In the fast-paced world of finance and trading, raw numerical tables, no matter how comprehensive, often obscure the deeper narrative. As developers, we understand that data's true power emerges when it's transformed into an accessible, digestible format. Data visualization simplifies complexity, enabling our brains to quickly identify trends, outliers, and regime shifts that are easily missed in a sea of figures. This capability is paramount in finance, directly influencing critical decisions regarding position sizing, timing, and overall confidence in trading strategies.

This guide explores a comprehensive approach to financial storytelling using data visualization. We'll leverage Financial Modeling Prep (FMP) APIs to interpret earnings data across nearly 1,000 stocks, aiming to uncover actionable patterns in post-earnings movements. Our journey will involve building a suite of visualizations:

• Sector Heatmap: Mapping strongest 3/10-day post-earnings reactions by sector and market-cap.
• EPS Scatter Plot: Testing if earnings beats correlate with returns, colored by sector.
• Return Violins: Illustrating 3-day post-earnings volatility and skew by sector and market-cap.
• Mega-Tech Time Series: Tracking post-earnings patterns for major tech stocks over time.
• Monthly Seasonality: Revealing calendar-based edges in post-earnings returns and surprises.
• Regime Cross-Section: Assessing sector robustness across varying market conditions.

Prerequisites

To follow along, familiarity with Python and pandas for data manipulation is essential. This is a code-first guide, emphasizing the workflow and the insights derived from charts rather than a line-by-line breakdown of Python code. Ensure you have:

• Python 3.10+
• A Financial Modeling Prep (FMP) API key
• Installed libraries: pandas, numpy, matplotlib, seaborn, scipy
• Sufficient compute resources and patience for API calls across a large stock universe.

Data Extraction and Preparation

The foundation of our visualization exercise is robust data collection. We begin by populating our stock universe, then gathering earnings reports and historical price data.

First, we retrieve NASDAQ stocks using FMP's Stock Screener API. This initial call yields approximately 1,000 stocks.

python import requests import pandas as pd import numpy as np import json from datetime import datetime, timedelta import seaborn as sns import matplotlib.pyplot as plt from scipy import stats

token = 'YOUR FMP TOKEN' url = f'https://financialmodelingprep.com/stable/company-screener' querystring = {"apikey":token,"country":"US", "exchange": "NASDAQ", "isActiveTrading": True, "isEtf": False, "isFund": False} resp = requests.get(url, querystring).json() df_universe = pd.DataFrame(resp) df_universe = df_universe[df_universe['exchangeShortName'] == 'NASDAQ'] df_universe

Next, we bin the market capitalization into predefined categories (Micro, Small, Mid, Large, Mega). This segmentation is crucial for granular analysis later, allowing us to understand how different market-cap segments react. We retain only essential columns: symbol, company name, market cap, and sector.

python bins = [0, 250_000_000, # 250M 2_000_000_000, # 2B 10_000_000_000, # 10B 200_000_000_000,# 200B float("inf")] labels = ["Micro", "Small", "Mid", "Large", "Mega"] df_universe["marketCap"] = pd.cut(df_universe["marketCap"], bins=bins, labels=labels, right=False) df_universe = df_universe[['symbol', 'companyName', 'marketCap', 'sector']] df_universe

Subsequently, we fetch earnings data using FMP’s Earnings Report API, looping through each symbol to gather all available earnings announcements. We filter out missing actual or estimated EPS/revenue values.

python symbols = df_universe['symbol'].to_list() all_dfs = []

for symbol in symbols: url = f"https://financialmodelingprep.com/stable/earnings?symbol={symbol}" params = {"apikey": token} resp = requests.get(url, params=params)

if resp.status_code != 200:
    print(f"Error for {symbol}: {resp.status_code} - {resp.text}")
    continue

data = resp.json()
if not data:
    print(f"No data for {symbol}")
    continue

df_symbol = pd.DataFrame(data)
df_symbol["symbol"] = symbol
all_dfs.append(df_symbol)

