{"id":940,"date":"2023-08-09T18:15:47","date_gmt":"2023-08-09T18:15:47","guid":{"rendered":"https:\/\/sharpely.in\/blogs\/does-analyst-recommendation-on-stocks-hold-any-importance\/"},"modified":"2026-06-03T15:48:21","modified_gmt":"2026-06-03T15:48:21","slug":"analyst-recommendation-stocks-hold-importance","status":"publish","type":"post","link":"https:\/\/sharpely.in\/blogs\/analyst-recommendation-stocks-hold-importance\/","title":{"rendered":"Does Analyst Recommendation on Stocks Hold Any Importance"},"content":{"rendered":"

Introduction:<\/h2>\n

In this blog, we will see if analyst recommendations on stocks can help you pick winners. The purpose will be to backtest a strategy that picks stocks where a high percentage of analysts have a positive recommendation.<\/p>\n

But before that, some definitions.<\/p>\n

What is Analyst Recommendation?<\/h2>\n

Analyst recommendations on stocks refer to the opinions provided by financial analysts (primarily sell-side analysts) regarding the potential performance outlook of a particular stock. <\/p>\n

These recommendations are usually based on a combination of fundamental analysis, market trends, company news, and other relevant factors. The recommendations are usually in the form of Buy, Overweight, Neutral (or hold), underweight, and sell. <\/p>\n

On sharpely, we capture analyst recommendations using 2 metrics \u2013 Buy Rating (%)<\/strong> and Strong BUY Rating (%)<\/strong>. The first one captures the percentage of analysts who have a favorable rating on the stock (buy, overweight, strong buy). <\/p>\n

The second one is more stringent and looks only at the percentage of analysts who have a buy (or strong buy) rating on the stock. Overweight ratings are excluded. Since different analysts have different rating systems (or naming systems), we consider only explicit \u201cBUY\u201d ratings as strong BUY and ignore \u201cOverweight\u201d ratings.<\/p>\n

Will Analyst Recommendation Help Me Pick Stocks?<\/h2>\n

While we don\u2019t have sufficient data to look at the efficacy of individual analysts, we do have data to check if an aggregate measure of analyst recommendation can be used for building stock portfolios.<\/p>\n

One such aggregate measure was introduced above – Strong BUY Rating (%)<\/strong>.<\/p>\n

So here is what we will do. We will use sharpely\u2019s metric performance tool to backtest the efficacy of this metric.<\/p>\n

Under the hood, here is what happens (more details on backtesting of metric can be found here<\/a>):<\/p>\n

1.\u00a0We first select a universe – Nifty 500 in this case.<\/span><\/p>\n

2. Stocks in the universe are sorted in ascending order and then portfolios are created based on percentiles. So, Quintile 1 will have stocks in 0 \u2013 20 percentiles while Quintile 5 will have stocks in 80 \u2013 100 percentiles. For each portfolio, stocks are assigned an <\/span>equal weight<\/strong> in the portfolio.<\/span><\/p>\n

3.\u00a0Using our standard walk-forward framework, 5 such portfolios are constructed <\/span>every quarter<\/strong> (based on quintile splitting) and portfolios are rebalanced to new stocks.<\/span><\/p>\n

Note that in our case, Quintile 5 portfolio is the one that contains stocks with the highest percentage of analysts with buy ratings. Quintile 1 portfolio, on the other hand, contains stocks with the lowest percentage of analysts with a buy rating.<\/p>\n

If analyst recommendation were a useful metric for stock selection, we would expect Quintile 5 portfolio to outperform Quintile 1 portfolio.<\/p>\n

Let\u2019s see the results.\u00a0<\/p>\n

<\/p>\n

In the last 10 years, Quintile 5 portfolio has delivered a CAGR of 17.21% which is almost 600 bps higher than Quintile 1 portfolio. <\/p>\n

Prima facie, this looks encouraging. But if we look at Quintile 2 portfolio, it has also delivered almost 16.84% and has outperformed Quintile 3.<\/p>\n

So, we have a mixed verdict. Here is our interpretation:<\/p>\n