Irrational fads, short‐term memory emulation, and asset predictability

• Author: Stelios D. Bekiros
• DOI: http://doi.org/10.1016/j.rfe.2013.05.005
• Published date: 01 November 2013
• Date: 01 November 2013

Irrational fads, short-term memory emulation, and asset predictability☆

Stelios D. Bekiros ⁎

,1,2

European University Institute, Florence, Italy

Athens University of Economics and Business, Athens, Greece

Rimini Centre for Economic Analysis (RCEA)

abstractarticle info

Available online 6 June 2013

JEL classification:

G10

G14

C53

C58

Keywords:

Machine learning

Neural networks

Volatility trading

Stock predictability

Opponents of the efficient markets hypothesis argue that predictability reflects the psychological factors and

“fads”of irrational investors in a speculative market. In that, conventional time series analysis often fails to

give an accurate forecast for financial processes due to inherent noise patterns, fat tails, and nonlinear com-

ponents. A recent stream of literature on behavioral finance has revealed that boundedly rational agents

using simple rules of thumb for their decisions under uncertainty provides a more realistic description of

human behavior than perfect rationality with optimal decision rules. Consequently, the application of techni-

cal analysis in trading could produce high returns. Machine learning techniques have been employed in eco-

nomic systems in modeling nonlinearities and simulating human behavior. In this study, we expand the

literature that evaluates return sign forecasting ability by introducing a recurrent neural network approach

that combines heuristic learning and short-term memory emulation, thus mimicking the decision-making

processof boundedly rationalagents. We investigate the relativedirection-of-changepredictabilityof the neural

networkstructure impliedby the Lee–White–Grangertest as well as compareit toother well-establishedmodels

for the DJIA index.Moreover, we examine the relationship betweenstock return volatility and returns.Overall,

the proposedmodel presents high profitability, in particular d uring “bear”marketperiods.

© 2013 Elsevier Inc. All rights reserved.

1. Introduction

In view of empirical studies that stock prices can be predicted with

a fair degree of reliability, advocates of efficient markets hypothesis

(e.g., Fama & French, 1995) claim that such results are based on

time-varying-equilibrium expected returns generatedby rational pric-

ing in an efficient market, which compensates for the level of risk

undertaken. On the contrary, opponents (e.g., La Porta, Lakonishok,

Shliefer,& Vishny, 1997; Shiller,2002) argue that predictabilityreflects

the psychological factors and fashions or “fads”of irrational investors

in a speculative market. This irrational behavior has been emphasized

by Shleifer and Summers(1990) and Black (1986) in their exposition

of noise traders who act on the basis of imperfect information and

consequently cause prices to deviate from their equilibrium values.

Arbitrageurs dilute a minor part of these shifts in prices, yet the

major component of deviation is tradable. Moreover, Black claimed

thatnoise traders play a useful rolein promoting market liquidity.Over-

all,there are two major typesof agents in heterogeneousmarkets: “fun-

damentalists,”who base their expectations upon dividends, earnings,

growth or even macroeconomic factors, and “chartists”(noise traders

and technicalanalysts), who instead base their tradingstrategies upon

historicalpatterns and heuristicsand try to extrapolate trendsin future

asset prices.

Conventional time series analysis, based on stationary processes,

does not always perform satisfactorily on economic and financial

time series (Harvey, 1989). Economic data are not generally described

by simple linear structural models, white noise or random walks. The

most commonly used techniques for financial forecasting are regression

methods and autoregressive integrated moving average (ARIMA)models

(Box & Jenkins, 1970). These models have been used extensively in the

past, but they often fail to give an accurate forecast for some series due

to inherent noise patterns, fat tails, and nonlinear components. The

major challenge for “chartists”is the development of new models, or

the modification of existing methods, that would enhance forecasting

ability particularly for time series with dynamic time variant patterns.

Simon (1957) argued that boundedly rational agents using simple rules

of thumb for their decisions under uncertainty, provides a more accurate

and realistic description of human behavior than perfect rationality with

optimal decision ru les. The key arguments of behavio ral agent-based

models reported by Hommes (2001, 2006) are c losely related to

Kahneman–Tversky analysis in p sychology that individual behavior

under uncertainty c an be described by simple heuristic s and biases.

Review of Financial Economics 22 (2013) 213–219

☆This research is supported by the Marie Curie Fellowship (FP7-PEOPLE-2011-CIG,

No. 303854) under the 7th European Community Framework Programme. The usual

disclaimers apply.

⁎Department of Economics, Via della Piazzuola 43, I-50133 Florence, Italy. Tel.: +39

055 4685916; fax: +39 055 4685 902.

E-mail addresses: stelios.bekiros@eui.eu,bekiros@aueb.gr.

1

Department of Accounting and Finance, 76 Patission str, GR104 34, Athens, Greece.

Tel./fax: +30 210 8203453.

2

RCEA, Via Patara, 3, 47900, Rimini, Italy. Tel.: +39 0541 434 142; fax: +39 0541 55

431.

1058-3300/$ –see front matter © 2013 Elsevier Inc. All rights reserved.

http://dx.doi.org/10.1016/j.rfe.2013.05.005

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