8 ms·

Stocks with Outperform Ratings Beat the Market

tomrod 9y ago
This is a great exploration of something many folks wonder about. Kudos to the author, especially in realizing that target prices aren't a great variable to use when optimizing investments.
chollida1 9y ago
I wrote this a few years ago about trying to predict earnings movements from stocks from about 10 years ago. https://news.ycombinator.com/item?id=9353569#9356652 https://news.ycombinator.com/item?id=9353569#9356652 It turns out the best I could do at the time was to rank the analysts and weight their preditions by their ranking. It actually worked well for a few years, which is a lifetime for a trading strategy. Sadly earnings movements have gotten a lot harder to trade on.
- dsacco 9y ago
  > Sadly earnings announcements have gotten a lot harder to trade on. They're absolutely more difficult for reversion strategies, but if you're armed with more granular data of a high enough signal specific to the company you can do targeted trades very successfully. Accurately forecasting the earnings results ahead of time on a per-equity basis requires more setup and data processing, but it is close to infallible when it comes to profiting on outperforms. Collect ultra-specified "alternative" data on individual companies with optimistic or pessimistic analyst forecasts, analyze it, then take a contrarian position if the data predicts an unexpected earnings result. This fails is in the case of extremely uninformed sentiment in the opposite direction of your own position (likely by unsophisticated investors not interpreting the results "correctly" en masse and opting for hype instead). But you can establish a win rate with a positive cushion. One of the interesting things I've also done is collecting this data and using it beyond discretionary earnings trades. For example, by selling option premium for equities with very high theta, conversely stable revenue as forecasted by the data and low overall sentiment activity in the market. This is harder to pull off but it allows you to conduct a greater number of trades on a rolling basis through the quarter.
  - inglor 9y ago
    Can confirm from my time at TipRanks that this data was useful for trading strategies.
- apaprocki 9y ago
  May or may not be related, but a number of years ago (< 10) Bloomberg started ranking analysts based on performance and that info is available on the ANR screen. Prior to that you had to do itself. So at some point that reference info was made easily available.
ikeboy 9y ago
Several flaws immediately jump out, all potentially fatal to the conclusion and title: 1. "We will do so by removing outliers in the 10th and 90th percentiles." you can't remove outliers when investing unless you have a time machine 2. Their "starting price" includes an average of 10 days, 5 of which are before the analyst releases the recommendation. Again, to buy before the release requires a time machine (or inside information). Suppose a stock is at $50 for 5 days, an analyst releases a $75 target, stock jumps to $60, then hits $70 over the next 12 months. The gain based on the average of $55 would be $70/$55= 27%. But the average based on what price you could actually buy it at is $70/$60= 16%. 3. They narrow down analysts apparently a second time to pick out only the top 10, which seems to be distinct from the first outlier removal (not entirely clear what's being removed in either case.) I see no statistical tests relating to removing outliers, significance, etc. This is all besides the considerable degrees of freedom (cutoff price, cutoff marketcap, 100 rating minimum for analysts which seems to have been implemented after collecting data, etc).
- JumpCrisscross 9y ago
  > you can't remove outliers To drive home this point, portfolio returns are driven at the margin. A minority of positions dictate the majority of performance.
- AznHisoka 9y ago
  Yep this is like coming up with a stock algorithm that says "every tech stock goes up on average 20% in the first week of September unless its an even year and the stock starts with the letter T"
- youngprogrammer 9y ago
  1. Removing outliers was for making the data easier to analyze as some outliers were skewing the average. Of course when investing you cannot ignore outliers, but you could possibly curb them with stop losses/stop limits. 2. A more in-depth analysis could be done on analyst releases' effect on prices but assuming that this does occur, then the performances are understated and provides further evidence to the conclusion that outperform ratings can do better than the market. 3. Not sure how narrowing down the top analysts is a flaw here. This blogpost is probably not as mathematically rigorous as it could be as I just wrote it as an exploratory analysis for fun and out of curiosity.
seanot 9y ago
Scanning the article, it appears that the author is using two years of data in order to reach conclusions and this is because only two years of data was available to the author. If these same criteria were measured with two years worth of data ending on December 31, 2008, the results would look quite a bit different.
- youngprogrammer 9y ago
  Unfortunately, the website I used to scrape the data only had 2 years of data. But each datapoint is looking at each individual analyst rating and price target for a stock and then comparing it to the price in 1 year. However, you are correct that datapoints during a downturn would be quite different.
