wehub-resource-sync
4571 lines
339 KiB
Plaintext
4571 lines
339 KiB
Plaintext
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"source": [
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"# Event Studies\n",
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"\n",
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"**Chapter 8: Feature Engineering**\n",
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"**Section Reference**: 8.6 - Combining Features and Controlling Search\n",
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"**Docker image**: `ml4t`\n",
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"\n",
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"## Purpose\n",
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"\n",
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"Event studies measure abnormal returns around specific events (signal triggers,\n",
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"macro announcements, earnings) to assess their predictive power. This is a key\n",
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"validation technique for trading signals.\n",
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"\n",
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"## Learning Objectives\n",
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"\n",
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"1. Understand event study methodology (MacKinlay 1997)\n",
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"2. Implement correct abnormal return computation\n",
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"3. Calculate CAAR with proper confidence bands\n",
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"4. Use event studies for signal validation\n",
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"5. Recognize common pitfalls (clustering, overlapping windows)\n",
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"\n",
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"## Key Concepts\n",
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"\n",
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"**Event Study Workflow**:\n",
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"1. Define events (signal triggers, announcements)\n",
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"2. Estimate \"normal\" returns in estimation window\n",
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"3. Calculate abnormal returns in event window\n",
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"4. Aggregate across events (CAAR)\n",
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"5. Test statistical significance\n",
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"\n",
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"## References\n",
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"\n",
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"- MacKinlay, A.C. (1997). \"Event Studies in Economics and Finance\"\n",
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"- Boehmer et al. (1991). Event-induced variance adjustments\n",
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"\n",
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"## Data Policy\n",
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"\n",
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"All examples use **real ETF data**."
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]
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},
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"outputs": [],
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"source": [
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"\"\"\"Event Studies — measure abnormal returns around signal triggers and macro announcements.\"\"\"\n",
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"\n",
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"from __future__ import annotations\n",
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"\n",
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"import warnings\n",
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"from datetime import datetime\n",
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"\n",
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"import numpy as np\n",
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"import plotly.graph_objects as go\n",
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"import polars as pl\n",
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"from scipy import stats\n",
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"\n",
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"from utils.reproducibility import set_global_seeds\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "8e7dd619",
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"tags": [
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"parameters"
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]
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},
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"outputs": [],
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"source": [
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"START_DATE = \"2018-01-01\"\n",
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"END_DATE = \"2024-01-01\"\n",
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"SEED = 42"
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]
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"source": [
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"set_global_seeds(SEED)"
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]
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},
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}
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},
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"source": [
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"## 1. Data Loading\n",
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"\n",
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"We use ETF data to demonstrate event studies. Events will be generated from\n",
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"momentum breakouts (trading signal) as a validation example."
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]
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"ETF data: 7,545 rows\n",
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"Symbols: 5\n",
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"Date range: 2018-01-02 to 2023-12-29\n"
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]
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}
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],
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"source": [
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"from data import load_etfs\n",
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"\n",
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"etfs = load_etfs()\n",
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"\n",
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"\n",
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"# Select liquid ETFs for event study\n",
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"SYMBOLS = [\"SPY\", \"QQQ\", \"IWM\", \"TLT\", \"GLD\"]\n",
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"\n",
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"# Filter\n",
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"etf_filtered = (\n",
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" etfs.filter(pl.col(\"symbol\").is_in(SYMBOLS))\n",
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" .filter(\n",
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" (pl.col(\"timestamp\") >= datetime.strptime(START_DATE, \"%Y-%m-%d\"))\n",
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" & (pl.col(\"timestamp\") < datetime.strptime(END_DATE, \"%Y-%m-%d\"))\n",
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" )\n",
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" .sort([\"symbol\", \"timestamp\"])\n",
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")\n",
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"\n",
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"print(f\"ETF data: {len(etf_filtered):,} rows\")\n",
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"print(f\"Symbols: {etf_filtered['symbol'].n_unique()}\")\n",
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"print(f\"Date range: {etf_filtered['timestamp'].min()} to {etf_filtered['timestamp'].max()}\")"
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]
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},
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{
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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||
"Returns: 7,540 observations\n",
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"Benchmark: 1,508 days\n"
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]
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||
}
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||
],
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"source": [
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"# Compute daily returns\n",
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"returns_df = (\n",
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" etf_filtered.select([\"timestamp\", \"symbol\", \"close\"])\n",
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" .with_columns(pl.col(\"close\").pct_change().over(\"symbol\").alias(\"return\"))\n",
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" .drop_nulls()\n",
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")\n",
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"\n",
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"print(f\"Returns: {len(returns_df):,} observations\")\n",
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"\n",
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"# Benchmark: SPY as market proxy\n",
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"benchmark_returns = (\n",
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" returns_df.filter(pl.col(\"symbol\") == \"SPY\")\n",
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" .select([\"timestamp\", \"return\"])\n",
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" .rename({\"return\": \"benchmark_return\"})\n",
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")\n",
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"\n",
