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Code examples, market analysis, and data quality deep-dives.

Which Part of a 13F Is Worth Copying? Top Holdings Against New Buys in Python
How Much of a Ticker History Belongs to Another Company? Entity Resolution in Python
Which Corporate Line Item Turns First? Lead-Lag Analysis of S&P 500 Fundamentals in Python
Financial Data API Licensing: What You Can Redistribute
How to Pick a Benchmark for a Backtest
Are the Market’s Best Days Always Rebounds? Drawdown-State Analysis Across Sectors in Python
How Much of EPS Growth Comes From Share Buybacks? EPS Growth Decomposition in Python
How Long Does an S&P 500 Membership Last? Kaplan-Meier Survival Analysis in Python
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Does Last Quarter’s Best Sector Stay on Top? Sector Rank Persistence in Python
How Much of the S&P 500’s Margin Expansion Is Index Turnover? Shift-Share Decomposition in Python
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How Much S&P 500 Profit Skips the Income Statement? Other Comprehensive Income in Python
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Does a Bollinger Band Squeeze Predict a Big Move? Band Width Analysis in Python
Do Sector Correlations Spike When the Market Falls? Conditional Beta Analysis in Python
Python Stock Data Libraries: What Each Gives You
Does the Balance Sheet Change Which Stocks Look Cheap? P/E Against EV/EBIT in Python
Can 2x Leveraged Sector ETFs Beat Their Sector? Volatility Drag Analysis in Python
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How to Run an Event Study in Python
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How Much Historical Stock Data Do You Need?
Does Mean Reversion Survive Trading Costs? Moving-Average Deviations in Python
How Much Does Survivorship Bias Add to a Backtest? Point-in-Time S&P 500 Returns in Python
Does High Profitability Persist? Five-Year Transition Analysis in Python
Log Returns vs Simple Returns: Which to Use
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How to Get a List of All US Stock Tickers
Do High-Idiosyncratic-Volatility Stocks Underperform? Residual Volatility Sorts in Python
Does Foreign Revenue Make a Stock Dollar-Sensitive? Firm-Level FX Beta in Python
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How to Get Stock Data Into Excel With Python
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Does Revenue Concentration Explain Earnings Volatility? Segment Herfindahl Analysis in Python
Is Residual Momentum Better Than Raw Momentum? Market-Adjusted Decile Sorts in Python
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Fama-French Factor Data: Download or Build Your Own?
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Does Trading Volume Predict Tomorrow's Volatility? Out-of-Sample Test in Python
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How Many Independent Bets Are There in the S&P 500? Principal Component Analysis in Python
Can a Company's Revenue Be Forecast From Its Own History? Out-of-Sample Test in Python
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What Expected Returns Does the S&P 500 Imply? Reverse Optimization in Python
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How to Build a Stock Dataset for Machine Learning
Comparing Companies With Different Fiscal Year Ends
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How Much Debt Is Hidden in Operating Leases? Lease-Adjusted Leverage in Python
How Much Do Profits Move When Sales Move? Operating Leverage Regression in Python
How Concentrated Is the S&P 500? Index Weight Analysis in Python
How Long Is Cash Tied Up in a Business? Cash Conversion Cycle Analysis in Python
Broker API vs Data API for Historical Stock Data
Where to Get Free Cash Flow Data for Stocks in Python
Are Stock Returns Skewed? Return Skewness in Python
Do High-Margin Companies Trade at Higher Multiples? EV/Sales in Python
How Much Profit Becomes Cash? Free Cash Flow Conversion in Python
Which Sectors Lead Out of a Market Bottom? Sector Recovery Analysis in Python
Are Buybacks Funded by Cash Flow or by Debt? S&P 500 Payout Analysis in Python
What to Look for in Fundamentals Data
Does Cash on the Balance Sheet Cushion a Crash? Quintile Sorts in Python
Do Corporate Insiders Time the Market? S&P 500 Insider Buying Breadth in Python
Does Unstable Volatility Warn of Deeper Drawdowns? Vol-of-Vol Sorts in Python
Does Fast Asset Growth Predict Weak Stock Returns? Decile Sorts in Python
How to Replace yfinance in a Python Script
Does the Nasdaq-100 Index Effect Still Exist? Event Study in Python
Does the Piotroski F-Score Still Work? Quality Screening in Python
How Many S&P 500 Stocks Beat the Index? Return Breadth Analysis in Python
Why Beta Differs Between Data Sources
Where to Get Historical Dividend Data for Stocks
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Do Old Ticker Symbols Still Point to the Same Company? S&P 500 Ticker Recycling in Python
What Growth Is Priced Into the S&P 500? Reverse DCF in Python
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What Data Do You Need to Measure Portfolio Risk?
Does a 60/40 Portfolio Actually Cut Drawdowns? Stocks and Bonds in Python
Do Steady Margins Mean Calmer Stocks? Cross-Sectional Analysis in Python
