ETFLogic also provides alternative ETF data and analytics which are derived from our proprietary calculations and technology. Below you will find a brief description of our main ETF data and derivative ETF analytics products. Typical use-cases range from systematic and quant-driven ETF strategies, actionable signals for tactical ETF trading to monitoring for pre-trade analysis, ETF due diligence and risk measurement.

FMD - Single Stock Flow Monitor Dataset - using daily ETF flows from creations and redemptions

Daily, single stock flow alpha signals for 3000+ US tickers due to ETF primary market activity. Incorporates creation, redemption and rebalancing activity. Use case: Systematic trading signal or individual stock flow monitoring. History: 5+ years of backfilled history. 1+ year of daily generated history.

J-FMD – Japan - Single Stock Flow Monitor Dataset

Our FMD signal also exists for the Japan market – where we produce daily signals on 1000+ Japanese single stock equities. History begins in April 2018.

S-FMD - Sector Flow Monitor Dataset

Sector scores in S-FMD are aggregated from the FMD data described above. Features are scored across 11 US sectors. Use case: Sector flow visualization and monitoring, sector rotation strategies, tactical risk management.

EFF - ETF Fundamental Financial Statement Features - based on the latest company financial statements

ETFLogic’s ETF Fundamental Features (EFF) produces a set of 23 daily features for over 500 US-Listed ETFs. The ETF universe is constrained to US equity funds. The features are backwards-looking fundamental single-stock metrics that are averaged up to the ETF using our basket weights database. The fundamental metrics are balance-sheet, income statement and cash-flow ratios such as Debt-To-Equity, Price-to-Sales and Quick Ratios. Each feature in the dataset incorporates the latest underlying fundamentals as reported to the SEC and is normalized for different accounting standards. History: 5+ years

EEE - ETF Earnings Consensus Estimates - based on latest company earnings consensus estimates

ETFLogic’s ETF Earnings Estimates (EEE) produces earnings estimate features for over 500 US-Listed ETFs. These estimates are generated daily and incorporate the latest consensus estimates which, in turn, are generated from the most recent equity analyst P/E forecasts. The ETF universe is constrained to US equity funds. The features are forwards-looking PE ratios that are averaged up to the ETF using our basket weights database.  History: 5+ years

ETF Factor Exposures and Scores - to compare exposures across US ETFs for risk measurement

Daily risk factor exposures file for all US listed ETFs, comprised of close to 100 different factors from our proprietary factor database. Use cases: ETF portfolio optimization, ranking and qualitative description of ETFs, monitoring style drift & visualization for “style box” analysis.  History: 5+ years

ETF Peer Group Taxonomy and Classification - for risk measurement and ETF comparisons

Labeling and taxonomy grouping of US listed ETFs based on their qualitative and quantitative characteristics. Use Cases: descriptive statistics, monitoring. Example: EWJ and HEWJ are both Japan-focused ETFs but would fall into different groups because one has a currency-hedged component.

ETFLogic Liquidity Metrics – advanced impact cost and implied liquidity estimates on ETFs

ETFLogic’s platform metrics, TruCost and TruLiquidity, are also available in an easy-to-parse daily file. TruCost is a 5-star ranking that calculates the lower of ETF and basket costs and makes a comparison to peer ETFs. TruLiquidity considers basket implied liquidity using market impact cost (share volume, volatility, urgency), creation fees, redemption fees, regional taxes and stamp duties. We generate daily cost runs for US listed ETFs and provide highlights on recent ETF block trades versus fair-value, primary trading volumes, etc. Use case: pre-trade, due-diligence, market monitoring.

Microstructure Tickdata Features - ETF analytics on US Trades and Quotes data for investment screening and due-diligence

Trade and quote analytics across daily, weekly, monthly time ranges. Calculation examples are average bid-ask spread, average spread at time of day (morning, midday, afternoon), top-of-book average sizes, top-of-book exchanges, exchanges at top % of time, large trade monitoring, NAV vs Close metrics. Use case: ETF screening, monitoring, pre-trade and due-diligence reports.

ETF ESG Ratings - screens funds for multiple warnings flags and ESG features

Not all ESG ETFs are created equally. We use Arabesque ESG ratings on global single stocks and percolate these ratings up to the global-equity ETFs. These ratings provide overall ESG scores and highlight individual warning flags for constituent stocks. The Arabesque methodology and ratings definitions can be found online.