Single DataFrame with all earnings

df_earnings = pd.concat(all_dfs, ignore_index=True) df_earnings = df_earnings.dropna(subset=['epsActual', 'epsEstimated', 'revenueActual','revenueEstimated']) df_earnings

We then calculate percentage surprises for both EPS and revenue, standardizing them for comparable analysis. We retain data from 2010 onwards.

python df_earnings["eps_surprise"] = ((df_earnings["epsActual"] - df_earnings["epsEstimated"]) / abs(df_earnings["epsEstimated"]) * 100).round(2) df_earnings["revenue_surprise"] = ((df_earnings["revenueActual"] - df_earnings["revenueEstimated"]) / abs(df_earnings["revenueEstimated"]) * 100).round(2) df_earnings = df_earnings[['symbol', 'date', 'eps_surprise', 'revenue_surprise']] df_earnings["date"] = pd.to_datetime(df_earnings["date"]) df_earnings = df_earnings[df_earnings["date"] > "2009-12-31"]

Finally, using FMP’s Historical Index Full Chart API, we fetch historical daily prices for each stock. This allows us to calculate 3-day and 10-day post-earnings returns, which are crucial for assessing market reactions. The final df_earnings DataFrame is then merged with our initial df_universe to include market cap and sector information.

python unique_symbols = df_earnings["symbol"].unique() price_results = [] print(f"Processing {len(unique_symbols)} symbols...")

for symbol in unique_symbols: # Fetch full historical prices url = f"https://financialmodelingprep.com/stable/historical-price-eod/full" params = {"apikey":token, "symbol":symbol, "from":'2009-10-01'} resp = requests.get(url, params=params) if resp.status_code != 200: print(f"Error for {symbol}: {resp.status_code}") continue data = resp.json() hist_df = pd.DataFrame(data) hist_df["date"] = pd.to_datetime(hist_df["date"]) hist_df = hist_df.sort_values("date").reset_index(drop=True)

# Get matching earnings rows
earnings_symbol = df_earnings[df_earnings["symbol"] == symbol].copy()

for _, row in earnings_symbol.iterrows():
    earn_date = pd.to_datetime(row["date"]).date()

    # === 3-DAY WINDOWS ===
    pre3_mask = (hist_df["date"].dt.date < earn_date) &
                (hist_df["date"].dt.date >= earn_date - timedelta(days=10))
    pre3 = hist_df[pre3_mask].tail(3)

    post3_mask = (hist_df["date"].dt.date > earn_date) &
                 (hist_df["date"].dt.date <= earn_date + timedelta(days=10))
    post3 = hist_df[post3_mask].head(3)

    pre3_start = pre3["close"].iloc[0] if len(pre3) >= 3 else None
    pre3_end = pre3["close"].iloc[-1] if len(pre3) >= 1 else None
    post3_end = post3["close"].iloc[-1] if len(post3) >= 3 else None

    pct_pre_3d = ((pre3_end - pre3_start) / pre3_start * 100) if pre3_start and pre3_end else None
    pct_post_3d = ((post3_end - pre3_end) / pre3_end * 100) if pre3_end and post3_end else None

    # === 10-DAY WINDOWS ===
    pre10_mask = (hist_df["date"].dt.date < earn_date) &
                (hist_df["date"].dt.date >= earn_date - timedelta(days=20))
    pre10 = hist_df[pre10_mask].tail(10)

    post10_mask = (hist_df["date"].dt.date > earn_date) &
                  (hist_df["date"].dt.date <= earn_date + timedelta(days=20))
    post10 = hist_df[post10_mask].head(10)

    pre10_start = pre10["close"].iloc[0] if len(pre10) >= 10 else None
    pre10_end = pre10["close"].iloc[-1] if len(pre10) >= 1 else None
    post10_end = post10["close"].iloc[-1] if len(post10) >= 10 else None

