Timing the Market with a Combination of Moving Averages

• DOI: http://doi.org/10.1111/irfi.12107
• Published date: 01 September 2017
• Author: Paskalis Glabadanidis
• Date: 01 September 2017

Timing the Market with a

Combination of Moving Averages*

PASKALIS GLABADANIDIS

Accounting and Finance Business School, University of Adelaide, Adelaide, Australia

ABSTRACT

A combination of simple moving average trading strategies with several win-

dow lengths delivers a greater average return and skewness as well as a lower

variance and kurtosis compared with buying and holding the underlying asset

using daily returns of value-weighted US decile portfolios sorted by market

size, book-to-market, momentum, and standard deviation as well as more than

1000 individual US stocks. The combination moving average (CMA) strategy

generates risk-adjusted returns of 2% to 16% per year before transaction costs.

The performance of the CMA strategy is driven largely by the volatility of stock

returns and resembles the payoffs of an at-the-money protective put on the

underlying buy-and-hold return. Conditional factor models with macroeco-

nomic variables, especially the market dividend yield, short-term interest

rates, and market conditions, can explain some of the abnormal returns. Stan-

dard market timing tests reveal ample evidence regarding the timing ability of

the CMA strategy.

JEL Codes: G11; G12; G14

I. INTRODUCTION

Technical analysis involves the use of past and current market price, trading vol-

ume, and potentially, other publicly available information to predict future mar-

ket prices. It is highly popular in practice with plentiful financial trading advice

that is based largely, if not exclusively, on technical indicators. From the stand-

point of classical economic and finance theory, it is not at all clear that technical

analysis in general and moving averages in particular will have any role or power

in predicting the returns of individual stocks as well as portfolios of stocks. Sev-

eral potential reasons come to mind in terms of justifying the use of moving av-

erages. First, investor heterogeneity as well as information asymmetry may lead

to the persistent manifestation of behavioral biases in stock market prices. Prior

studies that have touched upon these issues include Treynor and Ferguson

(1985); Brown and Jennings (1989), and Hong and Stein (1999) among many

others. Furthermore, the theoretical model in Wang (1993) shows explicitly

* I would like to thank the editor, Hong Yan, and one anonymous referee for their very detailed and

thoughtful comments. Any remaining errors are my own responsibility.

© 2016 International Review of Finance Ltd. 2016

International Review of Finance, 17:3, 2017: pp. 353–394

DOI: 10.1111/irfi.12107

how a rational economic agent inhabiting a classical model of choice under un-

certainty and differential information will find signals based on average past

prices quite useful, informative, and revealing of other agents’private informa-

tion. Secondly, active investors in practice very often follow price trends which

may lead to the continued persistence of trends, both upward as well as down-

ward. These trends present other investors with the ability to follow them at least

in the short-term. Academic work in this area is perhaps best exemplified by Fung

and Hsieh (2001), and their construction of trend following indicators based on

the returns of look back straddle options. Thirdly, the study by Brock et al. (1992)

document the performance of various implementations of the moving average

and conclude that it is the most popular strategy followed by investors who use

technical analysis. More formally, Brock et al. (1992) find evidence that some

technical indicators do have a significant predictive ability. Fourthly, Blume

et al. (1994) present a theoretical framework using trading volume and price data

leading to technical analysis being a part of a trader’s learning process. A more

thorough study of a large set of technical indicators by Lo et al. (2000) also found

some predictive ability especially when moving averages are concerned. Zhu and

Zhou (2009) provide a solid theoretical reason why technical indicators could be

a potentially useful state variable in an environment where investors need to

learn over time the fundamental value of the risky asset they invest in. More re-

cently,Nee ly et al. (2010, 2011) find that technical analysis has as much forecast-

ing power over the equity risk premium as the information provided by

economic fundamentals. The practitioner’s literature also includes Faber (2007)

and Kilgallen (2012) who thoroughly document the risk-adjusted returns to the

moving average strategy using various portfolios, commodities, and currencies.

