1212 lines
395 KiB
Plaintext
1212 lines
395 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "a36f5610",
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"metadata": {},
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"source": [
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"# Factor Analysis and Diagnostics"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d09530ec",
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"metadata": {},
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"source": [
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"## Purpose\n",
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"\n",
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"A **factor** (aka **signal**) is a vector $f_t \\in \\mathbb{R}^{N_t}$. We have one score per stock, assigned at each date $t$. The central question of this notebook is:\n",
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"\n",
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"> does the direction of $f_t$ predict the direction of next month's return vector $r_{t+1}$?\n",
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"\n",
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"In linear-algebraic terms, this is a question about the **angle** between two vectors. If $f_t$ and $r_{t+1}$ point in similar directions (small angle, high cosine similarity), the factor has predictive power. If they're nearly orthogonal, it doesn't. \n",
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"\n",
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"We test four standard cross-sectional factors, most of which we compute as price-based proxies since we are using pretty basic data. They are momentum, value, quality, and low volatility.\n",
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"\n",
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"> **Spoiler**: Momentum wins, and the rest have negative or insignificant information coefficients over this period. The methodology for diagnosing a factor is the same whether it works or not, and showing *why* the proxies fail is more instructive than silently dropping them.\n",
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"\n",
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"The main goals of this notebook are:\n",
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"1. To define and compute four cross-sectional factors (momentum, value, quality, low-vol) as vectors $f_t$.\n",
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"2. To measure each factor's information coefficient (**IC**) — the cosine similarity between $f_t$ and $r_{t+1}$.\n",
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"3. To examine **IC stability** across subperiods (walk-forward: is the factor consistent, or just lucky in one period?).\n",
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"4. To examine IC decay at longer periods.\n",
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"5. To quantify **turnover** via rank autocorrelation.\n",
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"6. To check **cross-factor correlations** (the Gram matrix).\n",
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"\n",
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"## Terms used\n",
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"\n",
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"| Term | Meaning |\n",
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"|------|---------|\n",
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"| **Factor / signal** | A vector $f_t \\in \\mathbb{R}^{N_t}$ assigning a score to each stock at date $t$ |\n",
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"| **Momentum** | Trailing 12-month return skipping the last month; \"winners keep winning\" |\n",
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"| **Value** | Cheap stocks (low price vs. fundamentals) may outperform; proxied here by inverse long-term return |\n",
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"| **Quality** | Profitable/stable firms may outperform; proxied by a return Sharpe ratio |\n",
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"| **Low volatility** | Low-risk stocks may outperform on a risk-adjusted basis |\n",
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"| **Information coefficient (IC)** | Spearman rank correlation between $f_t$ and $r_{t+1}$ — cosine similarity of rank vectors |\n",
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"| **Information ratio (IR)** | Mean IC / std(IC) — a signal-to-noise ratio for the factor |\n",
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"| **Sharpe ratio** ↻ | Mean return / volatility — used as the quality-factor proxy (12m Sharpe-like ratio) |\n",
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"| **Rank** | A permutation of $\\{1,\\dots,N\\}$; makes factors comparable and robust to outliers |\n",
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"| **Turnover (proxy)** | How much the signal changes; proxied here by rank autocorrelation |\n",
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"| **Walk-forward** | Split into sub-windows and test IC stability across regimes |\n",
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"| **Return panel** $\\mathbf{R}$ ↻ | The return matrix whose rows (cross-sections) we rank |\n",
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"| **Cross-section** ↻ | All stocks at one date — we rank within each row |\n",
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"\n",
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"## Outputs\n",
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"\n",
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"This notebook produces per-factor IC time series, subperiod IC stability tables, decay curves, turnover estimates, and a correlation matrix (the Gram matrix of factor vectors). These feed directly into constructions in later notebooks.\n",
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"\n",
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"## Notebook Structure\n",
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"1. [Setup and Imports](#setup-and-imports)\n",
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"2. [Load Data](#load-data)\n",
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"3. [Factor Definitions](#factor-definitions)\n",
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"4. [Information Coefficient Analysis](#information-coefficient-analysis)\n",
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"5. [Subperiod IC Stability (Walk-Forward)](#subperiod-ic-stability-walk-forward)\n",
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"6. [Factor Decay](#factor-decay)\n",
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"7. [Turnover via Rank Autocorrelation](#turnover-via-rank-autocorrelation)\n",
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"8. [Cross-Factor Correlations](#cross-factor-correlations)\n",
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"9. [Conclusion](#conclusion)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ff97ddb4",
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"metadata": {},
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"source": [
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"## Setup and Imports"
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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": 1,
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"id": "b6109fc9",
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"metadata": {},
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"outputs": [],
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"source": [
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"\"\"\"\n",
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"==================================\n",
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"Setup and imports\n",
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"==================================\n",
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"\"\"\"\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns\n",
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"import os\n",
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"\n",
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"pd.set_option('display.max_columns', None)\n",
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"pd.set_option('display.max_rows', 100)\n",
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"\n",
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"palette = ['steelblue', 'coral', 'seagreen']\n",
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"\n",
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"os.makedirs('../data/processed', exist_ok=True)\n",
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"os.makedirs('../images/02_factor_diagnostics', exist_ok=True)\n",
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"\n",
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"RANDOM_STATE = 3"
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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": "aebef262",
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"metadata": {},
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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: (251, 501)\n",
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"Prices: (252, 503)\n",
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"Sectors: 11\n"
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]
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}
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],
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"source": [
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"\"\"\"\n",
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"==================================\n",
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"Load return panel and sector mapping\n",
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"==================================\n",
