How CloudQuant Researchers Derived Signals out of DTCC Data Services’ Kinetics Datasets

CloudQuant Researchers have recently developed investor flow signals and research insights by studying DTCC Equity Kinetics and Investor Kinetics Datasets. Our Data Research Team utilized a combination of backtesting and artificial intelligence to derive these signals and insights from the data. Kevin Zhou, Senior Quantitative Researcher, provides background on the techniques and strategies he employed when generating the signals.

Markets are moved by large institutional players; when they decide to do something, there is a big price impact. DTCC Data Services’ Kinetics suite does a great job of capturing these movements based on data sourced from their clearing and trade matching infrastructures.

Equity Kinetics Study

Liquidity and market impact have always been the key factors in asset price performance.

DTCC’s Equity Kinetics provides insights by looking at clearing data, to potentially provide a deeper understanding of happenings behind the veil of a complex disaggregated US equity market and activity that may have been initiated by institutional investors. The features being studied are the anonymous aggregation of clearing activity grouped by different broker groups and trade sides. This data is the primary source of trade clearing activity and is sourced directly from DTCC’s National Securities Clearing Corporation subsidiary.

Our researchers utilize the features that include volume (active equity), broker count (how many brokers have been active in an equity), and concentration (summation of a broker’s market share squared), to try to identify and build models around the correlation of these values first.

By looking into the distribution of broker counts for stocks of different market cap levels, we found that there are several fundamental differences in the broker-volume structures between the large-cap universe and the small-cap universe. [include scatter graph described] The tail of the distribution of the concentration score in the large-cap universe may very well be the signal that could be implying a temporary surge in volume that is worth looking into. This also motivates us to examine the concentration score under different scopes of stock liquidity and market cap levels. Several models were constructed and strategies back-tested. Further research results are shown in our whitepaper for DTCC Equity Kinetics.


Investor Kinetics Study

Large institutional investors are a large part of the equity market alongside the top players. It’s always a good idea to try to understand what they’re doing and most importantly when and where they are placing their bets. DTCC Investor Kinetics sheds light on the distinctive flow patterns that emerge leveraging DTCC’s Institutional Trade Processing matching data. Review of the aggregation of anonymous flow dataserves as a timely and reliable source to identify large institutional flows and their direction so that investors may be better prepared when they decide to enter the market or get ready to exit. The benefit of flow data is clear and it provides insight into short-term market momentum.  It may also be a great catalyst risk monitoring tool by highlighting large institutional position movements. All that is left to answer is how to dig into this gold mine and how well has it performed. These answers can be found in the evaluation of DTCC Investor Kinetics Whitepaper by CloudQuant. Download the whitepapers by clicking the links below!


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