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||
"print(f\"Benchmark: {len(benchmark_returns):,} days\")"
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]
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},
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"cell_type": "markdown",
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},
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"source": [
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||
"## 2. Generate Events\n",
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||
"\n",
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||
"For demonstration, we generate events from **momentum breakouts** (new 20-day highs).\n",
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"In practice, events could be:\n",
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"- Trading signal triggers\n",
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"- Earnings announcements\n",
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"- FOMC meetings\n",
|
||
"- Index rebalances"
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||
]
|
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},
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{
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},
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"outputs": [],
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"source": [
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||
"def generate_momentum_breakout_events(\n",
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" prices: pl.DataFrame,\n",
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" lookback: int = 20,\n",
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" min_gap_days: int = 21,\n",
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") -> pl.DataFrame:\n",
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" \"\"\"\n",
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" Generate events when price makes a new N-day high.\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" prices : DataFrame with timestamp, symbol, close\n",
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" lookback : Days to look back for high\n",
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" min_gap_days : Minimum gap between events (avoid clustering)\n",
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"\n",
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" Returns\n",
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" -------\n",
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" DataFrame with timestamp, symbol, event_type\n",
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" \"\"\"\n",
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" events = []\n",
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"\n",
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" for symbol in prices[\"symbol\"].unique().to_list():\n",
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" if symbol == \"SPY\": # Skip benchmark\n",
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" continue\n",
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"\n",
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" symbol_data = prices.filter(pl.col(\"symbol\") == symbol).sort(\"timestamp\")\n",
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" close_prices = symbol_data[\"close\"].to_numpy()\n",
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" timestamps = symbol_data[\"timestamp\"].to_list()\n",
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"\n",
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" last_event_idx = -min_gap_days - 1\n",
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"\n",
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" for i in range(lookback, len(close_prices) - 30): # Leave room for event window\n",
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||
" # Check if new high\n",
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" if close_prices[i] >= max(close_prices[i - lookback : i]):\n",
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||
" # Check minimum gap\n",
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" if i - last_event_idx >= min_gap_days:\n",
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||
" events.append(\n",
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" {\n",
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" \"timestamp\": timestamps[i],\n",
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" \"symbol\": symbol,\n",
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" \"event_type\": \"momentum_breakout\",\n",
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" }\n",
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" )\n",
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" last_event_idx = i\n",
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"\n",
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" return pl.DataFrame(events)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"outputs": [
|
||
{
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"name": "stdout",
|
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"output_type": "stream",
|
||
"text": [
|
||
"Generated 126 events\n",
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||
"\n",
|
||
"Events by symbol:\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div><style>\n",
|
||
".dataframe > thead > tr,\n",
|
||
".dataframe > tbody > tr {\n",
|
||
" text-align: right;\n",
|
||
" white-space: pre-wrap;\n",
|
||
"}\n",
|
||
"</style>\n",
|
||
"<small>shape: (4, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>symbol</th><th>len</th></tr><tr><td>str</td><td>u32</td></tr></thead><tbody><tr><td>"GLD"</td><td>32</td></tr><tr><td>"IWM"</td><td>33</td></tr><tr><td>"QQQ"</td><td>34</td></tr><tr><td>"TLT"</td><td>27</td></tr></tbody></table></div>"
|
||
],
|
||
"text/plain": [
|
||
"shape: (4, 2)\n",
|
||
"┌────────┬─────┐\n",
|
||
"│ symbol ┆ len │\n",
|
||
"│ --- ┆ --- │\n",
|
||
"│ str ┆ u32 │\n",
|
||
"╞════════╪═════╡\n",
|
||
"│ GLD ┆ 32 │\n",
|
||
"│ IWM ┆ 33 │\n",
|
||
"│ QQQ ┆ 34 │\n",
|
||
"│ TLT ┆ 27 │\n",
|
||
"└────────┴─────┘"
|
||
]
|
||
},
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Generate events\n",
|
||
"events_df = generate_momentum_breakout_events(\n",
|
||
" etf_filtered.select([\"timestamp\", \"symbol\", \"close\"]),\n",
|
||
" lookback=20,\n",
|
||
" min_gap_days=30, # At least 30 days between events per symbol\n",
|
||
")\n",
|
||
"\n",
|
||
"print(f\"Generated {len(events_df)} events\")\n",
|
||
"print(\"\\nEvents by symbol:\")\n",
|
||
"events_df.group_by(\"symbol\").len().sort(\"symbol\")"
|
||
]
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},
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"cell_type": "markdown",
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"source": [
|
||
"## 3. Event Study: Manual Implementation\n",
|
||
"\n",
|
||
"**Key Fix**: Use proper indexing with MultiIndex or aligned DataFrames,\n",
|
||
"not `get_loc()` on potentially non-unique indices.\n",
|
||
"\n",
|
||
"The manual implementation shows the mechanics:\n",
|
||
"1. For each event, extract estimation and event windows\n",
|
||
"2. Estimate market model (CAPM) in estimation window\n",
|
||
"3. Calculate abnormal returns in event window\n",
|
||
"4. Aggregate across events"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bbecb68e",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.00176,
|
||
"end_time": "2026-06-13T03:33:20.898490+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:20.896730+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### 3a. Market Model Estimation\n",
|
||
"\n",
|
||
"For each event, estimate the CAPM parameters ($\\alpha$, $\\beta$) from\n",
|
||
"the pre-event estimation window. This establishes the \"normal return\"\n",
|
||
"baseline."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "66ddb9f2",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:33:20.902500Z",
|
||
"iopub.status.busy": "2026-06-13T03:33:20.902368Z",
|
||
"iopub.status.idle": "2026-06-13T03:33:20.905157Z",
|
||
"shell.execute_reply": "2026-06-13T03:33:20.904875Z"
|
||
},
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.005262,
|
||
"end_time": "2026-06-13T03:33:20.905453+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:20.900191+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def _estimate_market_model(\n",
|
||
" returns_wide: pl.DataFrame,\n",
|
||
" event_idx: int,\n",
|
||
" symbol: str,\n",
|
||
" estimation_window: tuple[int, int],\n",
|
||
" min_estimation_obs: int,\n",
|
||
") -> tuple[float, float] | None:\n",
|
||
" \"\"\"Estimate market model alpha and beta from estimation window.\"\"\"\n",
|
||
" est_start = event_idx + estimation_window[0]\n",
|
||
" est_end = event_idx + estimation_window[1]\n",
|
||
"\n",
|
||
" if est_start < 0:\n",
|
||
" return None\n",
|
||
"\n",
|
||
" est_slice = returns_wide.slice(est_start, est_end - est_start + 1)\n",
|
||
" asset_est = est_slice[symbol].to_numpy()\n",
|
||
" bench_est = est_slice[\"benchmark_return\"].to_numpy()\n",
|
||
"\n",
|
||
" valid = np.isfinite(asset_est) & np.isfinite(bench_est)\n",
|
||
" if np.sum(valid) < min_estimation_obs:\n",
|
||
" return None\n",
|
||
"\n",
|
||
" try:\n",
|
||
" slope, intercept, _, _, _ = stats.linregress(bench_est[valid], asset_est[valid])\n",
|
||
" return intercept, slope # alpha, beta\n",
|
||
" except Exception:\n",
|
||
" return None"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "ed3eb77a",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.001607,
|
||
"end_time": "2026-06-13T03:33:20.908898+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:20.907291+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### 3b. Abnormal Return Computation\n",
|
||
"\n",
|
||
"Given estimated $\\alpha$ and $\\beta$, compute abnormal returns in the event\n",
|
||
"window: $AR_t = R_{actual} - (\\alpha + \\beta \\cdot R_{market})$."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "4d7708e5",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:33:20.912484Z",
|
||
"iopub.status.busy": "2026-06-13T03:33:20.912421Z",
|
||
"iopub.status.idle": "2026-06-13T03:33:20.915194Z",
|
||
"shell.execute_reply": "2026-06-13T03:33:20.914900Z"
|
||
},
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.004956,
|
||
"end_time": "2026-06-13T03:33:20.915407+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:20.910451+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def _compute_abnormal_returns(\n",
|
||
