In any analysis project, raw tables of numbers often don’t tell the full story. Visualisations simplify complexity by transforming data into shapes that our brains can quickly understand, emphasising trends, outliers, and regime shifts that might be overlooked in raw data. This is especially vital in finance and trading, where clear visuals can uncover risks, opportunities, and patterns, directly affecting decisions on position sizing, timing, and confidence. Today, we will use FMP APIs to interpret earnings data: extracting announcements, surprises, and price reactions across almost 1,000 stocks to identify actionable patterns in post‑earnings movements. FMP APIs Here’s exactly what we’ll build: Sector Heatmap: Maps strongest 3/10-day post-earnings reactions by sector/market-cap buckets. EPS Scatter: Tests if earnings beats drive returns (sector-colored, with regression). Return Violins: Shows 3-day post-earnings volatility/skew by sector and market-cap. Mega-Tech Time Series: Tracks AAPL/MSFT/NVDA post-earnings patterns over time. Monthly Seasonality: Reveals calendar edges in post-earnings returns/surprises. Regime Cross-Section: Tests sector robustness across bull/bear/sideways markets. Sector Heatmap: Maps strongest 3/10-day post-earnings reactions by sector/market-cap buckets. Sector Heatmap EPS Scatter: Tests if earnings beats drive returns (sector-colored, with regression). EPS Scatter Return Violins: Shows 3-day post-earnings volatility/skew by sector and market-cap. Return Violins Mega-Tech Time Series: Tracks AAPL/MSFT/NVDA post-earnings patterns over time. Mega-Tech Time Series Monthly Seasonality: Reveals calendar edges in post-earnings returns/surprises. Monthly Seasonality Regime Cross-Section: Tests sector robustness across bull/bear/sideways markets. Regime Cross-Section Let’s code In the first part of this article, we need to collect all the data required for our visualisation exercise. Using FMP’s Stock Screener API, we will retrieve NASDAQ stocks. The first API call will return 1,000 stocks. FMP’s Stock Screener API FMP’s Stock Screener API , 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 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 This will give us 1,000 stocks! Next, we will bin the market capitalisation to gain a better understanding of the results later on, and we will keep only four columns that are necessary: the symbol, name, market cap, and sector. 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 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 Now it is time to retrieve the earnings using FMP’s Earnings Report API. We will loop through each symbol and collect all the earnings the endpoint provides to us. FMP’s Earnings Report API FMP’s Earnings Report API 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 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 Now we will calculate the surprise, both for earnings and revenue in percentage terms, so we can later compare apples with apples! We will keep everything from 2010 onwards. 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"] 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"] Lastly, as a final step in gathering the data needed for visualization, using FMP’s Historical Index Full Chart API, we will loop through the stocks in our dataframe, retrieve the historical daily prices, and calculate the return of the stock 3 and 10 trading days before and after the earnings announcement. FMP’s Historical Index Full Chart API FMP’s Historical Index Full Chart API 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 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 As you can see, at the end of the code, we have also merged the initial dataset, so all the information, such as name, marketCap, and sector, is now in a single dataset. Let’s plot! i. Sector Heatmap First, we will present the Sector Heatmap of average 3-day post-earnings returns segmented by sector and market-cap category. This basic visualisation highlights areas with the most significant reactions, enabling traders to swiftly identify high-alpha sectors and market caps for earnings strategies. Sector Heatmap # 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 heatmap_3d = agg.pivot(index='sector', columns='marketCap', values='avg_post3d') plt.figure(figsize=(12, 8)) sns.heatmap( heatmap_3d, 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() # 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 heatmap_3d = agg.pivot(index='sector', columns='marketCap', values='avg_post3d') plt.figure(figsize=(12, 8)) sns.heatmap( heatmap_3d, 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() Consumer Cyclical and Materials are performing really well, with small and mid caps seeing positive reactions over 1.1%. Real Estate is also doing great, jumping up to +4.0% in mid caps. Energy and Financials are holding steady, staying close to zero. Technology, on the other hand, is showing more muted gains, under 1.1%, indicating there might be limited immediate upside from the big tech earnings. Building on the 3‑day heatmap, we will now present the Sector Heatmap for average 10‑day post‑earnings returns by sector and market‑cap category. This extends the timeframe to capture momentum persistence, revealing which sectors maintain or reverse short‑term reactions. Sector Heatmap 10‑day # 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() # 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() Consumer Cyclical stands out with peaks at 3.2% (mega caps), and Industrials and Health Care show consistent gains in mid and large caps around 1.1%. Real Estate has eased after its 3-day surge. Technology has seen a small boost in mega caps (+1.8%) but remains less active overall compared to cyclicals. ii. Mega‑Cap Tech Time Series Mega‑Cap Tech Time Series Extending the heatmaps, we now present the Mega‑Cap Tech Time Series: 10‑day post‑earnings returns over time for AAPL, MSFT, NVDA, and others. Bubble visualisation works perfectly here, point size by absolute EPS surprise magnitude, colour by direction (red=miss, blue=beat) — capturing three dimensions at a glance for effective pattern recognition. Mega‑Cap Tech Time Series # 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\n(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() # 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\n(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() That large red bubble around 2018 is almost certainly AAPL’s Q4 2018 earnings miss (Jan 2019 announcement, but fiscal Q4 2018 data) and it stands out because: large red bubble around 2018 AAPL’s Q4 2018 earnings miss Large size = massive EPS surprise magnitude (Apple cut guidance dramatically, ~10% miss) Red colour = negative surprise Low Y position = poor 10‑day return (~-10% range visible) Large size = massive EPS surprise magnitude (Apple cut guidance dramatically, ~10% miss) Large size Red colour = negative surprise Red colour Low Y position = poor 10‑day return (~-10% range visible) Low Y position This was Apple’s infamous “iPhone demand warning” that triggered the January 2019 market panic. Perfect example of how one outlier event can anchor the whole trend line downward in your visualisation. one outlier event iii. EPS Surprise Scatter Plot EPS Surprise Scatter After identifying major tech trends, we will now present the EPS Surprise Scatter plots: EPS surprise against 3‑day and 10‑day post‑returns, coloured by sector with overall regression lines. These examine the core hypothesis — do earnings beats reliably forecast price movements? — and show sector‑specific relationships at a glance. EPS Surprise Scatter # 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}\nR²={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}\nR²={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) # 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}\nR²={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}\nR²={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 illustrates the typical relationship: for every 1% EPS beat, stocks tend to gain about 0.05–0.1% over 3 to 10 days. The gentle slope suggests that while surprises can give a little boost, they don’t guarantee large moves. You’ll notice that Consumer Cyclical dots mainly cluster in the upper right (beats leading to gains), and Real Estate shows a steeper increase. The wide spread around the line indicates that other factors often influence stock movements beyond surprises. trend line typical they don’t guarantee large moves iv. Return Distribution Violins Return Distribution Violins After confirming weak overall surprise‑return links, we will now show Return Distribution Violins for 3‑day post‑earnings returns — split by sector (left) and market‑cap (right). Violins reveal full distribution shapes (density, quartiles, tails) beyond just averages, helping assess realistic risk/reward for each group. Return Distribution Violins # 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) # 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) All violins concentrate near zero with modest variations (±5%), indicating that post-earnings reactions are generally noisy and lack a clear direction. Markets efficiently incorporate expectations, resulting in little predictable advantage. Consumer Cyclical and Materials sectors display slightly more frequent upside surprises, while small caps exhibit the greatest variability, reflecting higher risk and occasional gains. Not every visualization reveals alpha; this one honestly illustrates the difficulty involved. generally noisy and lack a clear direction. M v. Monthly Seasonality Monthly Seasonality After observing narrow return distributions near zero, we will now present Monthly Seasonality in four panels: average 3/10‑day post‑returns, EPS surprises, and event counts by month. This reveals calendar effects — systematic seasonal biases — that can influence timing of entries despite noisy individual responses. Monthly Seasonality # 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() # 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() Jan/Oct tend to have the best 3‑day returns, about 0.8%, while May/Jul usually see weaker results. The 10‑day trends show a similar but gentler pattern, with February and August reaching peaks. EPS surprises are slightly negative in January and May, possibly due to tough comparisons, and there are fewer events in July, August, and December because of holidays. While there’s a hint of seasonality, its impact is quite small, around 0.5%. vi. Regime Cross-Section Finally, after subtle monthly patterns, we will present the Regime Cross‑Section: sector 10‑day post‑earnings returns by market regime (heatmap at the top, bars below). This stress‑tests earlier findings — do patterns persist across bull, bear, and COVID eras? — revealing rotation opportunities and regime dependence. Regime Cross‑Section # 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("\nTop/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"\n{regime}:") print(regime_data[['sector', 'mean', 'count']].round(2).head(3)) # 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("\nTop/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"\n{regime}:") print(regime_data[['sector', 'mean', 'count']].round(2).head(3)) Consumer Cyclical does well during Bull (2023+) and COVID Recovery (~1.5–2%), but it’s less favorable in Bear 2022. Utilities turned negative before COVID. The bottom bars show the COVID era led overall gains (~1%), with Basic Materials and Industrials being the strongest. The recent Bull remains positive but less so. Sector leadership shifts depending on the market regime — there are no consistent winners. The bottom bars show the What did we get out of all this storyline? Guiding you through six interconnected visualizations, we’ve turned 15 years of earnings data into a clear and engaging story. Each chart responds to a specific question, yet together, they paint a bigger picture: earnings surprises influence markets, but not in the same way everywhere. Some sectors, periods, and regimes often provide consistent advantages, while others don’t. Here’s what the data shows us: No definitive alpha here, but specific opportunities are present: Markets are mostly efficient — returns hover near zero with weak surprise correlations — yet Consumer Cyclicals and Materials consistently show upside potential across different timeframes and market sizes. Timing your sector choice is important. Timing windows alter the story: 3-day reactions benefit Real Estate mid-caps (+4%), while 10-day reactions shift leadership to Consumer Cyclical mega-caps (+3.2%). Don’t assume all earnings reactions occur at the same pace. Mega-tech hype isn’t eternal: The bubble chart shows AAPL/MSFT/NVDA delivered strong returns from 2020–2022, but the falling trend since then indicates waning market enthusiasm. Don’t chase yesterday’s overhyped stocks. Calendar patterns reward patience: January and October deliver slightly stronger post-earnings returns (~0.8%), while July and August tend to have lower liquidity. Combine seasonal timing with sector choices for additional gains. Market regimes change winners: Cyclicals underperformed during COVID recovery and the bull run (2023+), while Industrials peaked during the recovery. There are no universal “best performers,” only the best performers for now. Adjust to the regime. The actionable setup: Small to mid-cap cyclical longs in January during bull markets combine all these signals for maximum conviction — where sector timing, seasonality, and regime alignment converge. No definitive alpha here, but specific opportunities are present: Markets are mostly efficient — returns hover near zero with weak surprise correlations — yet Consumer Cyclicals and Materials consistently show upside potential across different timeframes and market sizes. Timing your sector choice is important. No definitive alpha here, but specific opportunities are present Timing windows alter the story: 3-day reactions benefit Real Estate mid-caps (+4%), while 10-day reactions shift leadership to Consumer Cyclical mega-caps (+3.2%). Don’t assume all earnings reactions occur at the same pace. Timing windows alter the story Mega-tech hype isn’t eternal: The bubble chart shows AAPL/MSFT/NVDA delivered strong returns from 2020–2022, but the falling trend since then indicates waning market enthusiasm. Don’t chase yesterday’s overhyped stocks. Mega-tech hype isn’t eternal Calendar patterns reward patience: January and October deliver slightly stronger post-earnings returns (~0.8%), while July and August tend to have lower liquidity. Combine seasonal timing with sector choices for additional gains. Calendar patterns reward patience Market regimes change winners: Cyclicals underperformed during COVID recovery and the bull run (2023+), while Industrials peaked during the recovery. There are no universal “best performers,” only the best performers for now. Adjust to the regime. Market regimes change winners for now The actionable setup: Small to mid-cap cyclical longs in January during bull markets combine all these signals for maximum conviction — where sector timing, seasonality, and regime alignment converge. The actionable setup Final Thoughts This exercise shows why visualization is important in finance: raw tables of returns and surprises wouldn’t reveal these patterns. Heatmaps instantly highlighted sector winners. Scatter plots demonstrated the weak surprise‑return connection. Bubble charts narrated the mega‑tech story over time. Violins unveiled the harsh truth — markets are noisy. Cross‑sectional regime analysis reminded us that yesterday’s approach doesn’t ensure tomorrow’s returns. Heatmaps instantly highlighted sector winners. Scatter plots demonstrated the weak surprise‑return connection. Bubble charts narrated the mega‑tech story over time. Violins unveiled the harsh truth — markets are noisy. Cross‑sectional regime analysis reminded us that yesterday’s approach doesn’t ensure tomorrow’s returns. The effort to interpret this data pays off: you shift from passive observation to active pattern recognition. You see not just what occurred, but where and when it happened. In trading and analysis, understanding the shape of complexity often surpasses having a perfect formula. active pattern recognition where Visual storytelling turns data into intuition — and intuition, based on evidence, outperforms guesswork every time.
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