    pct_pre_10d = ((pre10_end - pre10_start) / pre10_start * 100) if pre10_start and pre10_end else None
    pct_post_10d = ((post10_end - pre10_end) / pre10_end * 100) if pre10_end and post10_end else None

    price_results.append({
        "symbol": symbol,
        "earn_date": earn_date,
        "month": earn_date.month,
        "pct_pre_3d": round(pct_pre_3d, 2) if pct_pre_3d else None,
        "pct_post_3d": round(pct_post_3d, 2) if pct_post_3d else None,
        "pct_pre_10d": round(pct_pre_10d, 2) if pct_pre_10d else None,
        "pct_post_10d": round(pct_post_10d, 2) if pct_post_10d else None,
        "eps_surprise": row["eps_surprise"],
        "revenue_surprise": row["revenue_surprise"]
    })

df_earnings = pd.DataFrame(price_results) df_earnings.dropna(inplace=True) df_earnings = df_universe.merge(df_earnings, on="symbol") df_earnings

Storytelling with Charts and Visuals

With our data prepared, let's dive into the visualizations that bring our financial story to life.

Sector Heatmap

Heatmaps offer a concise overview of average post-earnings reactions. We start with a 3-day post-earnings return heatmap, segmented by sector and market-cap. This helps quickly identify high-alpha areas for earnings strategies.

python

Aggregate: average post-earnings returns and EPS surprise

agg = ( df_earnings .dropna(subset=['pct_post_3d', 'pct_post_10d', 'eps_surprise', 'marketCap', 'sector']) .groupby(['sector', 'marketCap']) .agg( avg_post3d=('pct_post_3d', 'mean'), avg_post10d=('pct_post_10d', 'mean'), avg_eps_surprise=('eps_surprise', 'mean') ) .reset_index() )

Heatmap: average 3-day post-earnings return

plt.figure(figsize=(12, 8)) sns.heatmap( agg.pivot(index='sector', columns='marketCap', values='avg_post3d'), annot=True, fmt='.2f', cmap='RdYlGn', center=0, linewidths=0.5, linecolor='grey' ) plt.title('Average 3-Day Post-Earnings Return by Sector and Market-Cap Bucket') plt.xlabel('Market-cap bucket') plt.ylabel('Sector') plt.xticks(rotation=45, ha='right') plt.tight_layout() plt.show()

Our 3-day heatmap reveals Consumer Cyclical and Materials performing strongly, particularly in small and mid caps with over 1.1% positive reactions. Real Estate mid caps show a notable +4.0% jump. Technology generally exhibits more muted gains, under 1.1%, suggesting limited immediate upside from major tech earnings. Energy and Financials remain close to zero.

Extending our view, the 10-day post-earnings return heatmap helps capture momentum persistence.

python

Heatmap: average 10-day post-earnings return

heatmap_10d = agg.pivot(index='sector', columns='marketCap', values='avg_post10d') plt.figure(figsize=(12, 8)) sns.heatmap( heatmap_10d, annot=True, fmt='.2f', cmap='RdYlGn', center=0, linewidths=0.5, linecolor='grey' ) plt.title('Average 10-Day Post-Earnings Return by Sector and Market-Cap Bucket') plt.xlabel('Market-cap bucket') plt.ylabel('Sector') plt.xticks(rotation=45, ha='right') plt.tight_layout() plt.show()

Over ten days, Consumer Cyclical mega caps peak at 3.2%, while Industrials and Health Care show consistent mid-to-large cap gains around 1.1%. Real Estate's initial surge moderates. Technology sees a small boost in mega caps (+1.8%), but overall remains less active compared to cyclicals, indicating that short-term reactions might not always persist.

