By Irene Aldridge
“The Trump effect” has captured news headlines. The unprecedented rise in the U.S. stock markets following the November 8, 2016, election has taken many investors by surprise. Some portfolio managers and commentators question how long it will last. Others proclaim it a bubble that has just hit a natural ceiling for stock prices. Still others call it a “suckers’ rally”, a stock rally with little fundamental information to back up the price movements. Even the legendary Carl Icahn himself proclaimed on December 10, 2016, that “The Trump rally in stocks may have gone too far” (http://www.businessinsider.com/carl-icahn-trump-rally-2016-12). Of course, the market has reached its all-time highs over and over since.
Many pose an interesting question: who is behind this rally? By inference, a suckers’ rally is driven by, well, suckers. Since institutional investing is considered to be sophisticated, the suckers in question are implicitly assumed to be retail investors, perhaps those from the backcountry, an often-portrayed anti-elite bastion of Trump voters.
An analysis of data, however, paints a different picture., a Big Data for Capital Markets Company, produces an index of Institutional activity for all stocks, commodity futures, foreign exchange and even fixed income. Institutions are typically considered to be large professional money managers, such as hedge funds, pension funds and endowments. Using Big Data techniques, AbleMarkets parses every limit order to extract institutional orders from raw exchange data. Such inferences are no easy task. Every institutional order to buy or sell a particular stock can be as big as several million dollars and creates what researchers call “a credible signal” to other market participants of the institution’s research and opinions. Even though all orders on the exchanges are anonymous, the sheer size of institutional orders screams “I believe in my opinion, and I back it up with a massive amount of money”, encouraging instantaneous followers and other nosy traders. To conceal their steps, institutions break down their orders into small bits following two common algorithms: Value-Weighted Average Price or VWAP, popular in equities, or Time-Weighted Average Price, common in other financial instruments. The resulting pieces or “slices” of orders are indistinguishable to the human eye. However, to a sophisticated Big Data algorithm, such as the one continuously processing data at AbleMarkets, the institutional orders stand out vividly out of the large pool of order data.
Institutional participation, as measured by AbleMarkets Institutional Activity Index, comes in two sets: institutional order flow as a percentage of buyer-initiated volume, and a percentage of seller-initiated volume that is driven by institutions. Measured and delivered every 30 minutes intraday, the numbers can also be aggregated over a daily, weekly or monthly averages that are very descriptive of the market activity.
So what did the institutions do immediately before and after the 2016 election? Using AbleMarkets Institutional Activity Index across the most commonly traded 3000 stocks, collectively known as the Russell 3000 index, we observe the following:
– For the one month immediately prior to the elections, institutional investors were mostly selling U.S. stocks
– Immediately after the elections, institutional investors reversed course and started buying stocks in greater numbers, supporting, if not causing, the suckers’ rally.
Figures 1 and 2 illustrate the point. The Figures show the histogram of the difference in average institutional participation between the buyer and the seller-initiated orders. If over the course of the month, average institutional participation in buyer-initiated volume exceeded that of seller-initiated volume by 3%, the stock would be added to the “3%” bucket on the x-axis.
As Figure 1 shows, for the month preceding the election, the most frequent number of buy-sell incidences (the highest vertical line) corresponded to stocks where the proportion of buyer-initiated institutional volume equal that of seller-initiated volume. Out of the entire Russell 3000 universe, there were 468 stocks where the proportion of institutional buying activity approximately matched that of institutional selling activity. In contrast, 2,265 stocks out of Russell 3000 had institutional selling activity dominating institutional buying activity prior to the election. Only 200-some stocks out of Russell 3000 index were predominantly bought by institutions within 1 month before the election date.
In the month following the election, the dynamic reversed considerably. As Figure 2 shows, in contrast to pre-election behavior, only 1,299 stocks were sold more than bought by institutions following the election. Another 1,121 stocks from the Russell 3000 were bought rather than sold by institutions from November 9, 2016, through December 8, 2016.
A more dramatic illustration of institutional activity in response to the election results is tracking the AbleMarkets Institutional Activity Index on a day-by-day basis surrounding the election. Figures 3-5 show histograms of net institutional participation (buy – sell activity) in the Russell 3000 stocks on November 7-9, 2016. The Figures vividly show the institutional investors’ change of heart with respect to the market from heavily pessimistic on November 7, just one day before the elections, to neutral on the Election Day, to furiously optimistic on November 9, immediately post-election. Call them suckers, but the institutions appeared to know what they were doing when they prepared their funds for an unprecedented rally!
Irene Aldridge is Managing Director, Head of Research at AbleMarkets, a Big Data for Capital Markets company, specializing in real-time and near-real time Software-as-a-Service improving execution, portfolio allocation and risk management. She is a co-author of “Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading and Flash Crashes” (Wiley, 2017, https://www.amazon.com/Real-Time-Risk-Investors-FinTech-High-Frequency/dp/1119318963), and an author of “High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems” (Wiley, 2nd edition, 2013, https://www.amazon.com/High-Frequency-Trading-Practical-Algorithmic-Strategies/dp/1118343506).