In addition, Huang and Zhou (2013) use the moving average indicator to predict

the return on the US stock market while Goh et al. (2012) apply the same idea to

government bond yields and risk premia. Motivated in part by the predictive

power of the moving average indicator, Han et al. (2016) and Jiang (2013) con-

struct a trend factor with considerable cross-sectional explanatory power and

substantial historical performance. In a similar vein, Glabadanidis (2014,

2015a, 2015b) investigates and documents the performance of the simple mov-

ing average strategy with various US and international portfolios as well as indi-

vidual US stocks.

The contribution of this paper is three-fold. First, I propose a novel strategy

which is an equal-weighted average of simple moving average. The novelty here

is that the investment or disinvestment in the underlying risky asset is propor-

tional to the number of moving average windows that have generated a buy or

sell signal, respectively. This is in stark contrast with trading signals generated

by a single moving average window which involve either being completely

invested in the risky asset or completely invested in the risk-free asset. Secondly,

I report on the performance of the combination moving average strategy with a

large number of portfolios and individual stocks. Finally, I provide a link between

technical indicators and fundamental indicators by presenting evidence that the

performance of the combination moving average strategy can be partially

International Review of Finance

© 2016 International Review of Finance Ltd. 2016354

explained by a conditional asset pricing model with the market’s dividend yield,

short-term interest rates, and a recession indicator.

This paper is similar in spirit to Glabadanidis (2014, 2015a, 2015b) and Han

et al. (2013). However, several important differences stand out. First, I use daily

value-weighted returns of decile portfolios constructed by various characteristics

like size, book-to-market, momentum, and standard deviation of return. Value-

weighted portfolios at a daily frequency have a much smaller amount of trading

going on inside the portfolio compared with the daily equal-weighted portfolios

investigated by Han et al. (2013). Secondly, the cross-sectional results in this

study are just an artefact of the decile portfolios and not the main focus of this

paper, while Han et al. (2013) is mostly concerned with the inability of stan-

dard empirical tests to account for the moving average strategy average returns

differences across portfolios. I argue that this is largely due to using the wrong

benchmark pricing model. Using a dynamic market-timing tests and condi-

tional asset pricing models with macroeconomic state variables leads to mostly

negative or statistically insignificant risk-adjusted returns for the moving aver-

age strategy. In light of this, my take on the performance of the combination

moving average strategy is that it is not an anomaly, but instead a dynamic

trading strategy that exposes investors to potential upside returns derived from

risky assets via its market timing ability. Similarly, the combination moving av-

erage strategy manages to avoid substantial market downturns more often than

not, thus, insulating investors from periods of sustained bear markets. This per-

formance is more pronounced the more volatile the returns of the underlying

risky assets are. A final caveat is that I assume the moving average trading has

no price impact. Large investors using this strategy will necessarily experience

an inferior performance. This is largely due to the adverse price impact of liqui-

dating and initiating large positions, especially for less liquid assets with lower

trading volumes.

The highlights of this study are the superior performance of the combina-

tion moving average portfolios relative to buying and holding the underlying

portfolios, the fact that the switching strategy returns resemble an imperfect

at-the-money protective put, and that cross-sectional differences are not a

new anomaly as maintained in Han et al. (2013), but are due to volatility dif-

ferences in the underlying portfolios and stocks as well as factor exposure

differences to a few macroeconomicstate variables. The returns of the combina-

tion moving average strategy relative to the buy-and-hold strategy are quite

convex with respect to the return of the buy-and-hold strategy and, hence, will

be hard to explain using standard linear asset pricing models. The anomalous

risk-adjusted performance relative to standard linear asset pricing models ap-

pears to be largely due to omitting market timing factors in a simple piece-wise

linear framework that captures the moving average strategy’s convexity.

Furthermore, the moving average strategy appears to be antifragile in the sense

of Taleb (2012) meaning that for securities with more volatile returns there is a

greater improvement of the moving average returns relative to buy-and-hold

returns.

Combination of Moving Averages

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