" returns_wide: pl.DataFrame,\n",
|
||
" event_idx: int,\n",
|
||
" event_date,\n",
|
||
" symbol: str,\n",
|
||
" alpha: float,\n",
|
||
" beta: float,\n",
|
||
" event_window: tuple[int, int],\n",
|
||
") -> tuple[list[dict], dict | None]:\n",
|
||
" \"\"\"Compute abnormal returns in the event window.\"\"\"\n",
|
||
" evt_start = event_idx + event_window[0]\n",
|
||
" evt_end = event_idx + event_window[1]\n",
|
||
"\n",
|
||
" if evt_end >= len(returns_wide):\n",
|
||
" return [], None\n",
|
||
"\n",
|
||
" evt_slice = returns_wide.slice(evt_start, evt_end - evt_start + 1)\n",
|
||
" asset_evt = evt_slice[symbol].to_numpy()\n",
|
||
" bench_evt = evt_slice[\"benchmark_return\"].to_numpy()\n",
|
||
" evt_dates = evt_slice[\"timestamp\"].to_list()\n",
|
||
"\n",
|
||
" ars = []\n",
|
||
" car = 0.0\n",
|
||
" for i, (date, r_actual, r_market) in enumerate(\n",
|
||
" zip(evt_dates, asset_evt, bench_evt, strict=False)\n",
|
||
" ):\n",
|
||
" if not (np.isfinite(r_actual) and np.isfinite(r_market)):\n",
|
||
" continue\n",
|
||
" r_expected = alpha + beta * r_market\n",
|
||
" ar = r_actual - r_expected\n",
|
||
" car += ar\n",
|
||
" day_relative = event_window[0] + i\n",
|
||
" ars.append(\n",
|
||
" {\n",
|
||
" \"event_date\": event_date,\n",
|
||
" \"symbol\": symbol,\n",
|
||
" \"day\": day_relative,\n",
|
||
" \"ar\": ar,\n",
|
||
" \"car_to_day\": car,\n",
|
||
" }\n",
|
||
" )\n",
|
||
"\n",
|
||
" event_car = {\n",
|
||
" \"event_date\": event_date,\n",
|
||
" \"symbol\": symbol,\n",
|
||
" \"car\": car,\n",
|
||
" \"alpha\": alpha,\n",
|
||
" \"beta\": beta,\n",
|
||
" }\n",
|
||
" return ars, event_car"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "b75dd59f",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.00156,
|
||
"end_time": "2026-06-13T03:33:20.918564+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:20.917004+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### 3c. Aggregation: AAR and CAAR\n",
|
||
"\n",
|
||
"Average across events to get the Average Abnormal Return (AAR) per relative\n",
|
||
"day, then cumulate to get the CAAR with correct standard errors:\n",
|
||
"\n",
|
||
"$$\\text{SE}(\\text{CAAR}_t) = \\sqrt{\\sum_{s=1}^{t} \\frac{\\sigma_s^2}{n_s}}$$"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "c1b533ea",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:33:20.922090Z",
|
||
"iopub.status.busy": "2026-06-13T03:33:20.922017Z",
|
||
"iopub.status.idle": "2026-06-13T03:33:20.924553Z",
|
||
"shell.execute_reply": "2026-06-13T03:33:20.924269Z"
|
||
},
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.004655,
|
||
"end_time": "2026-06-13T03:33:20.924788+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:20.920133+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def _aggregate_to_caar(\n",
|
||
" all_ars: list[dict],\n",
|
||
" event_cars: list[dict],\n",
|
||
") -> tuple[pl.DataFrame, pl.DataFrame, pl.DataFrame]:\n",
|
||
" \"\"\"Aggregate abnormal returns to AAR and CAAR.\"\"\"\n",
|
||
" ar_df = pl.DataFrame(all_ars) if all_ars else pl.DataFrame()\n",
|
||
" car_df = pl.DataFrame(event_cars) if event_cars else pl.DataFrame()\n",
|
||
"\n",
|
||
" if len(ar_df) > 0:\n",
|
||
" daily_aar = (\n",
|
||
" ar_df.group_by(\"day\")\n",
|
||
" .agg(\n",
|
||
" [\n",
|
||
" pl.col(\"ar\").mean().alias(\"aar\"),\n",
|
||
" pl.col(\"ar\").std().alias(\"std\"),\n",
|
||
" pl.len().alias(\"n\"),\n",
|
||
" ]\n",
|
||
" )\n",
|
||
" .sort(\"day\")\n",
|
||
" .with_columns((pl.col(\"std\") / pl.col(\"n\").sqrt()).alias(\"se\"))\n",
|
||
" )\n",
|
||
" # CAAR and its standard error\n",
|
||
" daily_aar = daily_aar.with_columns(pl.col(\"aar\").cum_sum().alias(\"caar\"))\n",
|
||
" daily_aar = daily_aar.with_columns(\n",
|
||
" (pl.col(\"std\").pow(2) / pl.col(\"n\")).cum_sum().sqrt().alias(\"caar_se\")\n",
|
||
" )\n",
|
||
" daily_aar = daily_aar.with_columns(\n",
|
||
" [\n",
|
||
" (pl.col(\"aar\") / pl.col(\"se\")).alias(\"t_stat\"),\n",
|
||
" (pl.col(\"caar\") / pl.col(\"caar_se\")).alias(\"caar_t_stat\"),\n",
|
||
" ]\n",
|
||
" )\n",
|
||
" else:\n",
|
||
" daily_aar = pl.DataFrame()\n",
|
||
"\n",
|
||
" return ar_df, car_df, daily_aar"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d3cd7d8a",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.001555,
|
||
"end_time": "2026-06-13T03:33:20.927939+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:20.926384+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"### 3d. Event Study Wrapper\n",
|
||
"\n",
|
||
"The wrapper orchestrates the three stages: estimate market model, compute\n",
|
||
"abnormal returns, and aggregate to CAAR."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "9bea23b5",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:33:20.931539Z",
|
||
"iopub.status.busy": "2026-06-13T03:33:20.931477Z",
|
||
"iopub.status.idle": "2026-06-13T03:33:20.935207Z",
|
||
"shell.execute_reply": "2026-06-13T03:33:20.934937Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.006079,
|
||
"end_time": "2026-06-13T03:33:20.935593+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:20.929514+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def compute_event_study(\n",
|
||
" returns_df: pl.DataFrame,\n",
|
||
" benchmark_df: pl.DataFrame,\n",
|
||
" events_df: pl.DataFrame,\n",
|
||
" estimation_window: tuple[int, int] = (-60, -6),\n",
|
||
" event_window: tuple[int, int] = (-5, 10),\n",
|
||
" min_estimation_obs: int = 30,\n",
|
||
") -> dict:\n",
|
||
" \"\"\"\n",
|
||
" Compute event study using the three-stage pipeline above.\n",
|
||
"\n",
|
||
" Parameters\n",
|
||
" ----------\n",
|
||
" returns_df : Long-format returns (timestamp, symbol, return)\n",
|
||
" benchmark_df : Benchmark returns (timestamp, benchmark_return)\n",
|
||
" events_df : Events (timestamp, symbol)\n",
|
||
" estimation_window : (start, end) days relative to event\n",
|
||
" event_window : (start, end) days relative to event\n",
|
||
" min_estimation_obs : Minimum observations in estimation window\n",
|
||
"\n",
|
||
" Returns\n",
|
||
" -------\n",
|
||
" Dict with:\n",
|
||
" - abnormal_returns: DataFrame of AR by event-day\n",
|
||
" - event_cars: DataFrame of CAR by event\n",
|
||
" - daily_aar: DataFrame of AAR by relative day\n",
|
||
" \"\"\"\n",
|
||
" # Create wide-format returns for efficient lookup\n",
|
||
" returns_wide = returns_df.pivot(on=\"symbol\", index=\"timestamp\", values=\"return\").sort(\n",
|
||
" \"timestamp\"\n",
|
||
" )\n",
|
||
" returns_wide = returns_wide.join(benchmark_df, on=\"timestamp\", how=\"inner\")\n",
|
||
"\n",
|
||
" dates = returns_wide[\"timestamp\"].to_list()\n",
|
||
" date_to_idx = {d: i for i, d in enumerate(dates)}\n",
|
||
" symbols = [c for c in returns_wide.columns if c not in [\"timestamp\", \"benchmark_return\"]]\n",
|
||
"\n",
|
||
" all_ars = []\n",
|
||
" event_cars = []\n",
|
||
"\n",
|
||
" for row in events_df.iter_rows(named=True):\n",
|
||
" event_date = row[\"timestamp\"]\n",
|
||
" symbol = row[\"symbol\"]\n",
|
||
" if symbol not in symbols or event_date not in date_to_idx:\n",
|
||
" continue\n",
|
||
" event_idx = date_to_idx[event_date]\n",
|
||
"\n",
|
||
" # Check event window upper bound\n",
|
||
" if event_idx + event_window[1] >= len(dates):\n",
|
||
" continue\n",
|
||
"\n",
|
||
" # Stage 1: Estimate market model\n",
|
||
" model = _estimate_market_model(\n",
|
||
" returns_wide, event_idx, symbol, estimation_window, min_estimation_obs\n",
|
||
" )\n",
|
||
" if model is None:\n",
|
||
" continue\n",
|
||
" alpha, beta = model\n",
|
||
"\n",
|
||
" # Stage 2: Compute abnormal returns\n",
|
||
" ars, event_car = _compute_abnormal_returns(\n",
|
||
" returns_wide, event_idx, event_date, symbol, alpha, beta, event_window\n",
|
||
" )\n",
|
||
" all_ars.extend(ars)\n",
|
||
" if event_car:\n",
|
||
" event_cars.append(event_car)\n",
|
||
"\n",
|
||
" # Stage 3: Aggregate\n",
|
||
" ar_df, car_df, daily_aar = _aggregate_to_caar(all_ars, event_cars)\n",
|
||
"\n",
|
||
" return {\n",
|
||
" \"abnormal_returns\": ar_df,\n",
|
||
" \"event_cars\": car_df,\n",
|
||
" \"daily_aar\": daily_aar,\n",
|
||
" \"n_events\": len(car_df),\n",
|
||
" }"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"id": "c5b28fa6",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:33:20.939285Z",
|
||
"iopub.status.busy": "2026-06-13T03:33:20.939216Z",
|
||
"iopub.status.idle": "2026-06-13T03:33:20.968919Z",
|
||
"shell.execute_reply": "2026-06-13T03:33:20.968490Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.031952,
|
||
"end_time": "2026-06-13T03:33:20.969223+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:20.937271+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Processed 122 events\n",
|
||
"\n",
|
||
"CAR Summary:\n",
|
||
" Mean CAR: 1.11%\n",
|
||
" Median CAR: 0.61%\n",
|
||
" Std CAR: 3.72%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Run event study\n",
|
||
"result = compute_event_study(\n",
|
||
" returns_df.select([\"timestamp\", \"symbol\", \"return\"]),\n",
|
||
" benchmark_returns,\n",
|
||
" events_df,\n",
|
||
" estimation_window=(-60, -6),\n",
|
||
" event_window=(-5, 10),\n",
|
||
")\n",
|
||
"\n",
|
||
"print(f\"Processed {result['n_events']} events\")\n",
|
||
"\n",
|
||
"if len(result[\"event_cars\"]) > 0:\n",
|
||
" print(\"\\nCAR Summary:\")\n",
|
||
" cars = result[\"event_cars\"][\"car\"].to_numpy()\n",
|
||
" print(f\" Mean CAR: {np.mean(cars) * 100:.2f}%\")\n",
|
||
" print(f\" Median CAR: {np.median(cars) * 100:.2f}%\")\n",
|
||
" print(f\" Std CAR: {np.std(cars) * 100:.2f}%\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "bb264889",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.001638,
|
||
"end_time": "2026-06-13T03:33:20.972673+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:20.971035+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 4. Visualize CAAR with Correct Confidence Bands\n",
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"\n",
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"**Key Fix**: The variance of CAAR is the **cumulative sum** of daily AAR variances,\n",
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"not a rolling calculation.\n",
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"\n",
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"$$\\text{Var}(\\text{CAAR}_t) = \\sum_{s=1}^{t} \\text{Var}(\\text{AAR}_s)$$\n",
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"\n",
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"$$\\text{SE}(\\text{CAAR}_t) = \\sqrt{\\sum_{s=1}^{t} \\frac{\\sigma_s^2}{n_s}}$$"
