Real problems cannot be solved with government regulation, only market solutions. After the tech bubble burst in 2000, the government was forced to attempt limiting the ability for sell side analysts to manipulate stocks by publishing Buy/Sell/Hold recommendations or price targets to the public. This was an attempt at reform, but it did not remove the major conflict behind the scandal in the first place: Analysts needed to support investment banking fees from the same companies they were producing research on.
So when the government wrote new rules around the conduct of analysts, Chinese walls between research and banking, Reg FD, etc., they knew that if the rules were actually ever enforced, it would put the equity trading side of the banks and brokerages out of business, as well as the marketing arm for investment banking and corporate access: the money makers.
You need market solutions to solve these conflicts, because market solutions don’t give a hoot about incumbent businesses.
Five years ago I started Estimize to solve a piece of this problem. Instead of relying on the biased “sell side” analysts for earnings and revenue estimates on a quarterly basis, we built a platform to collect them from the crowd (hedge funds, corporate finance professionals, independent traders, students, etc.). We now have more than 35,000 contributors to our data set, and Estimize Consensus is more accurate than institutional crowdsourcing tools, which have been verified by half a dozen published academic papers.
We solved the regulatory problem by producing a better data set, by crowdsourcing it, not by attempting to change the behavior of a group of people that had no economic incentive to do so. We’re removing the influence of sell side analysts on the market as our Estimize Consensus is replacing it in trading models and investors’ decision making. We’ve been asked to do the same for Buy/Sell/Hold recommendations. We’ve said “No” every time. Why? Because Buy/Sell/Hold is an awful system that was never meant to produce real information. It was designed as marketing so that sell side analysts could push the stocks that investment bankers were raising capital for. Or worse, stocks their institutional clients were invested in. There is zero efficacy to this ratings system. There is no persistence of accuracy amongst analysts across time, because the system itself is random and useless.
We can do better. The correct way to judge which stocks an analyst expects to outperform is to ask directly vs. its peers. We developed a new crowdsourcing system as a competition. We score each competitor at the end of the week based on how accurate their Forcerank is versus what actually happened in the market.
Investors in general are great at stock selection, but terrible at market timing, risk management and position sizing. The latter three are not intuitive things, are extremely difficult to learn and go against human nature. But investors really are good at equity selection.
If I asked you how Apple (AAPL) was going to trade this week or month, you’d probably shrug your shoulders. But if I forced you to decide whether Apple will outperform Google (GOOG), you’ll probably have a decent opinion. Why? Because I’m not asking you to predict the direction of Apple’s over a period of time, which depends on a lot of different factors; I’m asking you how it will perform vs. Google. By asking this question of 10 stocks with homogeneous characteristics from thousands of people, Forcerank is able to collect the best possible sample of what stocks investors believe will outperform the rest.
You could build a consensus rankings list of all stocks in the S&P 500 each week and look at how stocks move up or down that list. You could build an ETF that was not market cap weighted, but sentiment weighted. If the data was accurate, you could go long the top five stocks and short the bottom five stocks each week. You could take the top 20 percentile of stocks based on their consensus ranking and call those your overweight sectors, the bottom 20 percentile and call them underweight. You would remove the influence of sell side analyst ratings. Most important for investors, you would get a solid understanding of how the street views specific stocks in a certain sector.
It will take time to prove our hypothesis regarding the predictive nature and representativeness of the data. It will take time to grow the number of contests and list of stocks being ranked.