HONG KONG,
CHINA -�Media OutReach�- 27 August 2020 -�It's been called the holy grail of finance. Is it possible
to harness the promise of artificial intelligence to make money trading stocks?
Many have tried with varying degrees of success. For example, BlackRock, the
world's largest money manager, has said its Artificial Intelligence (AI)
algorithms have consistently beaten
portfolios managed by human stock pickers. However, a recent research study by
The Chinese University of Hong Kong (CUHK) reveals that the effectiveness of
machine learning methods may require a second look.
�
The study, titled "Machine Learning versus Economic Restrictions: Evidence from
Stock Return Predictability",
analysed a large sample of U.S. stocks between 1987 and 2017. Using three
well-established deep-learning methods, researchers were able to generate a
monthly value-weighted risk-adjusted return of as much as 0.75 percent to 1.87
percent, reflecting the success of machine learning in generating a superior
payoff. However, the researchers found that this performance would attenuate if
the machine learning algorithms were limited to working with stocks that were
relatively easy and cheap to trade.
�
"We find that the return
predictability of deep learning methods weakens considerably in the presence of
standard economic restrictions in empirical finance, such as excluding
microcaps or distressed firms," says Si
Cheng, Assistant Professor at CUHK
Business School's Department of Finance and one of the study's authors.
�
Disappearing Returns
Prof. Cheng, along with her
collaborators Prof. Doron Avramov at IDC Herzliya and Lior Metzker, a research
student at Hebrew University of Jerusalem, found the portfolio payoff declined
by 62 percent when excluding microcaps -- stocks which can be difficult to trade
because of their small market capitalisations, 68 percent lower when excluding
non-rated firms -- stocks which do not receive Standard & Poor's long-term
issuer credit rating, and 80 percent lower excluding distressed firms around
credit rating downgrades.
�
According to the study, machine
learning-based trading strategies are more profitable during periods when
arbitrage becomes more difficult, such as when there is high investor
sentiment, high market volatility, and low market liquidity.
�
One caveat of the machine-learning
based strategies highlighted by the study is high transaction costs. "Machine
learning methods require high turnover and taking extreme stock positions. An
average investor would struggle to achieve meaningful alpha after taking
transaction costs into account," she says, adding, however, that this
finding did not imply that machine learning-based strategies are unprofitable
for all traders.
�
"Instead, we show that machine
learning methods studied here would struggle to achieve statistically and
economically meaningful risk-adjusted performance in the presence of reasonable
transaction costs. Investors thus should adjust their expectations of the
potential net-of-fee performance," says Prof. Cheng.
�
The Future of Machine Learning
"However,
our findings should not be taken as evidence against applying machine learning
techniques in quantitative investing," Prof. Cheng explains. "On the contrary, machine learning-based trading
strategies hold considerable promise for asset management." For instance,
they have the capability to process and combine multiple weak stock trading
signals into meaningful information that could form the basis for a coherent
trading strategy.
�
Machine learning-based strategies
display less downside risk and continue to generate positive payoff during
crisis periods. The study found that during several major market downturns,
such as the 1987 market crash, the Russian default, the burst of the tech
bubble, and the recent financial crisis, the best machine-learning investment
method generated a monthly value-weighted return of 3.56 percent, excluding
microcaps, while the market return came in at a negative 6.91 percent during
the same period.
�
Prof. Cheng says that the
profitability of trading strategies based on identifying individual stock
market anomalies -- stocks whose behaviour run counter to conventional capital
market pricing theory predictions -- is primarily driven by short positions and
is disappearing in recent years. However, machine-learning based strategies are
more profitable in long positions and remain viable in the post-2001 period.
�
"This could be particularly
valuable for real-time trading, risk management, and long-only institutions. In
addition, machine learning methods are more likely to specialise in stock
picking than industry rotation," Prof. Cheng adds, referring to strategy
which seeks to capitalise on the next stage of economic cycles by moving funds
from one industry to the next.
�
The study is the first to provide
large-scale evidence on the economic importance of machine learning methods,
she adds.
�
"The collective evidence shows
that most machine learning techniques face the usual challenge of
cross-sectional return predictability, and the anomalous return patterns are
concentrated in difficult-to-arbitrage stocks and during episodes of high
limits to arbitrage," Prof. Cheng says. "Therefore, even though
machine learning offers unprecedented opportunities to shape our understanding
of asset pricing formulations, it is important to consider the common economic
restrictions in assessing the success of newly developed methods, and confirm
the external validity of machine learning models before applying them to
different settings."
Reference:
Avramov, Doron and Cheng, Si and
Metzker, Lior, Machine Learning versus Economic Restrictions: Evidence from
Stock Return Predictability (April 5, 2020). Available at SSRN: https://ssrn.com/abstract=3450322 or http://dx.doi.org/10.2139/ssrn.3450322
�
This article was first published in the China
Business Knowledge (CBK) website by CUHK Business School: https://bit.ly/3fX2ydr.
About CUHK Business School
CUHK
Business School comprises two schools -- Accountancy and Hotel and
Tourism Management -- and four departments -- Decision Sciences and
Managerial Economics, Finance, Management and
Marketing. Established in Hong Kong in 1963, it is the first business school to
offer BBA, MBA and Executive MBA programmes in the region. Today, the School
offers 11 undergraduate programmes and 20
graduate programmes including MBA, EMBA,
Master, MSc, MPhil and Ph.D.
�
In
the Financial Times Global MBA Ranking 2020,
CUHK MBA is ranked 50th. In FT's 2019
EMBA ranking, CUHK EMBA is ranked 24th in the world. CUHK Business
School has the largest number of business alumni (37,000+)
among universities/business schools in Hong Kong -- many of whom are
key business leaders. The School currently has about 4,800
undergraduate and postgraduate students and Professor Lin
Zhou is the Dean of CUHK Business School.
�
More information
is available at http://www.bschool.cuhk.edu.hk or by connecting with CUHK Business School
on:
Facebook: www.facebook.com/cuhkbschool
Instagram: www.instagram.com/cuhkbusinessschool
LinkedIn: http://www.linkedin.com/school/cuhkbusinessschool
WeChat:
CUHKBusinessSchool
