Momentum Trading with the ℓ1‐Filter: Are the Markets Efficient?*

Date01 December 2020
AuthorAbhishek Rohit,Subrata K. Mitra
Published date01 December 2020
Momentum Trading with the
Filter: Are the Markets Efcient?*
Department of Finance, Indian Institute of Management Raipur, Raipur, India and
Department of Finance & Strategy, T A Pai Management Institute, Manipal, India
This paper explores the possibility of generating consistent momentum
prots by trading on nine major indices across the globe using the
This methodology penalizes slope reversion of the ltered trend and iden-
ties piecewise linear trends in the asset prices. We nd the buy strategy to
offer considerably higher momentum returns compared to the sell strategy.
Our strategy beats the buy-and-hold (BH) strategy on all fronts and, thus,
highlights the inefciencies in nancial markets in recent years (20002016).
Comparing the momentum prots across a set of advanced economies (AEs)
and emerging market economies (EMEs), we nd that the developed and ef-
cient nancial markets of the AEs provide lower opportunities for momen-
tum prots. The momentum prots are more than double in the EMEs as
compared to the AEs. Highlighting the instability of the momentum strategy
in different market states by using the global nancial crisis (GFC) as a turn-
ing point, we further nd that considerable opportunity exists for momen-
tum strategies in the bullish runs that precede the crisis, as happened before
the GFC. However, the momentum prots reduce signicantly as the crisis
sets in, increasing the degree of market uncertainty, fear, and risk-
Accepted: 17 October 2018
Technical analysis strategies based on the trend in asset prices have been inves-
tigated extensively by researchers in the last two decades. The trend component
of equity indices represents the global factors or common factors that inuence
a large number of stocks to co-move in a particular direction. The deviations
from the trend, that is, noise, represent the idiosyncratic component and do
not offer any meaningful information for technical analysis strategies. In accor-
dance with the efcient market hypothesis (EMH) (Fama 1965, 1970), current
* We are thankful to the managing editor, Prof. Sudipto Dasgupta and the anonymous referee for
extremely helpful comments and suggestions. The study benetted considerably from their expert
insights and valuable feedbacks. Any unintended errors or omissions are our own doing. This
research did not receive any specic grant from funding agencies in the public, commercial, or not-
for-prot sectors.
© 2018 International Review of Finance Ltd. 2018
International Review of Finance, 20:4, 2020: pp. 827856
DOI: 10.1111/ir.12245
prices in the nancial markets accurately reect all of the known information
directly or indirectly related to an asset. Moreover, any adjustment to incoming
information is rapid and complete. Thus, in addition to the current and past
information, current prices are also expected to reect future expectations. As a
consequence, only new and unknown information can affect equity prices, and
since it is unknown, the prediction of equity prices is futile. Hence, there
should be no scope to earn abnormal prots.
The EMH provides an explanation for the supposedly random walk behavior
of equity prices. Even the weak form of EMH rejects the notion of proting via
technical analysis. However, many academic studies have empirically demon-
strated the inefciencies existing in the nancial markets using technical strate-
gies. Momentum is one of the three important anomalies discussed in the
literature, the other two being size and value. Numerous studies have
highlighted the existence of momentum anomaly in various asset classes and
have explained it using behavioral models. Persistence of momentum anomaly
can be exploited by the traders to generate cumulative positive returns. This is
done by continuing a chain of trades via buying low and selling high when the
trend is rising (long positions) and selling high and buying low when the trend
is declining (short positions).
This paper is quite closely related to the recent research work on the use
of technical trading rules in generating abnormal returns. Ample evidence
has been forwarded that highlights the protability of such rules, (e.g., Brock
et al. 1992; Frankel and Froot 1986; Lo et al. 2002; Neftci 1991). Brock
et al. (1992) use moving average and trading range break on Dow Jones
Industrial Average (DJIA) from 1897 to 1986 and report support for technical
trading strategies. They also nd strong evidence for higher returns generated
by the buy strategy in comparison to the sell strategy. Critically analyzing
their study, Hudson et al. (1996) argue that the returns generated using such
strategies fade away when they are adjusted for transaction costs. In a similar
study, Mills (1998) suggests that the effectiveness of technical trading rules,
as compared to the buy and hold (BH) strategy, has faded in recent years.
Jegadeesh and Titman (2001) (JT) present support for the protability of the
momentum strategy based on past winners and past losers for a holding
period of 312 months.
The efcacy of such strategies spans a range of asset classes and is not limited
only to equity returns. Moskowitz et al. (2012) highlight signicant time-series
momentum in a total of 58 instruments including equity indices, currencies,
commodities, and bond futures. According to them, the momentum effect
based on excess returns in the past 12 months persists for 112 months. Vari-
ous techniques have been explored to identify the momentum anomaly in the
previous studies. Most of the studies (e.g., Faber 2007; Glabadanidis 2014,
2015, 2017; Han et al. 2013; Kilgallen 2012) have primarily focused on the
moving average technique. Other techniques that have been implemented in
the empirical literature to a lesser extent include the high-frequency trading
scalping strategy (Manahov 2016), the technical pattern recognition model
© 2018 International Review of Finance Ltd. 2018828
International Review of Finance

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