Mega-Cap Tech Time Series

To understand individual stock dynamics, we track 10-day post-earnings returns for key mega-cap tech stocks (AAPL, MSFT, NVDA, AMZN, GOOG/GOOGL, META) over time. A bubble chart effectively encodes earnings date, return, EPS surprise magnitude (size), and beat/miss status (color).

python

Define mega-cap tech tickers (top ones from data: AAPL, MSFT, NVDA, AMZN, GOOG/GOOGL, META)

tech_tickers = ['AAPL', 'MSFT', 'NVDA', 'AMZN', 'GOOG', 'GOOGL', 'META']

Filter data for mega-cap tech

df_tech = ( df_earnings[df_earnings['symbol'].isin(tech_tickers)] .dropna(subset=['earn_date', 'pct_post_10d', 'eps_surprise']) .sort_values('earn_date') .assign( earn_date=lambda x: pd.to_datetime(x['earn_date']) ) )

Create time-series plot: pct_post_10d vs earn_date, sized/color by eps_surprise

plt.figure(figsize=(14, 8))

Scatter plot

scatter = plt.scatter( df_tech['earn_date'], df_tech['pct_post_10d'], s=np.abs(df_tech['eps_surprise']) * 50 + 20, # Size by abs(eps_surprise) c=df_tech['eps_surprise'], cmap='RdYlBu_r', alpha=0.7, edgecolors='black', linewidth=0.5 ) plt.colorbar(scatter, label='EPS Surprise (%)') plt.xlabel('Earnings Date') plt.ylabel('10-Day Post-Earnings Return (%)') plt.title('Mega-Cap Tech: 10-Day Post-Earnings Returns vs Time (Point size/color by EPS Surprise)') plt.grid(True, alpha=0.3)

Add trend line

z = np.polyfit(pd.to_numeric(df_tech['earn_date']), df_tech['pct_post_10d'], 1) p = np.poly1d(z) plt.plot(df_tech['earn_date'], p(pd.to_numeric(df_tech['earn_date'])), "r--", alpha=0.8, linewidth=2, label=f'Trend: {z[0]:.3f}x + {z[1]:.1f}') plt.legend() plt.xticks(rotation=45) plt.tight_layout() plt.show()

This chart effectively highlights outlier events, such as Apple's Q4 2018 earnings miss (January 2019 announcement). Its large red bubble indicates a significant negative EPS surprise and a substantial negative 10-day return, underscoring how one major event can dramatically influence perceived trends.

EPS Surprise Scatter Plot

This plot investigates the simple hypothesis: do earnings beats lead to positive returns, and misses to negative returns? We visualize EPS surprise against post-earnings returns, adding a regression line to show the average relationship.

python

Prepare data: drop NaNs and convert earn_date if needed (not used here)

df_plot = ( df_earnings .dropna(subset=['eps_surprise', 'pct_post_3d', 'pct_post_10d', 'sector']) .copy() )

1. Scatter: EPS Surprise vs 3-Day Post-Return, colored by sector

plt.figure(figsize=(12, 5)) plt.subplot(1, 2, 1) sns.scatterplot( data=df_plot, x='eps_surprise', y='pct_post_3d', hue='sector', alpha=0.6, s=40 )

Regression line (overall)

slope, intercept, r_value, p_value, std_err = stats.linregress(df_plot['eps_surprise'], df_plot['pct_post_3d']) line = slope * df_plot['eps_surprise'] + intercept plt.plot(df_plot['eps_surprise'], line, 'red', linestyle='--', linewidth=2, label=f'y = {slope:.3f}x + {intercept:.2f} R²={r_value**2:.3f}') plt.xlabel('EPS Surprise (%)') plt.ylabel('3-Day Post-Earnings Return (%)') plt.title('EPS Surprise vs 3-Day Post-Return by Sector') plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left') plt.grid(True, alpha=0.3)

2. Scatter: EPS Surprise vs 10-Day Post-Return, colored by sector

plt.subplot(1, 2, 2) sns.scatterplot( data=df_plot, x='eps_surprise', y='pct_post_10d', hue='sector', alpha=0.6, s=40 )

