Who Owns the Nasdaq-99 Companies?
Use Neo4j to investigate institutional holdings in the stock market
As a retail stock investor, I always keep an eye on what the “smart money” is buying or selling. “Smart money” refers to institutional investors — pension funds, mutual funds, hedge funds, banks, insurance companies, and other big investors. They are also called the whales of Wall Street or the pro athletes of the investing game. These institutions manage billions of dollars and invest them in almost all asset classes — stocks, bonds, and commodities. Before the GameStop short squeeze by r/WallStreetBets, they had very much the say in the market: they account for more than 85–90% of daily trading volume on the New York Stock Exchange and their in- and divestment can trigger a rally of a sell-off.
Institutions invest in all large, publicly-traded companies. On the one hand, because they usually conducted extensive research prior to their decisions, their investment can be seen as an endorsement of the company. Furthermore, institutions hold their core investment for the long haul. As a result, blue-chip stocks with a large amount of institutional ownership tend to be less volatile and therefore favored by many defensive retail investors. On the other hand, as shown by the GameStop short squeeze, institutions can also compress the stock prices artificially. And the case of Archegos under Bill Hwang also illustrated that the collapse of a large hedge fund could trigger seismic waves across the stock market.
For these reasons, retail investors can learn a lot about the market by watching the movement of the smart money. Major institutions with assets over $100 millions are required to file form 13F with the Securities and Exchange Commission (SEC) to disclose their stock holdings. A caveat though: the reports come with a delay. They are filed within 45 days after calendar quarter end with the vast majority of updates occurring near the 45th day of the quarter. We can comb through these reports to get a delayed sense of what the institutions were doing. This information can without doubt help us to make better-informed investment decisions.
The Nasdaq-100 index (NDX) contains the 100 largest non-financial companies traded on the Nasdaq, such as Apple, Microsoft, and Micron. Because both Fox Corporation and Alphabet Inc. have issued two stocks, the index has in fact 102 tickers. In this article, I am going to use Neo4j to show how institutional investors distribute their money to the Nasdaq-100 companies. In order to get a sense of their investment strategies, I also augmented the data with the financial details of all these companies, such as earnings, stock prices and short interests. All these data are public and can be collected from the SEC. The data for this project are hosted on Kaggle (CC BY 4.0) under:
institutional holdings and financials of the nasdaq-100 companies
1. Data import and statistics
First, create a project called
Nasdaq in Neo4j Browser. You need the APOC plugin for this project. Download the data from the link above and put them into the
import folder (see “2. Import data into Neo4j” if you need assistance). Import the data into Neo4j by issuing the following commands in Neo4j Browser:
We can run some statistics to test whether our import was successful. For example, the following query retrieves the amounts of nodes:
Instead of 102, there are 101 stocks. The reason was that I removed Zoom (ZM) because I could not find any institutional holding data for the stock. And these 101 stocks belonged to 99 companies. The query also shows that there are 5,070 institutional investors in our dataset. These institutions have established 130,071 positions across the Nasdaq-99 companies.
2. The strategy of Berkshire Hathaway
Let’s first look at the biggest whales. These institutions collectively control a large amount of capital, and their movement can cause ripples across the market. Here are the top 20 institutions in terms of their total investments in the Nasdaq-99 companies.
There are many famous names on the list. Vanguard and BlackRock are the largest. And they are the only two institutions here that managed over $1 trillion just in the Nasdaq-99 companies. Berkshire Hathaway, led by the legendary Warren Buffett, stood out. In terms of total investment in the Nasdaq-99, its $154 billion only won the sixteenth place. And it only invested in eight of the companies: Amazon, Apple, Charter, Mondelez, Sirius, T-Mobile, Kraft Heinz and VeriSign. It is known that Buffett only invested in companies that he researched and understood well. So how did Buffett invest?
We can calculate some common metrics for all the portfolios. Through comparison we may learn a thing or two about Buffett’s strategies. The following query examines the weighted averages of
earnings per share (EPS) and
price to earnings (PE) of each portfolio.
Beta measures the volatility of a stock in relation to the overall market. The higher the beta, the more volatile the stock. The
days-to-cover of a stock is the ratio of its amount of shorted shares to its average daily trading volume. The higher the ratio, the more heavily shorted the stock, and the more bearish the sentiment. To get the value of
EPS, we can simply divide the profit after tax of a company by its shares outstanding. The higher the value, the more profitable a company. Similarly, dividing the stock price by EPS would give us the
PE ratio. It shows the premium that investors are willing to pay for each dollar earned by the company. Finally, I considered that the weighted averages could reflect the different allocation strategies better than the simple averages. I therefore weighted the investments with their market values.