Do Sectors Diversify When It Matters? Conditional Correlation in Python
How to Get Historical Market Cap Data in Python
How Much of the S&P 500 Survives 20 Years? Index Turnover Analysis in Python
Does Joining the S&P 500 Bring New Institutional Owners? 13F Event Study in Python
Is Volatility Seasonal? Calendar Month Analysis of Realized Volatility in Python
Does Fast Revenue Growth Force Companies to Borrow? Cash Funding Analysis in Python
How Far Back Does SEC EDGAR Data Go?
Are One-Time Charges Really One-Time? Charge Frequency Analysis in Python
Does Buying the Dip Work? Short-Term Reversal by Volatility Regime in Python
Alpha Vantage vs Massive vs xfinlink for Fundamentals
How Long Does a Stock Take to Recover From a 50% Fall? Drawdown Analysis in Python
Do Companies That Shrink Their Share Count Outperform? Net Buyback Yield in Python
How Much Does the Dow's Price Weighting Distort It? Index Weighting Analysis in Python
How to Get SEC Form 4 Insider Trading Data in Python
Can Anything Predict Next Month's Stock Returns? Out-of-Sample R-Squared Testing in Python
How Much of a Stock's Return Comes From Its Sector? Variance Decomposition in Python
Altman Z-Score: Where To Get It in Python
Do Value Screens Agree on Which Stocks Are Cheap? Multiple Overlap Analysis in Python
Annual vs Quarterly Financial Data: Which to Use
Do High Returns on Capital Persist? ROIC Fade Analysis in Python
Does Past Beta Predict Future Beta? Beta Stability Testing in Python
Do Defensive Sectors Actually Defend? Up and Down Capture in Python
How to Choose a Financial Data API
Do Small Caps Actually Beat Large Caps? Size Premium Test in Python
What If You Miss the Market's Best Days? Extreme-Day Analysis in Python
Does Rebalancing Add Return? Fixed-Weight vs Drift Portfolios in Python
Which S&P 500 Companies Are Closest to Default? Merton Distance-to-Default in Python
What Happens to Stocks Removed From the S&P 500? Replacement Pair Analysis in Python
Does the Golden Cross Work? 50/200 Moving Average Crossover Backtest in Python
Financial Data for Academic Finance Research
Does Skipping the Most Recent Month Improve Momentum? S&P 500 Decile Sorts in Python
Does Cointegration Survive Out of Sample? Pairs Trading Validation in Python
Which Dividends Are Not Covered by Cash? Free-Cash-Flow Coverage Screening in Python
GICS vs SIC vs NAICS: Which Industry Classification to Use
How Many Stocks Does It Take to Diversify? Random Portfolio Simulation in Python
How Many Days of Data Does a Volatility Estimate Need? Range-Based Estimators in Python
How Much of S&P 500 Cash Flow Is Stock Compensation? Cross-Sectional Analysis in Python
What Is a 13F Filing? Institutional Holdings Explained
Does Revenue Growth Explain Profit Growth? Cross-Sectional Decomposition in Python
How Much of the Nasdaq 100 Is Already in the S&P 500? Index Overlap Analysis in Python
How Often Does a 99% Value-at-Risk Limit Actually Break? VaR Backtesting in Python
Real-Time vs End-of-Day Market Data: Which Do You Need?
How Concentrated Are S&P 500 Earnings? Point-in-Time Index Analysis in Python
Does Volatility Scale With the Square Root of Time? Variance Ratio Test in Python
Does Goodwill Distort the Price-to-Book Screen? Goodwill-Adjusted Valuation in Python
How Are Shares Outstanding Reported (and Why They Disagree)
Do Low-Volatility Stocks Deliver Better Risk-Adjusted Returns? S&P 500 Quintile Sorts in Python
Does Trend Following Beat Buy and Hold? Time-Series Momentum in Python
Has the Stock-Bond Correlation Flipped? 60/40 Portfolio Risk in Python
What API to Use for a Stock Screener
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Does Covariance Shrinkage Beat the Sample Covariance? Minimum-Variance Portfolios in Python
Do Faster Inventory Turns Mean Thinner Margins? Gross Margin Return on Inventory in Python
SEC EDGAR API vs Fundamentals API: Which to Use
Does Fast Earnings Growth Persist? Rank Correlation Analysis in Python
Split Adjustment Explained: Adjusted Close vs Close
Which Trading Day of the Month Pays Best? Turn-of-the-Month Analysis in Python
Can You Use Yahoo Finance Data Commercially?
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How Concentrated Are Institutional Equity Portfolios? Form 13F Concentration Analysis in Python
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When Do Corporate Insiders Actually Trade? Form 4 Timing Analysis in Python
Data Requirements for Backtesting a Trading Strategy
What Is Survivorship Bias in Backtesting?
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Do Insider Buying Clusters Predict Returns? Signal Testing in Python
Does the S&P 500 Index Effect Still Exist? Event Study in Python
Are Companies Leaving the S&P 500 Faster Than They Used To? Index Survival Analysis in Python
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What Growth Rate Is the Market Pricing In? Reverse DCF in Python
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Does Ticker Recycling Corrupt a Mean-Reversion Backtest? Entity-Resolved Z-Scores in Python
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Why Do Leveraged ETFs Decay? Measuring Volatility Drag in Python
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Which Commodity ETFs Have the Worst Tail Risk? Expected Shortfall in Python
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← All articles

