Institutional Hedging End-of-Month Daily Flows Predict Near-term Returns
Examining institutional activity toward the end of the month sheds light on a persistent approach by institutional traders. Execution acting on behalf of Institutional Investors appears to depress the prices for end-of-day execution until about 15:30 PM ET, new research from AbleMarkets shows. Specifically, following the end-of-month average of the daily institutional activity measured by AbleMarkets at 15:30 and prior in the day (buy if the institutions are buying and sell if the institutions are selling then), produces a strategy with a consistently negative average Sharpe ratio in stocks, ETFs, currencies and beyond. In contrast, end-of-month institutional activity from 15:30 PM to 4 PM ET delivers strong directional predictability in following the institutional trends. The analysis suggests that institutions and their execution managers trade to hedge and move the prices ahead of actual trading at the end of the day. This allows institutions to capture favorable prices while implementing their investment reallocation objectives at the end of each month.
What kind of execution appear to help institutions depress the price? A whole range is possible. One thing appears certain, however, is that the “support” of the institutional trading at the end of each month appears to reverse during the high-volume end-of-day trading, potentially neutralizing previously exposed market influencing positions. Further fine-tuning is possible with detailed intraday analysis.
Table 1 shows performance of FAANG stocks (FB, AAPL, AMZN, NFLX and GOOGL), as well as SPY and EUR/USD. Other U.S. equity names with similar highly-persistent patterns of end-of-month behavior include: VZ, CAT, CVX, DIS, HPQ, HD, UTX, INTC, C, HON, ABT, AFL, CHTR, CHE, CBB, APC, EXPR, GGG, MTX, BAX, MTH, BMS, IDA, BSX, INT, CPB, CTL, ALE, MLI, CL, OXM, PVH, DHR, PKI, SXI, THO, FDX, M, GD, INGN, GWW, NCLH, KNX, SON, IPG, ATNI, K, KMB, KSS, LLY, LB, CASY, MAT, MDT, MU, EGHT, NWL, COHU, NBL, NSC, NOC, ASNA, DGII, OKE, FICO, FUL, HVT, BBT, VVI, ICUI, TIF, ITRI, UNP, and many more.
The results immediately suggest an end-of-month hedging strategy following the AbleMarkets Institutional Activity Index:
- For the 25th and higher trading days of each month, observe Institutional participation at 15:30 PM ET.
- Compute average Institutional Buyer Activity and average Institutional Seller Activity from 8:30 AM ET to 15:30 PM ET from AbleMarkets Institutional Activity estimates.
- If the average Institutional Buyer Activity exceeds that of the average Institutional Seller Activity for the given financial instrument, sell a quantity of the instrument between 15:30 and 16:00 and purchase it back on the following trading day before 15:30 PM.
- If the average Institutional Seller Activity exceeds that of the average Institutional Buyer Activity for the given financial instrument, buy a quantity of the instrument between 15:30 and 16:00 and purchase it back on the following trading day before 15:30 PM.
Table 1. Performance of FAANG, SPY and EUR/USD achieved by following average Institutional Activity estimated by AbleMarkets at 15:30 PM ET at the end of each month (when the day is greater or equal to 25). The performance is calculated using the closing price on the day when AbleMarkets Institutional Activity is estimated at 15:30 PM ET to the closing price on the following trading day.
* indicates 95% statistically-significant result, **: 99%, ***: 99.99% statistical significance
Performance shown in Table 1 is computed from mid-quote at 16:00 PM ET on the day the institutional activity is observed at the end of each month to 16:00 PM ET on the very next trading day, following the average Institutional Activity as estimated by AbleMarkets Institutional Activity Index at 15:30 PM ET on the first day. Estimating the average Institutional Activity at 16:00 PM ET instead of 15:30 PM ET erases all statistically-significant evidence indicating that the directional trading switches to “full-on” mode.
AbleMarkets measures Institutional Activity by tracing the footprints of electronic execution in the markets. Since institutional positions tend to be large and move the markets significantly, if announced, institutional managers prefer to break down their positions into small chunks with the explicit aim of avoiding detection by other market participants. Most institutions today deploy some kind of algorithmic trading in an attempt to avoid detection or rely on specialized exchanges and dark pools to avoid detection of their orders. While algorithms such as VWAP remain go-to standards for institutional execution, AbleMarkets research shows that they are easily detected using Big Data techniques. AbleMarkets continuously analyzes the market data in real time to pinpoint likely institutional activity in the otherwise anonymous data flow and report it to our clients.
This flow analysis is what makes it possible for AbleMarkets clients to accurately predict items like
- the end-of-day direction of the market,
- impending volatility (several days ahead), and even
- price movements at the end of the month, when institutional activity typically picks up as many institutions move in and out of their positions
- Other applications abound
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Irene Aldridge is Managing Director of AbleMarkets, a pioneer Big Data and Machine Learning Platform for Finance. She is a co-author of Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading and Flash Crashes (with Steve Krawciw, Wiley 2017) and author of High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems (2nd edition, Wiley 2013). She can be reached by email at Irene@AbleMarkets.com.