Again, Berkshire’s metrics stood out immediately. Although Berkshire invested in eight of the Nasdaq-99 companies (Table 2), 86% of its money was tied up in Apple (AAPL). As a result, Berkshire’s metrics were largely determined by Apple. Berkshire’s portfolio had the second highest beta of 1.18 among its peers. It means that by concentrating its investments, Berkshire took on slightly more risk than its competitors, but that could be the secret to its excellent returns. Charter Communications and Sirius were the two stocks with relatively high
days-to-cover values but Berkshire invested only modestly in them (Table 3). Overall, Berkshire’s low average
days-to-cover of 1.66 indicated that the market was quite bullish on Berkshire’s choices. Apple also dragged Berkshire’s average EPS to $6.2, the lowest among the pack — all the other institutional investors had values above $15. But
EPS alone was not the whole story: Berkshire had the lowest
PE in this analysis, which means that Berkshire had paid the least for each dollar profit earned by its companies. To put it simply, Buffett bought the stocks cheap. This echoes one of Buffett’s rules:
“It’s Far Better to Buy a Wonderful Company at a Fair Price Than a Fair Company at a Wonderful Price.”
The other 19 institutions have spread their money more broadly. For example, the first thirteen institutions in Table 1 invested in all 101 stocks, while Vanguard only missed the Dutch photolithography supplier ASML, who is also the sole supplier of extreme ultraviolet lithography (EUV) photolithography machines in the world. The rest of the pack invested in at least 55 companies. The portfolio of Capital World Investors had a high average PE because of its large stake in Tesla (TSLA).
The rest on the list all shared similar metrics. By diversifying their portfolios, these institutions also diversified risks — their average beta values were closer to 1 than those of Berkshire and Capital World Investors. It meant that their portfolios fluctuated more closely with the broader market.
3. Did institutions move the needle?
In 92 out of the 101 Nasdaq stocks, institutional investors had controlled the majorities of shares outstanding (>50%). The institutional ownership of DexCom’s (DXCM) was even as high as 98%. On the other end of the spectrum were stocks such as NetEase (NTES, 8%), IDEXX Laboratories (IDXX, 10%) and Sirius (JD, 14%). We can do a company-centric analysis to see whether the institutional movements had anything to do with the stock prices. The query below shows the relative volumes, the implied market value change among institutional investors and the year-to-date return of the stocks.
As Table 4 showed, there was little discernible pattern between the movements of institutional investors and the price actions. For example, Moderna (MRNA) had surged over 200% in the first ten months of 2021, while the institutions had only moved around about 12% of the shares outstanding in the same period. All in all, they had bought in a net total of 4%. Another example was the Chinese company Pinduoduo (PDD). Since the start of 2021, the stock only knew one direction: down. It lost over 40% of its value. But meanwhile only 5% of its stocks had changed hands among the institutions. And in fact, the smart money had bought even some more PDD (0.3%) in this period.
Figure 3 shows that the institutional volumes were not indicative of the price trends: the R-square of the YTD returns versus the absolute institutional volume percentages was only 0.03, and that of the YTD return versus the net institutional volume percentage was 0.007. Even the average daily volume was not a good predictor either.
Let’s look at one specific example, the NAND and DRAM maker Micron (MU). The price of MU had declined around 4% since the start of the year. At first glance, the institutional investors were to blame. While they were bullish on Micron at the end of 2020, 2021 witnessed an exodus of capital from MU. Institutional investors collectively dumped a net total of 2.5 million shares during this period, which amounted to $178 million at its current share price. But Micron had an average daily volume of 17 million. So the 2.5 million shares sold by the institutional investors during the last ten months amounted to fewer than one day’s worth of trading. The $178 million was also relatively small compared to Micron’s market capitalization of $77 billion.
Although we can estimate the total investments of each institutional investors, we could not calculate their gains and losses accurately because they did not file the purchase dates and prices in 13F. We could estimate their year-to-date performance if they did not make any trade since the beginning of the year though. But if they had traded frequently, our estimate would be way off.
It was a surprise to me that the institutional stock in-and-out gave us no hint about the direction the stock prices went. It is noteworthy that the data here did not contain the put and call options and short positions. So they did not reveal all the institutional maneuvers that could influence the price. On a separate note, the data reinforced the our impression that generally, institutional investors traded infrequently.
In addition, because of their delayed nature, the 13F data are not that useful for short-term investors and day traders. Those investors need real-time options data, Tweets streams and news feeds. But, as I demonstrated in the text above, 13F data could reveal the long-term strategies of some institutional investors. They therefore provide long-term investors with useful information about the stock market. General investors should make use of these data and learn to analyze them for better informed investment decisions.