Estimating S&P 500 Earnings Yield from Constituents

# How are Quandl monthly S&P500 earnings estimates derived?


# How are Quandl monthly S&P500 earnings estimates derived?












Can someone explain how the monthly earnings estimates are derived for S&P500? Quandl sources multpl.com, who state:

```
Yields following March 2015 (including current yield) are estimated based on 12 month earnings through March 2015 — the latest reported by S&P.
```

Is there a release schedule for S&P500 (ttm) earnings somewhere?

I would like to be able to manually derive and match the Quandl estimates on the first of each month.

Also, how stable are the Quandl estimates? That is to say, if I use some historical record, but try to replicate each estimate on the first of each month, are they often revised such that the 1st of the month estimates would not reflect the historical values?

## Answer by David Addison (score 1)

https://quant.stackexchange.com/a/33870

Just wanted to provide a little color to @assylias' answer, which is a correct one.

The S&P 500 index is constructed according to an adjusted float weighted methodology, in which a change in the index level is defined by a Laspeyres index:

$\frac{I + \Delta I}{I} = \frac{\sum_i P_{i,t+1}*Q_{i,t}}{\sum P_{i,t}*Q_{i,t}} \,; \forall i \in I$

where: $I$ is the index level; $P_i$ is the price of asset $i$; and, $Q_i$ is the float adjusted share count of asset $i$.

Please reference this following S&P document for a more robust definition: http://us.spindices.com/documents/methodologies/methodology-index-math.pdf

Therefore, the S&P earnings yield can be restated as follows:

$\frac{E}{I}= \frac{\sum_i e_{i,t}*Q_{i,t}}{\sum P_{i,t}*Q_{i,t}} \,; \forall i \in I$

Where now, $e_{i,t}$ is the earnings per share of constituent, $i$.

Also, the simple aggregation of Earnings over Market Cap will provide the yield of a cap-weighted index... it serves as a very good approximation of the modified float-weighted Laspeyres index. The sum-product of corporate earnings by the ratio of float to market cap (i.e., index weightings) would be an equivalent workthrough:

$E = \sum_i ( E_{i,t}*\frac{Q_{i,t} P_{i,t}}{I}) \,; \forall i \in I$

I cannot speak exactly to Multpl's approach, but my estimates using the aforementioned methodology converge with Multpl's using S&P Capital IQ data. As an aside, I've never been able to perfectly replicate the S&P 500 total return index over time, but my average absolute daily tracking error using the above methodology is less $1 *10^{-6}\%$. I presume there are some adjustments missing or lags in my dataset. It stands to reason that results will very somewhat depending on selection of which float and which earnings to use. For example, expect different results if using GAAP earnings (i.e., net income) versus net income before non-controlling interests and extra-ordinary items. Furthermore, the calculation of float can be very nuanced.

## Answer by assylias (score 0)

https://quant.stackexchange.com/a/25142

The S&P500 is an index so it does not have earnings per se. What you can do is find the earning yield of each constituent of the index and calculate the weighted average yield of the constituents (using their weight in the index). That is one way of estimating the earning yield of the index.

If the index is market cap weighted (not sure if it's strictly the case for the S&P 500) you can also sum the earnings of all the companies in the index and divide that sum by the total market cap of those companies.