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--- |
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license: other |
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task_categories: |
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- feature-extraction |
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- summarization |
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- tabular-to-text |
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- table-to-text |
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- text-retrieval |
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tags: |
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- finance |
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- investing |
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- portfolio |
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- factors |
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- synthetic-data |
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- hedge-funds |
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- buffett |
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- alpha |
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--- |
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# Synthetic Buffett's Alpha Dataset |
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This dataset contains **realistic synthetic data** inspired by the paper *“Buffett’s Alpha” (Frazzini, Kabiller, Pedersen, 2018)*. |
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It simulates Berkshire Hathaway–style returns, leverage, financing, factor exposures, portfolio decomposition, and systematic replication performance. |
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All data is **artificially generated** using stochastic processes (GBM, GARCH-like models) and contains **no proprietary or private financial data**. |
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--- |
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## Dataset overview |
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- The dataset includes **100 independent synthetic companies** (`BuffettCo_1` … `BuffettCo_100`). |
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- Each company has **1,000 monthly observations** (~83 years of synthetic data). |
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- All data is stored in a **single unified file**: `buffett_alpha_synthetic.csv`. |
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- Each row corresponds to one **company × month** observation. |
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**Total size:** ~100,000 rows × ~25 columns. |
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--- |
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## Columns included |
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### Identification |
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- `scenario_id` — numeric ID of the simulated company |
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- `company_name` — company label (e.g., `BuffettCo_17`) |
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- `date` — monthly timestamp |
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### Returns & performance |
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- `market_excess_return` |
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- `berkshire_excess_return` |
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- `sharpe_ratio` (constant across a company’s rows) |
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- `info_ratio` (constant across a company’s rows) |
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### Leverage & financing |
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- `leverage_ratio` |
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- `insurance_float_cost` |
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- `debt_outstanding` |
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- `float_share_of_liabilities` |
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### Factor exposures |
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- `MKT` (market beta) |
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- `SMB` (size) |
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- `HML` (value) |
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- `UMD` (momentum) |
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- `BAB` (betting against beta) |
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- `QMJ` (quality minus junk) |
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- `Alpha` |
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### Portfolio decomposition |
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- `public_stock_return` |
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- `private_company_return` |
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- `public_weight` |
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- `private_weight` |
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- `combined_return` |
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### Replication comparison |
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- `actual_berkshire_return` |
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- `systematic_buffett_style_return` |
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- `tracking_error` |
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--- |
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## Relation to the original paper |
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In the original *Buffett’s Alpha* study: |
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- The authors used **real-world data** from 1976–2017 (~500 monthly observations). |
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- There was only **one company**: Berkshire Hathaway. |
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In this dataset: |
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- There are **100 Buffett-style companies**, not just one. |
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- Each has **1,000 months of synthetic data**, much larger than the original sample. |
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This enables: |
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- Reproducing the types of analyses from the paper (Sharpe ratios, factor regressions, leverage effects). |
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- Exploring alternative “what if” Buffett-style histories. |
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- Using long time series for machine learning, econometrics, or stress testing. |
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--- |
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## Example usage |
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```python |
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from datasets import load_dataset |
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# Load the dataset (replace with your HF username/repo) |
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ds = load_dataset("your-username/buffett-alpha-synthetic") |
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# Convert to pandas for analysis |
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df = ds["train"].to_pandas() # single split |
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print(df.head()) |
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# Example: compute mean excess return by company |
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print(df.groupby("company_name")["berkshire_excess_return"].mean().head()) |
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