  - MatthewDPX 9y ago
    Founder of MarketBeat (website mentioned) here. We do have more than two years of data (more like 7), but profile pages get too large when we publish it all in one shot. We can make the full history available as a giant CSV file if there's a researcher that wants to do a similar analysis over a long period of time.
    - youngprogrammer 9y ago
      Hey Matthew, thanks for running your website! It was very helpful for obtaining the data I needed for my blogpost.
      - MatthewDPX 9y ago
        You're welcome. Email me if you want the full data set. matt [at] mattpaulson [dot] com.
  - inglor 9y ago
    Hey, I'm no longer affiliated with any site. If you're looking for data - I'd compare the TipRanks data set as well. I've found it to be a lot bigger than MarketBeat's and fairly more accurate. Data collection is automated through natural language processing and cross-validated with machine learning techniques that guarantees a much higher accuracy. I'm not sure who you should reach out to there - but I think support [at] tipranks.com should work. Things might have changed since I parted ways with TipRanks though - but you probably want both data sets for comparison.
codecamper 9y ago
Careful going from programming to investing. In programming you have a confidence & that makes sense because the results are often up to your own skills. With investing.. there is so much that is beyond what you know. Things go against you all the time. There is all the finance stuff. But then there are mechanics to the market that can surprise you. Triple witching? (etc etc) It's often not about the fundamentals of a company but what the perception or the trend is. If I could give myself advice a year ago it would be to start with a paper account, & seriously try to make "money" that way. It requires a ton of research & time. Everyone else is trying the same thing so not only must you figure out which company is under or over valued, you must figure it out before others do. People with entire staffs doing just that. As programmers, it can be a good idea to stick to software & service companies, because we have a leg up on the understanding of how big those things can get. A lot of investors fail to realize how software companies can grow. Check out Square. Square has some of the best programming talent around. They have a Point of Sale system but are also expanding into small business services (like running payroll). I imagine they will tackle inventory, maybe wholesale ordering, do they have a loyalty system? They are using AI to choose who gets small business loans. They offer loans that are smaller than a typical bank would, thus carving out a new niche. As long as they don't hand out too much money to the wrong people, I see this business growing massively. No other software companies seem to combine a great UI, with a mobile / tablet focus. The moat is just once you get set up with it, it'd be a pain to switch out. Curious what others think. (hijack the thread!) It's my top pick at the moment. I saw it at 9 a share last summer & did not invest enough in it. Even with a market cap of 7 billion, it would seem that it could fit those shoes & then some.
- YZF 9y ago
  If I could give myself advice 20 years ago it'd be 70/30 (world) index/(world) bonds and keep doing so. Go a little heavier (80/20) on stocks in times of crisis (>20% correction). That's all. You can just get lucky over a year and confuse that with skill.
  - seanp2k2 9y ago
    This is what I've basically gone with and I've had good results so far. I basically wanted the most return with the least effort, so I just went for index funds with the lowest expense ratios. """ There are some potential advantages to the share class structure of the Vanguard S&P 500 ETF. Although VOO is only a fraction of the size of IVV and SPY, VOO offers the lowest expense ratio of the group, charging just five basis points. """ http://etfdb.com/equity-etfs/closer-look-at-sp-500-options/ http://etfdb.com/equity-etfs/closer-look-at-sp-500-options/ I get that SPY is better if you need to liquidate millions on a moments notice, but I'm definitely not there, so the nearly-double expense ratio didn't make sense IMO.
loeg 9y ago
I suspect the model comes from overfitting the training data, that is, you would not see expect to see 15-20% outperformance from the same group of analysts next year. Or BigBankCo would immediately hire them to invest their cash. There are a number of other flaws that require time traveling to make this scheme work, and if you have a time traveling machine, beating the stock market is even easier than this.
gaetanrickter 9y ago
Would be nice to correlate analysts ratings that are similar to rare earth or chemical correlations to public companies here "Profiting from Python & Machine Learning in the Financial Markets" https://hackernoon.com/unsupervised-machine-learning-for-fun-profit-with-basket-clusters-17a1161e7aa1 https://hackernoon.com/unsupervised-machine-learning-for-fun...
odammit 9y ago
I have two investment philosophies[1]: - invest in things that help people be lazy and/or self centered - invest in "evil" companies Essentially invest in the shittiest components of being human wrapped up in a corporation. All of my stocks besides Snap are up between 40%-800%. I'm banking on protein powder MLMs and camwhores cranking that Snap up over the next few years. [1] I am not Warren Buffett or Michael Burry
