Implied Equity Risk Premium Estimate Using Finbox

We can estimate the implied equity risk premium used for estimating the cost of equity in corporate finance and valuation using the Finbox API service.

Equity Risk Premium

Simply put, the equity risk premium is the price of risk in equity markets. It captures the premium investors demand to invest in equity over a risk-free safe haven investment such as treasury bills. It can also be understood as the expected return on equity compared to the expected return on risk-free assets. Estimating the premium plays an important role in estimating the cost of equity and cost of capital in corporate finance and valuation.

Most equity risk premium estimates are backwards looking historical risk premium estimates based on the historical performance of stocks. As Damodaran points out in his lengthy paper on equity risk premiums (Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2019 Edition), historical risk premiums carry an inherent bias towards the user’s preference of time window, type of average, and the chosen risk-free rate. Even for the most data-rich estimates, the standard error is still significant and thus the statistical value low.

Comparison of historical equity risk premium estimates (Mr. Aswath Damodaran)
Comparison of historical equity risk premium estimates (Mr. Aswath Damodaran)

The implied equity risk premium is a forward-looking method of estimating equity risk premiums. The idea is as follows:

“If you know the price paid for an asset and have estimates of the expected cash flows on the asset, you can estimate the IRR of these cash flows. If you paid the price, this is what you have priced the asset to earn (as an expected return). If you assume that stocks are correctly priced in the aggregate and you can estimate the expected cashflows from buying stocks, you can estimate the expected rate of return on stocks by finding that discount rate that makes the present value equal to the price paid.”

Mr. Aswath Damodaran
Example of an implied equity risk premium calculation by Mr. Damodaran
Example of an implied equity risk premium calculation by Mr. Damodaran

Every month Damodaran updates the Implied ERP on his NYU Stern website. I urge you to read through the many teaching materials available on his website to get a better understanding of the mechanics of the implied equity risk premium.

Finbox Inc

Finbox Inc ( ) is a Chicago-based online toolbox for investment and financial professionals that covers over 95,000 companies worldwide through their partnership with Standard & Poor’s Market Intelligence. I found this platform late 2018 and signed up for a membership in June 2019. I personally use their powerful stock screener to identify potential investment targets and pull relevant financial data into excel spreadsheets using their Excel spreadsheet add-on. Here you can find a complete list of supported API metrics.

For the purpose of estimating the implied equity risk premium, I use Finbox’ API services primarily to pull in data of the companies included in the S&P 500.

Data Gathering

To estimate the implied equity risk premium of a mature market, we need the following data inputs:

  • Mature market index: S&P 500 (Yahoo Finance)
  • Long-term risk-free rate: 10Y US bond yield (Wikipedia)
  • Company information (Finbox API)
    • Current market capitalization (marketcap)
    • Dividends paid LTM (total_div_paid_cf)
    • Stock buybacks LTM (common_rep)
    • Stock issues LTM (common_issues)
    • Net income LTM (ni)
    • Book value of equity FY (total_equity)
S&P500 company data provided by API service
S&P500 company data provided by API service

To estimate the current premium, we want to use the most up to date information available. That’s why for most metrics we use the last twelve months data points.

Dividend and Buyback Computation

After gathering the raw data, we normalize by weighing the current S&P 500 index against the S&P 500 total market capitalization.

From raw data to index unit adjusted data points

At the moment of writing, March 22, 2020, the index units adjusted data points are:

  • Earnings: $142.92
  • Dividends: $57.44
  • Buybacks: $81.50
  • Issuances: $138.94
  • Cash to Equity: $138.95 (dividends + buybacks)
  • Net Cash to Equity: $129.52 (dividends + buybacks – issuances)

Estimating Implied Equity Risk Premium

To estimate the intrinsic value of the S&P500 index, we only need a couple of inputs:

  • Current level of the index
  • Expected earnings growth for the next 5 years (top-down analyst forecast provided by Finbox)
  • Expected earnings growth in terminal year (equal to the long-term risk-free rate)
  • Expected returns to equity for the next 5 years (net cash to equity)
  • Expected returns to equity in terminal year (sustainable payout ratio)
  • Discount rate (implied equity risk premium)

The current level of the index is a single data point anyone can find in a matter of seconds.

The expected growth in earnings is a bit more difficult as we’re trying to predict future cash flows. In Damodaran’s worksheets you’ll find several options to determine your choice of growth rate though he seems to prefer a top-down analyst forecast of the S&P 500 index level. Following this idea, I rely on Finbox’ Net Income Forecast CAGR 5Y (ni_proj_cagr_5y) as main growth input.

Net income growth forecast by finbox as foundation for expected growth
Net income growth forecast by Finbox API

The forecast data is sourced from Standard & Poor’s aggregate of forecasts by various brokers and equity research institutions. Finbox’ Data Explorer allows you to check how many analysts contribute to the forecast of a specific stock. For example, Apple’s net income forecast is based on 73 estimates. While not all companies are covered equally (or accurately for that matter), the sample size of total estimates for all S&P 500 companies should give us a solid base as growth input.

As terminal growth rate we choose the risk-free rate (US 10Y gov’t bond yield).

To estimate expected returns, I slightly diverge from Damodaran’s preferred choice and opt to include dividends, stock buybacks as well as stock issuances (Net Cash to Equity). I feel it’s a more fair representation of return to all shareholders (existing and new). To estimate the returns between year 1 and year 5, I draw upon what Damodaran calls the sustainable payout level. The sustainable payout is computed using the stable growth rate and the trailing 12-month ROE and equal to 1 – g/ ROE. The payout ratio is adjusted over the next 5 years in linear increments to this value.

Once all this is set up, we’re ready to solve for the equity risk premium using Excel’s built-in Goal Seek function.

Example of estimating the implied equity risk premium using excel goal seek function
Example of estimating the implied equity risk premium using excel goal seek function

On March 22, 2020, my estimate for the implied equity risk premium is 7.18%. For your reference, you can compare this number with the implied equity risk premium published by Damodaran on his website at any time (5.77% for March 2020).

Conclusive Thoughts and Spreadsheets

I don’t think there’s a fundamental difference between the method for estimating the implied equity risk premium outlined in this blog post and the method used by Damodaran. However, the API service provided by Finbox facilitates gathering relevant data. Also, it offers a larger sample size of analysts for the top-down S&P 500 earnings growth forecast. Lastly, with the latest updated company and analyst information we can calculate the implied equity risk premium on a daily basis.

The value of this “instant” data point remains to be seen. Does it really matter to have the absolute latest information? Is having today’s estimate more useful than relying on Damodaran’s monthly ERP update? I’m not entirely sure so I’ll leave that up to you to decide. In case you’re also a Finbox user, I will link my spreadsheets below, so you open them and play around with them yourself.

To end this blog post, I would like to express my gratitude to Mr. Aswath Damodaran for his generous attitude towards teaching and sharing information. I don’t have the opportunity to join his classes at NYU Stern, but I do have access to his study materials and classroom via the online videos and webcasts. I highly recommend you to check those out if you want to learn more about corporate finance and valuation. While you’re at it, also check out his YouTube channel and blog for more content and insights.