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"\"\"\"\n",
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"df_returns = pd.read_csv('../data/processed/returns_monthly.csv', index_col=0, parse_dates=True)\n",
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"df_prices = pd.read_csv('../data/processed/prices_monthly.csv', index_col=0, parse_dates=True)\n",
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"df_sector = pd.read_csv('../data/processed/sector_mapping.csv')\n",
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"\n",
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"print(f\"Returns: {df_returns.shape}\")\n",
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"print(f\"Prices: {df_prices.shape}\")\n",
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"print(f\"Sectors: {df_sector['sector'].nunique()}\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ac67fd55",
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"metadata": {},
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"source": [
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"## Factor definitions\n",
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"We'll now compute our four standard factors. All are cross-sectional ranks (permutations) at each month-end, so they live on the same scale and can be combined later without further normalization.\n\nEach factor below is an economic hypothesis about a vector $f_t \\in \\mathbb{R}^{N_t}$; the rest of this notebook measures the angle between that vector and $r_{t+1}$ (their cosine similarity is the IC).\n",
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"\n",
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"- **Momentum**: Trailing 12-month return skipping the most recent month ($t - 12$ to $t - 2$). The intuition behind it is that stocks that went up over the past year tend to keep going up for another month or two\n",
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"- **Values**: we use price-based inverse momentum as a value proxy: 60-month trailing return, inverted. The intuition is that stocks that went down over 5 years are \"cheap\" and may mean-revert.\n",
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"- **Quality**: 12-month Sharpe-like ratio of monthly returns (mean / std). The intuition is that stocks with smooth positive returns are \"higher quality\".\n",
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"- **Low-volatility**: Inverse of 60-month trailing volatility, ranked. Intuition is that low-risk stocks tend to outperform on a risk-adjusted basis.\n",
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"\n",
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"\n",
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"The following code builds four factor matrices, each matching the shape of `df_returns`, where every cell holds a cross-sectional rank in $[-0.5, 0.5]$. The helper function converts any raw signal to percentile ranks centered at zero. Applied with `axis=1` so ranking happens across stocks within each month, not down time.\n",
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"\n",
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"**The output:**\n",
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"A dict `factor_dict` holding four DataFrames, plus a print loop confirming shapes and non-null month counts. Momentum needs ~12 months of history; value and low-vol need ~60; quality needs ~12.\n",
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"\n",
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"**Key caveat:** Momentum is the only academically faithful signal. Value, quality, and low-vol are price-based proxies — useful for learning the methodology, but expect weak or negative ICs because the proxies conflate the true factors with mean-reversion and volatility effects."
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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": 3,
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"id": "dbc1fa9f",
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||
"metadata": {},
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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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"momentum: (251, 501), non-null months: 240\n",
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"value: (251, 501), non-null months: 191\n",
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"quality: (251, 501), non-null months: 239\n",
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"lowvol: (251, 501), non-null months: 191\n"
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]
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}
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],
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"source": [
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"\"\"\"\n",
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"==================================\n",
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"Compute factor exposures\n",
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"==================================\n",
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"\"\"\"\n",
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"def cross_sectional_rank(series):\n",
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" \"\"\"Rank cross-sectionally, scaled to [-0.5,0.5]\"\"\"\n",
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" return series.rank(pct=True) - 0.5\n",
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"\n",
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"# Momentum (12-1): sum of returns from t-12 to t-2\n",
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"mom_12_1 = df_returns.rolling(11).sum().shift(1)\n",
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"df_momentum = mom_12_1.apply(cross_sectional_rank, axis=1)\n",
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"\n",
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"# Value (proxy): inverse 60-month momentum\n",
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"mom_60 = df_returns.rolling(60).sum().shift(1)\n",
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"df_value = (-mom_60).apply(cross_sectional_rank, axis=1)\n",
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"\n",
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"# Quality (proxy): 12m sharpe-like\n",
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"mean_12 = df_returns.rolling(12).mean().shift(1)\n",
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"std_12 = df_returns.rolling(12).std().shift(1)\n",
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"quality_raw = mean_12 / std_12.replace(0, np.nan)\n",
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"df_quality = quality_raw.apply(cross_sectional_rank, axis=1)\n",
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"\n",
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"# Low volatility: inverse 60m vol\n",
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"vol_60 = df_returns.rolling(60).std().shift(1)\n",
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"df_lowvol = (-vol_60).apply(cross_sectional_rank, axis=1)\n",
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"\n",
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"\n",
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"factor_dict = {\n",
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" 'momentum': df_momentum,\n",
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" 'value': df_value,\n",
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" 'quality': df_quality,\n",
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" 'lowvol': df_lowvol,\n",
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"}\n",
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"\n",
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"for name, df_f in factor_dict.items():\n",
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" print(f\"{name}: {df_f.shape}, non-null months: {df_f.notna().any(axis=1).sum()}\")"
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]
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},
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||
{
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||
"cell_type": "markdown",
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||
"id": "e9ea22e1",
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||
"metadata": {},
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"source": [
|
||
"## Information Coefficient Analysis\n",
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"\n",
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"The **information coefficient (IC)** at date $t$ is the Spearman rank correlation between the factor vector $f_t$ and the realized return vector $r_{t+1}$. From the linear algebraic perspective, this is just the Pearson correlation applied to rank vectors. For two centered vectors $u,v$, this is the cosine of the angle between them:\n",
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"$$ \\rho(u,v) = \\frac{\\langle u,v \\rangle}{\\|u\\|\\|v\\|} = \\cos\\theta. $$\n",
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"So the IC is $\\cos\\theta$, where $\\theta$ is the angle between the factor rank vector and the return rank vector. An IC of 1 means perfect alignment (zero angle); IC of 0 means orthogonality (no predictive power); IC of -1 means anti-alignment. \n",
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"\n",
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"The **information ratio (IR)** is the mean IC divided by the standard deviation of IC across dates: \n",
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"$$ \\text{IR} = \\frac{\\text{Mean IC}}{\\text{Std IC}} \\times \\sqrt{12}. $$\n",
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"This is a ratio of signal and noise, which tells us whether the factor reliably predicts returns or is just noise."