davidw 9y ago
"We can beat the market"... Those are bold words.
rexstjohn 9y ago
One of the important properties of the stock market is that it is a chaotic system which changes based on observations people make of the system itself. This has fascinating implications. To understand why this is important, compare the following: If everyone looks up at the sky and sees rainclouds, this does nothing to effect the likelihood of rain. The weather doesn't depend on what people think about it. With the stock market things are different. If Tesla stock goes to $400, you might have a pool of people looking at charts, comparing Tesla's performance to it's 200 day moving average and deciding that this movement is "too quick" - so they start selling Tesla and thus change the stock's behavior. It is a self-reflective variety of chaos. The same is true of stock analyst opinions: A publicly voiced opinion of a stock directly effects the stock because people look to analysts for guidance. If 20 analysts from important firms like Goldman Sachs appear on CNN / Bloomberg swearing that Tesla is an outrageous BUY stock, it will cause more people to buy the stock because analysts opinions effect the price behavior. Long story short, what the author may actually be observing to some degree is that analyst opinions may CAUSE stock outperformance.
- youngprogrammer 9y ago
  In my analysis, I do hypothesize that analyst opinions become a self-fulfilling prophecy as you described. However, I would like to believe that analysts do some sort of sophisticated breakdown and analysis of a company's financial statements and market outlook when releasing a justifiable rating.
- TomK32 9y ago
  The dynamics of the stock market are even more complicated. Say TSLA does hit the $400 then you'll see a bunch of sell orders hitting month old targets. Certain dates when a lot of short/long contracts are resolved (Sept 15th is the next I think? and there is a bunch of call options with a $400 base price) can somewhat have an influence. And of course with a stock like TSLA there might be a lot of people being in the short doing everything they can to get the stock down to make their profit. Analysts are such a tiny part in this game. Right now the whole tech industry is overheating, just like the dot-com bubble back in the days.
- LVB 9y ago
amorphid 9y ago
They beat the market until they don't. Patterns that generate better returns don't last. In this case, everyone sees that outperform rated stocks (OSR): - OSR yield better returns - OSR demand increases - price of OSR goes up - higher prices yield lower returns - in near future, non-OSR have more attractive returns - article is published saying non-OSR - repeat in perpetuity
TomK32 9y ago
buy bitcoins! /s
- phaed 9y ago
  You have eaten your words, and will continue to eat your words.
  - TomK32 9y ago
    I know, friend told me about that weirdo stuff back in 2010 and I don't fall for the trap. Never will. Booze, that's what I invest in.
dforrestwilson 9y ago
https://www.tipranks.com/ https://www.tipranks.com/ <- You can see large data sets analyzing the same thing at work here.
Spooky23 9y ago
The secret to making a lot of money in stocks is simple: don't buy the stock that go down.
inglor 9y ago
So, until recently I was employee #1 at TipRanks who was founded to answer the question "Are Analysts bullshitting us?" - https://www.tipranks.com https://www.tipranks.com TipRanks has loads of great and interesting information - literally what _any_ analyst ranks on _any_ stock since 2009 - huge amounts of verified data from multiple objective sources. > You could possibly beat the market by only buying stocks with sector outperforms or buy ratings and selling in one year. This is true in theory (and very easily benchmarkable with TipRanks). If you follow the top 25 analysts you can beat the market pretty regularly (easily confirmed with a regression test). You might be surprised, but you won't beat it by that much and your beta will be higher. Also, Marketbeat has maybe 1/3rd of all the ratings and a study based on their data would be worthless anyway. Basing a study on their bought data would not be indicative of a whole market trend. The data source is simply not a reliable one. The average analyst does not outperform the market (they lose), the average analyst says "buy" 85% of the time and are wrong most of the time about outperforms. The average analyst is pretty bad. Some analysts like Mark Mahaney https://www.tipranks.com/analysts/mark-mahaney https://www.tipranks.com/analysts/mark-mahaney or https://www.tipranks.com/analysts/jonathan-atkin?benchmark=none&period=yearly https://www.tipranks.com/analysts/jonathan-atkin?benchmark=n... are pretty decent. In general - be very wary of recommendations - I've not found analyst opinions a good indicator of market performance over time (and I worked in a company that ranked analysts for 5 years). I have found however that combining that with several other signals (news sentiment, bloggers, insider opinions etc) can create strategies that outperform the market (TipRanks sells such strategies to big organizations). I've sent my old employer an email to see if they can open some data regarding this - and reproducing the above study with real market data.

