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
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.
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.
So, until recently I was employee #1 at TipRanks who was founded to answer the question "Are Analysts bullshitting us?" - 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 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.
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
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.
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.
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
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.
https://www.tipranks.com/ <- You can see large data sets analyzing the same thing at work here.
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...
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.
"We can beat the market"... Those are bold words.
The secret to making a lot of money in stocks is simple: don't buy the stock that go down.
buy bitcoins! /s
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).