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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": 4,
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||
"id": "a7146b0c",
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||
"metadata": {},
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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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"Computing IC for momentum... \n",
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"Computing IC for value... \n",
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"Computing IC for quality... \n",
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"Computing IC for lowvol... \n",
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"\n",
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"IC Summary\n",
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"\n"
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]
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},
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{
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|
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
|
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" <th>mean</th>\n",
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" <th>std</th>\n",
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" <th>min</th>\n",
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" <th>max</th>\n",
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" <th>ic_ir</th>\n",
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" </tr>\n",
|
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" </thead>\n",
|
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" <tbody>\n",
|
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" <tr>\n",
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" <th>momentum</th>\n",
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" <td>0.0060</td>\n",
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" <td>0.1905</td>\n",
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" <td>-0.6010</td>\n",
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" <td>0.4032</td>\n",
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" <td>0.1091</td>\n",
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" </tr>\n",
|
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" <tr>\n",
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" <th>value</th>\n",
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" <td>-0.0216</td>\n",
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" <td>0.1576</td>\n",
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||
" <td>-0.3878</td>\n",
|
||
" <td>0.6297</td>\n",
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" <td>-0.4750</td>\n",
|
||
" </tr>\n",
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||
" <tr>\n",
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" <th>quality</th>\n",
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" <td>-0.0025</td>\n",
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||
" <td>0.1945</td>\n",
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||
" <td>-0.5120</td>\n",
|
||
" <td>0.4798</td>\n",
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||
" <td>-0.0448</td>\n",
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||
" </tr>\n",
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||
" <tr>\n",
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" <th>lowvol</th>\n",
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" <td>-0.0259</td>\n",
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" <td>0.2360</td>\n",
|
||
" <td>-0.5288</td>\n",
|
||
" <td>0.5478</td>\n",
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||
" <td>-0.3808</td>\n",
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],
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"text/plain": [
|
||
" mean std min max ic_ir\n",
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"momentum 0.0060 0.1905 -0.6010 0.4032 0.1091\n",
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||
"value -0.0216 0.1576 -0.3878 0.6297 -0.4750\n",
|
||
"quality -0.0025 0.1945 -0.5120 0.4798 -0.0448\n",
|
||
"lowvol -0.0259 0.2360 -0.5288 0.5478 -0.3808"
|
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]
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}
|
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],
|
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"source": [
|
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"\"\"\"\n",
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"==================================\n",
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"Compute monthly IC (Spearman rank correlation)\n",
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"==================================\n",
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"\"\"\"\n",
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"from scipy.stats import spearmanr\n",
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"\n",
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"def compute_monthly_ic(factor_df, return_df):\n",
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" \"\"\"Compute Spearman IC between factor and next-month returns.\"\"\"\n",
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" common_dates = factor_df.index.intersection(return_df.index)\n",
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" common_tickers = factor_df.columns.intersection(return_df.columns)\n",
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"\n",
|
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" ic_series = []\n",
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" for date in common_dates:\n",
|
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" f = factor_df.loc[date, common_tickers]\n",
|
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" r = return_df.loc[date, common_tickers]\n",
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"\n",
|
||
" mask = f.notna() & r.notna()\n",
|
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" if mask.sum() < 20: # sample size filter\n",
|
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" ic_series.append(np.nan)\n",
|
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" continue\n",
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" \n",
|
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" ic, _ = spearmanr(f[mask], r[mask])\n",
|
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" ic_series.append(ic)\n",
|
||
" return pd.Series(ic_series, index=common_dates)\n",
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"\n",
|
||
"ic_results = {}\n",
|
||
"for name, df_f in factor_dict.items():\n",
|
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" print(f\"Computing IC for {name}... \")\n",
|
||
" ic_results[name] = compute_monthly_ic(df_f,df_returns)\n",
|
||
"\n",
|
||
"df_ic = pd.DataFrame(ic_results)\n",
|
||
"df_ic = df_ic.dropna(how='all')\n",
|
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"\n",
|
||
"print(\"\\nIC Summary\\n\")\n",
|
||
"ic_summary = df_ic.describe().T[['mean', 'std', 'min', 'max']]\n",
|
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"ic_summary['ic_ir'] = ic_summary['mean']/ic_summary['std'] * np.sqrt(12)\n",
|
||
"display(ic_summary.round(4))"
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]
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||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "5659b941",
|
||
"metadata": {},
|
||
"source": [
|
||
"Note that we added a sample size filter with `mask.sum() < 20`. The `mask` identifies stocks that have both a valid factor score and a valid forward return in a given month, and `mask.sum()` counts how many usable pairs you actually have. The `< 20` threshold prevents the code from computing a correlation on a tiny sample, which is statistically meaningless and numerically unstable (e.g., Spearman correlation on 2 stocks is always exactly $\\pm$1). If you don't gate this, early-history months, mass delistings, or data gaps inject garbage $\\pm 1.0$ values into your IC time series, which then contaminate every downstream statistic like the mean IC, Information Ratio, and decay curves. Setting a floor of 20 filters out those degenerate months while retaining enough valid data to produce a reliable signal.\n",
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||
"\n",
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||
"We do this sort of masking throughout."