This is a great exploration of something many folks wonder about. Kudos to the author, especially in realizing that target prices aren't a great variable to use when optimizing investments.

chollida1 9y ago

I wrote this a few years ago about trying to predict earnings movements from stocks from about 10 years ago. https://news.ycombinator.com/item?id=9353569#9356652 https://news.ycombinator.com/item?id=9353569#9356652 It turns out the best I could do at the time was to rank the analysts and weight their preditions by their ranking. It actually worked well for a few years, which is a lifetime for a trading strategy. Sadly earnings movements have gotten a lot harder to trade on.

dsacco 9y ago
> Sadly earnings announcements have gotten a lot harder to trade on. They're absolutely more difficult for reversion strategies, but if you're armed with more granular data of a high enough signal specific to the company you can do targeted trades very successfully. Accurately forecasting the earnings results ahead of time on a per-equity basis requires more setup and data processing, but it is close to infallible when it comes to profiting on outperforms. Collect ultra-specified "alternative" data on individual companies with optimistic or pessimistic analyst forecasts, analyze it, then take a contrarian position if the data predicts an unexpected earnings result. This fails is in the case of extremely uninformed sentiment in the opposite direction of your own position (likely by unsophisticated investors not interpreting the results "correctly" en masse and opting for hype instead). But you can establish a win rate with a positive cushion. One of the interesting things I've also done is collecting this data and using it beyond discretionary earnings trades. For example, by selling option premium for equities with very high theta, conversely stable revenue as forecasted by the data and low overall sentiment activity in the market. This is harder to pull off but it allows you to conduct a greater number of trades on a rolling basis through the quarter.
- inglor 9y ago
  Can confirm from my time at TipRanks that this data was useful for trading strategies.
apaprocki 9y ago
May or may not be related, but a number of years ago (< 10) Bloomberg started ranking analysts based on performance and that info is available on the ANR screen. Prior to that you had to do itself. So at some point that reference info was made easily available.

ikeboy 9y ago

Several flaws immediately jump out, all potentially fatal to the conclusion and title: 1. "We will do so by removing outliers in the 10th and 90th percentiles." you can't remove outliers when investing unless you have a time machine 2. Their "starting price" includes an average of 10 days, 5 of which are before the analyst releases the recommendation. Again, to buy before the release requires a time machine (or inside information). Suppose a stock is at $50 for 5 days, an analyst releases a $75 target, stock jumps to $60, then hits $70 over the next 12 months. The gain based on the average of $55 would be $70/$55= 27%. But the average based on what price you could actually buy it at is $70/$60= 16%. 3. They narrow down analysts apparently a second time to pick out only the top 10, which seems to be distinct from the first outlier removal (not entirely clear what's being removed in either case.) I see no statistical tests relating to removing outliers, significance, etc. This is all besides the considerable degrees of freedom (cutoff price, cutoff marketcap, 100 rating minimum for analysts which seems to have been implemented after collecting data, etc).

JumpCrisscross 9y ago
> you can't remove outliers To drive home this point, portfolio returns are driven at the margin. A minority of positions dictate the majority of performance.
AznHisoka 9y ago
Yep this is like coming up with a stock algorithm that says "every tech stock goes up on average 20% in the first week of September unless its an even year and the stock starts with the letter T"
youngprogrammer 9y ago
1. Removing outliers was for making the data easier to analyze as some outliers were skewing the average. Of course when investing you cannot ignore outliers, but you could possibly curb them with stop losses/stop limits. 2. A more in-depth analysis could be done on analyst releases' effect on prices but assuming that this does occur, then the performances are understated and provides further evidence to the conclusion that outperform ratings can do better than the market. 3. Not sure how narrowing down the top analysts is a flaw here. This blogpost is probably not as mathematically rigorous as it could be as I just wrote it as an exploratory analysis for fun and out of curiosity.

seanot 9y ago

Scanning the article, it appears that the author is using two years of data in order to reach conclusions and this is because only two years of data was available to the author. If these same criteria were measured with two years worth of data ending on December 31, 2008, the results would look quite a bit different.