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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"if len(result[\"daily_aar\"]) > 0:\n",
|
||
" daily_aar = result[\"daily_aar\"]\n",
|
||
"\n",
|
||
" fig = go.Figure()\n",
|
||
"\n",
|
||
" days = daily_aar[\"day\"].to_list()\n",
|
||
" caar = daily_aar[\"caar\"].to_numpy() * 100 # Convert to percent\n",
|
||
" caar_se = daily_aar[\"caar_se\"].to_numpy() * 100\n",
|
||
"\n",
|
||
" # CAAR line\n",
|
||
" fig.add_trace(\n",
|
||
" go.Scatter(\n",
|
||
" x=days,\n",
|
||
" y=caar,\n",
|
||
" mode=\"lines+markers\",\n",
|
||
" name=\"CAAR\",\n",
|
||
" line=dict(color=\"steelblue\", width=2),\n",
|
||
" )\n",
|
||
" )\n",
|
||
"\n",
|
||
" # 95% confidence band (CORRECT formula)\n",
|
||
" upper = caar + 1.96 * caar_se\n",
|
||
" lower = caar - 1.96 * caar_se\n",
|
||
"\n",
|
||
" fig.add_trace(\n",
|
||
" go.Scatter(\n",
|
||
" x=days + days[::-1],\n",
|
||
" y=np.concatenate([upper, lower[::-1]]).tolist(),\n",
|
||
" fill=\"toself\",\n",
|
||
" fillcolor=\"rgba(70, 130, 180, 0.2)\",\n",
|
||
" line=dict(width=0),\n",
|
||
" name=\"95% CI\",\n",
|
||
" )\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Event day marker\n",
|
||
" fig.add_vline(x=0, line_dash=\"dash\", line_color=\"red\", annotation_text=\"Event Day\")\n",
|
||
" fig.add_hline(y=0, line_dash=\"dot\", line_color=\"gray\")\n",
|
||
"\n",
|
||
" fig.update_layout(\n",
|
||
" title=\"Cumulative Average Abnormal Return (CAAR) - Momentum Breakouts\",\n",
|
||
" xaxis_title=\"Days Relative to Event\",\n",
|
||
" yaxis_title=\"CAAR (%)\",\n",
|
||
" height=500,\n",
|
||
" )\n",
|
||
"\n",
|
||
" fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"id": "a8d2e574",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:33:21.684378Z",
|
||
"iopub.status.busy": "2026-06-13T03:33:21.684296Z",
|
||
"iopub.status.idle": "2026-06-13T03:33:21.686911Z",
|
||
"shell.execute_reply": "2026-06-13T03:33:21.686653Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.005916,
|
||
"end_time": "2026-06-13T03:33:21.687297+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:21.681381+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Daily AAR and CAAR:\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
" Day AAR (%) t-stat CAAR (%) CAAR t n\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
" -5 0.030 0.41 0.030 0.41 122\n",
|
||
" -4 0.097 1.68 0.127 1.37 122\n",
|
||
" -3 0.153 2.01* 0.280 2.33* 122\n",
|
||
" -2 0.085 1.60 0.365 2.78* 122\n",
|
||
" -1 0.191 3.12* 0.556 3.84* 122\n",
|
||
" 0 0.739 8.84* 1.295 7.74* 122\n",
|
||
" 1 0.033 0.39 1.328 7.08* 122\n",
|
||
" 2 -0.094 -1.16 1.234 6.04* 122\n",
|
||
" 3 0.015 0.20 1.249 5.71* 122\n",
|
||
" 4 -0.080 -1.23 1.169 5.12* 122\n",
|
||
" 5 0.013 0.16 1.182 4.88* 122\n",
|
||
" 6 -0.110 -1.21 1.072 4.14* 122\n",
|
||
" 7 -0.120 -1.70 0.952 3.55* 122\n",
|
||
" 8 0.001 0.01 0.953 3.34* 122\n",
|
||
" 9 0.080 0.91 1.034 3.46* 122\n",
|
||
" 10 0.075 1.01 1.109 3.60* 122\n",
|
||
"--------------------------------------------------------------------------------\n",
|
||
"* = significant at 5%\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Print daily AAR and CAAR table\n",
|
||
"if len(result[\"daily_aar\"]) > 0:\n",
|
||
" daily_aar = result[\"daily_aar\"]\n",
|
||
" print(\"\\nDaily AAR and CAAR:\")\n",
|
||
" print(\"-\" * 80)\n",
|
||
" print(f\"{'Day':>5} {'AAR (%)':>10} {'t-stat':>10} {'CAAR (%)':>12} {'CAAR t':>10} {'n':>8}\")\n",
|
||
" print(\"-\" * 80)\n",
|
||
"\n",
|
||
" for row in daily_aar.iter_rows(named=True):\n",
|
||
" sig = \"*\" if abs(row[\"t_stat\"]) > 1.96 else \"\"\n",
|
||
" caar_sig = \"*\" if abs(row[\"caar_t_stat\"]) > 1.96 else \"\"\n",
|
||
" print(\n",
|
||
" f\"{row['day']:>5} {row['aar'] * 100:>10.3f} {row['t_stat']:>10.2f}{sig}\"\n",
|
||
" f\" {row['caar'] * 100:>12.3f} {row['caar_t_stat']:>10.2f}{caar_sig} {row['n']:>8}\"\n",
|
||
" )\n",
|
||
"\n",
|
||
" print(\"-\" * 80)\n",
|
||
" print(\"* = significant at 5%\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "082296ef",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.002423,
|
||
"end_time": "2026-06-13T03:33:21.692162+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:21.689739+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 4b. Library Alternative: EventStudyAnalysis\n",
|
||
"\n",
|
||
"The manual implementation above teaches the MacKinlay (1997) mechanics.\n",
|
||
"The `ml4t-diagnostic` library adds robust variance adjustment (BMP test,\n",
|
||
"Boehmer et al. 1991) and non-parametric testing (Corrado rank test)."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"id": "32db8ff1",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:33:21.697377Z",
|
||
"iopub.status.busy": "2026-06-13T03:33:21.697308Z",
|
||
"iopub.status.idle": "2026-06-13T03:33:23.175821Z",
|
||
"shell.execute_reply": "2026-06-13T03:33:23.175427Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 1.481802,
|
||
"end_time": "2026-06-13T03:33:23.176345+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:21.694543+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"from ml4t.diagnostic.config import EventConfig\n",
|
||
"from ml4t.diagnostic.config.event_config import WindowSettings\n",
|
||
"from ml4t.diagnostic.evaluation import EventStudyAnalysis\n",
|
||
"\n",
|
||
"# Prepare data in library format\n",
|
||
"# Returns: date, asset, return\n",
|
||
"lib_returns = returns_df.select(\n",
|
||
" pl.col(\"timestamp\").alias(\"date\"),\n",
|
||
" pl.col(\"symbol\").alias(\"asset\"),\n",
|
||
" pl.col(\"return\"),\n",
|
||
")\n",
|
||
"\n",
|
||
"# Benchmark: date, return\n",
|
||
"lib_benchmark = benchmark_returns.rename({\"timestamp\": \"date\", \"benchmark_return\": \"return\"})\n",
|
||
"\n",
|
||
"# Events: date, asset\n",
|
||
"lib_events = events_df.select(\n",
|
||
" pl.col(\"timestamp\").alias(\"date\"),\n",
|
||
" pl.col(\"symbol\").alias(\"asset\"),\n",
|
||
")\n",
|
||
"\n",
|
||
"# Configure event study\n",
|
||
"config = EventConfig(\n",
|
||
" window=WindowSettings(\n",
|
||
" estimation_start=-60,\n",
|
||
" estimation_end=-6,\n",
|
||
" event_start=-5,\n",
|
||
" event_end=10,\n",
|
||
" ),\n",
|
||
" model=\"market_model\",\n",
|
||
" min_estimation_obs=30,\n",
|
||
")\n",
|
||
"\n",
|
||
"# Run library event study\n",
|
||
"lib_analysis = EventStudyAnalysis(\n",
|
||
" returns=lib_returns,\n",
|
||
" events=lib_events,\n",
|
||
" benchmark=lib_benchmark,\n",
|
||
" config=config,\n",
|
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")\n",
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"lib_result = lib_analysis.run()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "7275bad3",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-06-13T03:33:23.182208Z",
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},
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"duration": 0.00549,
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"end_time": "2026-06-13T03:33:23.184520+00:00",
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"exception": false,
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"status": "completed"
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}
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},
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"outputs": [
|
||
{
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"name": "stdout",
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||
"output_type": "stream",
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"text": [
|
||
"=== Library EventStudyAnalysis Results ===\n",
|
||
"\n",
|
||
"==================================================\n",
|
||
"EVENT STUDY RESULTS\n",
|
||
"==================================================\n",
|
||
"Events analyzed: 122\n",
|
||
"Event window: [-5, 10]\n",
|
||
"Model: market_model\n",
|
||
"\n",
|
||
"CUMULATIVE AVERAGE ABNORMAL RETURN (CAAR)\n",
|
||
" Event day AAR (t=0): +0.0074 (+0.74%)\n",
|
||
" Final CAAR: +0.0111 (+1.11%)\n",
|
||
" 95% CI: [0.0096, 0.0125]\n",
|
||
"\n",
|
||
"STATISTICAL TEST\n",
|
||
" Test: boehmer\n",
|
||
" Test statistic: 3.0177\n",
|
||
" P-value: 0.0025\n",
|
||
" Result: significant at α=0.05\n",
|
||
"==================================================\n",
|
||
"\n",
|
||
"=== Additional Statistical Tests ===\n"
|
||
]
|
||
}
|
||
],
|
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"source": [
|
||
"# Compare results\n",
|
||
"print(\"=== Library EventStudyAnalysis Results ===\\n\")\n",
|
||
"print(lib_result.summary())\n",
|
||
"\n",
|
||
"# The library adds tests not in the manual implementation:\n",
|
||
"print(\"\\n=== Additional Statistical Tests ===\")\n",
|
||
"if hasattr(lib_result, \"bmp_test\"):\n",
|
||
" print(f\"BMP test (robust to event-induced variance): t={lib_result.bmp_test['t_stat']:.2f}\")\n",
|
||
"if hasattr(lib_result, \"corrado_test\"):\n",
|
||
" print(f\"Corrado rank test (non-parametric): z={lib_result.corrado_test['z_stat']:.2f}\")"
|
||
]
|
||
},
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||
{
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"cell_type": "markdown",
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"id": "40b4c41b",
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"metadata": {
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"papermill": {
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"duration": 0.002528,
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||
"end_time": "2026-06-13T03:33:23.189516+00:00",
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"exception": false,
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"start_time": "2026-06-13T03:33:23.186988+00:00",
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"status": "completed"
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||
}
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||
},
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"source": [
|
||
"The manual implementation teaches the market model ($R_i = \\alpha + \\beta R_m$)\n",
|
||
"and CAAR computation. The library adds:\n",
|
||
"\n",
|
||
"| Feature | Manual | Library |\n",
|
||
"|---------|--------|---------|\n",
|
||
"| Market model | Yes | Yes |\n",
|
||
"| Mean-adjusted model | No | Yes |\n",
|
||
"| BMP test (robust variance) | No | Yes |\n",
|
||
"| Corrado rank test | No | Yes |\n",
|
||
"| Event clustering handling | No | Yes |"
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||
]
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},
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{
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"cell_type": "markdown",
