The information content of analysts recommendations Vadim 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?

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 1.careful exanimate information about assets. 2.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 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-side analyst gives recommendations to buy-side investor. Buy-side analyst has executed a trade with this recommendations. – reward is directly dependent on the volume of transactions – an indirect motivation to do qualitative research – a great 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 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/sellside analysts? Is there any sign of price manipulation? Are analysts biased? What determines [un]successful recommendation?

Prior evidence: “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 output outperform the comparable

Prior evidence: 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 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

“Sharpe Ratio” Rule of Thumb

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

Findings Q1, Qn… Kolmogoro v-Smirnov Q-Spread Sharpe rule of thumb 2-sample Welsh) (t-test p-value) p-value Americas0.7465Q1=Q TRUE (0.4845) Asia-Pasific Q1<>Q5 (почти) TRUE ( ) EMEA0.3394Q2=Q3=Q TRUE ( ) World0.743 Q1=Q2= TRUE Q3=Q4=( ) Universe

Findings TotalShR TRUE Welsh 2-sample t-Student KS- no diff KS-at least 1 W/S/KS Dev %23.53%35.29%41.18%17.65%41.18% Em&Fr %10.26%33.33%15.38%25.64%43.59% 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

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

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?