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||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "d0fe3ee3",
|
||
"metadata": {},
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"outputs": [
|
||
{
|
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"data": {
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"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1400x1200 with 4 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"==================================\n",
|
||
"Plot IC time series\n",
|
||
"==================================\n",
|
||
"\"\"\"\n",
|
||
"fig, axes = plt.subplots(4,1, figsize=(14,12), sharex = True)\n",
|
||
"\n",
|
||
"for ax, (name, ic_series) in zip(axes, ic_results.items()):\n",
|
||
" ax.bar(ic_series.index, ic_series.values, color='steelblue', alpha=0.6, width=20)\n",
|
||
" ax.plot(ic_series.index, ic_series.rolling(12).mean(), color='coral', linewidth=2, label='12m MA')\n",
|
||
" ax.axhline(y=0, color='black', linestyle='-', linewidth=0.5)\n",
|
||
" ax.set_ylabel('IC')\n",
|
||
" ax.set_title(f'{name.capitalize()} Factor IC')\n",
|
||
" ax.legend(loc='upper right')\n",
|
||
" ax.grid(alpha=0.3)\n",
|
||
"\n",
|
||
"axes[-1].set_xlabel('Date')\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.savefig('../images/02_factor_diagnostics/ic_time_series.png', dpi=150, bbox_inches='tight')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "2cce2714",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Subperiod IC Stability (Walk-Forward)\n",
|
||
"\n",
|
||
"A factor with positive full-sample IC could still be unreliable as it might have earned its entire IC in one lucky subperiod. Walk-forward IC analysis splits the sample into non-overlapping windows and computes the IC in each. A factor that's positive in most windows is robust; one that's positive in only one is fragile.\n",
|
||
"\n",
|
||
"We'll do an overly-simplified form of walk-forward validation. It will tell us whether the factor's predictive power is a stable feature of the data or a consequence of a specific time period."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "fba556cd",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Mean IC by Subperiod\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
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||
"<style scoped>\n",
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" }\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th>period</th>\n",
|
||
" <th>2006-2011</th>\n",
|
||
" <th>2011-2016</th>\n",
|
||
" <th>2016-2021</th>\n",
|
||
" <th>2021-2026</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>factor</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>lowvol</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>-0.0029</td>\n",
|
||
" <td>-0.0361</td>\n",
|
||
" <td>-0.0295</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>momentum</th>\n",
|
||
" <td>-0.0131</td>\n",
|
||
" <td>0.0276</td>\n",
|
||
" <td>-0.0079</td>\n",
|
||
" <td>0.0174</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>quality</th>\n",
|
||
" <td>-0.0287</td>\n",
|
||
" <td>0.0258</td>\n",
|
||
" <td>-0.0258</td>\n",
|
||
" <td>0.0183</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>value</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>-0.0179</td>\n",
|
||
" <td>-0.0331</td>\n",
|
||
" <td>-0.0095</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
"period 2006-2011 2011-2016 2016-2021 2021-2026\n",
|
||
"factor \n",
|
||
"lowvol NaN -0.0029 -0.0361 -0.0295\n",
|
||
"momentum -0.0131 0.0276 -0.0079 0.0174\n",
|
||
"quality -0.0287 0.0258 -0.0258 0.0183\n",
|
||
"value NaN -0.0179 -0.0331 -0.0095"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
" Information Ration by Subperiod\n",
|
||
"\n"
|
||
]
|
||
},
|
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|
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|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th>period</th>\n",
|
||
" <th>2006-2011</th>\n",
|
||
" <th>2011-2016</th>\n",
|
||
" <th>2016-2021</th>\n",
|
||
" <th>2021-2026</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>factor</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>lowvol</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>-0.043</td>\n",
|
||
" <td>-0.560</td>\n",
|
||
" <td>-0.420</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>momentum</th>\n",
|
||
" <td>-0.237</td>\n",
|
||
" <td>0.547</td>\n",
|
||
" <td>-0.130</td>\n",
|
||
" <td>0.325</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>quality</th>\n",
|
||
" <td>-0.593</td>\n",
|
||
" <td>0.461</td>\n",
|
||
" <td>-0.390</td>\n",
|
||
" <td>0.353</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>value</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>-0.576</td>\n",
|
||
" <td>-0.676</td>\n",
|
||