youngprogrammer 9y ago
Unfortunately, the website I used to scrape the data only had 2 years of data. But each datapoint is looking at each individual analyst rating and price target for a stock and then comparing it to the price in 1 year. However, you are correct that datapoints during a downturn would be quite different.
- MatthewDPX 9y ago
  Founder of MarketBeat (website mentioned) here. We do have more than two years of data (more like 7), but profile pages get too large when we publish it all in one shot. We can make the full history available as a giant CSV file if there's a researcher that wants to do a similar analysis over a long period of time.
  - youngprogrammer 9y ago
    Hey Matthew, thanks for running your website! It was very helpful for obtaining the data I needed for my blogpost.
    - MatthewDPX 9y ago
      You're welcome. Email me if you want the full data set. matt [at] mattpaulson [dot] com.
- inglor 9y ago
  Hey, I'm no longer affiliated with any site. If you're looking for data - I'd compare the TipRanks data set as well. I've found it to be a lot bigger than MarketBeat's and fairly more accurate. Data collection is automated through natural language processing and cross-validated with machine learning techniques that guarantees a much higher accuracy. I'm not sure who you should reach out to there - but I think support [at] tipranks.com should work. Things might have changed since I parted ways with TipRanks though - but you probably want both data sets for comparison.

codecamper 9y ago

Careful going from programming to investing. In programming you have a confidence & that makes sense because the results are often up to your own skills. With investing.. there is so much that is beyond what you know. Things go against you all the time. There is all the finance stuff. But then there are mechanics to the market that can surprise you. Triple witching? (etc etc) It's often not about the fundamentals of a company but what the perception or the trend is. If I could give myself advice a year ago it would be to start with a paper account, & seriously try to make "money" that way. It requires a ton of research & time. Everyone else is trying the same thing so not only must you figure out which company is under or over valued, you must figure it out before others do. People with entire staffs doing just that. As programmers, it can be a good idea to stick to software & service companies, because we have a leg up on the understanding of how big those things can get. A lot of investors fail to realize how software companies can grow. Check out Square. Square has some of the best programming talent around. They have a Point of Sale system but are also expanding into small business services (like running payroll). I imagine they will tackle inventory, maybe wholesale ordering, do they have a loyalty system? They are using AI to choose who gets small business loans. They offer loans that are smaller than a typical bank would, thus carving out a new niche. As long as they don't hand out too much money to the wrong people, I see this business growing massively. No other software companies seem to combine a great UI, with a mobile / tablet focus. The moat is just once you get set up with it, it'd be a pain to switch out. Curious what others think. (hijack the thread!) It's my top pick at the moment. I saw it at 9 a share last summer & did not invest enough in it. Even with a market cap of 7 billion, it would seem that it could fit those shoes & then some.

YZF 9y ago
If I could give myself advice 20 years ago it'd be 70/30 (world) index/(world) bonds and keep doing so. Go a little heavier (80/20) on stocks in times of crisis (>20% correction). That's all. You can just get lucky over a year and confuse that with skill.
- seanp2k2 9y ago
  This is what I've basically gone with and I've had good results so far. I basically wanted the most return with the least effort, so I just went for index funds with the lowest expense ratios. """ There are some potential advantages to the share class structure of the Vanguard S&P 500 ETF. Although VOO is only a fraction of the size of IVV and SPY, VOO offers the lowest expense ratio of the group, charging just five basis points. """ http://etfdb.com/equity-etfs/closer-look-at-sp-500-options/ http://etfdb.com/equity-etfs/closer-look-at-sp-500-options/ I get that SPY is better if you need to liquidate millions on a moments notice, but I'm definitely not there, so the nearly-double expense ratio didn't make sense IMO.

loeg 9y ago

I suspect the model comes from overfitting the training data, that is, you would not see expect to see 15-20% outperformance from the same group of analysts next year. Or BigBankCo would immediately hire them to invest their cash. There are a number of other flaws that require time traveling to make this scheme work, and if you have a time traveling machine, beating the stock market is even easier than this.

gaetanrickter 9y ago

Would be nice to correlate analysts ratings that are similar to rare earth or chemical correlations to public companies here "Profiting from Python & Machine Learning in the Financial Markets" https://hackernoon.com/unsupervised-machine-learning-for-fun-profit-with-basket-clusters-17a1161e7aa1 https://hackernoon.com/unsupervised-machine-learning-for-fun...

odammit 9y ago

I have two investment philosophies[1]: - invest in things that help people be lazy and/or self centered - invest in "evil" companies Essentially invest in the shittiest components of being human wrapped up in a corporation. All of my stocks besides Snap are up between 40%-800%. I'm banking on protein powder MLMs and camwhores cranking that Snap up over the next few years. [1] I am not Warren Buffett or Michael Burry

davidw 9y ago

"We can beat the market"... Those are bold words.