Regression line (overall)

slope10, intercept10, r_value10, p_value10, std_err10 = stats.linregress(df_plot['eps_surprise'], df_plot['pct_post_10d']) line10 = slope10 * df_plot['eps_surprise'] + intercept10 plt.plot(df_plot['eps_surprise'], line10, 'red', linestyle='--', linewidth=2, label=f'y = {slope10:.3f}x + {intercept10:.2f} R²={r_value10**2:.3f}') plt.xlabel('EPS Surprise (%)') plt.ylabel('10-Day Post-Earnings Return (%)') plt.title('EPS Surprise vs 10-Day Post-Return by Sector') plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left') plt.grid(True, alpha=0.3) plt.tight_layout() plt.show()

Optional: Summary table of correlations by sector

corr_3d = df_plot.groupby('sector')[['eps_surprise', 'pct_post_3d']].corr().unstack().xs('pct_post_3d', level=1, axis=1)['eps_surprise'] corr_10d = df_plot.groupby('sector')[['eps_surprise', 'pct_post_10d']].corr().unstack().xs('pct_post_10d', level=1, axis=1)['eps_surprise'] corr_df = pd.DataFrame({ 'Corr_EPS_3Day': corr_3d.round(3), 'Corr_EPS_10Day': corr_10d.round(3) }).sort_values('Corr_EPS_10Day', ascending=False)

The red dashed trend line shows a typical relationship: a 1% EPS beat generally leads to a modest 0.05–0.1% gain over 3 to 10 days. The gentle slope highlights that while surprises can provide a small boost, they don't guarantee significant moves. The broad dispersion of points indicates that many other factors influence post-earnings stock performance.

Return Distribution Violins

While averages are useful, they can conceal risk. Violin plots reveal the full distribution of returns, including spread and tail characteristics. Here, we plot 3-day post-earnings return distributions by sector and market-cap bucket.

python

Prepare data

df_plot = ( df_earnings .dropna(subset=['pct_post_3d', 'sector', 'marketCap']) .copy() )

1. Violin plot: 3-day post-returns by sector

plt.figure(figsize=(15, 6)) plt.subplot(1, 2, 1) sns.violinplot( data=df_plot, x='sector', y='pct_post_3d', inner='quartile', palette='Set2' ) plt.title('Distribution of 3-Day Post-Earnings Returns by Sector (Violin)') plt.xlabel('Sector') plt.ylabel('3-Day Post-Earnings Return (%)') plt.xticks(rotation=45, ha='right') plt.grid(True, alpha=0.3)

2. Violin plot: 3-day post-returns by market-cap group

plt.subplot(1, 2, 2) sns.violinplot( data=df_plot, x='marketCap', y='pct_post_3d', inner='quartile', palette='Set3' ) plt.title('Distribution of 3-Day Post-Earnings Returns by Market-Cap (Violin)') plt.xlabel('Market-cap bucket') plt.ylabel('3-Day Post-Earnings Return (%)') plt.xticks(rotation=45, ha='right') plt.grid(True, alpha=0.3) plt.tight_layout() plt.show() plt.show()

Summary statistics table

summary = df_plot.groupby(['sector', 'marketCap'])['pct_post_3d'].agg(['mean', 'median', 'std', 'count']).round(2) print("Summary Statistics: Mean/Median/Std/Count of 3-Day Returns by Sector & Market-Cap") print(summary)

The violin plots show most distributions clustered near zero with modest variations (typically ±5%). This indicates that post-earnings reactions are often noisy, lacking a consistent, clear direction, possibly due to market efficiency in pricing expectations. Small caps display the highest variability, reflecting their higher risk and occasional outsized gains. Consumer Cyclical and Materials show slightly more frequent upside surprises. This visualization honestly portrays the challenge of finding predictable alpha in post-earnings movements.

