NASDAQ Composite Index Daily (FRED)

Rows: 14,428
Columns: 2

Daily closing value of the NASDAQ Composite Index from 1971 to present, covering all stocks listed on the NASDAQ exchange. Long-running government-distributed financial time series.

AI analysis

NASDAQ Composite Index Daily — Dataset Analysis

1. Dataset Overview & Value for Correlation Studies This dataset captures the full historical arc of the NASDAQ Composite Index from its inception in 1971 through the present day, representing daily closing values across all stocks listed on the NASDAQ exchange. Sourced directly from the Federal Reserve Bank of St. Louis's FRED platform (https://fred.stlouisfed.org/graph/fredgraph.csv?id=NASDAQCOM), this is an authoritative, government-distributed financial time series — the FRED provenance signals both high credibility and near-real-time freshness, as FRED datasets are typically updated on a next-business-day lag. With 14,428 daily observations spanning over five decades, this dataset is exceptionally well-suited for long-horizon correlation studies, regime-change analysis, and macroeconomic signal detection. Its breadth makes it a natural anchor dataset against which a wide range of economic, sentiment, and sector-level variables can be benchmarked.

2. Data Quality Observations Overall data quality is good but warrants attention in one specific area. The NASDAQCOM column carries 485 null values (~3.4% of all rows), which accounts for all 485 total null cells across the dataset — the Date column is perfectly complete with zero nulls and 14,428 distinct values, confirming no duplicate date entries exist. The nulls in NASDAQCOM are almost certainly attributable to non-trading days (weekends, federal holidays, and market closures), which is a structurally expected and well-understood pattern for daily financial time series rather than a data integrity failure. However, analysts should be deliberate about how these gaps are handled — forward-filling, interpolation, or explicit exclusion of non-trading days are all valid strategies depending on the analytical use case. Duplicate row counts are pending final recomputation (Phase D), but the fact that Date has 14,428 distinct values matching total row count strongly implies zero date-level duplicates.

3. Key Column Distributions & Statistical Highlights The NASDAQCOM column tells a compelling statistical story that mirrors the index's real-world history. The mean of 3,267.42 sits well above the median of 1,507.58 — a gap of over 1,700 points — immediately signaling a strong right skew, confirmed by a skewness coefficient of 2.26. This divergence reflects the exponential growth phase of the index post-2010, particularly the dramatic run-up toward the maximum of 26,656.20, which pulls the mean upward significantly from the median. The minimum of 54.87 anchors to the early 1970s baseline. The quartile spread is stark: Q1 = 286.96 versus Q3 = 3,417.40, a roughly 12x range, underscoring how heavily the distribution is compressed in the early decades and stretched in recent years. The standard deviation of 4,870.91 — nearly 1.5× the mean — further reflects this extreme dispersion. The 1,670 flagged outliers are likely concentrated in the post-2017 era where values exceed historical norms by wide margins, and should be treated as genuine market data rather than data errors, though they will heavily influence any linear modeling without transformation.

4. Recommended Join Key for Cross-Dataset Correlation The Date column is the clear and unambiguous join key — it is complete (0 nulls), fully distinct (14,428 unique values), and typed as a proper Date format, making it immediately usable for time-series alignment. When joining to other datasets, analysts should account for the trading-day calendar embedded in this data: joins to daily datasets from non-financial domains (e.g., economic releases, weather, sentiment scores) will require a date-spine or asof-join strategy to handle the ~3.4% of calendar days with no NASDAQ observation. For lower-frequency datasets (monthly, quarterly), date truncation to period-level keys (e.g., year-month) will be necessary.

5. Suggested Correlation Pairings Several dataset categories would pair powerfully with this data. Macroeconomic indicators from FRED itself — such as Federal Funds Rate (FEDFUNDS), CPI (CPIAUCSL), or Unemployment Rate (UNRATE) — would enable classic finance-macro correlation studies, particularly around rate hike cycles and index drawdowns. S&P 500 or Dow Jones daily data would allow index divergence analysis, revealing periods when tech-heavy NASDAQ decoupled from broader market trends. VIX (Volatility Index) data would be a natural complement for studying fear-index relationships and volatility clustering. Earnings or revenue data for major NASDAQ-listed companies (Apple, Microsoft, Meta) could support bottom-up vs. index-level correlation work. Finally, social media sentiment or Google Trends data on financial keywords could test whether retail investor attention leads or lags index movements — a particularly rich question in the post-2020 meme-stock era.

Columns

Date (date)
NASDAQCOM (decimal)