By Irene Aldridge
Many ETF and active portfolio managers need accurate end-of-day price direction forecasts to optimally execute their daily portfolio reallocation requirements. Predicting the end-of-day direction of the price is crucial to solidify performance, as the last half-hour is known to be marked by extreme volatility. The end-of-day volatility, if met on the wrong side of the price movement, may reduce the performance gains of even the most seasoned and talented portfolio managers to rubble.
The main challenge with predicting market direction at the close of the day is to estimate the objectives and motivation of market participants. To simplify, most market participants in the U.S. equity markets can be classified into one of the following three groups:
- Institutional investors (large, sophisticated portfolio managers with long-term or medium-term investment horizon)
- High-Frequency Traders (sophisticated, but very near-term traders, formerly known as prop traders)
- Retail traders (traders that do not fit into the two categories above).
Some companies estimate institutional flows, but only on a much-delayed T+3 basis. AbleMarkets is the first firm to develop and make available near-real-time estimation of the participation of the Institutional and Aggressive High-Frequency Traders in the markets. Armed with the AbleMarkets Institutional Activity Index (IAI) and Aggressive HFT Index (AHFT), investors can monitor breakdown of activity throughout the day. Most interestingly, both AHFT Index and IAI Index provide a worthwhile estimate of the price direction at the formal end of the North American trading day, 3:30 PM ET to 4 PM ET.
For the AbleMarkets Aggressive HFT Index, computed as a percentage of buyer-initiated and, separately, seller-initiated AHFT-driven trading volume throughout the day, the difference between buyer-AHFT and seller-AHFT points to the direction of the mid-quote in the last 30 minutes of the trading day. So does the AbleMarkets Institutional Activity Index. Either or the two Indexes taken together:
- generate 2-5% per annum just in the last 30 minutes if trading
- result in no overnight positions and no associated risk
- tie up minimum capital for a short time period
- can be used as a “portable alpha” by combining with other strategies
- are essential for end-of-day execution
- work well even in retail-preferred stocks, such as Facebook, Apple, Amazon, Netflix and Google (the so-called FAANG stocks).
Aggressive High-Frequency Trading (HFT) is a subclass of high-frequency strategies that feeds on rapidly-fleeting informational advantage (“rapidly-decaying alpha”) and volatility. As described in High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems by Aldridge (2nd edition, 2013, Wiley), unlike their passive cousins, aggressive HFTs scour the news and pounce on tradeable information, often much ahead of the broader market. As a result, not only do Aggressive HFT make the markets more efficient by impounding all available information as soon as possible, but their activity is also highly predictive of the short-term price direction. AbleMarkets estimates Aggressive HFT by tracing the AHFT footprint in the markets obtained using sophisticated Big Data techniques.
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 execution 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
To start benefiting from the AbleMarkets Institutional and Aggressive HFT Indexes today, please sign up here: http://www.AbleMarkets.com/platform/pricing
AbleMarkets products are presently available for U.S. equities, major European indexes, Top 40 currencies, major commodity futures and more.
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.