Monthly Seasonality

Exploring monthly seasonality can uncover systematic biases that might influence trading strategies. We present a four-panel view: average 3/10-day post-returns, EPS surprises, and event counts by month.

python

1. Ensure earn_date is datetime

df_month = ( df_earnings .dropna(subset=['earn_date', 'pct_post_3d', 'pct_post_10d', 'eps_surprise']) .copy() ) df_month['earn_date'] = pd.to_datetime(df_month['earn_date'])

2. Derive month number and name

df_month['month_num'] = df_month['earn_date'].dt.month df_month['month_name'] = df_month['earn_date'].dt.strftime('%b')

3. Aggregate averages by month

monthly_agg = ( df_month .groupby('month_num') .agg( pct_post_3d_mean=('pct_post_3d', 'mean'), pct_post_10d_mean=('pct_post_10d', 'mean'), eps_surprise_mean=('eps_surprise', 'mean'), n_obs=('earn_date', 'count') ) .reset_index() .sort_values('month_num') )

Keep a stable month order and names

month_order = monthly_agg['month_num'].tolist() month_labels = df_month.drop_duplicates('month_num').set_index('month_num')['month_name'].reindex(month_order) monthly_agg['month_name'] = month_labels.values

4. Plot bar charts

fig, axes = plt.subplots(2, 2, figsize=(14, 10)) fig.suptitle('Monthly Seasonality of Post-Earnings Returns and EPS Surprise', fontsize=16)

Avg 3-day return

axes[0, 0].bar(monthly_agg['month_name'], monthly_agg['pct_post_3d_mean'], color='skyblue') axes[0, 0].set_title('Avg 3-Day Post-Earnings Return by Month') axes[0, 0].set_ylabel('Return (%)') axes[0, 0].grid(alpha=0.3)

Avg 10-day return

axes[0, 1].bar(monthly_agg['month_name'], monthly_agg['pct_post_10d_mean'], color='lightgreen') axes[0, 1].set_title('Avg 10-Day Post-Earnings Return by Month') axes[0, 1].set_ylabel('Return (%)') axes[0, 1].grid(alpha=0.3)

Avg EPS surprise

axes[1, 0].bar(monthly_agg['month_name'], monthly_agg['eps_surprise_mean'], color='salmon') axes[1, 0].set_title('Avg EPS Surprise by Month') axes[1, 0].set_ylabel('EPS Surprise') axes[1, 0].grid(alpha=0.3)

Number of observations

axes[1, 1].bar(monthly_agg['month_name'], monthly_agg['n_obs'], color='gold') axes[1, 1].set_title('Number of Earnings Events by Month') axes[1, 1].set_ylabel('Count') axes[1, 1].grid(alpha=0.3)

for ax in axes.ravel(): ax.set_xlabel('Month') ax.tick_params(axis='x', rotation=0)

plt.tight_layout() plt.show()

January and October tend to exhibit the best 3-day returns (around 0.8%), while May and July typically see weaker results. Similar patterns, though gentler, are observed in 10-day trends, with February and August peaking. EPS surprises are slightly negative in January and May, possibly due to difficult comparative periods. Earnings event counts are lower in July, August, and December due to holiday seasons. While hints of seasonality exist, their impact is generally small, around 0.5%.

Regime Cross-Section

Finally, we analyze 10-day post-earnings returns by market regime. This stress-tests our findings, determining if patterns persist across different market environments (e.g., bull, bear, COVID-recovery). This reveals regime-dependent rotation opportunities.

python

Prepare data with year extraction

df_regimes = ( df_earnings .dropna(subset=['earn_date', 'pct_post_10d', 'sector']) .copy() ) df_regimes['earn_date'] = pd.to_datetime(df_regimes['earn_date']) df_regimes['year'] = df_regimes['earn_date'].dt.year

Define market regimes (adjust years based on your data/market history)

Example: Bull (2023-2025), Bear/Transition (2022), COVID (2020-2021), etc.

def assign_regime(year): if year >= 2023: return 'Bull (2023+)' elif year == 2022: return 'Bear (2022)' elif 2020 <= year <= 2021: return 'COVID Recovery' elif 2018 <= year <= 2019: return 'Pre-COVID' else: return 'Earlier' df_regimes['market_regime'] = df_regimes['year'].apply(assign_regime)

