Cash-Flow Data Needs for an Implied Equity Premium Estimate
Summary
The document outlines data requirements for estimating an implied equity risk premium with a cash-flow valuation approach. The author is building a free-cash-flow stock valuation library and, following a Damodaran framework, has gathered historical S&P 500 dividends and share repurchases to estimate cash returned to equity holders. The stated motivation is to automate the collection of quarterly or annual inputs that are currently assembled from recurring spreadsheets and reports.
It asks whether APIs provide historical dividends and buybacks, index market capitalization, and earnings growth forecasts, including for international indexes. These are relevant inputs to a cash-flow-based premium estimate, but the text provides no API recommendations, validated data source, or calculation procedure. It mentions comparisons between current, historical, and subsequent implied premiums, but gives no evidence that the cited correlations establish one ERP method as more reliable. Data definitions, coverage, revisions, and index composition would need checking before using any provider’s series.
Key ideas
- A cash-flow-based implied equity premium estimate needs equity cash-flow and valuation inputs.
- The author seeks historical index dividends and buybacks, market capitalization, and earnings growth forecasts.
- The document asks about automating data collection for the S&P 500 and international indexes.
- It provides no API sources or validation of the proposed data inputs.
- Correlations between premium estimates do not by themselves establish that one method is more reliable.
Tags
Full text
# Are there any APIs to retrieve stock buybacks and dividends to calculate ERP? # Are there any APIs to retrieve stock buybacks and dividends to calculate ERP? I'm in the process of building a Python library to value stocks using a FCF model and one of the first steps is calculating an implied equity premium. I know there a few ways of doing this, but looking at previous correlations (current implied ERPs apparently have 0.763 correlation to next year's implied premium, historical ERP has -0.497) it seems like a cash-flow based valuation model on an index like the S&P 500 is the most reliable way to calculate ERP. Following Aswath Damodaran's notes and spreadsheet, I collected a list of dividends and stock buy-backs for the S&P 500 for the past 10 years in order to get the TTM and previous years cash to equity. However it's quite toilsome to populate this data because S&P release it in Excel spreadsheets and PDF documents every quarter. Here are some examples: - https://www.spglobal.com/spdji/en/documents/additional-material/sp-500-buyback.xlsx - https://www.spglobal.com/spdji/en/documents/research/research-sp-examining-share-repurchases-and-the-sp-buyback-indices.pdf This got me thinking: - Are there any APIs to retrieve historical dividends and stock buybacks per quarter or year? It seems the dividend yield is widely published but that doesn’t include buybacks (I also couldn’t find a reliable data source). I'm also wondering if there's a way to calculate this for any world index like the FTSE 100 or Nikkei. - Are there any APIs to retrieve the overall market cap of an index? I know this can be done for the S&P 500 with its index divisor and current price, but the divisor changes every quarter and I want to automate where possible. - Are there any ways to retrieve earnings growth forecasts for indexes? I’ve found some in S&P and GS press releases but I’m wondering if I can find a growth rate more consistently from a specific source. I guess I could compare 2020 earnings to 2010 and use that historical rate.
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