" <td>-0.170</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
"period 2006-2011 2011-2016 2016-2021 2021-2026\n",
|
||
"factor \n",
|
||
"lowvol NaN -0.043 -0.560 -0.420\n",
|
||
"momentum -0.237 0.547 -0.130 0.325\n",
|
||
"quality -0.593 0.461 -0.390 0.353\n",
|
||
"value NaN -0.576 -0.676 -0.170"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
" % Months with Positive IC\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th>period</th>\n",
|
||
" <th>2006-2011</th>\n",
|
||
" <th>2011-2016</th>\n",
|
||
" <th>2016-2021</th>\n",
|
||
" <th>2021-2026</th>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>factor</th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" <th></th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>lowvol</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>51.7</td>\n",
|
||
" <td>43.3</td>\n",
|
||
" <td>41.7</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>momentum</th>\n",
|
||
" <td>48.3</td>\n",
|
||
" <td>61.7</td>\n",
|
||
" <td>45.0</td>\n",
|
||
" <td>58.3</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>quality</th>\n",
|
||
" <td>47.5</td>\n",
|
||
" <td>63.3</td>\n",
|
||
" <td>43.3</td>\n",
|
||
" <td>60.0</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>value</th>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>40.0</td>\n",
|
||
" <td>46.7</td>\n",
|
||
" <td>48.3</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
"period 2006-2011 2011-2016 2016-2021 2021-2026\n",
|
||
"factor \n",
|
||
"lowvol NaN 51.7 43.3 41.7\n",
|
||
"momentum 48.3 61.7 45.0 58.3\n",
|
||
"quality 47.5 63.3 43.3 60.0\n",
|
||
"value NaN 40.0 46.7 48.3"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"==================================\n",
|
||
"Subperiod IC stability (walk-forward)\n",
|
||
"==================================\n",
|
||
"\"\"\"\n",
|
||
"windows = [(2006,2011), (2011,2016),(2016,2021),(2021,2026)]\n",
|
||
"subperiod_results = []\n",
|
||
"\n",
|
||
"for name in factor_dict:\n",
|
||
" ic = ic_results[name]\n",
|
||
" for start, end in windows:\n",
|
||
" mask = (ic.index.year >= start) & (ic.index.year < end)\n",
|
||
" sub = ic[mask].dropna()\n",
|
||
" if len(sub) < 12:\n",
|
||
" continue\n",
|
||
" subperiod_results.append({\n",
|
||
" 'factor': name,\n",
|
||
" 'period': f'{start}-{end}',\n",
|
||
" 'mean_ic': sub.mean(),\n",
|
||
" 'std_ic': sub.std(),\n",
|
||
" 'ir': sub.mean() / sub.std() * np.sqrt(12) if sub.std() > 0 else np.nan,\n",
|
||
" 'n': len(sub),\n",
|
||
" 'pct_positive': (sub > 0).mean()\n",
|
||
" })\n",
|
||
"\n",
|
||
"df_subperiod = pd.DataFrame(subperiod_results)\n",
|
||
"\n",
|
||
"# Pivot for display\n",
|
||
"pivot_ic = df_subperiod.pivot(index='factor', columns='period', values='mean_ic')\n",
|
||
"pivot_ir = df_subperiod.pivot(index='factor', columns='period', values='ir')\n",
|
||
"pivot_pct = df_subperiod.pivot(index='factor', columns='period', values='pct_positive')\n",
|
||
"\n",
|
||
"print(\"Mean IC by Subperiod\\n\")\n",
|
||
"display(pivot_ic.round(4))\n",
|
||
"\n",
|
||
"print(\"\\n Information Ration by Subperiod\\n\")\n",
|
||
"display(pivot_ir.round(3))\n",
|
||
"\n",
|
||
"print(\"\\n % Months with Positive IC\\n\")\n",
|
||
"display((pivot_pct * 100).round(1))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "7b0bc01a",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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|
||
"text/plain": [
|
||
"<Figure size 1200x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# Plot subperiod IC\n",
|
||
"fig,ax = plt.subplots(figsize=(12,6))\n",
|
||
"x = np.arange(len(windows))\n",
|
||
"width = 0.2\n",
|
||
"colors = ['steelblue', 'coral', 'seagreen', 'goldenrod']\n",
|
||
"for i, name in enumerate(factor_dict):\n",
|
||
" vals = [pivot_ic.loc[name, f'{s}-{e}'] if f'{s}-{e}' in pivot_ic.columns else 0 for s, e in windows]\n",
|
||
" ax.bar(x + i * width, vals, width, label=name, color=colors[i], alpha=0.8)\n",
|
||
"\n",
|
||
"ax.set_xticks(x + width * 1.5)\n",
|
||
"ax.set_xticklabels([f'{s} - {e}' for s,e in windows])\n",
|
||
"ax.set_ylabel('Mean monthly IC')\n",
|
||
"ax.set_title('Factor IC by Subperiod')\n",
|
||
"ax.axhline(y=0, color='black', linewidth=0.5)\n",
|
||
"ax.legend()\n",
|
||
"ax.grid(alpha=0.3, axis='y')\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.savefig('../images/02_factor_diagnostics/ic_subperiod.png', dpi=150, bbox_inches='tight')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6c6cc2c9",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Factor Decay\n",
|
||
"\n",
|
||
"IC at 1-month tells you the signal exists. IC at longer horizons tells you how fast it dies. This sets your rebalance frequency. A factor with positive IC out to 12 months is slow-moving — you can trade it quarterly and capture most of what you need it to. A factor that's dead after 2 months is fast — you need monthly rebalancing, and transaction costs can play a role. \n",
