The Libra Apollo DataFeed provides the outputs from an asset pricing model that, with its associated forecast and signal generation models, seeks to provide the end investor with a comprehensive, stock level data set that not only provides a series of systematic expected return forecasts but a range of valuation, volatility, growth and momentum measures alongside it.
The Libra Apollo DataFeed provides the outputs from an asset pricing model that, with its associated forecast and signal generation models, seeks to provide the end investor with a comprehensive, stock level data set that not only provides a series of systematic expected return forecasts but a range of valuation, volatility, growth and momentum measures alongside it. The Apollo data set provides daily updated information derived from the Libra Investment Services global equity model, Apollo.
The product information and content statistics contained in this document are as of August 2019.
Libra Apollo covers approximately 7000 equities globally with daily historical data going back to 2004. The DataFeed consists of over 25 unique factors, the majority of which are provided as a daily timeseries.
The Apollo global equity model is based on the principle that the price of a stock is the sum of future cash flows discounted by a (risk-adjusted) discount rate. The price moves due to investors updating their expectations of future cash flows and/or adjusting an assumed discount rate being applied to those cash flows. As neither future cash flows nor discount rate are directly observable for a company, Apollo aims to create a proxy value for each variable as a daily time series.
To derive the expected cash flows and discount rates, Apollo leverages FactSet Estimates data to generate a multi-factor measure of future cash flows and then uses an implied cost of capital, present value-based approach in order to determine a measure of the discount rate being applied to the estimate of future cash-flows. These values, in addition to a set of fundamental factor weights, make up the Core Components of the Libra Apollo DataFeed.
In addition to the Core Components, Apollo provides three datatypes leveraging the Apollo model: Key variables, Apollo Forecasts, and Apollo Signals. The DataFeed contains over 25 derived indicators and signals within these four categories covering style, rating, value, volatility, growth, and potential alpha providing a proprietary stock level data resource.
Example Use Case
Apollo’s derived forecasts and signals are useful for active managers looking for unique timing indicators, risk measures, and forecasts of expected returns. The data makes for valuable input into any stock selection, risk management, or portfolio construction process. To better understand the variety of use cases, this section takes a deeper dive into a few key factors offered in the Apollo DataFeed.
Fair Value and Intrinsic Value fields provide a daily proxy measure of stock level future cash flows. To create the Apollo Fair Value, a multifactor approach is used. This generates a time series of weights which are provided in the DataFeed in the form of Fair Value drivers based upon: Earnings, EBITDA, Sales, Cash-flow and Book value. Understanding how these fundamental drivers are weighted and how those weights have changed through time provides valuable insight into what is driving a stock’s valuation currently as well as historically.
The Apollo Sentiment indicator measures the trend in short-term forward-looking expectations of both the growth outlook and the implied cost of capital. This value is normalized across companies, allowing it to be used to directly compare securities within a portfolio. Additionally, an upper and lower bound to this indicator provide a signal corresponding to upside and downside risk, respectively. The lower boundary serving as a stop-loss level for a strongly trending stock for example.
Apollo also provides expected return forecasts on a 2 frequency which additional return horizons being added in the future. These forecasted returns are delivered on a daily basis and can provide for ranked returns at an index or portfolio or for time-series historical analysis. Each return in the DataFeed has a corresponding daily period target price which then allows for derivations of benchmark and portfolio target pricing or validations of one’s own internal forecasts.
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