The information content of analysts recommendations

1. The information content of analysts recommendations VadimSurin International Financial Laboratory

2. 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?

3. 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.

4. 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

5. 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?

6. 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

7. 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)

8. 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

9. “Sharpe Ratio” Rule of Thumb

10. 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

11. Findings

12. Findings • 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

13. Discussion • 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

14. 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?