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"id": "c8d8a1e1",
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"metadata": {
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"duration": 0.002407,
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"end_time": "2026-06-13T03:33:23.194360+00:00",
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"start_time": "2026-06-13T03:33:23.191953+00:00",
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"status": "completed"
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}
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},
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"source": [
|
||
"## 5. CAR Distribution\n",
|
||
"\n",
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||
"Examining the distribution of individual event CARs reveals whether the\n",
|
||
"aggregate effect is driven by many small effects or few large ones."
|
||
]
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||
},
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{
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"status": "completed"
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},
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z3x99prdy5sx+3iPCwhV4zZMWeg+b7jya7OlalZw+zUfVK9Z9qHVvjnVWqc3rp1//UqfnJun3v3fHPZYsqfEldNNal4GTnaBkbs669n9fvnHq1GnVazNY/+zap/ULxznbOxKbW7CB14wr2L5SO/CasZgbGM2+VPNFJNmzZ433+C3z8+6DpzpPZHh5dDdnj/PZL7Pyb1Z4zZMZUhJ4B497zXmc2+ZlLyp3rhxBvddNSJ0x9x3nq6ef79HMuYEw8DJ/GK1672PnRkzz2DGzGvzhZ187nwqcG1TNdW2eUHHpRQU1/6UznxIkdtNaYAW8asX7NKJvS5mV4WDfG0FNioMQQAABBBDwiYBnA2/gW6NOR0Vp3/7D+mTnt86jxa67+jK9NKKz8yzVwOvcwBsIAGVK3OHcaJQta1Zt++J7J1ScvTIWuAPffBmAWSE8fOS4syUg1MBrHudlvrntooIX6qff/nEej2X2W74+sU9cOA98RG5Ce9n77tA/u/dr44c7nbBmPsoOPIfXzC2x8SUUeM1zguu0HOCEmppVyzof05sbrszNS+ab5ALbOMIReIPtK9TAG8xNa4HaB24qM//77GfvBn5uVi/rtR6kPfsOOU9vMM8ANn8IbN32jXNNBJ7dnJLAG3iihXm+svlylD/+3uM8FzpvnoQft2bGYvZIm5vfzCPKzKPRzP5a82i9fQcOOeH2j793O18bbLbcmJsHzY12Jvwm9DJPmDDPCl45Z6SuvKxwooH3zDX0uua8tdZ5HJp5LFqw7w2f/P5imAgggAACCAQl4NnAGxi9WeEyK3Am6FYqd4/zzNyzV8fMcecG3v0Hj+i1Bau1YcsO/fbXLudbqsxKnlllNatdgdfho8c1ZPxs52uLzVcY33/37Zr4fIeQA6/5ggezSvvXP3ucMT5Y9i51bV3XCZ9nv8wXU5gxmzB2wzWXqWm9qtr62VfxvnjCHJ/Y+BL74olffv/H+fIE88UIp05H6cZrLnf2s1YoXTyu+3AEXnOyYPoKNfAmdeV+snJq3BMHnCB5Osp5fJdZoQ48e/fc9mbrg7lxzFwP5ktI8l6Q29nfWqm8uZ7KOauoKQm85vzmS00Wrnhf5lozX9trntObVOA1bcxKrlltNttMzPORzRdSXJA7l/NJxCMV7tUjD5Rw/igzTyF5cWjHeF8YcvacAl+uYbY8mPomtsJr2pj9us8+N1HrNm1z/tir/lDJoN4bQf324CAEEEAAAQR8IuC5wOsTN4aJAAIIIIAAAggg4BMBAq9PCsUwEUAAAQQQQAABBEITIPCG5kYrBBBAAAEEEEAAAZ8IEHh9UiiGiQACCCCAAAIIIBCaAIE3NDdaIYAAAggggAACCPhEgMDrk0IxTAQQQAABBBBAAIHQBAi8obnRCgEEEEAAAQQQQMAnAgRenxSKYSKAAAIIIIAAAgiEJkDgDc2NVggggAACCCCAAAI+ESDw+qRQDBMBBBBAAAEEEEAgNAECb2hutEIAAQQQQAABBBDwiQCB1yeFYpgIIIAAAggggAACoQkQeENzoxUCCCCAAAIIIICATwQIvD4pFMNEAAEEEEAAAQQQCE2AwBuaG60QQAABBBBAAAEEfCJA4PVJoRgmAggggAACCCCAQGgCBN7Q3GiFAAIIIIAAAggg4BMBAq9PCsUwEUAAAQQQQAABBEITIPCG5kYrBBBAAAEEEEAAAZ8IEHh9UiiGiQACCCCAAAIIIBCaAIE3NDdaIYAAAggggAACCPhEgMDrk0IxTAQQQAABBBBAAIHQBAi8obnRCgEEEEAAAQQQQMAnAgRenxSKYSKAAAIIIIAAAgiEJkDgDc2NVggggAACCCCAAAI+ESDw+qRQDBMBBBBAAAEEEEAgNAECb2hutEIAAQQQQAABBBDwiQCB1yeFYpgIIIAAAggggAACoQkQeENzoxUCCCCAAAIIIICATwQIvD4pFMNEAAEEEEAAAQQQCE2AwBuaG60QQAABBBBAAAEEfCJA4PVJoRgmAggggAACCCCAQGgCBN7Q3GiFAAIIIIAAAggg4BMBAq9PCsUwEUAAAQQQQAABBEITIPCG5kYrBBBAAAEEEEAAAZ8IEHh9UiiGiQACCCCAAAIIIBCaAIE3NDdaIYAAAggggAACCPhEgMDrk0IxTAQQQAABBBBAAIHQBAi8obnRCgEEEEAAAQQQQMAnAgRenxSKYSKAAAIIIIAAAgiEJkDgDc2NVggggAACCCCAAAI+ESDw+qRQDBMBBBBAAAEEEEAgNAECb2hutEIAAQQQQAABBBDwiQCB1yeFYpgIIIAAAggggAACoQkQeENzoxUCCCCAAAIIIICATwQIvD4pFMNEAAEEEEAAAQQQCE2AwBuaG60QQAABBBBAAAEEfCJA4PVJoRgmAggggAACCCCAQGgCBN7Q3GiFAAIIIIAAAggg4BMBAq9PCsUwEUAAAQQQQAABBEITIPCG5hbX6u99/1megeYIIOAHgUsL5nSG+ffe49bD/S/quK6fWlA5M+fSD632WJ+PEyCAQOQLXFogR+RPMhVnSOC1xCXwWgLSHAGfCIQ18J4+rutfKqicWXLph5YEXp9cAgwTgTQVIPDa8RN47fxE4LUEpDkCPhEg8PqkUAwTgQgVIPDaFZbAa+dH4LX0ozkCfhEg8PqlUowTgcgUIPDa1ZXAa+dH4LX0ozkC6VHgP7Y0pMeyM2cErAQIvFZ8IvDa+RF4Lf1ojoAfBY4cO66aDbskOPS2TevosSrlk5xWQoG32bODtX//Qc15aYhy5MjutI+KjtYzbfrp2PETWjJ7rGeozPy7D5iguo9XVrmSxZMdV3LHv7thq95cskYvj+8X71yr12/RgqVrFRsbq+oPl9VjVR5wfn702HG16zFck0f1Vs7/WSU7CA5AwOcCBF67AhJ47fwIvJZ+NEfAjwImgB09Hv8JLds//07jpryuicN76PJLC4cUeE+cPKnHHimvWo8+6LRfv+kTvfrGUh05etwzgfeVN5ZpzXtbdODQEfV6tmmygTep4/fuP6Su/cfo0OFjKpA/X7zAa8J+zYZdNWvSQGXJkln1W/bRG9OGKlfOHHpt/nLnv9Wr+bAfLx/GjEBIAgTekNjiGhF47fwIvJZ+NEcgEgR279mvtj2Gq3ObBrr/7jucKZkwN3H6XH317c/Knj2r6tWsrKoPlXF+VrNRF/1w8Qbl232d8hwrrClj+mjgyGnKlTO7vvvhN+XPn1fVK5fV5o93qFL5+zVz7rK4wJvUeZe8s0HzF6/SocNHlfeC3KryYGk9Xaea06f52Qdbt+vyyy7SB1t3KFvWzOrQop5K3FUkrgRV6rZXi4ZPJLtCbRq06TZMdYJc4U3u+K2ffK6X5yyNF3j3Hzikxu0HaOnr45zx1W/ZW8P7d1DevHnUoecITRnVRzlyZIuEy4c5IBCUAIE3KKZEDyLw2vkReC39aI6A3wVOnTqtTn1H657it6tR3erOdGJiYp0AXPb+Yqpd4yH9++9ede4/VsP6ttN111zhBN5dGX7V4eu/1YpG61S4QH7VbdFL1111mTJlyqTiRW/Winc3KXOWTOrWrpF6D57oBN7kzvvbH38rc+YsKpA/rw6aFdjBE9WpVX3dcdsNTuA1wbnxUzV0T7FbtX7Tx3pnzWZn1TTw6jPkRSeUl7y3aLJlSe3AGx0do9pNumvSyJ7KmiWzmj87WHNeGqq5i1crT64cjisvBNKTAIHXrtoEXjs/Aq+lH80R8LvAmMmvy6xGDu7VRhkzZnCm8833v2jouBmaPeX5uOlNmDZXFxcuoDqPVXIC786b5yvjhSed5/CaIFu5dht1b9dIBQrm1aTp85U5S2ZFR0epc5uGcYE3ufPu3ndAi5at1Y4vv9fhw0d0+OhxtWpUy1ktNoF3++ffaGDP1s6Y9u0/qLrNe+ntOROcFeiUvlI78JrxbPl4p+YsfEcxsVKdGg+p+B03q33vUXppdB+teX+rVqz5QFkyZ1KTBo+p2O03pXQKHI+ArwQIvHblIvDa+RF4Lf1ojoCfBZa/u0kLlq7RiyN7Kk+uM9/EZl7vb/5MI1+cpXx588T9t9Ono/RwhZJqUr/GeYHXBOYnm/bU0L7tdW/xW9W2+zAdPmK+0S1WvTo1jQu8SZ23Ub1H1bTjQN1+y/V6unYVFSp4oQaPnq47brvR2aJwbuD9778TerRBJ7312ph4Yw+2Hm4E3nPHMv21xc7qddmSd6lF58GaNWmQfv/jHw2f8Gq8Py6CnQPHIeAnAQKvXbUIvHZ+BF5LP5oj4CeBxpPXxw331KH92rNjowrd9YCy5s4bbxonDu7Vga8/0SUlH0lwen9tXKKf7nxLp3MfV8WMCxUbE6s/31uoC2+5R7kvvVqm/Ym9/+jUwT3Ke0NR7d2xSZeVe8z572ef99U2FeLOv2ffQT3VopfefmOCsmc7s2I7aNS0iAm8ZovGs31G66WxffXDT79p0isLNGVUL52OilaVOu208s0XlTlTJj9dTowVgRQJEHhTxHXewQReOz8Cr6UfzRHwk0Ag8EafOqndH69VnqtuVI6Lrog3hQwZM0sZM2r3x2uUo9Clyn3FDZIy6PTh/YrNmFE58l+kcwOvOcGf7y9Rllx5VbBoKcWcPqV9n29R7iuuV5bceeMCb0xMTLzzvtikrL798Vdn+0ORm6/XE426ql2zOrr7zlu15ZPPNW3WW86KcrArvLZ7eH/8+XdNe22xundopIL54/8RkNSKcEI3rZ17XUyduVCXXFRINR4pp12796lzvzHOnt7dew+oY68Rmjt9uJ8uJcaKQIoFCLwpJov/uznWPF+HV8gCfLVwyHQ0RMBXAoFvWqs8aLlO7NulvTs2Jjj+3FfcqHw3FlXUf8d06MedOnlgr2JiopUldz7lu+EOZctbIMHA+8+Hq5U5Ww6dOnpAGTJmUu7LrlWeq27RqSP74wKv6fDs82bLlEHXXXO5mjesqVtvvEbvb9mmyTPm6+Sp07r/nju0e+9+lbmveNCBN5inNLz48jyt/+BTHT/+n7JmyercWDd9bF/nsWLbdn6jHoNe0MwXB+qyS848mi2p401YbdV1iKKjomUeyZYrV049VK6EWjeuHc/WbPno0n+cpo3t6zyOzLxmz1+uH37+QydPn1aNh8sFdaOdry44BovAOQIEXrtLghVeOz9WeC39aI6AXwTODry2Y46OPaF1sbWUSdmdLQ2hvs7e0hDqOWiHAAL+ECDw2tWJwGvnR+C19KM5An4RIPD6pVKME4HIFCDw2tWVwGvnR+C19KM5An4RIPD6pVKME4HIFCDw2tWVwGvnR+C19KM5An4RIPD6pVKME4HIFCDw2tWVwGvnR+C19KM5An4RIPD6pVKME4HIFCDw2tWVwGvnR+C19KM5An4RIPD6pVKME4HIFCDw2tWVwGvnR+C19KM5An4RIPD6pVKME4HIFCDw2tWVwGvnR+C19KM5An4RIPD6pVKME4HIFCDw2tWVwGvnR+C19KM5An4SOPurhW3GzXN4bfRoi0D6FCDw2tWdwGvnR+C19KM5An4SIPD6qVqMFYHIEiDw2tWTwGvnR+C19KM5An4SIPD6qVqMFYHIEiDw2tWTwGvnR+C19KM5An4SIPD6qVqMFYHIEiDw2tWTwGvnR+C19KM5An4SIPD6qVqMFYHIEiDw2tWTwGvnR+C19KM5An4SIPD6qVqMFYHIEiDw2tWTwGvnR+C19KM5An4SIPD6qVqMFYHIEiDw2tWTwGvnR+C19KM5An4SIPD6qVqMFYHIEiDw2tWTwGvnR+C19KM5An4SIPD6qVqMFYHIEiDw2tWTwGvnR+C19KM5An4R4JvW/FIpxolAZAoQeO3qSuC18yPwWvrRHAG/CBB4/VIpxolAZAoQeO3qSuC18yPwWvrRHAG/CBB4/VIpxolAZAoQeO3qSuC18yPwWvrRHAG/CBB4/VIpxolAZAoQeO3qSuC18yPwWvrRHAG/CBB4/X/hOwYAACAASURBVFIpxolAZAoQeO3qSuC18yPwWvrRHAG/CBB4/VIpxolAZAoQeO3qSuC18yPwWvrRHAG/CBB4/VIpxolAZAoQeO3qSuC18yPwWvrRHAG/CBB4/VIpxolAZAoQeO3qSuC18yPwWvrRHAG/CBB4/VIpxolAZAoQeO3qSuC18yPwWvrRHAE/CfBNa36qFmNFILIECLx29Uz3gTc2Nla9h72szz7/Trv3HVSBfBfoyUcfUIsG1ZQhQwbFxMRq9JR5Wrxyk05HRalimbs0sGtjZc+W1ZH/e99/dhWgNQII+EaAwOubUjFQBCJOgMBrV9J0H3hNoH1t4WqVKXGHCl6YV9/88Jtadh+j1yf1VZGbr9G8pes1681VmjSsk3LmyKZug6ao6G3Xq2urOgReu2uP1gj4ToDA67uSMWAEIkaAwGtXynQfeM/mO3XqtD7a/o2eG/2qFk4fpPz58uiZjsNUsXRxNaxd2Tn0/Q93atDYWVq3YCyB1+7aozUCvhMg8PquZAwYgYgRIPDalZLA+z+/j7d/q8adhqtg/rx6cUhHFbnlWucn5Wp21ODuTVT2vqLO//7tz12q0qCHPl01TTmyZ2VLg931R2sEfCVA4PVVuRgsAhElQOC1KyeB9yw/s8K7+dMv1XvodC16eZAuvbig7q3SShOHdFSJYrc4R+7ac0AVanfSpiUTnRXg/05G21WA1ggg4BuBx0auCstYo2NPaF1sLWVSdlXMuDDkcy7p8XDIbSOuYWzEzYgJIRBPIEe2TIhYCBB4E8Cr23qQHnu4tOrWqOCs8A7p2Uyl7y2S4Arv/iMnLfhpigACfhKoN35tWIYbrsA7t+ODYRlPRJwkQ0TMgkkgkKhA/jzZ0LEQIPAmgFe9YS81qVdFjz9SRg07DNVDZe/W07UqOUe+t2W7Bo6ZpQ2Lxjv/m6c0WFx9NEXAZwJsafBZwRguAhEkwJYGu2Km+8C77YvvtXbjZ6r20P0qVCCfFry9QbMWrNaK2Wf28855a41eX7RWU4abpzRkV5eBk3XrjVepV/v6BF67a4/WCPhOgMDru5IxYAQiRoDAa1fKdB94//p3r4a9MEdffPuzjh3/T7fccJW6t6kXd9NadHSMRkyaq2XvblZUVJQeKFXMeQ6vCb+s8NpdfLRGwE8CfNOan6rFWBGIPAECr11N033gteNjS4OtH+0R8IsAgdcvlWKcCESmAIHXrq4EXjs/9vBa+tEcAb8IEHj9UinGiUBkChB47epK4LXzI/Ba+tEcAb8IEHj9UinGiUBkChB47epK4LXzI/Ba+tEcAb8IEHj9UinGiUBkChB47epK4LXzI/Ba+tEcAb8IEHj9UinGiUBkChB47epK4LXzI/Ba+tEcAb8IEHj9UinGiUBkChB47epK4LXzI/Ba+tEcAb8IEHj9UinGiUBkChB47epK4LXzI/Ba+tEcAb8IEHj9UinGiUBkChB47epK4LXzI/Ba+tEcAT8J8E1rfqoWY0UgsgQIvHb1JPDa+RF4Lf1ojoCfBAi8fqoWY0UgsgQIvHb1JPDa+RF4Lf1ojoCfBAi8fqoWY0UgsgQIvHb1JPDa+RF4Lf1ojoCfBAi8fqoWY0UgsgQIvHb1JPDa+RF4Lf1ojoCfBAi8fqoWY0UgsgQIvHb1JPDa+RF4Lf1ojoCfBAi8fqoWY0UgsgQIvHb1JPDa+RF4Lf1ojoCfBAi8fqoWY0UgsgQIvHb1JPDa+RF4Lf1ojoCfBAi8fqoWY0UgsgQIvHb1JPDa+RF4Lf1ojoCfBAi8fqoWY0UgsgQIvHb1JPDa+RF4Lf1ojoBfBPimNb9UinEiEJkCBF67uhJ47fwIvJZ+NEfALwIEXr9UinEiEJkCBF67uhJ47fwIvJZ+NEfALwIEXr9UinEiEJkCBF67uhJ47fwIvJZ+NEfALwIEXr9UinEiEJkCBF67uhJ47fwIvJZ+NEfALwIEXr9UinEiEJkCBF67uhJ47fwIvJZ+NEfALwIEXr9UinEiEJkCBF67uhJ47fwIvJZ+NEfALwIEXr9UinEiEJkCBF67unou8HZ67kXVebSCShS/RRkyZLCbnQut/973nwu90AUCCKS1AIE3rStA/wikbwECr139PRd42/d9Qe9/uEOXXVxIdR59QI89XFr58ua2m2UqtibwpiIup073AuH6oodwQK7uX805TeVBy61PFx17QutiaymTsqtixoUhn+/VNhVCbktDBBDwlwCB165engu8Zjp79x/SW+9s1KIVG7Vr7wFVLn+Ps+pbvMgNdrNNhdYE3lRA5ZQI/E/AS4E3nEUh8IZTk3MhkD4ECLx2dfZk4A1MKTY2Vh9+9pVmL1yjjVt36vprLnOC76OVSip3rhx2Mw9TawJvmCA5DQIJCBB4k74sWOHlbYNA+hEg8NrV2tOB99sff9eCtzdo+doPFRMTo7L3FdW2L77XkaPHVaXifRrUrYnd7MPQmsAbBkROgUAiAgReAi9vDgQQOCNA4LW7EjwXeI8dP6EV67Zq4fIN+uq7X3XbTVerVrXyqvbgfcqZI7uioqO1duNnmvPWWs2e2Ntu9mFoTeANAyKnQIDAG9I1wApvSGw0QsCXAgReu7J5LvDe/XALZcyYUdUeKqknq5fXzddfaTfDVG5N4E1lYE6frgVY4WWFN12/AZg8AmcJEHjtLgfPBd7FKzfp4QdKKEf2rHYzc6k1gdclaLpJlwIEXgJvurzwmTQCCQgQeO0uC88F3nWbtumDjz9Xv07PKGPGM8/hjYmJ1bCJr6vUPUVUvuSddjMOc2sCb5hBOR0CZwkQeAm8vCEQQOCMAIHX7krwXOCt23qQKpYurub1zzzzMvCaOX+V1n3wmWZP7GM34zC3JvCGGZTTIUDgDfoaYA9v0FQciIDvBQi8diX0XOA1e3hfHd9LRW6+Jt7MvvnhNzXsMEyfrJxqN+MwtybwhhmU0yFA4A36GiDwBk3FgQj4XoDAa1dCzwXeko+21ej+rVXy7tvjzezDT79Sv5EztPbNsXYzDnNrAm+YQTkdAh4NvHzTGpcmAgikpQCB107fc4G3z/CX9f3Pf+qFwe11yUUFnNn9s3u/nu03UVddfpFG9mtlN+MwtybwhhmU0yFA4A36GmCFN2gqDkTA9wIEXrsSei7wHjx0VM26jtL3P/+hyy4u6Mzur3/36srLLtIrY3uocMF8djMOc2sCb5hBOR0CBN6grwECb9BUHIiA7wUIvHYl9FzgNdOJjo7Rxo926pvvf5MyZND1V1+mB0reqSxZMtvNNhVaE3hTAZVTIvA/AS89pYEtDVyWCCCQlgIEXjt9TwZeuym525rA6643vaUvAQJv0vVmhTd9vR+YbfoWIPDa1d9zgdd8dfDyNR9qx5c/6six4+fNbsxzbexmHObWBN4wg3I6BM4SIPASeHlDIIDAGQECr92V4LnAO3DsLL21YqOK33GDCubPq4wZMsab4Yi+Le1mHObWBN4wg3I6BAi8QV8DrPAGTcWBCPhegMBrV0LPBd77q7VR62dqqGHtynYzc6k1gdclaLpJlwKs8Pqr7ARwf9WL0fpLgMBrVy/PBd5SNdppaM/mKnd/UbuZudSawOsSNN2kSwECr7/KTuD1V70Yrb8ECLx29fJc4B01ZZ4OHT6m53s0tZuZS60JvC5B0026FPBS4A1nAaJjT2hdbC1lUnZVzLgwnKdO03MReNOUn84jXIDAa1dgzwVes4d3wdsbVO+xCsqaJct5s+vWpq7djMPcmsAbZlBOh8BZAgRef10OBF5/1YvR+kuAwGtXL88F3qadRyY5oxlju9vNOMytCbxhBuV0CBB4fXsNEHh9WzoG7gMBAq9dkTwXeO2m435rAq/75vSYfgRY4fVXrQm8/qoXo/WXAIHXrl6eDbwnTp7Srj0HdNXlF9nNMJVbE3hTGZjTp2sBAq+/yk/g9Ve9GK2/BAi8dvXyXOA9cOiI+o14Re9t2e7M7KsNM52vGq7d4jmVu/9OdWz2hN2Mz2n934lT6jN8unZ89aMOHjqqa6+6VF1b1dF9d93qHBkTE6vRU+Zp8cpNOh0VpYpl7tLAro2VPVtW5+cE3rCWg5MhEE+AwOuvC4LA6696MVp/CRB47erlucDbY8hL2rv/kLq3qaeaTfs5gde8Fq3YqNkL39WSV5+3m/E5rU3InTp7mR57uLQKFcjnBNuXZi/TewvHK3euHJq3dL1mvblKk4Z1Us4c2dRt0BQVve16JxQTeMNaCk6GwHkCBF5/XRQEXn/Vi9H6S4DAa1cvzwXe0jXa65VxPXTjtZfrtvKN4gLvNz/8pgbthuiz1dPsZpxMa/PVxkUrNtXC6QN1yw1X6ZmOw1SxdPG4L8J4/8OdGjR2ltYtGEvgTdVKcHIEJAKvv64CAq+/6sVo/SVA4LWrl+cCb/FKzbV05hBdcWnheIF300dfqPvgKfpw+WS7GSfT+vOvf1LjTiP0/lsTnBXecjU7anD3Jip735kvwvjtz12q0qCHPl01TTmyZ2VLQ6pWg5OndwECr7+uAAKvv+rFaP0lQOC1q5fnAm/zrqN1d9Gb1PLp6nGB9+Sp02rdY6xy5cyuiUM62s04idbHjp9Q/bbPq0rFEmrRoLpz5L1VWjl9lih2i/O/zY10FWp30qYlE5U/Xx4dPxmVauPhxAikd4HHR672DMHq/tWcsVQetNx6TJH6xROLu/vjK+GtC8gJEEgDgZzZMqdBr5HTpecC73c//aFGHYfpnmI3a92mbapZpaw+3v6NzM1scyb11Q3XXJ4q+ibstuox1nkqhFnRzZAhg9OPWeEd0rOZSt9bxPnf567wHjx2OlXGw0kRQECqM/ZdzzAQeJMvxfzOlZI/iCMQQCAkgXy5zv8yrpBOlE4beS7wmjr8s2uf5ixeq6+/+1UxsbG66bor1OCJh5xtDqnxMjfJmbBbvMgN6tW+flzYNX017DBUD5W9W0/XOvOL3Dw9YuCYWdqwaLzzv3lKQ2pUhHMicEbAS1saCLzJX5VsaUjeiCMQCFWALQ2hyp1p58nAazellLX++9+9avTscOcpDY3qPBzXOHOmTMqaNYvmvLVGry9aqynDzVMasqvLwMm69carnGBM4E2ZNUcjkFIBAm9KxdL2eAJv2vrTe2QLEHjt6uu5wPvOuo+SnJHZXxvOl9k20aHfC+ed8snq5fVcl0bOM4BHTJqrZe9uVlRUlB4oVcx5Dq8JvwTecFaCcyFwvgCB119XBYHXX/VitP4SIPDa1ctzgbdE1dbnzch8+cPx/044T034aMUUuxmHuTVbGsIMyukQOEuAwOuvy4HA6696MVp/CRB47erlucCb0HTMs3FrNu2vLi2fVLn7zzwezCsvAq9XKsE4IlGAwOuvqhJ4/VUvRusvAQKvXb18EXjNFJes+kCvLVitt2YMtptxmFsTeMMMyukQYIXXt9cAgde3pWPgPhAg8NoVyTeB1+y17TZ4ira9O91uxmFuTeANMyinQ4DA69trgMDr29IxcB8IEHjtiuS5wPvx9m/jzShWsdp/4LCmvf62cmTPpjcm97ObcZhbE3jDDMrpEPBo4A1nYSL1iycIvOG8SjgXAvEFCLx2V4TnAu9t5RudNyPziLA7br1WA7s10bVXXmI34zC3JvCGGZTTIUDg9e01QOD1bekYuA8ECLx2RfJc4DVPYzj3lT1bNmXMeOabz7z2IvB6rSKMJ5IEvHTTWjhdWeENpybnQiB9CBB47ersucBrNx33WxN43Tenx/QjQOD1V61Z4fVXvRitvwQIvHb18lzgfXXeyqBn1LjuI0Efm1oHEnhTS5bzIuCtrxYOZz1Y4Q2nJudCIH0IEHjt6uy5wFuqRjvFxsaqYP58cTOLjYnRz7//o+uuulTK8P9bG5bNHGI3+zC0JvCGAZFTIJCIACu8/ro0WOH1V70Yrb8ECLx29fJc4H2iWX8982RlPVqpVLyZteg2WqXvLaKGtSvbzTjMrQm8YQbldAicJUDg9dflQOD1V70Yrb8ECLx29fJc4C1WqblmT+yt22+6Jt7M3n53iybMWKS188fYzTjMrQm8YQbldAgQeH17DRB4fVs6Bu4DAQKvXZE8F3hL12ivbm3qqkbl+Cu8JvD2H/2qtvPFE3YVpzUCPhJghddHxZJE4PVXvRitvwQIvHb18lzgnfLaUr2+aI16tK2nu++4SRkzZdSX3/6ioS+8rpuvv1KTh3Wym3GYW7PCG2ZQToeAR1d4V/ev5oys8qDl1jXipjVrQk6AQLoTIPDaldxzgTcmJlaTZy7Ry3NX6PTpqLjZPVT2bvXr1FAFLrzAbsZhbk3gDTMop0OAwOvba4AVXt+WjoH7QIDAa1ckzwXewHSOHT+hH375U5kyZtTllxbShXnz2M00lVoTeFMJltMiIG89lowV3uQvSQJv8kYcgUCoAgTeUOXOtPNk4DXftvbZ5z/o3z37VLtaeUVHx2jbF9/rmisvUcH8ee1mHObWBN4wg3I6BFjh9e01QOD1bekYuA8ECLx2RfJc4P32x9/VqsdYnTp1WoeOHNNXG2Y6M2zXe4LyX5hHg7o1sZtxmFsTeMMMyukQIPD69hog8Pq2dAzcBwIEXrsieS7wNnp2uG694SrnSQ23P9A4LvCu27RNIyfP1eq5o+xmHObWBN4wg3I6BAi8vr0GCLy+LR0D94EAgdeuSJ4LvPc80kqLXxmsyy8ppNvKN4oLvGblt26rgdqxdobdjMPcmsAbZlBOhwCB17fXAIHXt6Vj4D4QIPDaFclzgbdk9baaOaGXbrz28niBd8W6rRo9ZZ7eWzjebsZhbk3gDTMop0OAwOvba4DA69vSMXAfCBB47YrkucDbd8QMmZvWRvRtpTsfbOqs8P70299q03OcSt1bRP07NbSbcZhbE3jDDMrpECDw+vYaIPD6tnQM3AcCBF67Inku8B46fEwtuo3W37v2av/BI7ri0sL68589uum6K/TKuB7KmyeX3YzD3JrAG2ZQToeARwNvOAvDF0+EU5NzIZA+BAi8dnX2XOA10zGPIdv8yRf66vtfFRsTq5uuu1LlShZV5kyZ7GabCq0JvKmAyikR+J8AXy3sr0uBFV5/1YvR+kuAwGtXL88F3oYdhqppvaoqd39Ru5m51JrA6xI03aRLAQKvv8pO4PVXvRitvwQIvHb18lzgNTetDereRA+WuctuZi61JvC6BE036VKAwOuvshN4/VUvRusvAQKvXb08F3i7D56qQgXzqVvrunYzc6k1gdclaLpJlwIEXn+VncDrr3oxWn8JEHjt6uW5wLtw+fsaNnGOBnRppDy5c543u/Il77SbcZhbE3jDDMrpEDhLgMDrr8uBwOuvejFafwkQeO3q5bnAW6RC4yRn9MX6V+1mHObWBN4wg3I6BAi8vr0GCLy+LR0D94EAgdeuSJ4LvHbTcb81gdd9c3pMPwKs8Pqr1gRef9WL0fpLgMBrVy/PBN7ew6arVcMauvKywnYzcrk1gddlcLpLVwIEXn+Vm8Drr3oxWn8JEHjt6uWZwHtb+UZ6Y3I/Fb31Ouc5vA/W6aypI7o4Xzjh5ReB18vVYWx+F/BS4F3dv5rDWXnQcmtWvnjCmpATIJDuBAi8diX3ZOCNio5W0YpNNf+l53T7TdfYzTCVWxN4UxmY06drAQKvv8rPCq+/6sVo/SVA4LWrF4HXzk8EXktAmiOQhACB11+XB4HXX/VitP4SIPDa1YvAa+dH4LX0ozkCSQkQeP11fRB4/VUvRusvAQKvXb08FXjr1qigiwvnV2xsrCa8vEhPPV5RhQteGG+Gzeuf2UfnlRcrvF6pBOOIRAECr7+qSuD1V70Yrb8ECLx29fJM4H2wTpegZrJ2/pigjnPrIAKvW9L0kx4FCLz+qjqB11/1YrT+EiDw2tXLM4HXbhpp15rAm3b29Bz5AgRef9WYwOuvejFafwkQeO3qReC182MPr6UfzRFISoDA66/rg8Drr3oxWn8JEHjt6kXgtfMj8Fr60RwBAu/CiLkICLwRU0om4kEBAq9dUQi8dn4EXks/miPgl8AbzkrxxRPh1ORcCKQPAQKvXZ0JvHZ+BF5LP5ojQOBlhZd3AQIIJC9A4E3eKKkjCLx2fgReSz+aI0DgJfDyLkAAgeQFCLzJGxF47YySbM1TGlIRl1OnewEv3bQWzmKwpSGcmpwLgfQhQOC1qzMrvHZ+rPBa+tEcAVZ4WeHlXYAAAskLEHiTN2KF186IFd5U9OPUCBB4Cby8CxBAIHkBAm/yRgReOyMCbyr6cWoECLwEXt4FCCCQvACBN3kjAq+dEYE3Ff04NQIEXgIv7wIEEEhegMCbvBGB186IwJuKfpzaWwKRepOYt5QlblrzWkUYDwLeFyDw2tWIm9b+53f46HE16TRCzetXU+Xy98SpxsTEavSUeVq8cpNOR0WpYpm7NLBrY2XPltU5hqc02F2AtPaWAIE38Xqs7l/N+WHlQcuti0bgtSbkBAikOwECr13JCbySJry8SEtWbdK+A4c1ql/reIF33tL1mvXmKk0a1kk5c2RTt0FTVPS269W1VR0Cr921R2sPChB4Cbw2lyVfLWyjR1sEkhYg8NpdIQTes/xqtxigZk9VjRd4n+k4TBVLF1fD2pWdI9//cKcGjZ2ldQvGEnjtrj1ae1CAwEvgtbksCbw2erRFgMCbmtcAgTeZwFuuZkcN7t5EZe8r6hz525+7VKVBD326appyZM/KlobUvDo5t+sCBF4Cr81FR+C10aMtAgTe1LwGCLzJBN57q7TSxCEdVaLYLc6Ru/YcUIXanbRpyUTlz5dHR/+LSs36cG4EXBV4YvRqV/vzU2fs4U2+Wou6nvkkjBcCCIRfIHeOzOE/aTo6I4E3mcBrVniH9Gym0vcWcY48d4X38PHT6ehyYaqRLlB7zLuRPsWQ50fgTZ5uQZdKyR/EEQggEJLABTmzhNSORmcECLzJBN6GHYbqobJ36+laZ36Rv7dluwaOmaUNi8Y7/5unNPBWiiQBtjQkXk0Cb/JXOlsakjfiCARCFeCmtVDlCLznySV009qct9bo9UVrNWW4eUpDdnUZOFm33niVerWvT+C1u/Zo7UEBAi+B1+ayJPDa6NEWgaQFCLx2VwgrvJKGTJitFeu26uix/5Qta1ZlyZJJS18dokIF8ik6OkYjJs3Vsnc3KyoqSg+UKuY8h9eEX1Z47S4+WntPgMBL4LW5Kgm8Nnq0RYDAm5rXAIHXUpctDZaANPeUAIHXnXLwxRPuONMLApEkwAqvXTUJvHZ+7OG19KO5twQIvO7Ug8DrjjO9IBBJAgReu2oSeO38CLyWfjT3lgCB1516RGrgdUfPv72w5cO/tfPCyAm8dlUg8Nr5EXgt/WjuLQECrzv1IPC64+y1Xgi8XquIv8ZD4LWrF4HXzo/Aa+lHc28JEHjdqQeB1x1nr/VC4PVaRfw1HgKvXb0IvHZ+BF5LP5p7S4DA6049CLzuOHutFwKv1yrir/EQeO3qReC18yPwWvrR3FsCBF536kHgdcfZa70QeL1WEX+Nh8BrVy8Cr50fgdfSj+beEiDwulMPAq87zl7rhcDrtYr4azwEXrt6EXjt/Ai8ln4095YAgdedehB43XH2Wi8EXq9VxF/jIfDa1YvAa+dH4LX0o7m3BAi8iddjdf9qzg8rD1puXTQCrzWhL09A4PVl2TwzaAKvXSkIvHZ+BF5LP5p7S4DAS+D11hUZWaMh8EZWPd2eDYHXTpzAa+dH4LX0o7m3BAi8BF5vXZGRNRoCb2TV0+3ZEHjtxAm8dn4EXks/mntLgMBL4PXWFRlZoyHwRlY93Z4NgddOnMBr50fgtfSjubcECLwEXm9dkZE1GgJvZNXT7dkQeO3ECbx2fgReSz+ae0uAwEvg9dYVGVmjIfBGVj3dng2B106cwGvnR+C19KO5twQIvAReb12RkTUaAm9k1dPt2RB47cQJvHZ+BF5LP5p7S4DAS+D11hUZWaMh8EZWPd2eDYHXTpzAa+dH4LX0o7m3BAi8BF5vXZGRNRoCb2TV0+3ZEHjtxAm8dn4EXks/mntLgMDrTj344gl3nL3WC4HXaxXx13gIvHb1IvDa+RF4Lf1o7i0BAq879SDwuuPstV4IvF6riL/GQ+C1qxeB186PwGvpR3NvCRB43akHgdcdZ6/1QuD1WkX8NR4Cr129CLx2fgReSz+ae0uAwOtOPQi87jh7rRcCr9cq4q/xEHjt6kXgtfMj8Fr60dxbAgRed+pB4HXH2Wu9EHi9VhF/jYfAa1cvAq+dH4HX0o/m3hIg8LpTDwKvO85e64XA67WK+Gs8BF67ehF47fwIvJZ+NPeWAIHXnXoQeN1x9lovBF6vVcRf4yHw2tWLwGvnR+C19KO5twQIvO7Ug8DrjrPXeiHweq0i/hoPgdeuXgReOz8Cr6Ufzb0lQOB1px4EXnecvdYLgddrFfHXeAi8dvUi8Nr5EXgt/WjuLQECb+L1WN2/mvPDyoOWWxeNwGtN6MsTEHh9WTbPDJrAa1cKAq+dH4HX0o/m3hIg8BJ4vXVFRtZoCLyRVU+3Z0PgtRMn8Nr5EXgt/WjuLQECL4HXW1dkZI2GwBtZ9XR7NgReO3ECr50fgdfSj+beEiDwEni9dUVG1mgIvJFVT7dnQ+C1Eyfw2vkReC39aO4tAQIvgddbV2RkjYbAG1n1dHs2BF47cQKvnR+B19KP5t4SIPASeL11RUbWaAi8kVVPt2dD4LUTJ/Da+RF4Lf1o7i0BAi+B11tXZGSNhsAbWfV0ezYEXjtxAq+dH4HX0o/m3hIg8BJ4vXVFRtZoCLyRVU+3Z0PgtRMn8Nr5EXgt/WjuLQECL4HXW1dkZI2GwBtZ9XR7NgReO3ECr50fgdfSj+beEiDwulMPvnjCHWev9ULg9VpF/DUeAq9dvQi8dn4EXks/mntLgMDrTj0IvO4400vSAgRwf10hBF67ehF46eIeVAAAG0NJREFU7fwIvJZ+NPeWAIHXnXoQeN1xphcCbyRdAwReu2oSeO38CLyWfjT3lgCB1516EHjdcaYXAm8kXQMEXrtqEnjt/Ai8ln4095YAgdedehB43XGmFwJvJF0DBF67ahJ47fwIvJZ+NPeWAIHXnXoQeN1xphcCbyRdAwReu2oSeO38CLyWfm43J9C5LU5/CQkQeLkuvCDATWteqELwYyDwBm+V0JEEXjs/Aq+ln9vNCbxui9MfgZdrwKsCBF6vVibhcRF47epF4LXzI/Ba+rndnMDrtjj9EXi5BrwqQOD1amUIvKlRGQKvperf+/6zPAPN3RQg8LqpHVl9re5fzZlQ5UHLrSfGlgZrQk4QBgECbxgQXTwFK7x22AReOz9WeC393G5O4HVbPHL6I/BGTi2ZyRkBAq+/rgQCr129CLx2fgReSz+3mxN43RaPnP4IvJFTS2ZC4PXjNUDgtasagdfOT5UHr7A8Q3ibe+0vdgJmeOvL2dJOgMCbdvb0nDoCXvv3InVmGfpZvfbv1+p+VUOfDC1F4E3mIoiJidXoKfO0eOUmnY6KUsUyd2lg18bKni2r05LAmzSg135h8J5HIFQBAm+ocrTzqgCB11//fhF47d5JBN5k/OYtXa9Zb67SpGGdlDNHNnUbNEVFb7teXVvVIfAGce0ReINA4hBfCBB4fVEmBpkCAQIvgTcFl4vvDyXwJlPCZzoOU8XSxdWwdmXnyPc/3KlBY2dp3YKxBN4gLn8CbxBIHOILAQKvL8rEIFMgQOAl8KbgcvH9oQTeZEpYrmZHDe7eRGXvK+oc+dufu1SlQQ99umqacmTPypaGZPwIvL7/HcEE/idA4OVSiDQBAi+BN9Ku6aTmQ+BNptr3VmmliUM6qkSxW5wjd+05oAq1O2nTkonKny+P5wKv1/b4eG2Pc3p6czPX8AqENfDqpNbFPKFMyq6KGReGd6CcDYEgBbz270WQw3btMK/9+0W97EpP4A1ihXdIz2YqfW+RBFd47fhpjQACCCCAAAIIIJDaAgTeZIQbdhiqh8reradrVXKOfG/Ldg0cM0sbFo1P7dpwfgQQQAABBBBAAIEwCBB4k0Gc89Yavb5oraYMN09pyK4uAyfr1huvUq/29cPAzykQQAABBBBAAAEEUluAwJuMcHR0jEZMmqtl725WVFSUHihVzHkOrwm/vBBAAAEEEEAAAQS8L0Dg9X6NfD3Cpas3a8bcd7Rs5pB48zhw6Ij6jXhFWz79Urlz5XC2jDSvX83Xc/XC4Me+9Kbjffar1D23a9qorl4Yni/HkNyXz/hyUh4c9KaPvlCrHmPijSxLlszaseZlD47Wf0Pid7E7NUvMmd/N7vgn1QuBN+1rEJEj2L33oJ7pOFQHDh1V4YIXnhd4Ow+YrOjoaPXr1FD/7N6v1j3GamivZnGPf4tIFBcmZX6p7t53UL07NIjrLXOmTM6XpvAKTSC5L58J7ay0OlfABN7B42Zp4cuD4n6UQVKe3DnBshDgd7EFXgqaJufM7+YUYKbSoQTeVILltGcENmzZobHTFsQLvKdOnda9VVtr3pT+uvn6K53jRk2Zp30HDmt47xbQWQiYX6rmjwzz7Ghe4RFI7stnwtMLZzGBd8iE2Vr1xkgwUkGA38WpgJrAKRNyNofxu9kd/6R6IfCmfQ0iegQJvfl/+f0fVWvYK+7LOwzAguUbtGj5+5o39bmI9kjtyZlfqm8sXqvcuXKqwIUXqOqD96lJ3Sqp3W1Enz+5L5+J6Mm7ODkTeNv2HqcL8+bRBblz6t5it6hj81rO/8/LXoDfxfaGwZwhqcDL7+ZgBFPvGAJv6tlG5JlPnjqt4/+dSHBumTJlOu8fp4Te/N/88JtqNX9OX773qjJkMB9aSsvXfKhpr7+tZbOGRqSb7aSOHT+hU6dPJ3iabFmzxm1Z+P2vXYqKjlH2rFn07U9/aOCYmWr59KN66vGKtkNIt+2T+/KZdAsT5ombff3mi33yXpBb/+7epzFT31ShAnk1bmC7MPeUPk/H72J36p5Y4OV3szv+SfVC4E37GvhqBG+/u0WTZi5JcMzXXX2pJg19Nt7PklpV2P7udGXNmsU53qzwLnz7fc1/iRXehHDNR71mBSyh1+OPlFHLp6sn+DPzR8TWz77WK+N6+Oo689JgzQovXz7jfkW2f/mDzHaSnWtnxP1h7P4oIqdHfhe7U8vEAu+5vfO72Z16nN0Lgdd983TVY6L7xqq0crYvBPbwjpw0V3v3H9LIfq3SlU9qT3b89IUyKwtjB7RN7a4i9vx8+UzalHbzJ1+qx/Mv6YOlE9NmABHWK7+L3SlosIGX383u1IPA675zuu0xsTd/x34TlTFjBvV9tqF27z2gFt1Ga0CXxqpYpni6tQrHxAePe02Vyt+j6666VN/88Lt6PD9Vg7s3xdUCly+fscBLQVPzOL3LLymoorddr4OHjqrviBm6644b+ZKfFBgmdSi/i8MEmcxpEnPmd7M7/kn1wgpv2tcgIkdgHjX2RLN+ioqK1n8nTjqPFnq0Uin1bPeUM1+zmttv5Cvauu1r5cqRXfWfeFCtG9aISAs3JzVs4hyt37zd8b24UH41erKy6tSo4OYQIq4vvnzGnZIuXrlJM99cpT//3uP8vqhU7h51alFbObJndWcAEdoLv4vdKWxyzvxudqcOBN60d2YECCCAAAIIIIAAAmkkwApvGsHTLQIIIIAAAggggIA7AgRed5zpBQEEEEAAAQQQQCCNBAi8aQRPtwgggAACCCCAAALuCBB43XGmFwQQQAABBBBAAIE0EiDwphE83SKAAAIIIIAAAgi4I0DgdceZXhBAAAEEEEAAAQTSSIDAm0bwdIsAAggggAACCCDgjgCB1x1nekEAAQQQQAABBBBIIwECbxrB0y0CCCCAAAIIIICAOwIEXnec6QUBBBBAAAEEEEAgjQQIvGkET7cIIIAAAggggAAC7ggQeN1xphcEEEAAAQQQQACBNBIg8KYRPN0igAACCCCAAAIIuCNA4HXHmV4QQAABBBBAAAEE0kiAwJtG8HSLAAIIIIAAAggg4I4