over-weighted:
XOM
JPM
JNJ
MSFT
AAPL
[/quote]Check out the attached screenshot. You should see a correlation between FloatPct and under/over weighing. That makes sense. FloatPct measures the percentage of shares outstanding that are publicly traded. Companies with a smaller FloatPct are smaller weightings in the index compared to their market cap and vice versa.
Here is a screen to calculate mktcap and float for S&P 500 stocks.
There are issues with accuracy. For example, GOOGL is showing a float of 602.70M shares according to P123 but only 297.20M according to morningstar.com. Some other stocks probably also have different float numbers for different data sources, which would explain why SPY weights don’t perfectly match P123’s version of float adjusted market cap.
Chipper,
Thank you for the screen.
Logic dictates that the under-weighted stocks should perform better than the over-weighted ones, which is indeed the case.
The 1-year return for the 5 under-weighted is 44.7%.
The 1-year return for the 5 over-weighted is 33.7%.
The 1-year return for SPY is 24.8%.
On prima facie, it would appear as though the index is purely a float-weighted passive vehicle. On deeper inspection, regulatory distortions may compel some limitations only on very large issuers (e.g., AAPL).
Despite the discrepancies, the SPY does not qualify as actively managed.
[quote]
It surprises me that this argument has drawn out with no one actually referencing the Prospectus.
[/quote]David,
Thanks for setting us straight. Note that I did post links to the S&P index methodology–which is practically the same thing. I left out the part about RIC diversification rules because it does not affect SPY.
All,
The attached chart is a ranking system with a single factor: “FloatPct” within the S&P 500. Notice how the five smallest floatPct stocks dramatically underperformed.
Theory: Stocks that are controlled by insiders do not have to answer to public shareholders. For example, Larry Page and Sergie Brim of Google already have more money than they will ever need. Therefore, they don’t mind blowing Google money on many projects where the goal is not necessarily to make money, but to advance the world’s technologies. On the other hand, CEOs of stocks with more outsider control need to perform in order to keep their jobs.
Evidence: The data does not conclusively prove or disprove this theory. Yes, the five stocks with the highest insider ownership did dramatically underperform, but the pattern does not continue for other stocks like it should. Furthermore, Google is not even in this group. Furthermore the outperformance of the highest FloatPct stocks relative the index seems to be similar to buckets 2-99, which owe their outperformance to the fact that the buckets are equal weighted while the index is cap weighted.
Primus, thanks for posting the SEC rules.
Nowhere does it say that a fund’s stock weights have to be proportional to the weights in the Index. So by adjusting weightings a fund manager can easily outperform the S&P 500 with dividends. If his portfolio shows a higher return than what is necessary to keep track with the index, he simply removes the excess into a side account. Should the manager underperform, then he adds funds from the side account back into the ETF.
This is what the fact-sheet says: The SPDR® S&P 500® ETF Trust seeks to provide investment results that, before expenses, correspond generally to the price and yield performance of the S&P 500® Index.
So all he has to do is track the S&P before expenses. There is no requirement to do anything else.
Sorry if I came came off harshly. Wasn’t my intent… just found it silly to be arguing about something that’s already out there in plain English.
Also, it was a good exercise for me to learn about RIC, even though – as you educated me – it does not affect the SPY (largest holding is AAPL; about 3.82% of trust).
Regarding insider and “beneficial party” ownership, it’s hard to deduce anything concrete regarding the correlation between it and future performance. On one hand, a company with high insider ownership is not beholden to the market’s imperatives for earnings and growth. On the other hand, a company which is free from often myopic and capricious investor demands is potentially able to better focus on strategic objectives (or likewise continue to wallow in judgement-free mismanagement). On balance, performance-based incentives drive performance, yet these same incentives can cause executives to lose sight of the long term.
…two sides of the same coin, thus I would not expect performance to be linked simply to the public float.
Here is an alternate theory: companies with low public floats (as a % of shares outstanding) tend to have more volatile stock prices due to the aforementioned phenomena of the freedom to either extremely fail or extremely succeed.
On a related note, information asymmetry on behalf of insiders is known to exist. Many studies have assessed the relationships between insider actions and future equity market returns.
I think the problem with volatility harvesting is purely theoretical; naysayers are probably just confusing the theory of “a random walk” with models which presume Brownian Motion (BM). Under pure BM, a thing is equally to wobble up from its mean as it is to wobble down. Volatility harvesting would be impossible under such a schema.
Confusing models with reality is nowhere more prominent than in academia. Academics who teach us that the market is efficient usually just mean to say that it is difficult to beat. They then present us with a model of market efficiency which presumes that asset prices are a Geometric Brownian Motion (i.e., lognormally distributed through a Wiener Process). After a while, we begin to conflate the concrete model with the amorphous theory.
However, there is a ton evidence that prices mean revert (stocks in the short term and commodities over the short and long terms). If this mean reversion exists, then volatility harvesting actually should be possible.
Due to the shortcomings of conventional models, I recently replaced GBM with exponential Ornstein-Uhlenbeck (O-U) processes in my commodities price models. An O-U process is simply a Brownian Motion which is subjected to friction such that a wobble in any direction encounters external resistance. There are several possible interpretations of an exponential O-U. There’s no reason that one could not also calibrate such a model on securities prices and thereby improve upon the execution of a volatility harvesting strategy.