How Long Does a Stock Take to Recover From a 50% Fall? Drawdown Analysis in Python

What’s the question?

A drawdown is the fall from a running high to a later low, and its recovery is the month the price first closes back above that old high. Index history makes both look survivable: the S&P 500 has recovered every fall it ever suffered, and the worst took close to seven years.

Individual stocks are a different problem, for a mechanical reason. An index is a portfolio with a maintenance rule, so companies that fail get removed and replaced; it recovers partly because its failures are deleted from it, while the shareholder who owned the failure keeps the loss. Two numbers decide what a 50% fall in one company costs: the wait when the price does come back, and how often it never comes back.

The approach

A study built on today’s index members would measure the recovery record of companies selected for having recovered.

  1. Take the S&P 500 roster as it stood at each year end from 1995 to 2025, plus July 2026. The union is 1,133 companies, keyed on a persistent company identifier rather than a ticker string, so a symbol reused by a later listing cannot contaminate an earlier one.
  2. Pull monthly split-adjusted closes from January 1996 to July 2026. A raw close steps across every split and would manufacture falls that never happened.
  3. Track each company from the month it joined the index, and stop its series where trading stops. A company delisted in 2009 has a series that ends in 2009.
  4. Cut every series into episodes: from a running high, to the lowest point before that high is regained, to the month the price closes back above it. Falls shallower than 20% are ignored.
  5. Handle censoring. A stock that hit its low in 2024 cannot be watched for five years, so it drops from the five-year figure rather than counting as a failure, while a company that stopped trading below its old high counts as a failure at every horizon.

Series without a clean single-symbol monthly record for the window drop out, leaving 871 companies, 524 of them still trading in July 2026. Prices are adjusted for splits and not for spin-offs, so a company that handed a large division to its own shareholders registers a fall its holders did not suffer, which makes the figures conservative.

