Market Timing With Moving Averages

Date01 September 2015
AuthorPaskalis Glabadanidis
Published date01 September 2015
DOIhttp://doi.org/10.1111/irfi.12052
Market Timing With
Moving Averages*
PASKALIS GLABADANIDIS
Department of Accounting and Finance, Business School, University of Adelaide,
Adelaide, SA, Australia
ABSTRACT
I present evidence that a moving average (MA) trading strategy has a greater
average return and skewness as well as a lower variance compared to buying
and holding the underlying asset using monthly returns of value-weighted
US decile portfolios sorted by market size, book-to-market, and momentum,
and seven international markets as well as 18,000 individual US stocks. The
MA strategy generates risk-adjusted returns of 3–7% per year after transaction
costs. The performance of the MA 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
macroeconomic variables, especially the default premium, can explain some
of the abnormal returns. Standard market timing tests reveal ample evidence
regarding the timing ability of the MA strategy.
JEL Classification: G11, G12, G14.
I. INTRODUCTION
Technical analysis involves the use of past and current market price, trading
volume and, potentially, other publicly available information to predict future
market prices. It is highly popular in practice with plentiful financial trading
advice that is based largely, if not exclusively, on technical indicators. In a
perhaps belated testament to this fact, consider the following quote from the
New York Times issue dated March 11, 1988: ‘Starting today the New York Times
will publish a comprehensive three-column market chart every Saturday. . .
History has shown that when the S&P index rises decisively above its (moving)
average the market is likely to continue on an upward trend. When it is below
* I would like to thank Syed Zamin Ali, Tze Chuan ‘Chewie’ Ang, B. Ross Barmish, Jean Canil,
Don Chance, Sudipto Dasgupta, Daisy Doan, Victor Fang, Berowne Hlavaty, Daniel Orlovsky,
James Primbs, Bruce Rosser, Vincent Xiang, Takeshi Yamada, Alfred Yawson, Xinwei Zheng,
Edward Zychowicz as well as the seminar participants at Deakin University and the University of
Adelaide and participants in the 2012 Australasian Finance and Banking conference in Sydney,
the 2014 J.P. Morgan quantitative conference in Sydney and the 2013 Midwest Finance Associa-
tion meetings in Chicago. In addition, I would like to express my gratitude to the editor, Huining
Cao, and two anonymous referees for their very detailed and thoughtful comments. Any remain-
ing errors are my own responsibility.
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International Review of Finance, 15:3, 2015: pp. 387–425
DOI: 10.1111/irfi.12052
© 2015 International Review of Finance Ltd. 2015
the average that is a bearish signal.’ According to Brock et al. (1992), the moving
average (MA) in its various implementations is the most popular strategy fol-
lowed by investors who use technical analysis. More formally, Brock et al.
(1992) find evidence that some technical indicators do have a significant pre-
dictive ability. 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 MAs are
concerned. Zhu and Zhou (2009) provide a solid theoretical reason why tech-
nical 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 recently, Neely et al. (2010, 2011) find that technical
analysis has as much forecasting power over the equity risk premium as the
information provided by economic fundamentals. The practitioners’ literature
also includes Faber (2007) and Kilgallen (2012) who thoroughly document the
risk-adjusted returns to the MA strategy using various portfolios, commodities
and currencies. In addition, Huang and Zhou (2013) use the MA 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 MA indicator, Han and Zhou (2013) and Jiang (2013)
construct a trend factor with considerable cross-sectional explanatory power
and substantial historical performance.
The main findings of this study are as follows. First, I present evidence that
the returns to a simple MA switching strategy dominate in a mean-variance
sense the returns to a buy-and-hold strategy of the underlying portfolio.
Second, I demonstrate that the switching strategy involves infrequent trading
with relatively long periods when the MA strategy is invested in the under-
lying assets and the break-even transaction costs (BETCs) are on the order of
3–7% per transaction. Third, even though there is overwhelming evidence of
imperfect market timing ability of the MA switching strategy for a single
portfolio or individual stocks, cross-sectional differences remain between the
abnormal returns of different portfolios. These differences persist when con-
trolling for the four-factor Carhart (1997) model for portfolios formed on past
price returns and are mostly driven by differences in the volatility of portfolio
and stock returns. Fourth, conditional models explain to a certain degree the
MA abnormal returns but do not completely eliminate them. Fifth, I docu-
ment the performance of the MA strategy using more than 18,000 individual
stocks from the Center for Research in Security Prices. Sixth, I present evi-
dence of the robustness of the performance of the MA strategy in seven inter-
national stock markets. Seventh, I show that the lagged indicator regarding
the switch into or out of the risky asset has substantial predictive ability over
subsequent portfolio returns over and above the predictability contained in
standard instrumental variables, like the default spread, investor sentiment,
recession dummy variable and liquidity risk. Last, but not least, the strategy is
robust to randomly generated stock returns and bootstrap historical returns.
International Review of Finance
388 © 2015 International Review of Finance Ltd. 2015
Nevertheless, a random switching strategy leads to negative and statistically
significant returns. The inferior performance of random switching is a testa-
ment to the market timing ability of the MA strategy. Furthermore, random
switching generates increasingly poorer average returns as we investigate its
performance with riskier underlying assets. This is also consistent with the
performance being driven by volatility.
This paper is similar in spirit to Han et al. (2013). However, several impor-
tant differences stand out. First, I use monthly value-weighted returns of
decile portfolios constructed by various characteristics like size, book-to-
market and momentum.1Value-weighted portfolios at a monthly frequency
should have a much smaller amount of trading going on inside the portfolio
compared to the daily equal-weighted portfolios investigated by Han et al.
(2013). Second, the cross-sectional results in this study are just an artifact of
the decile portfolios and not the main focus of this paper while Han et al.
(2013) are mostly concerned with the inability of standard empirical tests to
account for the MA strategy average return differences across portfolios. I
argue that this is largely due to using the wrong benchmark pricing model.
Using dynamic market timing tests and conditional asset pricing models with
macroeconomic state variables leads to mostly negative or statistically insig-
nificant risk-adjusted returns for the MA strategy. In light of this, my take on
the performance of the MA 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. This performance is
more pronounced the more volatile the returns of the underlying risky assets
are. A final caveat I need to make is that the performance of the strategy is
investigated using historical returns rather than actually trading in financial
markets. It is likely that in reality there may be adverse price impact of liq-
uidating and initiating large positions, especially for less liquid assets with
lower trading volumes. This possibility is in the spirit of limits to arbitrage as
another potential explanation for the performance of the MA strategy. The
nature of this empirical study is such that this potential explanation cannot
be eliminated.
The highlights of this study are the superior performance of the MA portfo-
lios relative to buying and holding the underlying portfolios, the infrequency of
trading and the very large BETCs, 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 differences in the underlying portfolios and stocks. An asset
with 10% higher standard deviation of returns will experience on average
between 2% and 3.5% mean return improvement between the buy-and-hold
and the MA strategy. The returns of the MA strategy relative tothe buy-and-hold
strategy exhibit a lot of convexity and, hence, will be hard to explain using
1 Further findings using cash-flow-to-price, earnings-to-price, dividend-price, past return and
industry are broadly consistent with those reported in the text and are available from the
author upon request.
Market Timing With Moving Averages
389© 2015 International Review of Finance Ltd. 2015

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