S&P 500 Daily Returns (datahub.io)

Rows: 1,865
Columns: 10

S&P 500 monthly price, dividend, earnings, and PE ratio going back to 1871. Long-run historical equity index time series.

AI analysis

S&P 500 Historical Data — Analyst Report

1. Dataset Overview & Research Value

This dataset, sourced from [datahub.io/core/s-and-p-500/r/data.csv](https://datahub.io/core/s-and-p-500/r/data.csv), represents one of the most comprehensive long-run equity index time series publicly available, spanning monthly observations from 1871 to the present — roughly 155 years of financial history across 1,865 rows. The publisher, DataHub.io, is a well-known open data repository that curates and maintains structured financial datasets, lending reasonable credibility to the source. The breadth of variables — nominal and inflation-adjusted prices, dividends, earnings, interest rates, CPI, and the cyclically adjusted PE ratio (PE10/CAPE) — makes this dataset exceptionally well-suited for macroeconomic correlation studies, long-run valuation research, and regime analysis. It is particularly valuable for testing hypotheses about the relationship between interest rates and equity valuations, dividend yield cycles, and inflation's impact on real returns over multi-decade horizons.

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2. Data Quality Observations

The dataset presents a remarkably clean quality profile at first inspection: zero null cells across all 10 columns and 1,865 distinct Date values (matching total row count exactly), which strongly suggests no duplicate rows exist in the date-indexed time series. This is a positive signal for time-series integrity. However, the outlier counts deserve attention — SP500 alone has 361 flagged outliers (roughly 19% of rows), and Earnings carries 283 outliers (~15%), which is disproportionately high and reflects the extreme right-skew of nominal financial data compounding over 150 years rather than true data errors. More subtly, minimum values of 0 across SP500, Dividend, Earnings, CPI, Long Interest Rate, Real Price, Real Dividend, and Real Earnings warrant scrutiny — a true zero in any of these fields for a functioning market is economically implausible and may indicate missing value substitution or sentinel encoding in early historical records, particularly pre-1900. This should be validated before any regression analysis is run on the full date range.

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3. Key Column Distributions

The SP500 column illustrates the compounding nature of equity prices dramatically: the mean of 471.16 sits far above the median of 19.83, with Q1 at just 8.04 and Q3 at 263.70 — a classic hallmark of log-normal financial price distributions with a skewness of 3.38. This means the bulk of the dataset's history predates the modern index level, and log-transforming this column will be essential before any linear correlation work. The PE10 (CAPE ratio) is arguably the most analytically interesting column: with a mean of 15.98, median of 15.76, Q1 of 11.03, and Q3 of 20.55, it is the most symmetrically distributed variable in the dataset (skew of only 0.339) and has just 47 outliers — making it the most regression-ready column out of the box. Its range of 0–44.2 captures both Depression-era lows and the dot-com bubble peak. Long Interest Rate shows a mean of 4.41% and median of 3.77%, but with 190 outliers and a skew of 1.63, it clearly captures the anomalous 1970s–80s rate spike (max 15.32%) that separates structurally from most of the series. Real Price vs. SP500 (nominal) comparisons are especially useful: the inflation-adjusted series has a lower mean (731 vs. 471 nominal is counterintuitive — reflecting deflation in early periods) and a more compressed distribution, confirming CPI normalization is working as expected.

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4. Recommended Join Key Columns

The Date column is the natural and only join key here, with 1,865 distinct values matching row count perfectly — confirming it functions as a clean primary key. For cross-dataset joins, two strategies are advisable: (a) Monthly grain joins using a standardized YYYY-MM format, since this dataset is monthly and many financial datasets share that cadence; and (b) Annual aggregations (averaging or summing monthly values to a YYYY key) when joining with annual macroeconomic series such as GDP, federal budget data, or labor statistics. Care should be taken to align calendar conventions — some series use end-of-month dates while others use first-of-month — which can cause silent mismatches if date formatting is not normalized before joining.

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5. Recommended Pairing Datasets for Correlation Discovery

Several dataset categories would pair powerfully with this data:

- U.S. GDP / Real GDP Growth (BEA or FRED) — Monthly or quarterly GDP could be interpolated to test whether earnings growth (Real Earnings column) leads or lags economic output cycles, a classic macro-finance research question. - Federal Reserve Federal Funds Rate (FRED) — Joining on Date to compare the Long Interest Rate series against the Fed Funds Rate would illuminate the transmission mechanism between monetary policy and equity valuations via the PE10 column. - U.S. Unemployment Rate (BLS/FRED) — A classic counter-cyclical indicator; correlating unemployment spikes with Real Earnings drawdowns or PE10 compression could identify recession signal patterns. - Gold Price or Commodity Index data — Inflation hedge comparisons against the Real Price and CPI columns would test portfolio diversification hypotheses across rate regimes. - Shiller Bond Return Data — Robert Shiller, who originated much of this dataset's methodology, also publishes bond return series that would allow direct equity risk premium calculations using the existing Long Interest Rate and PE10 columns.

Columns

Date (date)
SP500 (decimal)
Dividend (decimal)
Earnings (decimal)
Consumer Price Index (decimal)
Long Interest Rate (decimal)
Real Price (decimal)
Real Dividend (decimal)
Real Earnings (decimal)
PE10 (decimal)