Code

import numpy as np
import pandas as pd
import xfinlink as xfl

xfl.set_api_key("YOUR_API_KEY")  # free at https://xfinlink.com/signup

roster = pd.concat([xfl.index("sp500", as_of=d)[["entity_id", "added_date"]]
                    for d in [f"{y}-12-31" for y in range(1995, 2026)] + ["2026-07-31"]])
joined = pd.to_datetime(roster.groupby("entity_id")["added_date"].min()).dt.to_period("M")
ids = sorted(joined.index)

frames = []
for i in range(0, len(ids), 40):
    frames.append(xfl.prices(entity_id=ids[i:i + 40], start="1996-01-01",
                             end="2026-07-31", interval="1mo",
                             fields=["adj_close"], max_rows=500000))
px = pd.concat(frames, ignore_index=True)
px["m"] = pd.to_datetime(px["date"]).dt.to_period("M")
px = px[px["m"] >= px["entity_id"].map(joined)].sort_values(["entity_id", "m"])

def episodes(values):
    out, peak, pi, low, li, live = [], values[0], 0, values[0], 0, False
    for i in range(1, len(values)):
        x = values[i]
        if x >= peak:
            if live:
                out.append((pi, li, i, 1 - low / peak))
                live = False
            peak, pi, low, li = x, i, x, i
        elif not live:
            live, low, li = True, x, i
        elif x < low:
            low, li = x, i
    if live:
        out.append((pi, li, None, 1 - low / peak))
    return out

def share_back(frame, h):
    hit = frame["recovered"] & (frame["months"] <= h)
    countable = hit | ~frame["trading"] | (frame["watched"] >= h)
    return hit.sum() / countable.sum() * 100

Full script with formatting and visualisation: how-long-do-stock-drawdowns-take-to-recover-python.py

Output

Recovery curves showing the share of falls back above the old high against months since the low, one line per depth bucket, with a stacked bar panel splitting each bucket into recoveries within one, two and five years and those still down after five
point-in-time S&P 500 rosters, 32 dates 1995-2026: 1133 distinct companies
monthly split-adjusted closes 1996-01 to 2026-07: 271,842 bars on 1047 companies
each series starts the month the company joined the index: 198,464 bars
52 companies truncated at a break in trading
set aside: 41 where a symbol reappears after another, 36 with a price step above 50% at a symbol change,
           39 with a single month beyond +200% or -90%, 117 with under 36 months
sample: 871 companies, 174,859 monthly bars, 524 still trading at 2026-07

falls of 20% or deeper from a running high: 2,611 episodes on 868 companies

fall      episodes   within 1yr    within 2yr    within 5yr   median  never
20-30%         872   82.5% ( 815)   96.3% ( 812)   98.6% ( 812)       7   8.0%
30-50%         826   44.1% ( 765)   76.5% ( 754)   94.1% ( 731)      13  14.9%
50-70%         449   10.2% ( 400)   32.2% ( 385)   71.6% ( 370)      34  29.6%
70%+           464    0.7% ( 424)    4.4% ( 407)   23.4% ( 398)      61  59.9%
counts in brackets are the episodes countable at that horizon; median months is measured from the low, over recoveries only

where every episode stands at 2026-07
fall      episodes   back above the old high   below it, still trading   series ends first
20-30%         872         802 (92.0%)                60 ( 6.9%)             10 ( 1.1%)
30-50%         826         703 (85.1%)                95 (11.5%)             28 ( 3.4%)
50-70%         449         316 (70.4%)                83 (18.5%)             50 (11.1%)
70%+           464         186 (40.1%)               121 (26.1%)            157 (33.8%)

falls of 50% or more: 913 episodes on 653 companies
  back above the old high within  1 year :   5.3%  (44/824)
  back above the old high within  2 years:  17.9%  (142/792)
  back above the old high within  5 years:  46.6%  (358/768)
  back above the old high within 10 years:  62.4%  (458/734)
  median months from the low, over recoveries only: 47
  never got back: 411 of 913 (45.0%)