AgdcdZ3pBIKIFWnQbrWuuvES92tePiHlu++J7Pd1+qD5ZOVU5c2SPiDkFJnHnQ800pn8bVSxTPKLmldxk3n3/U3UfPEXTRnXTvcVuTu7wJH9+4uQpNe40QoUK5NULgztYnYvGCCDgjgCB1x1nekHASmD/wSN6ec5yvbdlu/7ZvV8X5M7pBMzK5e/VE1XLKlvWLFbnt22c0sD76ryVWrzqAy2bOSSu6+P/ndA9j7TSwK6NVataOdshJdt+w5Ydatt7vC6/pJBWvTFSGTJkiGuTngPvX//uVaW6XeMssmfLqpuvv1KtGj6qMiXuSNY1cEBCNQ66cZgPPHDoiKo26Km2jR9T/ZoPxZ3dXAMjJs3VkaPHVfyOG/R8j2bOe8u8oqKjVb1hLw3q1kT33Hl+QN67/5CqNezl/JFXo3KpMI+Y0yGAQLgFCLzhFuV8CIRZYNeeA3qqzWBlyJhBrZ5+VDdee7lOnDytrdu+0ryl6/XikI4qXuTGMPeastOFI/CagLFy3Ucqetv1uvKywikbQAhHd3ruRR0+elxbP/tar73QW3fd8f+GBN6uGtG3pW667godPHRU85e9p3ff/0SzJvRSsdtvCErbS4F37EtvatNHn2vh9EHKlCmjM36zSvvAE89qWO8WTpBv0X20itx8rZ5tXsv5uXlvvbd5u14a2SXR+b6+aI1eW7Ba78wZocyZMgXlwkEIIJA2AgTetHGnVwSCFjDB7JsfftfC6QOVO1eOeO3Mym9MTIxy5sjmrI6+Oq5nvI9rS1Rtrec6N1KViiUUCHGTh3XS5JlL9N3Pf+ima6/QyH6t9MW3P2va7Lf15z97VOSWazW8T0tdUji/09eoyfP05Xe/OGEn8DL/yJsQtGL2cOc/nRt4ew2dro+3f6N9Bw8rT64cKlH8VvVs95QK5s+r1Rs+UecBk+LNo32Tms4KYuDj9gdKFVOlul1Us2o5tXmmRtyxP/7yl2o07qO3Zgx2wtihw8c0aso8rd+8TVFR0br9pmvUs31954+CpF6HjhxTuZod9fLobnphxiJddfnFGty9SVyTgNXEIR019bWl+v6nP3T1FZdoYLfGKnrrdc5xU19bprWbPlP9mg9q2uvLtWffAd11x00a0rOZM8/Aa+Hy9zVj7gr9/e8+XXpxQbVoUE2PP1Im7udFKjTWgC6NHa91H3ymW2+82gng5r/3bFdfH23/Wh989IUK5M+r3h3qq1CBfBo9Zb52fv2TLr2ogPp3fkYlit3inO/Tnd9p6Auv6+9d+3T6dJQuv7SQmtWrquqVSsb1l9yWhsAK7xuT+8XNNSYmVuZaali7kkytzCsp+8RqbNoHe52aa3nSzMXa8eWPala/qkrdc7uzzWTqiC5OTb7+4TddfflFGtD1/2uSUM2jo2NU5rH26tzyyXifHHz/8596vElf7Vg7Q1kyZ9Ir897R5o+/1Iyx3fXfiVOq0qC7pgzv7KxuJ/Yyn0qUqtFe4wa0VfmSdwb9nuZABBBwX4DA6745PSIQtID5B/X+am3Vo91Teurxikn+wxtskLji0sJqXr+aE8rGTVugXXv2q1DBC9XsqSrKljWr899MqDMrfOYVSuAdM/VN3X7z1U6Q3HfgsEa8+IazBWPC4PY6deq0ExBXvvdRXIg2+2RNaD87jE185S2tWLvV2W4QeI2eOl+fbP9W8196TibImJXvyy4pqFYNayhH9qyau3idc14TxJPae2tW72bMfUfvzh2lt97ZpBGT3tDGxS/IfHxvXoHAe8M1l6veYxVUMH8+vbZwtX77c5fWzButLFkyO4HXjNGsDD/1+IMyoXDUlLkqefftTug1r3Wbtqnr4Cnq0baec9wnO77TyMlzNX5gu7iAZIJt1ixZVLdGBRUrcoOyZM6scvcXdQJv9mzZ1Oypqrr95mu0aMVGbdy6Q7ly5lDjuo/o6ssv1ryl6/TDz39p7ZtjnC0ZH23/Rju/+tFZ8Td/HK3d+Jmmzl7m/LEUCG6hBF6z+m6ur07Na6lh7crJ2pvVzoRqLMUGHXgLF8ynZ558WFdcUljm/z8dFeUE3ssuLqhm9as5+2dnzl+lf3bt0+q5o+JtSTn7jWL+MHi6/RBtWjJRefPkivvRD7/8qcca99WONS879TTXw5ZPv9SMMd017fW3ZX4+ql/rZN+r7fu+oIsKXqi+zz6d7LEcgAACaSdA4E07e3pGIFmB7376QzWb9tOcSX11523XhyXwbl76ovLlze2ca8HyDRoy4XV98s5U5x998zL/8JtVyZVzRoQceM8dqDmfWUk1odK8Evu4++wwZlabK9frptkTezsBzgTKCrU7OSu+Tz76gDZu3anew17We4vGOyt05hUbG6tSNdppRJ9WKlOiSKJedVsNVOl771C7Jo/r6LH/VPbxDhrUvYmqPXh/vMD78TtTlSvnmZvW9uw76PT/wvMd9EDJYk7gXfXex1ry6vNx/UyetVTL12zRO6+fsavTcqCzRcOszAZeQybM1lff/SqzgmpeJtgO7dk83ips4L+Pea6tKpW72znOhO0qDXo4fyTcXfQm57998c3Pqtt6kN5bON4JhQm9Hqj1rLMVpk6NCs6PUxp4zV7VF19Z7GxpWDZrqPOHUjD2Se3TDuaTiHULxuriQmc+ZTCvwB8hZ1+/ZkX7mY7DtGHReGflO6FXYIV95Zz//8PJHHfy1GlVrN1ZA7o2Urn7iqpl9zHOHyUNnqikqk/3dOrzz+59GjlprvYfPKx777xF/To1PO9TFrMyvHHr55o5vmei1xs/QACBtBcg8KZ9DRgBAokKpEbgPfvJA+YjebO94PN1r8SNYfHKTc4qbyCchrLCa7ZAzF/6nr7+/leZ7QPm4+/Y2Bh9umqa008wgdcc17jTcJkVaXPj0Acff6EOfV/Q+29NUJ7cOTXltaVOEEvoZUJM7WrlE/zZz7//49yMZAJQYK9w10FTdPjIMU0bdeZmrcT28Jrw2LReVTV44iEn8K77YJsWTBsQ18+bb2/Qi6+8FWd3V+UWzjaIQJA2By5f86EGjZslE6bNywTeic93PO8j8XP/u3EsWb2tZk/so+JFzuyjDfxR8PZrw3TtlZc4IW7+0vXa+NHn+vvfvTp2/IQT1jq3eNJZFTavYANvxowZFBt75o8Is9I9un9rXX/NZc45grG3DbznPiEjoZr8+se/TjgNzD+hgk+fs1ybP/kywUD64adfyXxqYIzMtpv+nRpq0swlOnHilLMFp+KTnZ1tJQ+WKa6mXUY6N691aPpEvG5MPc2KsPljgBcCCHhXgMDr3dowMgScwHJ/9Tbq3qaeE7ISewWecPDKuB5x+znNsQnt4T07SJiP3J99bqK+WP9qkoHX7PE1+0oDr6T28JpVxwbthqjaQ/fr4QdKOCuPZiVu3LQ3Uxx43353iwaPf80JkP1HvqJMmTJpWO/mzjDMPuS312xxgmtKXibMv/zGivOamC0B6xeMc8abWOA1K8FmO8jTtSolGHjPXclOKPAG5pTSwGtusLu/Wpu4FW8zgcB+20Dga9d7grM3u1m9Krr5hqucPwza9R7vhP+UBt7ATWtvLF6nFWs/dOof2BYRjH1SgTel16mZa0I1+f2vXXqkfo8kA68Jox9t+8bZm5vcy9wg+njTvlo2c6hzQ6P5w2j7u9OVNWsWmeC8ddvXzpaHs19mq8msBavjPXEkuX74OQIIuC9A4HXfnB4RSJGACTGff/OT849wYCtC4ATmcUtmL2v+fBeo2EPNNLxPCz1SoYTzY7Pv0uz/NY/5OvumtZQGXrOSuXzth1r+2rC4cZvAaFaCE7ppzXysv3LdVieEBF7mJqY+w6fHBV4TomYvXH1eWD139dHcSW9uLuvSqo6zD9jcMR/4ON+E9Y79Jzora2Z18+yXMQncjX/2fzfbIh6s01mPPVxalcrdE6+NWcFrUreKmtarkmC4CmwpCNzMldAK77mB98mWA5ytKL07NIjr6/nxs52V77O3NASzwptc4L3miot1R8Umzr7Thx+4N66/Rxv10eMPl05x4A3M05gZ58+//snZWmMe4xaMfUI1NucK5Tq1CbymJmbbQWCbSVJvvv6jXlGBC/OqY7Mn9NNvf+vRZ3o7n36Ya8kEePMpw7nBedKri/XJzu/Y0pCi32ocjID7AgRe983pEYEUCZiPrc3NWZkzZ1LLBtV143VX6OTJ0/p4xzfOkxImPt/B2eNqbswx+1HbNa6pI8eO65V5K/XTr385Acgm8JrHdpkw2LbRY84THDZ99IXzsbl5AkBCgdfsa+055CX17vi0s2Xg2x9+d274MlsGAlsaPt7+rbNdwTwhwgQ1c9OW+bg8oY/bB46Z6QRus3f07NVcE+jNHlmzCm4eJXX1FRc7TycwQbxhrUoJPjvV3JRk9mqaPa9nP0nBFGTwuNf0yY5vnQAdWE3s1KK2s6/zwKGjmjhjkbNPNLDtIZjAu2bjp+r+/Evq3qauE9QDN62NHdBWFUoVc66DYLc0JBd4Teg3K5IXFc6vpnWr6PDRYzJftmDq0bVVnZADrxmj+cOjaeeRMk8FMaH3gjw5k7VPrMahXKc2gdfctGb2+W5Z9mKSNzL+8vs/athhqBOMzcq42R5iHltmAu4tN1ylHs+/pEsuKhD32LLAm7h9nwm6uHB+9enITWsp+sXGwQi4LEDgdRmc7hAIRWD33oN6afYyZ2/m7j0HlCNHNt1yw5Wq9mBJVX/ofucjV7Mi1XfEDH374+9O0OzYrJZ6DZ123mPJUrrCa8ZrVrHmLlnv3ClvVg8LXHiB83ixhAKv2fNp9kUufmeTTkdFq+TdtzlPGTDjDwRec85hE+do2erNioqOUbc2dfVk9fIJBt4vvv1F5iYzE2rNdoKzX2Zv8PiXFzrPSz146IgKF7zQ2YtpVujODbSmnQktJrRNH/3/X6wQON/2L39wtmKYJ0CYJ0mYJwKYG992fvWTTp2OUrn773T2eAZW2YMJvObcby57z/njwzxN4NKLCzh7gM/+Yo1wBl6zcvzc6JnOEwYuKVxADZ54UPOWrFfNKmWtAq+Zh7Gu3+5552ka5gat06ejk7VPqMahXKc2gdesKpeu0c75goizH8927vvw2f4vOk8nCWz9MD83X0xhnnJhblw0/3f2F1OYn5svrCjzeAdNGNTeebIGLwQQ8K4Agde7tWFkCCCAAAJhEBg/faGz/3bu5H6JPr4slG7Mvl6zZWLF68P54olQAGmDgIsCBF4XsekKAQQQQMB9AfNtceaRbuZ51uH6GuB/9+x3tpD07/RMkivH7s+WHhFAICEBAi/XBQIIIIBAxAuY/dS9h03XK+N6qsjN11jN1+wbN3uRzT72FwZ3sDoXjRFAwB0BAq87zvSCAAIIIIAAAgggkEYCBN40gqdbBBBAAAEEEEAAAXcECLzuONMLAggggAACCCCAQBoJEHjTCJ5uEUAAAQQQQAABBNwRIPC640wvCCCAAAIIIIAAAmkkQOBNI3i6RQABBBBAAAEEEHBHgMDrjjO9IIAAAggggAACCKSRAIE3jeDpFgEEEEAAAQQQQMAdAQKvO870ggACCCCAAAIIIJBGAgTeNIKnWwQQQAABBBBAAAF3BAi87jjTCwIIIIAAAggggEAaCRB40wiebhFAAAEEEEAAAQTcESDwuuNMLwgggAACCCCAAAJpJEDgTSN4ukUAAQQQQAABBBBwR4DA644zvSCAAAIIIIAAAgikkQCBN43g6RYBBBBAAAEEEEDAHQECrzvO9IIAAggggAACCCCQRgIE3jSCp1sEEEAAAQQQQAABdwQIvO440wsCCCCAAAIIIIBAGgkQeNMInm4RQAABBBBAAAEE3BEg8LrjTC8IIIAAAggggAACaSRA4E0jeLpFAAEEEEAAAQQQcEeAwOuOM70ggAACCCCAAAIIpJEAgTeN4OkWAQQQQAABBBBAwB0BAq87zvSCAAIIIIAAAgggkEYCBN40gqdbBBBAAAEEEEAAAXcECLzuONMLAggggAACCCCAQBoJEHjTCJ5uEUAAAQQQQAABBNwRIPC640wvCCCAAAIIIIAAAmkkQOBNI3i6RQABBBBAAAEEEHBHgMDrjjO9IIAAAggggAACCKSRAIE3jeDpFgEEEEAAAQQQQMAdAQKvO870ggACCCCAAAIIIJBGAgTeNIKnWwQQQAABBBBAAAF3BAi87jjTCwIIIIAAAggggEAaCRB40wiebhFAAAEEEEAAAQTcESDwuuNMLwgggAACCCCAAAJpJPB/fGiy9A8cJlYAAAAASUVORK5CYII="
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
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{
|
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"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Statistical Test (H0: Mean CAR = 0):\n",
|
||
" Mean CAR: 1.11%\n",
|
||
" Median CAR: 0.61%\n",
|
||
" T-statistic: 3.28\n",
|
||
" P-value: 0.0014\n",
|
||
" Significant at 5%: Yes\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"if len(result[\"event_cars\"]) > 0:\n",
|
||
" cars = result[\"event_cars\"][\"car\"].to_numpy() * 100\n",
|
||
"\n",
|
||
" fig = go.Figure()\n",
|
||
"\n",
|
||
" fig.add_trace(\n",
|
||
" go.Histogram(\n",
|
||
" x=cars,\n",
|
||
" nbinsx=25,\n",
|
||
" marker_color=\"steelblue\",\n",
|
||
" name=\"CAR Distribution\",\n",
|
||
" )\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Mean and zero lines\n",
|
||
" fig.add_vline(x=0, line_dash=\"dash\", line_color=\"red\", annotation_text=\"Zero\")\n",
|
||
" fig.add_vline(\n",
|
||
" x=np.mean(cars),\n",
|
||
" line_dash=\"solid\",\n",
|
||
" line_color=\"green\",\n",
|
||
" annotation_text=f\"Mean: {np.mean(cars):.2f}%\",\n",
|
||
" )\n",
|
||
"\n",
|
||
" fig.update_layout(\n",
|
||
" title=\"Distribution of Event CARs\",\n",
|
||
" xaxis_title=\"Cumulative Abnormal Return (%)\",\n",
|
||
" yaxis_title=\"Frequency\",\n",
|
||
" height=400,\n",
|
||
" )\n",
|
||
"\n",
|
||
" fig.show()\n",
|
||
"\n",
|
||
" # Statistical test: Mean CAR = 0\n",
|
||
" t_stat, p_value = stats.ttest_1samp(cars, 0)\n",
|
||
"\n",
|
||
" print(\"\\nStatistical Test (H0: Mean CAR = 0):\")\n",
|
||
" print(f\" Mean CAR: {np.mean(cars):.2f}%\")\n",
|
||
" print(f\" Median CAR: {np.median(cars):.2f}%\")\n",
|
||
" print(f\" T-statistic: {t_stat:.2f}\")\n",
|
||
" print(f\" P-value: {p_value:.4f}\")\n",
|
||
" print(f\" Significant at 5%: {'Yes' if p_value < 0.05 else 'No'}\")"
|
||
]
|
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},
|
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{
|
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"cell_type": "markdown",
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"id": "6c318a4c",
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"metadata": {
|
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"papermill": {
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"duration": 0.003175,
|
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"end_time": "2026-06-13T03:33:23.245650+00:00",
|
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"exception": false,
|
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"start_time": "2026-06-13T03:33:23.242475+00:00",
|
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"status": "completed"
|
||
}
|
||
},
|
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"source": [
|
||
"**Interpretation**: A right-skewed CAR distribution with a statistically significant\n",
|
||
"positive mean suggests that momentum breakouts are followed by genuine abnormal\n",
|
||
"returns -- not just a few outlier events. If the distribution were bimodal or\n",
|
||
"heavily skewed by a handful of events, the aggregate CAAR would be unreliable\n",
|
||
"for strategy design. The mean/median comparison also matters: if the median is\n",
|
||
"near zero but the mean is positive, a few large events drive the result."
|
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]
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},
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{
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"id": "6468b158",
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"duration": 0.00311,
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"end_time": "2026-06-13T03:33:23.251915+00:00",
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"start_time": "2026-06-13T03:33:23.248805+00:00",
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"status": "completed"
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}
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},
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"source": [
|
||
"## 6. Event Study Heatmap\n",
|
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"\n",
|
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"Visualize abnormal returns across events and days to identify patterns."
