It simulates Berkshire Hathaway–style returns, leverage, financing, factor exposures, portfolio decomposition, and systematic replication performance.

All data is artificially generated using stochastic processes (GBM, GARCH-like models) and contains no proprietary or private financial data.

Dataset overview

The dataset includes 100 independent synthetic companies (BuffettCo_1 … BuffettCo_100).
Each company has 1,000 monthly observations (~83 years of synthetic data).
All data is stored in a single unified file: buffett_alpha_synthetic.csv.
Each row corresponds to one company × month observation.

Total size: ~100,000 rows × ~25 columns.

Columns included

Identification

scenario_id — numeric ID of the simulated company
company_name — company label (e.g., BuffettCo_17)
date — monthly timestamp

Returns & performance

market_excess_return
berkshire_excess_return
sharpe_ratio (constant across a company’s rows)
info_ratio (constant across a company’s rows)

Leverage & financing

leverage_ratio
insurance_float_cost
debt_outstanding
float_share_of_liabilities

Factor exposures

MKT (market beta)
SMB (size)
HML (value)
UMD (momentum)
BAB (betting against beta)
QMJ (quality minus junk)
Alpha

Portfolio decomposition

public_stock_return
private_company_return
public_weight
private_weight
combined_return

Replication comparison

actual_berkshire_return
systematic_buffett_style_return
tracking_error

Relation to the original paper

In the original Buffett’s Alpha study:

The authors used real-world data from 1976–2017 (~500 monthly observations).
There was only one company: Berkshire Hathaway.

In this dataset:

There are 100 Buffett-style companies, not just one.
Each has 1,000 months of synthetic data, much larger than the original sample.

This enables:

Reproducing the types of analyses from the paper (Sharpe ratios, factor regressions, leverage effects).
Exploring alternative “what if” Buffett-style histories.
Using long time series for machine learning, econometrics, or stress testing.

Example usage

from datasets import load_dataset

# Load the dataset (replace with your HF username/repo)
ds = load_dataset("your-username/buffett-alpha-synthetic")

# Convert to pandas for analysis
df = ds["train"].to_pandas()  # single split
print(df.head())

# Example: compute mean excess return by company
print(df.groupby("company_name")["berkshire_excess_return"].mean().head())

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