longest waits from the low back to the old high
              company  sym    peak     low    back fall % months
          CORNING INC  GLW 2000-08 2002-07 2026-02   98.5  283.0
    CISCO SYSTEMS INC CSCO 2000-03 2002-09 2026-01   86.4  280.0
           NETAPP INC NTAP 2000-09 2001-09 2024-06   94.7  273.0
         QUALCOMM INC QCOM 1999-12 2002-07 2019-12   84.4  209.0
           INTEL CORP INTC 2000-08 2009-02 2026-04   83.0  206.0
 BANK OF AMERICA CORP  BAC 2006-10 2009-02 2025-12   92.7  202.0
TENET HEALTHCARE CORP  THC 2002-05 2009-01 2025-09   97.8  200.0
           CIENA CORP CIEN 2001-11 2009-02 2025-09   95.7  199.0

deepest falls on companies whose price series ends before recovery
                      company  sym    peak     low fall % last month
                RITE AID CORP  RAD 1998-12 2023-09  100.0    2023-10
    FLEETWOOD ENTERPRISES INC  FLE 1998-02 2009-01   99.9    2009-01
         CONEXANT SYSTEMS INC CNXT 2000-02 2009-02   99.9    2011-04
FRONTIER COMMUNICATIONS PRINT  FTR 2007-05 2020-04   99.9    2020-04
                 VISTEON CORP   VC 2001-07 2009-03   99.9    2009-03
          PEABODY ENERGY CORP  BTU 2008-06 2016-04   99.9    2016-04
       CHESAPEAKE ENERGY CORP  CHK 2008-06 2020-06   99.9    2020-06
            PENNEY J C CO INC  JCP 2007-03 2020-05   99.8    2020-05

What this tells us

Depth does not scale the wait, it changes the outcome. A fall of 20% to 30% is a routine interruption: 82.5% are over within a year, the median takes 7 months from the low, and 8.0% have not recovered. A fall of 50% or more sits elsewhere entirely, with 5.3% back inside a year, 46.6% inside five, and 45.0% of those 913 episodes never getting back.

The break comes between the 30-50% bucket and the 50-70% bucket. One-year recovery drops from 44.1% to 10.2% and the median wait from the low goes from 13 months to 34. Halving is not twice as bad as a quarter fall; it is a different kind of event, because a price that has halved usually reflects a change in what the business is worth rather than in what the market will pay for it.

Below 70% the arithmetic turns hostile, since regaining the old high then requires a 233% gain. Of the 464 falls that deep, 157 belong to companies whose price series ends first, the polite description of Rite Aid, Chesapeake Energy and the rest of that table; another 121 are still under water and trading. The 61-month median here covers only the 40.1% that made it back, so it understates the wait facing a holder at the low.

Recoveries that do arrive can take decades. Corning regained its August 2000 high in February 2026, 283 months after the 2002 low; Cisco needed 280 months and Bank of America 202, each of them large and continuously listed for the whole wait.

So what?

Size single positions against the tail rather than the average. A position that halves has a 45% historical chance of never returning to its old high, and a median wait near four years if it does. Depth is the strongest cheap signal about what follows, and it argues for cutting deep losers instead of averaging into them.

Index recovery statistics do not transfer to single names. The S&P 500 regained its August 2000 high in May 2007, 81 months later, because it dropped the companies that did not; Corning, Cisco and Intel, index members throughout, needed between 17 and 24 years from their lows.

For a tail-risk model, the split between the two failure modes matters more than the headline rate. Among 70%-plus falls, 33.8% ended with the series ending, a default-like outcome that belongs in a credit-shaped model, and 26.1% are still open, which is a live position with option value. Running this on a specific universe takes one roster pull and one price pull, and returns depth-conditional recovery odds for the book actually held.

Built with xfinlink — free financial data API for Python. pip install -U xfinlink

Built with xfinlink — free financial data API for Python. pip install -U xfinlink
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