|
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]
|
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},
|
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{
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},
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"start_time": "2026-06-13T03:33:23.255028+00:00",
|
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"status": "completed"
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}
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},
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|
||
"gridcolor": "white",
|
||
"gridwidth": 2,
|
||
"linecolor": "white",
|
||
"showbackground": true,
|
||
"ticks": "",
|
||
"zerolinecolor": "white"
|
||
},
|
||
"yaxis": {
|
||
"backgroundcolor": "#E5ECF6",
|
||
"gridcolor": "white",
|
||
"gridwidth": 2,
|
||
"linecolor": "white",
|
||
"showbackground": true,
|
||
"ticks": "",
|
||
"zerolinecolor": "white"
|
||
},
|
||
"zaxis": {
|
||
"backgroundcolor": "#E5ECF6",
|
||
"gridcolor": "white",
|
||
"gridwidth": 2,
|
||
"linecolor": "white",
|
||
"showbackground": true,
|
||
"ticks": "",
|
||
"zerolinecolor": "white"
|
||
}
|
||
},
|
||
"shapedefaults": {
|
||
"line": {
|
||
"color": "#2a3f5f"
|
||
}
|
||
},
|
||
"ternary": {
|
||
"aaxis": {
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"ticks": ""
|
||
},
|
||
"baxis": {
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"ticks": ""
|
||
},
|
||
"bgcolor": "#E5ECF6",
|
||
"caxis": {
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"ticks": ""
|
||
}
|
||
},
|
||
"title": {
|
||
"x": 0.05
|
||
},
|
||
"xaxis": {
|
||
"automargin": true,
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"ticks": "",
|
||
"title": {
|
||
"standoff": 15
|
||
},
|
||
"zerolinecolor": "white",
|
||
"zerolinewidth": 2
|
||
},
|
||
"yaxis": {
|
||
"automargin": true,
|
||
"gridcolor": "white",
|
||
"linecolor": "white",
|
||
"ticks": "",
|
||
"title": {
|
||
"standoff": 15
|
||
},
|
||
"zerolinecolor": "white",
|
||
"zerolinewidth": 2
|
||
}
|
||
}
|
||
},
|
||
"title": {
|
||
"text": "Abnormal Returns Heatmap (Events x Days)"
|
||
},
|
||
"xaxis": {
|
||
"title": {
|
||
"text": "Days Relative to Event"
|
||
}
|
||
},
|
||
"yaxis": {
|
||
"title": {
|
||
"text": "Event Date"
|
||
}
|
||
}
|
||
}
|
||
},
|
||
"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"if len(result[\"abnormal_returns\"]) > 0:\n",
|
||
" ar_df = result[\"abnormal_returns\"]\n",
|
||
"\n",
|
||
" # Create unique event identifier (date + symbol can have multiple events)\n",
|
||
" ar_df = ar_df.with_columns(\n",
|
||
" (pl.col(\"event_date\").dt.strftime(\"%Y-%m-%d\") + \"_\" + pl.col(\"symbol\")).alias(\"event_id\")\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Pivot to wide format (event x day)\n",
|
||
" ar_pivot = ar_df.pivot(on=\"day\", index=\"event_id\", values=\"ar\").sort(\"event_id\")\n",
|
||
"\n",
|
||
" # Convert to numpy for heatmap\n",
|
||
" day_cols = sorted([c for c in ar_pivot.columns if c != \"event_id\"], key=lambda x: int(x))\n",
|
||
" ar_matrix = ar_pivot.select(day_cols).to_numpy() * 100\n",
|
||
"\n",
|
||
" # Limit to 30 events for readability\n",
|
||
" if len(ar_matrix) > 30:\n",
|
||
" ar_matrix = ar_matrix[:30]\n",
|
||
" event_ids = ar_pivot[\"event_id\"].to_list()[:30]\n",
|
||
" else:\n",
|
||
" event_ids = ar_pivot[\"event_id\"].to_list()\n",
|
||
"\n",
|
||
" fig = go.Figure(\n",
|
||
" data=go.Heatmap(\n",
|
||
" z=ar_matrix,\n",
|
||
" x=day_cols,\n",
|
||
" y=event_ids,\n",
|
||
" colorscale=\"RdBu\",\n",
|
||
" zmid=0,\n",
|
||
" colorbar=dict(title=\"AR (%)\"),\n",
|
||
" )\n",
|
||
" )\n",
|
||
"\n",
|
||
" fig.add_vline(x=0, line_dash=\"dash\", line_color=\"yellow\", line_width=2)\n",
|
||
"\n",
|
||
" fig.update_layout(\n",
|
||
" title=\"Abnormal Returns Heatmap (Events x Days)\",\n",
|
||
" xaxis_title=\"Days Relative to Event\",\n",
|
||
" yaxis_title=\"Event Date\",\n",
|
||
" height=600,\n",
|
||
" )\n",
|
||
"\n",
|
||
" fig.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "e1541a52",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.00407,
|
||
"end_time": "2026-06-13T03:33:23.355394+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:23.351324+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 7. Caveats and Best Practices\n",
|
||
"\n",
|
||
"### Event Clustering\n",
|
||
"\n",
|
||
"When multiple events occur on the same day (e.g., sector-wide announcements),\n",
|
||
"the cross-sectional correlation inflates the t-statistics. Solutions:\n",
|
||
"- Use portfolio-level returns\n",
|
||
"- Adjust standard errors for clustering\n",
|
||
"- Aggregate to one \"event\" per day\n",
|
||
"\n",
|
||
"### Overlapping Windows\n",
|
||
"\n",
|
||
"If events are close together, estimation and event windows may overlap,\n",
|
||
"contaminating the \"normal return\" estimate. Solutions:\n",
|
||
"- Enforce minimum gap between events (we used 30 days)\n",
|
||
"- Use shorter estimation windows\n",
|
||
"- Use calendar-time portfolio approach\n",
|
||
"\n",
|
||
"### Confounding Events\n",
|
||
"\n",
|
||
"Other events in the window (earnings, macro news) can confound results.\n",
|
||
"Solutions:\n",
|
||
"- Screen for confounding events\n",
|
||
"- Use matched controls\n",
|
||
"- Analyze subsamples"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "82746242",
|
||
"metadata": {
|
||
"lines_to_next_cell": 2,
|
||
"papermill": {
|
||
"duration": 0.004071,
|
||
"end_time": "2026-06-13T03:33:23.363516+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:23.359445+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 8. Using Event Studies for Signal Validation\n",
|
||
"\n",
|
||
"Event studies validate trading signals by testing whether signal-generated\n",
|
||
"\"events\" produce abnormal returns."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"id": "dc7b34b7",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:33:23.372367Z",
|
||
"iopub.status.busy": "2026-06-13T03:33:23.372266Z",
|
||
"iopub.status.idle": "2026-06-13T03:33:23.375189Z",
|
||
"shell.execute_reply": "2026-06-13T03:33:23.374911Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.008014,
|
||
"end_time": "2026-06-13T03:33:23.375557+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:23.367543+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"def validate_signal_with_event_study(\n",
|
||
" returns_df: pl.DataFrame,\n",
|
||
" benchmark_df: pl.DataFrame,\n",
|
||
" signal_df: pl.DataFrame,\n",
|
||
" signal_column: str = \"signal\",\n",
|
||
" threshold: float = 2.0,\n",
|
||
" event_window: tuple[int, int] = (-5, 10),\n",
|
||
") -> dict:\n",
|
||
" \"\"\"\n",
|
||
" Validate a trading signal using event study methodology.\n",
|
||
"\n",
|
||
" Parameters\n",
|
||
" ----------\n",
|
||
" returns_df : Long-format returns\n",
|
||
" benchmark_df : Benchmark returns\n",
|
||
" signal_df : Signal values (timestamp, symbol, signal)\n",
|
||
" signal_column : Column name for signal\n",
|
||
" threshold : Z-score threshold for event trigger\n",
|
||
" event_window : Days around event to analyze\n",
|
||
"\n",
|
||
" Returns\n",
|
||
" -------\n",
|
||
" Dict with long and short event study results\n",
|
||
" \"\"\"\n",
|
||
" # Z-score signals cross-sectionally\n",
|
||
" signal_zscored = signal_df.with_columns(\n",
|
||
" (\n",
|
||
" (pl.col(signal_column) - pl.col(signal_column).mean().over(\"timestamp\"))\n",
|
||
" / pl.col(signal_column).std().over(\"timestamp\")\n",
|
||
" ).alias(\"zscore\")\n",
|
||
" )\n",
|
||
"\n",
|
||
" # Generate events from extreme signals\n",
|
||
" long_events = (\n",
|
||
" signal_zscored.filter(pl.col(\"zscore\") > threshold)\n",
|
||
" .select([\"timestamp\", \"symbol\"])\n",
|
||
" .with_columns(pl.lit(\"long_signal\").alias(\"event_type\"))\n",
|
||
" )\n",
|
||
"\n",
|
||
" short_events = (\n",
|
||
" signal_zscored.filter(pl.col(\"zscore\") < -threshold)\n",
|
||
" .select([\"timestamp\", \"symbol\"])\n",
|
||
" .with_columns(pl.lit(\"short_signal\").alias(\"event_type\"))\n",
|
||
" )\n",
|
||
"\n",
|
||
" print(f\"Long signal events: {len(long_events)}\")\n",
|
||
" print(f\"Short signal events: {len(short_events)}\")\n",
|
||
"\n",
|
||
" results = {}\n",
|
||
"\n",
|
||
" if len(long_events) > 10:\n",
|
||
" results[\"long\"] = compute_event_study(\n",
|
||
" returns_df, benchmark_df, long_events, event_window=event_window\n",
|
||
" )\n",
|
||
"\n",
|
||
" if len(short_events) > 10:\n",
|
||
" results[\"short\"] = compute_event_study(\n",
|
||
" returns_df, benchmark_df, short_events, event_window=event_window\n",
|
||
" )\n",
|
||
"\n",
|
||
" return results"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"id": "5489ce91",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:33:23.384107Z",
|
||
"iopub.status.busy": "2026-06-13T03:33:23.384024Z",
|
||
"iopub.status.idle": "2026-06-13T03:33:23.388305Z",
|
||
"shell.execute_reply": "2026-06-13T03:33:23.388039Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.009173,
|
||
"end_time": "2026-06-13T03:33:23.388752+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:23.379579+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Momentum signal: 7,440 observations\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Example: Validate momentum signal\n",
|
||
"# Create momentum signal\n",
|
||
"prices_wide = (\n",
|
||
" etf_filtered.select([\"timestamp\", \"symbol\", \"close\"])\n",
|
||
" .pivot(on=\"symbol\", index=\"timestamp\", values=\"close\")\n",
|
||
" .sort(\"timestamp\")\n",
|
||
")\n",
|
||
"\n",
|
||
"symbols = [c for c in prices_wide.columns if c != \"timestamp\"]\n",
|
||
"\n",
|
||
"# 21-day momentum\n",
|
||
"momentum = prices_wide.select(\n",
|
||
" pl.col(\"timestamp\"), *[(pl.col(s) / pl.col(s).shift(21) - 1).alias(s) for s in symbols]\n",
|
||
")\n",
|
||
"\n",
|
||
"# Melt to long format\n",
|
||
"momentum_long = (\n",
|
||
" momentum.unpivot(index=\"timestamp\", variable_name=\"symbol\", value_name=\"momentum\")\n",
|
||
" .drop_nulls()\n",
|
||
" .filter(pl.col(\"momentum\").is_finite())\n",
|
||
")\n",
|
||
"\n",
|
||
"print(f\"Momentum signal: {len(momentum_long):,} observations\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"id": "d30ce07d",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2026-06-13T03:33:23.397826Z",
|
||
"iopub.status.busy": "2026-06-13T03:33:23.397736Z",
|
||
"iopub.status.idle": "2026-06-13T03:33:23.505935Z",
|
||
"shell.execute_reply": "2026-06-13T03:33:23.505618Z"
|
||
},
|
||
"papermill": {
|
||
"duration": 0.113867,
|
||
"end_time": "2026-06-13T03:33:23.506832+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:23.392965+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Long signal events: 228\n",
|
||
"Short signal events: 329\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"Long Signal Validation:\n",
|
||
" Final CAAR: 0.87%\n",
|
||
" CAAR t-stat: 3.30\n",
|
||
" Significant: Yes\n",
|
||
"\n",
|
||
"Short Signal Validation:\n",
|
||
" Final CAAR: -0.25%\n",
|
||
" CAAR t-stat: -1.31\n",
|
||
" Significant: No\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# Validate momentum signal\n",
|
||
"validation = validate_signal_with_event_study(\n",
|
||
" returns_df.select([\"timestamp\", \"symbol\", \"return\"]),\n",
|
||
" benchmark_returns,\n",
|
||
" momentum_long,\n",
|
||
" signal_column=\"momentum\",\n",
|
||
" threshold=1.5,\n",
|
||
")\n",
|
||
"\n",
|
||
"if \"long\" in validation and len(validation[\"long\"][\"daily_aar\"]) > 0:\n",
|
||
" print(\"\\nLong Signal Validation:\")\n",
|
||
" aar_long = validation[\"long\"][\"daily_aar\"]\n",
|
||
" final_caar = aar_long[\"caar\"].to_numpy()[-1] * 100\n",
|
||
" final_t = aar_long[\"caar_t_stat\"].to_numpy()[-1]\n",
|
||
" print(f\" Final CAAR: {final_caar:.2f}%\")\n",
|
||
" print(f\" CAAR t-stat: {final_t:.2f}\")\n",
|
||
" print(f\" Significant: {'Yes' if abs(final_t) > 1.96 else 'No'}\")\n",
|
||
"\n",
|
||
"if \"short\" in validation and len(validation[\"short\"][\"daily_aar\"]) > 0:\n",
|
||
" print(\"\\nShort Signal Validation:\")\n",
|
||
" aar_short = validation[\"short\"][\"daily_aar\"]\n",
|
||
" final_caar = aar_short[\"caar\"].to_numpy()[-1] * 100\n",
|
||
" final_t = aar_short[\"caar_t_stat\"].to_numpy()[-1]\n",
|
||
" print(f\" Final CAAR: {final_caar:.2f}%\")\n",
|
||
" print(f\" CAAR t-stat: {final_t:.2f}\")\n",
|
||
" print(f\" Significant: {'Yes' if abs(final_t) > 1.96 else 'No'}\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "829e7005",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.004873,
|
||
"end_time": "2026-06-13T03:33:23.519392+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:23.514519+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## 9. Summary\n",
|
||
"\n",
|
||
"### Methodology\n",
|
||
"\n",
|
||
"- **Estimation window**: 60 days before event (excluding 5-day gap)\n",
|
||
"- **Event window**: 5 days before to 10 days after\n",
|
||
"- **Model**: Market model ($R_i = \\alpha + \\beta \\cdot R_{market}$)\n",
|
||
"\n",
|
||
"### Key Formulas\n",
|
||
"\n",
|
||
"| Metric | Formula |\n",
|
||
"|--------|---------|\n",
|
||
"| Abnormal Return | $AR = R_{actual} - (\\alpha + \\beta \\cdot R_{market})$ |\n",
|
||
"| CAR | Cumulative sum of AR over event window |\n",
|
||
"| CAAR | Average CAR across events |\n",
|
||
"| CAAR SE | $\\sqrt{\\sum_{s=1}^{t} \\text{Var}(AAR_s) / n}$ |\n",
|
||
"\n",
|
||
"### Interpretation Guide\n",
|
||
"\n",
|
||
"| Pattern | Meaning | Trading Implication |\n",
|
||
"|---------|---------|---------------------|\n",
|
||
"| Pre-event drift | Information leakage | Limited post-event alpha |\n",
|
||
"| Event-day jump | Clean announcement | Event timing matters |\n",
|
||
"| Post-event drift | Underreaction | Post-event momentum |\n",
|
||
"| Reversal | Overreaction | Mean-reversion |\n",
|
||
"\n",
|
||
"### Caveats\n",
|
||
"\n",
|
||
"- Event clustering inflates t-stats\n",
|
||
"- Overlapping windows contaminate estimates\n",
|
||
"- Confounding events require screening"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "c0562820",
|
||
"metadata": {
|
||
"papermill": {
|
||
"duration": 0.00404,
|
||
"end_time": "2026-06-13T03:33:23.527482+00:00",
|
||
"exception": false,
|
||
"start_time": "2026-06-13T03:33:23.523442+00:00",
|
||
"status": "completed"
|
||
}
|
||
},
|
||
"source": [
|
||
"## Key Takeaways\n",
|
||
"\n",
|
||
"1. **Proper indexing matters**: Use aligned DataFrames, not `get_loc()` on\n",
|
||
" potentially non-unique indices.\n",
|
||
"\n",
|
||
"2. **CAAR variance is cumulative**: SE(CAAR_t) = sqrt(sum of daily variances),\n",
|
||
" not a simple propagation formula.\n",
|
||
"\n",
|
||
"3. **Event studies validate signals**: Signal-triggered \"events\" should produce\n",
|
||
" significant abnormal returns if the signal has predictive power.\n",
|
||
"\n",
|
||
"4. **Watch for clustering**: Events on the same day violate independence\n",
|
||
" assumptions underlying the t-tests.\n",
|
||
"\n",
|
||
"5. **Minimum gap prevents overlap**: Enforce at least 20-30 day gaps between\n",
|
||
" events per symbol to keep estimation windows clean.\n",
|
||
"\n",
|
||
"### Next Notebook\n",
|
||
"\n",
|
||
"- `case_study_feature_summary` — cross-case-study feature inventory"
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
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},
|
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"language_info": {
|
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"codemirror_mode": {
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|
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"mimetype": "text/x-python",
|
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"name": "python",
|
||
"nbconvert_exporter": "python",
|
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"pygments_lexer": "ipython3",
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|
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"environment_variables": {},
|
||
"exception": null,
|
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"input_path": "08_financial_features/07_event_studies.ipynb",
|
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|
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|
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|
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|
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|
||
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"ml4t_provenance": {
|
||
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|
||
"executed_at": "2026-06-13T03:33:26.375193+00:00",
|
||
"executor": "cpu-local",
|
||
"production": true,
|
||
"parameters": {},
|
||
"notes": "executed-state finalization batch (2026-06-13 run)"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|