|
||
"\n",
|
||
"Momentum is typically slow. Short-term reversal is fast. Low-vol is very slow. The decay curve tells you which is which. In vector terms, 'slow' means the angle between $\\mathrm{rank}(f_t)$ and $\\mathrm{rank}(r_{t+h})$ stays small as the horizon $h$ grows; 'fast' means it opens quickly."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "29d1c1d8",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 1000x600 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>1m</th>\n",
|
||
" <th>3m</th>\n",
|
||
" <th>6m</th>\n",
|
||
" <th>12m</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>momentum</th>\n",
|
||
" <td>0.0015</td>\n",
|
||
" <td>-0.0023</td>\n",
|
||
" <td>-0.0032</td>\n",
|
||
" <td>-0.0048</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>value</th>\n",
|
||
" <td>-0.0229</td>\n",
|
||
" <td>-0.0400</td>\n",
|
||
" <td>-0.0463</td>\n",
|
||
" <td>-0.0458</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>quality</th>\n",
|
||
" <td>-0.0041</td>\n",
|
||
" <td>-0.0128</td>\n",
|
||
" <td>-0.0167</td>\n",
|
||
" <td>-0.0251</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>lowvol</th>\n",
|
||
" <td>-0.0251</td>\n",
|
||
" <td>-0.0621</td>\n",
|
||
" <td>-0.0873</td>\n",
|
||
" <td>-0.1108</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" 1m 3m 6m 12m\n",
|
||
"momentum 0.0015 -0.0023 -0.0032 -0.0048\n",
|
||
"value -0.0229 -0.0400 -0.0463 -0.0458\n",
|
||
"quality -0.0041 -0.0128 -0.0167 -0.0251\n",
|
||
"lowvol -0.0251 -0.0621 -0.0873 -0.1108"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"==================================\n",
|
||
"IC decay at horizons 1, 3, 6, 12 months\n",
|
||
"==================================\n",
|
||
"\"\"\"\n",
|
||
"horizons = [1,3,6,12]\n",
|
||
"decay_results = {}\n",
|
||
"\n",
|
||
"for name, df_f in factor_dict.items():\n",
|
||
" decay_results[name] = []\n",
|
||
" for h in horizons:\n",
|
||
" # Forward returns at horizon h\n",
|
||
" fwd_returns = df_returns.rolling(h).sum().shift(-h)\n",
|
||
" ic_series = compute_monthly_ic(df_f, fwd_returns)\n",
|
||
" decay_results[name].append(ic_series.mean())\n",
|
||
"\n",
|
||
"df_decay = pd.DataFrame(decay_results, index=[f'{h}m' for h in horizons]).T\n",
|
||
"\n",
|
||
"fig,ax = plt.subplots(figsize=(10,6))\n",
|
||
"df_decay.T.plot(ax=ax, marker='o')\n",
|
||
"ax.set_xlabel('Horizon')\n",
|
||
"ax.set_ylabel('Mean IC')\n",
|
||
"ax.set_title('Factor IC Decay by Horizon')\n",
|
||
"ax.axhline(y=0, color='black', linestyle='--', linewidth=0.5)\n",
|
||
"ax.grid(alpha=0.3)\n",
|
||
"ax.legend(title='Factor')\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.savefig('../images/02_factor_diagnostics/ic_decay.png', dpi=150, bbox_inches='tight')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"display(df_decay.round(4))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "d0205e12",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Turnover via Rank Autocorrelation\n",
|
||
"\n",
|
||
"A factor that re-ranks the universe every month will produce high turnover, and this contributes to lowering net returns after transaction costs.\n",
|
||
"\n",
|
||
"**Rank autocorrelation** is the correlation between this month's factor rank vector and last month's. If the rank vector barely changes (autocorrelation $\\approx$ 1), the portfolio barely trades. If it changes a lot (autocorrelation $\\approx$ 0), turnover is high. In linear algebraic terms, the rank vector at $t$ is nearly parallel to the rank vector at $t-1$ for stable factors, and nearly orthogonal for unstable ones."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "65a97db6",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"momentum: rank autocorr = 0.888, turnover proxy = 0.112\n",
|
||
"value: rank autocorr = 0.977, turnover proxy = 0.023\n",
|
||
"quality: rank autocorr = 0.898, turnover proxy = 0.102\n",
|
||
"lowvol: rank autocorr = 0.997, turnover proxy = 0.003\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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",
|
||
"text/plain": [
|
||
"<Figure size 800x500 with 1 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"==================================\n",
|
||
"Rank autocorrelation as turnover proxy\n",
|
||
"==================================\n",
|
||
"\"\"\"\n",
|
||
"def rank_autocorrelation(factor_df, lag=1):\n",
|
||
" \"\"\"Averages cross-sectional rank correlation between t and t-lag.\"\"\"\n",
|
||
" common_dates = factor_df.index[:-lag]\n",
|
||
" autocorr = []\n",
|
||
" for date in common_dates:\n",
|
||
" f_now = factor_df.loc[date]\n",
|
||
" f_lag = factor_df.loc[factor_df.index[factor_df.index.get_loc(date) + lag]]\n",
|
||