rexstjohn 9y ago

One of the important properties of the stock market is that it is a chaotic system which changes based on observations people make of the system itself. This has fascinating implications. To understand why this is important, compare the following: If everyone looks up at the sky and sees rainclouds, this does nothing to effect the likelihood of rain. The weather doesn't depend on what people think about it. With the stock market things are different. If Tesla stock goes to $400, you might have a pool of people looking at charts, comparing Tesla's performance to it's 200 day moving average and deciding that this movement is "too quick" - so they start selling Tesla and thus change the stock's behavior. It is a self-reflective variety of chaos. The same is true of stock analyst opinions: A publicly voiced opinion of a stock directly effects the stock because people look to analysts for guidance. If 20 analysts from important firms like Goldman Sachs appear on CNN / Bloomberg swearing that Tesla is an outrageous BUY stock, it will cause more people to buy the stock because analysts opinions effect the price behavior. Long story short, what the author may actually be observing to some degree is that analyst opinions may CAUSE stock outperformance.

youngprogrammer 9y ago
In my analysis, I do hypothesize that analyst opinions become a self-fulfilling prophecy as you described. However, I would like to believe that analysts do some sort of sophisticated breakdown and analysis of a company's financial statements and market outlook when releasing a justifiable rating.
TomK32 9y ago
The dynamics of the stock market are even more complicated. Say TSLA does hit the $400 then you'll see a bunch of sell orders hitting month old targets. Certain dates when a lot of short/long contracts are resolved (Sept 15th is the next I think? and there is a bunch of call options with a $400 base price) can somewhat have an influence. And of course with a stock like TSLA there might be a lot of people being in the short doing everything they can to get the stock down to make their profit. Analysts are such a tiny part in this game. Right now the whole tech industry is overheating, just like the dot-com bubble back in the days.
LVB 9y ago

amorphid 9y ago

They beat the market until they don't. Patterns that generate better returns don't last. In this case, everyone sees that outperform rated stocks (OSR): - OSR yield better returns - OSR demand increases - price of OSR goes up - higher prices yield lower returns - in near future, non-OSR have more attractive returns - article is published saying non-OSR - repeat in perpetuity

TomK32 9y ago

buy bitcoins! /s

phaed 9y ago
You have eaten your words, and will continue to eat your words.
- TomK32 9y ago
  I know, friend told me about that weirdo stuff back in 2010 and I don't fall for the trap. Never will. Booze, that's what I invest in.

dforrestwilson 9y ago

https://www.tipranks.com/ https://www.tipranks.com/ <- You can see large data sets analyzing the same thing at work here.

Spooky23 9y ago

The secret to making a lot of money in stocks is simple: don't buy the stock that go down.

inglor 9y ago

So, until recently I was employee #1 at TipRanks who was founded to answer the question "Are Analysts bullshitting us?" - https://www.tipranks.com https://www.tipranks.com TipRanks has loads of great and interesting information - literally what _any_ analyst ranks on _any_ stock since 2009 - huge amounts of verified data from multiple objective sources. > You could possibly beat the market by only buying stocks with sector outperforms or buy ratings and selling in one year. This is true in theory (and very easily benchmarkable with TipRanks). If you follow the top 25 analysts you can beat the market pretty regularly (easily confirmed with a regression test). You might be surprised, but you won't beat it by that much and your beta will be higher. Also, Marketbeat has maybe 1/3rd of all the ratings and a study based on their data would be worthless anyway. Basing a study on their bought data would not be indicative of a whole market trend. The data source is simply not a reliable one. The average analyst does not outperform the market (they lose), the average analyst says "buy" 85% of the time and are wrong most of the time about outperforms. The average analyst is pretty bad. Some analysts like Mark Mahaney https://www.tipranks.com/analysts/mark-mahaney https://www.tipranks.com/analysts/mark-mahaney or https://www.tipranks.com/analysts/jonathan-atkin?benchmark=none&period=yearly https://www.tipranks.com/analysts/jonathan-atkin?benchmark=n... are pretty decent. In general - be very wary of recommendations - I've not found analyst opinions a good indicator of market performance over time (and I worked in a company that ranked analysts for 5 years). I have found however that combining that with several other signals (news sentiment, bloggers, insider opinions etc) can create strategies that outperform the market (TipRanks sells such strategies to big organizations). I've sent my old employer an email to see if they can open some data regarding this - and reproducing the above study with real market data.