Demand for high-quality, granular data is soaring; access to such data provides investment managers with added confidence to navigate the increasingly complex liquidity challenges that arise during "normal" periods and crises.
The latest reports from CloudQuant, an alternative data company serving global financial services and B2B clients, explore investment concentration and fund flow signals derived from DTCC Investor Kinetics and Equity Kinetics data.
CloudQuant Identifies Significant Momentum Signals in Equity Kinetics Data
- Methodology: The report evaluates the Equity Kinetics dataset over a training period. On each day during the training period, US equities were categorized into unique groups based on broker count, market capital, and dollar volume traded. CloudQuant then engineered features for each group resulting in predictions of over/under performance for different market cap stock universes. The portfolios were back tested, based on trading the extreme quantiles long (highest) and short (lowest), for the test period. The datasets’ potential ability to generate short-term (daily to 5 days) alpha was tested on the CloudQuant Artificial Intelligence (CQAI) high frequency back testing platform.
- Results: CloudQuant found that investment fund flow signals, based on Equity Kinetics, applied to a large-cap dollar neutral equal-weighted portfolio, outperformed the S&P 500 Index by an average of 21.0% per annum over ten years and averaged 35.68% per annum.
- Bottom Line: The report indicates that, fundamental and systematic investors’ can enhance their existing strategies’ performance by incorporating Equity Kinetics data.
CloudQuant Measures Informed Investor Fund Flow Signals with Investor Kinetics
- Methodology: The Investor Kinetics data was evaluated using a training and test set period. CloudQuant characterized US stock equities into unique groups based on market participant count, market capital, and dollar volume traded. CloudQuant applied various normalization forms to the signals to create predictions of over/underperformance for the different market cap-signal combination universes. CloudQuant then computed the performance of the signals during the training and test set periods for average holding periods of 1 to 5 days on their CQAI high-frequency back testing platform.
- Results: CloudQuant found that investment signals based on Investor Kinetics, when applied to a large-cap dollar neutral equal-weighted portfolio, returned 122.30% over nearly ten years with a Sortino ratio of 2.51.
- Bottom Line: The report found that these signals can be valuable to fundamental and systematic managers aiming to enhance their returns and performance statistics.
Comprehensive data can make or break investment models, predictions, and returns in today's market. Investors should attain multiple perspectives on the global equities market to help inform investment strategies, refine market positions, and outperform market benchmarks. Investment strategies are amplified through timely insights, tested hypotheses, and models built to extract signals.
Contact us to learn how DTCC Investor Kinetics and Equity Kinetics data can help your firm navigate today’s market complexity and illuminate tomorrow’s opportunities.