" mask = f_now.notna() & f_lag.notna()\n",
|
||
" if mask.sum() < 20:\n",
|
||
" continue\n",
|
||
" rho,_ = spearmanr(f_now[mask], f_lag[mask])\n",
|
||
" autocorr.append(rho)\n",
|
||
" return np.mean(autocorr)\n",
|
||
"\n",
|
||
"turnover_proxy = {}\n",
|
||
"for name, df_f in factor_dict.items():\n",
|
||
" rho = rank_autocorrelation(df_f, lag=1)\n",
|
||
" turnover_proxy[name] = rho\n",
|
||
" # Rough turnover estimate: 1 - rho\n",
|
||
" print(f\"{name}: rank autocorr = {rho:.3f}, turnover proxy = {1 - rho:.3f}\")\n",
|
||
"\n",
|
||
"fig,ax = plt.subplots(figsize=(8,5))\n",
|
||
"ax.bar(turnover_proxy.keys(), [1-v for v in turnover_proxy.values()], color='coral')\n",
|
||
"ax.set_ylabel('Turnover proxy (1 - Rank Autocorr)')\n",
|
||
"ax.set_title('Factor Turnover Proxy')\n",
|
||
"ax.grid(alpha=0.3, axis='y')\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.savefig('../images/02_factor_diagnostics/turnover_proxy.png', dpi=150, bbox_inches='tight')\n",
|
||
"plt.show()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "6ac614b4",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Cross-Factor Correlations\n",
|
||
"\n",
|
||
"The cross-factor correlation matrix is the **Gram matrix** of the factor vectors. If you stack the four factor vectors into a matrix $\\mathbf{F} = [f_1, f_2, f_3, f_4]$, the Gram matrix is $\\mathbf{G} = \\mathbf{F}^\\top \\mathbf{F}$, where each entry $G_{ij} = \\langle f_i, f_j \\rangle$ is the inner product of two factor vectors. Since our factors are cross-sectional ranks centered at zero, these inner products are proportional to Pearson correlations — so the Gram matrix is literally the correlation matrix between factors.\n",
|
||
"\n",
|
||
"High off-diagonal entries (near 1) mean two vectors are nearly collinear — they point in the same direction and carry redundant information. Low entries (near 0) mean they're nearly orthogonal — independent signal. If momentum and quality correlate at 0.86, combining them adds almost nothing; you're adding a vector to a near-parallel copy of itself. The ideal set of factors is a set of nearly orthogonal vectors, each with positive IC, so that a combined portfolio benefits from diversification rather than doubling down on the same bet."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"id": "2d488e92",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
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",
|
||
"text/plain": [
|
||
"<Figure size 800x600 with 2 Axes>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
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"<div>\n",
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"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
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||
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||
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|
||
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||
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|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>momentum</th>\n",
|
||
" <th>value</th>\n",
|
||
" <th>quality</th>\n",
|
||
" <th>lowvol</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>momentum</th>\n",
|
||
" <td>1.000</td>\n",
|
||
" <td>-0.444</td>\n",
|
||
" <td>0.860</td>\n",
|
||
" <td>-0.104</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>value</th>\n",
|
||
" <td>-0.444</td>\n",
|
||
" <td>1.000</td>\n",
|
||
" <td>-0.412</td>\n",
|
||
" <td>0.210</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>quality</th>\n",
|
||
" <td>0.860</td>\n",
|
||
" <td>-0.412</td>\n",
|
||
" <td>1.000</td>\n",
|
||
" <td>0.109</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>lowvol</th>\n",
|
||
" <td>-0.104</td>\n",
|
||
" <td>0.210</td>\n",
|
||
" <td>0.109</td>\n",
|
||
" <td>1.000</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" momentum value quality lowvol\n",
|
||
"momentum 1.000 -0.444 0.860 -0.104\n",
|
||
"value -0.444 1.000 -0.412 0.210\n",
|
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"quality 0.860 -0.412 1.000 0.109\n",
|
||
"lowvol -0.104 0.210 0.109 1.000"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"==================================\n",
|
||
"Cross-factor rank correlation (time-averaged)\n",
|
||
"==================================\n",
|
||
"\"\"\"\n",
|
||
"factor_names = list(factor_dict.keys())\n",
|
||
"corr_matrix = np.zeros((len(factor_names), len(factor_names)))\n",
|
||
"\n",
|
||
"for i, name_i in enumerate(factor_names):\n",
|
||
" for j, name_j in enumerate(factor_names):\n",
|
||
" if i == j:\n",
|
||
" corr_matrix[i,j] = 1.0\n",
|
||
" continue\n",
|
||
" df_i = factor_dict[name_i]\n",
|
||
" df_j = factor_dict[name_j]\n",
|
||
" common_dates = df_i.index.intersection(df_j.index)\n",
|
||
"\n",
|
||
" corrs = []\n",
|
||
" for date in common_dates:\n",
