‘Consistent’ Earnings Surprises

‘Consistent’ Earnings Surprises

Byoung-Hyoun Hwang 

Cornell University – Dyson School of Applied Economics and Management; Korea University – Department of Finance

Baixiao Liu 

Florida State University

Dong Lou 

London School of Economics & Political Science (LSE)
May 5, 2014

Abstract: 
We hypothesize that analysts with a bullish stock recommendation have an interest in not being subsequently contradicted by negative firm-specific news. As a result, these analysts report downward-biased earnings forecasts so that the company is less likely to experience a negative earnings surprise. Analogously, analysts with a bearish recommendation report upward biased earnings forecasts so that the firm is less likely to experience a strong positive earnings surprise. Consistent with this notion, we find that stock recommendations significantly and positively predict subsequent earnings surprises, as well as narrow beats versus narrow misses. This predictability is concentrated in situations where the motivation for such behavior is particularly strong. Stock recommendations also predict earnings-announcement-day returns. A long-short portfolio that exploits this predictability earns abnormal returns of 125 basis points per month.

 

Biased Analysts Create Big Profits?

Jack Vogel, Ph.D. 06/19/2014 22:04

‘Consistent’ Earnings Surprises

Hwang, Liu, and Lou

A version of the paper can be found here.

Want a summary of academic papers with alpha? Check out our free Academic Alpha Database!

Abstract:

We hypothesize that analysts with a bullish stock recommendation have an interest in not being subsequently contradicted by negative firm-specific news. As a result, these analysts report downward-biased earnings forecasts so that the company is less likely to experience a negative earnings surprise. Analogously, analysts with a bearish recommendation report upward biased earnings forecasts so that the firm is less likely to experience a strong positive earnings surprise. Consistent with this notion, we find that stock recommendations significantly and positively predict subsequent earnings surprises, as well as narrow beats versus narrow misses. This predictability is concentrated in situations where the motivation for such behavior is particularly strong. Stock recommendations also predict earnings-announcement-day returns. A long-short portfolio that exploits this predictability earns abnormal returns of 125 basis points per month.

Alpha Highlight:

The paper documents that analysts with a bullish recommendation walk down their EPS growth estimates (creating a positive surprise). For analysts with a bearish recommendation, EPS growth estimates  are too high (creating a negative surprise). The paper shows this in the figure below:
image001

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

Is there a way to take advantage of this? The paper recommends the following trading strategy:
From page 23 and 24:

image002

To better assess the economic magnitude of our finding, we employ the following calendar-time portfolio approach: We sort earnings announcements into two groups based on the consensus recommendation prior to the earnings announcement. On any given trading day, we purchase stocks that have a strong buy/buy consensus recommendation and that are announcing earnings in three trading days (i.e., we purchase stocks at time t=-3, where t=0 is the earnings announcement day or the next trading day if earnings are announced on a non-trading day; “long leg”). We short stocks that have a hold/underperform/sell consensus recommendation and that are announcing earnings in three trading days (“short leg”). Each stock is kept in the long/short-portfolio for seven trading days (i.e., until t=+3). If on any given day, there are less than or equal to 10 stocks on either the long or short side, we hold the 3-month Treasury bill instead of the long-short portfolio (this is the case for less than 5% of the trading days).

As shown below, this strategy produces significant monthly alphas (at 5% and 10% levels):

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

150bps x 12 months = some SERIOUS annual alpha!

 

About bambooinnovator
Kee Koon Boon (“KB”) is the co-founder and director of HERO Investment Management which provides specialized fund management and investment advisory services to the ARCHEA Asia HERO Innovators Fund (www.heroinnovator.com), the only Asian SMID-cap tech-focused fund in the industry. KB is an internationally featured investor rooted in the principles of value investing for over a decade as a fund manager and analyst in the Asian capital markets who started his career at a boutique hedge fund in Singapore where he was with the firm since 2002 and was also part of the core investment committee in significantly outperforming the index in the 10-year-plus-old flagship Asian fund. He was also the portfolio manager for Asia-Pacific equities at Korea’s largest mutual fund company. Prior to setting up the H.E.R.O. Innovators Fund, KB was the Chief Investment Officer & CEO of a Singapore Registered Fund Management Company (RFMC) where he is responsible for listed Asian equity investments. KB had taught accounting at the Singapore Management University (SMU) as a faculty member and also pioneered the 15-week course on Accounting Fraud in Asia as an official module at SMU. KB remains grateful and honored to be invited by Singapore’s financial regulator Monetary Authority of Singapore (MAS) to present to their top management team about implementing a world’s first fact-based forward-looking fraud detection framework to bring about benefits for the capital markets in Singapore and for the public and investment community. KB also served the community in sharing his insights in writing articles about value investing and corporate governance in the media that include Business Times, Straits Times, Jakarta Post, Manual of Ideas, Investopedia, TedXWallStreet. He had also presented in top investment, banking and finance conferences in America, Italy, Sydney, Cape Town, HK, China. He has trained CEOs, entrepreneurs, CFOs, management executives in business strategy & business model innovation in Singapore, HK and China.

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