The information content of analysts recommendations
1 / 14

The information content of analysts recommendations - PowerPoint PPT Presentation

  • Uploaded on

The information content of analysts recommendations. V a dim Surin International Financial Laboratory. The plan. Importance Buy-side/sell-side difference Typical research questions Prior evidence Our dataset and method Why our method is better Results Discussion What’s next?.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about ' The information content of analysts recommendations' - wynona

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
The information content of analysts recommendations

The information content of analysts recommendations


International Financial Laboratory

The plan
The plan

  • Importance

  • Buy-side/sell-side difference

  • Typical research questions

  • Prior evidence

  • Our dataset and method

  • Why our method is better

  • Results

  • Discussion

  • What’s next?

Why it s important
Why it’s important

  • The goal of market is timely and accurate reflection of relevant information in prices

  • Market is efficient, if it manages with it (efficient-market hypothesis)

  • Market manages, if there are a lot of self-dependent members, whose

    • careful exanimate information about assets.

    • quickly translate results of researches in transactions.

  • There are sell- and buy-side analysts on the stock market.

Buy side sell side difference
Buy-side/sell-side difference

  • Buy-side analyst gives closed recommendations for institutional investors

    • reward is directly dependent on the success of the recommendations

    • there is no motivation to report recommends to the market

  • Sell-sideanalyst gives recommendations to buy-side investor. Buy-side analyst has executeda trade with this recommendations.

    • reward is directly dependent on the volume of transactions

    • an indirect motivation to do qualitative research

    • agreat motivation to report recommendations to the market

  • Buy-side data are closed, sell-side – are opened

  • But for the effective market are important both

Typical research questions
Typical research questions

  • Do analysts add value on individual and aggregated value?

  • Do analysts add value in excess of publicly available information?

  • Is there any asymmetry in value, added by foreign/local, developed/emerging, buyside/sellsideanalysts?

  • Is there any signof price manipulation?

  • Are analysts biased?

  • What determines [un]successful recommendation?

Prior evidence 2000 2013
Prior evidence: 2000-2013

  • “glamour” stock effect

    • P/B ratio is the indicator for Buy and Strong Buy recommendation regressions

  • information in recommendations is largely orthogonal to the information in 8 other variables with proven ability to predict future stock returns

  • aggregated analyst recommendation relates to subsequent aggregate market change.

  • strategies, which combine the full analyst report and specific analytical outputoutperform the comparable

Prior evidence 2000 20131
Prior evidence: 2000-2013

  • reaction to sell is greater than to buy

  • foreign analysts’ buy recommendations more informative than local (opposite held for sell recommendations)

Our dataset and method
Our dataset and method

  • opinions are encoded and aggregated

    • strong buy = 5, strong sell = 1

  • quantile portfolios, rebalanced monthly

  • differential “abnormal” monthly return,

    • no a priori assumption about market model

  • KS-test on statistical significance of differences between return distributions of opinion portfolios

  • T-Student and Welsh tests on difference of returns between opinion portfolios and Q-Spread

  • “Sharpe ratio” rule of thumb

Why our method is better
Why our method is better

  • Test analysts aggregated ability to predict individual stocks outperformance

  • Test just significance of difference between aggregated opinion portfolios,

    • no implied assumption, e.g. “positive = buy, negative = sell”

  • Minimum assumptions = robust

    • any market model, any distribution law

  • Relative = free from positive bias

  • Useful in practice, as can be directly replicated to profit from any pattern


  • Strong evidence of excess return, “earned” by analyst recommendation, is rare

  • Evidence of no difference in opinion portfolios returns is quite frequent

  • “opinion portfolios” serve rare free lunch to the market by providing diversification venue


  • Possible reason for insignificance of “opinion portfolios” profits

    • market doesn’t respond to analyst recommendation

    • responds too fast to be captured by our method

      • marked is liquid and profit is arbitraged away before the end of the month

        • participants are “too rational”: well-informed, well-equipped

      • low liquidity: arbitraged away by one or two rational participants, others abstain due to high prices

What s next
What’s next?

  • the speed of price adjustment

  • daily? high-frequency?

  • “trading” back-test

  • what makes market “efficient”

    • liquidity impact

    • capital flows impact

  • the level of the consensus adds value only among stocks with positive quantitative characteristics

    • perhaps, markets that failed in our research had negative characteristics prevailing all the time

  • How to measure “closeness” of opinion portfolios returns

    • slightly positively skewed, almost-normal (with several negative outliers)

    • any distance metrics, like Mahalanobis?

  • ad