|
||
" a = df_i.loc[date]\n",
|
||
" b = df_j.loc[date]\n",
|
||
" mask = a.notna() & b.notna()\n",
|
||
" if mask.sum() < 20:\n",
|
||
" continue\n",
|
||
" rho, _ = spearmanr(a[mask], b[mask])\n",
|
||
" corrs.append(rho)\n",
|
||
" corr_matrix[i,j] = np.mean(corrs)\n",
|
||
"\n",
|
||
"df_corr = pd.DataFrame(corr_matrix, index=factor_names, columns=factor_names)\n",
|
||
"\n",
|
||
"fig,ax = plt.subplots(figsize=(8,6))\n",
|
||
"sns.heatmap(df_corr, annot=True, fmt='.2f', cmap='RdBu_r', center=0, ax=ax, square=True)\n",
|
||
"ax.set_title('Cross-Factor Rank Correlation (Time-Averaged)')\n",
|
||
"\n",
|
||
"plt.tight_layout()\n",
|
||
"plt.savefig('../images/02_factor_diagnostics/factor_correlation.png', dpi=150, bbox_inches='tight')\n",
|
||
"plt.show()\n",
|
||
"\n",
|
||
"display(df_corr.round(3))"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"id": "f5fea3fa",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Saved factor exposures, IC series, decay, and correlation matrix.\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\"\"\"\n",
|
||
"==================================\n",
|
||
"Save factor exposures and IC results\n",
|
||
"==================================\n",
|
||
"\"\"\"\n",
|
||
"for name, df_f in factor_dict.items():\n",
|
||
" df_f.to_csv(f'../data/processed/factor_{name}.csv')\n",
|
||
"\n",
|
||
"df_ic.to_csv('../data/processed/ic_monthly.csv')\n",
|
||
"df_decay.to_csv('../data/processed/ic_decay.csv')\n",
|
||
"df_corr.to_csv('../data/processed/factor_correlation.csv')\n",
|
||
"\n",
|
||
"print(\"Saved factor exposures, IC series, decay, and correlation matrix.\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "a76a4424",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Conclusion\n",
|
||
"\n",
|
||
"The diagnostics tell us that **momentum** is realistically the only signal worth carrying forward, and even it is modest. Its full-sample IC is $+0.006$ with an information ratio of $+0.11$ — positive, but weak. The encouraging part is consistency: momentum is the only factor with a positive mean IC in two of the four subperiods (2011–2016 at IR $0.55$ and 2021–2026 at IR $0.33$), and it decays slowly, staying near zero out to the 12-month horizon. That slow decay, combined with a moderate turnover proxy of $0.11$, means a monthly or quarterly rebalance captures most of the edge. Momentum is not a strong signal in this universe, but it is a *real* one.\n",
|
||
"\n",
|
||
"The other three factors fail, and the **why** is more instructive than the failure:\n",
|
||
"\n",
|
||
"- **Value** (IC $-0.022$, IR $-0.48$, negative in every subperiod): our proxy is inverse 60-month return, intended to capture mean reversion of \"cheap\" stocks. Instead it loaded on long-term *momentum continuation* — five-year winners kept winning, so the inverted signal predicted negative returns. A price-only value proxy cannot separate cheapness from drift; it needs fundamentals (book-to-market, earnings yield).\n",
|
||
"- **Quality** (IC $-0.003$, IR $-0.04$, statistically zero): the 12-month Sharpe proxy is dominated by the direction of recent returns, which is why it correlates **0.86** with momentum. It is not an independent signal — it is momentum with extra noise. Adding it to a portfolio doubles down on the same bet rather than diversifying.\n",
|
||
"- **Low-volatility** (IC $-0.026$, IR $-0.38$, decaying to $-0.11$ at 12m): low-vol names underperformed high-vol names in this window, the opposite of the low-vol anomaly. The inverse-60m-vol proxy is too crude to isolate the anomaly, and the period itself was risk-on. Its one virtue is extreme stability (turnover proxy $0.003$), but a stable negative-IC signal is just a reliably bad signal.\n",
|
||
"\n",
|
||
"The **Gram matrix** confirms the structural problem. Momentum and quality are near-collinear ($0.86$); value is anti-correlated with both ($-0.44$, $-0.41$) because it is inverse-momentum at a different horizon; only low-vol is close to orthogonal to the rest ($\\approx 0.1$). So of four factors, we effectively have one directional bet (momentum/quality), its mirror image (value), and one orthogonal but useless signal (low-vol). There is no diversification to harvest from this set.\n",
|
||
"\n",
|
||
"**What carries forward:** momentum is the candidate signal for portfolio construction in later notebooks. The value, quality, and low-vol proxies should be discarded or re-engineered with genuine fundamental and risk data before they can contribute. The methodology — IC, walk-forward stability, decay, turnover, and the correlation structure — is sound; the inputs were not."
|
||
]
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": ".venv",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.14.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|