1. Aggregate: average 10-day returns by sector and regime/year

agg_data = ( df_regimes .groupby(['sector', 'market_regime'])['pct_post_10d'] .agg(['mean', 'count']) .reset_index() .query('count >= 5') # Filter low-sample regimes )

2. Visualization: Heatmap first (quick overview)

plt.figure(figsize=(12, 8)) plt.subplot(2, 1, 1) pivot_heatmap = agg_data.pivot(index='sector', columns='market_regime', values='mean') sns.heatmap(pivot_heatmap, annot=True, fmt='.2f', cmap='RdYlGn', center=0, linewidths=0.5) plt.title('Average 10-Day Post-Earnings Returns: Sector x Market Regime Heatmap')

3. Bar charts: By regime (stacked by sector)

plt.subplot(2, 1, 2) regime_order = agg_data.groupby('market_regime')['mean'].mean().sort_values(ascending=False).index sns.barplot(data=agg_data, x='market_regime', y='mean', hue='sector', palette='Set2', order=regime_order) plt.title('Average 10-Day Returns by Market Regime (Colored by Sector)') plt.ylabel('10-Day Post-Return (%)') plt.xlabel('Market Regime') plt.xticks(rotation=45, ha='right') plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left') plt.grid(axis='y', alpha=0.3) plt.tight_layout() plt.show()

5. Summary tables

print("Average Returns by Sector x Market Regime (min 5 obs):") print(agg_data.pivot(index='sector', columns='market_regime', values='mean').round(2))

6. Ranking: Best/worst performing sectors by regime

print(" Top/Bottom Sectors by Regime:") for regime in regime_order: regime_data = agg_data[agg_data['market_regime'] == regime].sort_values('mean', ascending=False) print(f" {regime}:") print(regime_data[['sector', 'mean', 'count']].round(2).head(3))

This analysis shows how sector performance shifts across different market cycles. For instance, some sectors might thrive in bull markets but underperform in bear markets or during periods of high uncertainty. This helps validate and refine earnings strategies by accounting for broader market conditions, revealing which sectors maintain their post-earnings patterns and which diverge significantly under specific regimes.

What Did We Get Out of All This Storyline?

This comprehensive journey through financial data visualization underscores its profound utility beyond mere aesthetics. We've moved from raw figures to actionable insights by: identifying high-potential sectors and market caps in heatmaps, understanding the impact of outlier events in mega-tech stock trends, quantifying the limited direct correlation between EPS surprise and returns, exposing the inherent noise in post-earnings movements via violin plots, observing subtle monthly seasonal biases, and stress-testing sector performance across diverse market regimes. While alpha isn't always obvious or guaranteed, the ability to rapidly synthesize complex information, pinpoint risks, and spot opportunities through well-crafted visuals is an indispensable skill for any developer engaged in financial analysis.

FAQ

Q: What specific FMP APIs were utilized in this guide for data extraction?

A: This guide utilized three primary FMP APIs: the Stock Screener API to retrieve the NASDAQ stock universe, the Earnings Report API to collect historical earnings data including EPS and revenue surprises, and the Historical Index Full Chart API to fetch daily historical prices for calculating post-earnings returns.

Q: Why is market capitalization binned into categories like "Micro," "Small," and "Mega"?

A: Market capitalization is binned to segment stocks into distinct groups based on their size. This allows for a more granular analysis, helping to identify if post-earnings reactions or other financial patterns vary significantly across different market-cap segments, which can be crucial for tailoring trading strategies.

Q: What does the gentle slope of the regression line in the EPS Surprise Scatter Plot imply about market efficiency?

A: The gentle slope (e.g., 0.05-0.1% return for every 1% EPS beat) suggests that while earnings surprises can nudge stock prices, they don't guarantee massive, predictable moves. This indicates a relatively efficient market where expectations are largely priced in before announcements, leaving only a modest, short-term impact from surprises and highlighting that many other factors also influence stock performance.