Stocks with Outperform Ratings Beat the Market(blog.ayoungprogrammer.com) |
Stocks with Outperform Ratings Beat the Market(blog.ayoungprogrammer.com) |
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).
To drive home this point, portfolio returns are driven at the margin. A minority of positions dictate the majority of performance.
Your number 3 is basically, "the ten people who did the best did better than everybody else" really? You don't say...
One will find fairly large differences between data sets that collect analyst ratings and "corrections" as they happen (which you are going to have to collect yourself) and analyst ratings as reported by the various services and the differences almost always make the analysts seem more accurate.
It is possible things are different in Canada, but if you look at bias-free data for the US or Europe, analyst recommendations are mostly random. Analyst recommendation data is mostly useful so you can bet against the short term spike or drop caused by people reacting to a change in analyst recommendation. This was very effective up to the 2000s but doing this with a reasonable Sharpe Ratio today is very hard (otherwise I wouldn't write about it) though it may be useful as an add on to an otherwise profitable strategy.
The point was to hold analysts accountable for their actions by objectively collecting them. TipRanks sells this data and I'd love to run a study on it but unfortunately I no longer has access.
The Marketbeat data set used here is tiny in comparison and only consists of a third of analyst recommendations if I remember it correctly.
Bloomberg has a much better dataset which is nearly as good as TipRanks'.
Again, I can't really make claims on their behalf (I'm employed at Peer5 (YC@W17) at the moment and am no longer affiliated).
The legal threats held to merit - but to be fair they were very scary as a young startup before partnerships with E*Trade, Nasdaq and others started.
As an antidote, take a look at this short McKinsey piece analyzing the performance of sell-side analysts, concluding that "earnings forecasts exceed realized earnings per share:" http://www.mckinsey.com/business-functions/strategy-and-corp...
Obviously, predicting company earnings is not the same as predicting stock prices, but if sell-side analysts have such a terrible track record at the former , one would expect them to have a terrible track record at the latter too.
My blogpost shows that stock price predictions also show a terrible track record. They are wildly off and on average higher than actual results.
One can easily tweak the code to suit their needs, I appreciate CODE Author for the effort . The Pandas and Python code can be used as spring board to further one's reserach
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.
> Not sure how narrowing down the top analysts is a flaw here.
Potentially, because how did you decide what "top analysts" were? If it's using the same methods you used to determine they were successful, it just means analysts that come out of your math come out of your math.
If a 50K people flip 10 coins, one of them might flip 10 heads. It doesn't mean that person is better at flipping heads. We could in fact calculate the chances of one of 50K people flipping ten heads. If I decided it meant that some people really were better at flipping heads, I'd probably be wrong. (Although if I calculated the chances and discovered it was like a one in bazillion chance that even one of 50K people would flip ten heads... I'd probably at least consider that they might be better at flipping heads! But I'd probably run the experiment again. :) )
If I pick the top 100 heads-flippers from my 50K coin flippers, and show that they really are better at flipping heads because they flipped more heads in the same dataset that I used to pick them as the top 100 heads-flippers in the first place --- I haven't really shown that at all. By "narrowing down top analysts", depending on how you did it, it's possible you simply found the analysts who got lucky, while ignoring the ones who didn't.
Statistical analysis is _tricky_.
For 2, it's not just the effect of the release on prices, but could also be the effect of other things on both releases and prices - imagine great news came out which bumped up the price and also caused analysts to upgrade their ratings.
You should use price data from the day after release.
For top analysts, if you only know who's top after looking at their performance, then it's again not a repeatable strategy. Compare: "you can beat the market just by buying the top 10 stocks!"
It's nice to play with data, I'm just laying out some of the reasons these won't work in the "real world", and pointing towards where a future analysis could be improved.
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.
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.
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.
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 or 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.
- 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
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.
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.
- 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
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.
""" 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/
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.
The tough part will be staying the course if the market crashes, which it will at some point. It'll look like the world is ending and you should be investing in gun powder, canned foods and gold. You need to be comfortable with the idea that it may go down (even 40%-50%) and take a long time to come back up but you can keep buying through that and also get dividends...
Here are some basic questions for you, related to the points made above:
* WHY did you remove outliers in the 10th and 90th percentiles? What happens if you don't remove them?
* WHY did you use a 10-day window centered on dates of recommendation? What happens if you use the price on the same day?
* Why did you choose those return horizons? What happens if you choose different ones?
* WHY did you pick out only the top 10 analysts? What happens if you don't?
* WHY did you not do statistical tests relating to removing outliers, significance, etc.?
* WHY did you choose those cutoffs for price, marketcap, and minimum analyst rating?
10 day windows were used to reduce the amount of volatility/noise in a time frame
Return horizons for 1 years was used because price targets are for one year.
Theres only 15 or so analysts I looked at.
I was doing this as an exploratory data analysis and didn't want to pull out my old stats textbook.
Cutoffs were chosen to reduce volatility of measurements since I was looking at percentages. A stock going from $1.5 to $2.0 is a 33% increase whereas the movement of $100 to $133 is significantly more impactful. Stock with lower market cap have more volatility. The minimum analyst rating was chosen to eliminate analysts with very small number of ratings as they would be unreliable.
The analysis was more about measuring the performance of analysts which is why the price data for before and after the recommendation. For practical purposes of using this strategy, you are right that the price data from days after release would be better.
If the top 10 stocks you picked beat the market and have consistent earnings and dividends over a period of time, would this not be a repeatable strategy?
Most statistical analysis is about trying to distinguish meaningful results (implying a repeatable correlation of some kind that means something), from random chance with no meaning. The whole point is you _don't_ start out knowing if the thing you are investigating is random chance or not, if you did, you wouldn't need to analyze it. That's what statistical analysis is for. In part because we humans are really really good at finding patterns and assuming a meaningful correlation when in fact it's just random chance.
The coin example is useful because we all know (or define for the sake of the discussion) that it must be random chance, so any analysis that appeared to say it wasn't is probably in error. And using the same sort of analysis on something where you don't know how much of the effect is due to random chance--is not going to answer the question.
The top analysts were determined by the average performance from one year after their ratings have been made. This isn't the top analysts out of 50,000 it's the top out of 50 or so analyst-rating pairs. There were only 16 or so analysts in total that I looked at. This isn't an instance of survivor bias as your example states. If I were to be more rigorous I could give a statistical test for this.