIFEr.2012.6327799

Name of conference/published proceedings

Computational Intelligence for Financial Engineering & Economics (CIFEr), 2012 IEEE Conference on

Volume number

Issue number

First page of article

ISSN of proceedings

Year of publication

2012

URL

http://www-users.cs.york.ac.uk/~kazakov/papers/cifer2012_testing_amh.pdf.

Number of additional authors

Additional information

<24>Adaptive and learning approaches are common in financial forecasting, yet their usefulness is questioned by a widespread assumption, The Efficient Market Hypothesis (EMH), which implies that markets follow a random walk. This paper identifies two properties of financial data, variable efficiency and cyclical profitability, that contradict the EMH claim. Showing that nonlinear dependence in a time series improves the efficiency of supervised machine learning, as confirmed on six different approaches, is an important methodological contribution to AI as a whole. This article won the best student paper award at CIFEr (2012), the main IEEE conference in this area.

Interdisciplinary

Cross-referral requested

Research group

G - Artificial Intelligence

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract