# compute_volatility_labels.py # Computes lagged_vol_30d and fwd_vol_30d for every filing in filings_index.csv # where has_pickle=True. # # Price sources (in priority order): # 1. FINSABER all_sp500_prices_2000_2024_delisted_include.csv (2000-2024) # 2. WRDS CRSP crsp_2025_daily.csv (2025) # 3. yfinance fallback for ETN / PARA (no PERMNO in CCM) # # Volatility definition: # vol = std(log_returns over 30 trading days) * sqrt(252) [annualised] # lagged_vol_30d : 30 trading days strictly BEFORE filing_date # fwd_vol_30d : 30 trading days strictly AFTER filing_date # # Output: datasets/volatility_labels.csv # Run: python scripts/compute_volatility_labels.py import math import numpy as np import pandas as pd from tqdm import tqdm # ── Paths ────────────────────────────────────────────────────────────────────── FILINGS_PATH = r"D:\UoE AI\Dissertation\IPP Draft\datasets\filings_index.csv" FINSABER_PATH = r"D:\UoE AI\Dissertation\IPP Draft\datasets\all_sp500_prices_2000_2024_delisted_include.csv" CRSP_PATH = r"D:\UoE AI\Dissertation\IPP Draft\datasets\crsp_2025_daily.csv" OUT_PATH = r"D:\UoE AI\Dissertation\IPP Draft\datasets\volatility_labels.csv" WINDOW = 30 # trading-day window ANNUALIZE = math.sqrt(252) # annualisation factor # Tickers with no PERMNO in CCM that have Item 1A pickles -> yfinance fallback YFINANCE_TICKERS = {'ETN', 'PARA'} # ── Helper ───────────────────────────────────────────────────────────────────── def compute_vol(series: pd.Series, filing_date: pd.Timestamp, window: int = 30): """ series : pd.Series, DatetimeIndex ascending, values = daily log-returns Returns (lagged_vol, fwd_vol) as Python floats; np.nan if < window days. """ # Ensure 1-D Series (guards against yfinance returning DataFrame column) if isinstance(series, pd.DataFrame): series = series.iloc[:, 0] dates = series.index before = dates[dates < filing_date] after = dates[dates > filing_date] lagged_vol = float(series.loc[before[-window:]].std()) * ANNUALIZE \ if len(before) >= window else np.nan fwd_vol = float(series.loc[after[:window]].std()) * ANNUALIZE \ if len(after) >= window else np.nan return lagged_vol, fwd_vol # ── 1. Load filings (pickle only) ───────────────────────────────────────────── print("Loading filings index...") filings = pd.read_csv(FILINGS_PATH) filings = filings[filings['has_pickle'] == True].copy() filings['filing_date'] = pd.to_datetime(filings['filing_date']) print(f" Filings with pickle : {len(filings):,}") # ── 2. Load FINSABER (2000-2024) ────────────────────────────────────────────── print("\nLoading FINSABER prices (2000-2024)...") fin = pd.read_csv(FINSABER_PATH, usecols=['date', 'adjusted_close', 'symbol']) fin['date'] = pd.to_datetime(fin['date']) fin['symbol'] = fin['symbol'].str.upper().str.strip() fin = fin.sort_values(['symbol', 'date']) # Log-return from adjusted close fin['log_ret'] = fin.groupby('symbol')['adjusted_close'].transform( lambda x: np.log(x / x.shift(1)) ) # Drop NaN and -inf/-inf rows (adjusted_close == 0 produces log(0) = -inf) fin = fin[fin['log_ret'].notna() & np.isfinite(fin['log_ret'])][['symbol', 'date', 'log_ret']] fin.rename(columns={'symbol': 'ticker'}, inplace=True) print(f" Rows after log-ret : {len(fin):,} | Tickers: {fin['ticker'].nunique()}") # ── 3. Load CRSP 2025 ───────────────────────────────────────────────────────── print("\nLoading CRSP 2025 daily...") crsp = pd.read_csv(CRSP_PATH, usecols=['Ticker', 'DlyCalDt', 'DlyRet']) crsp.columns = ['ticker', 'date', 'dlyret'] crsp['date'] = pd.to_datetime(crsp['date']) crsp['ticker'] = crsp['ticker'].str.upper().str.strip() crsp = crsp[crsp['dlyret'].notna()].copy() crsp['log_ret'] = np.log(1 + crsp['dlyret']) crsp = crsp[['ticker', 'date', 'log_ret']] print(f" Rows : {len(crsp):,} | Tickers: {crsp['ticker'].nunique()}") # ── 4. Combine into a single lookup dict ────────────────────────────────────── print("\nMerging price sources...") prices = pd.concat([fin, crsp], ignore_index=True) prices = (prices .sort_values(['ticker', 'date']) .drop_duplicates(subset=['ticker', 'date'])) # dict: ticker (upper) -> pd.Series(log_ret, index=DatetimeIndex) price_dict = { tkr: grp.set_index('date')['log_ret'] for tkr, grp in prices.groupby('ticker') } print(f" Unique tickers in price dict: {len(price_dict):,}") # ── 5. yfinance fallback for ETN / PARA ─────────────────────────────────────── yf_needed = YFINANCE_TICKERS & set(filings['ticker'].str.upper()) if yf_needed: import yfinance as yf print(f"\nFetching yfinance for: {yf_needed}") for tkr in sorted(yf_needed): hist = yf.download(tkr, start='2005-01-01', end='2026-06-01', auto_adjust=True, progress=False) if hist.empty: print(f" {tkr}: no data returned") continue # Newer yfinance returns hist['Close'] as a DataFrame; squeeze to Series close = hist['Close'] if isinstance(close, pd.DataFrame): close = close.iloc[:, 0] s = np.log(close / close.shift(1)).dropna() s = s[np.isfinite(s)] # drop any -inf from zero prices s.index = pd.to_datetime(s.index) s.index.name = 'date' s.name = 'log_ret' price_dict[tkr] = s.sort_index() print(f" {tkr}: {len(s):,} rows ({s.index[0].date()} -> {s.index[-1].date()})") # ── 6. Compute volatility for every filing ──────────────────────────────────── print("\nComputing volatility windows...") records = [] for _, row in tqdm(filings.iterrows(), total=len(filings), desc="Vol"): ticker = str(row['ticker']).upper().strip() filing_date = row['filing_date'] if ticker in price_dict: lagged_vol, fwd_vol = compute_vol(price_dict[ticker], filing_date, WINDOW) else: lagged_vol, fwd_vol = np.nan, np.nan records.append({ 'ticker': row['ticker'], 'cik': row['cik'], 'sic': row['sic'], 'sic_description': row['sic_description'], 'fiscal_year': row['fiscal_year'], 'filing_date': row['filing_date'].date(), 'report_date': row['report_date'], 'lagged_vol_30d': round(lagged_vol, 6) if pd.notna(lagged_vol) else np.nan, 'fwd_vol_30d': round(fwd_vol, 6) if pd.notna(fwd_vol) else np.nan, }) df = pd.DataFrame(records) df = df.sort_values(['ticker', 'filing_date']).reset_index(drop=True) # ── 7. Summary ──────────────────────────────────────────────────────────────── total = len(df) has_both = (df['lagged_vol_30d'].notna() & df['fwd_vol_30d'].notna()).sum() lagged_only = (df['lagged_vol_30d'].notna() & df['fwd_vol_30d'].isna()).sum() fwd_only = (df['lagged_vol_30d'].isna() & df['fwd_vol_30d'].notna()).sum() neither = (df['lagged_vol_30d'].isna() & df['fwd_vol_30d'].isna()).sum() print(f"\nTotal filings processed : {total:,}") print(f" Both vol computed : {has_both:,}") print(f" Lagged only : {lagged_only:,} (fwd window crosses data gap)") print(f" Forward only : {fwd_only:,}") print(f" Neither (no prices) : {neither:,}") df.to_csv(OUT_PATH, index=False) print(f"\nSaved -> {OUT_PATH}") print(df.head(10).to_string(index=False))