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Prediction-Small-Cap-Biotech-Anomalies

Prediction Small Cap Biotech Anomalies

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Information

Property Value
Language Python
Stars 1
Forks 0
Watchers 1
Open Issues 0
License No License
Created 2026-03-02
Last Updated 2026-03-03
Last Push 2026-03-03
Contributors 1
Default Branch main
Visibility private

Datasets

This repository includes 13 dataset(s):

Dataset Format Size

| data | | 0.0 KB |

| samples | | 0.0 KB |

| .gitkeep | | 0.0 KB |

| data | | 0.0 KB |

| init.py | .py | 0.01 KB |

| clinical_trials.py | .py | 13.45 KB |

| delisting.py | .py | 11.35 KB |

| event_dates.py | .py | 12.67 KB |

| fama_french.py | .py | 5.85 KB |

| fundamentals.py | .py | 17.17 KB |

| openalex.py | .py | 11.08 KB |

| stock_prices.py | .py | 8.91 KB |

| universe.py | .py | 5.6 KB |

Reproducibility

This repository includes reproducibility tools:

  • Python requirements.txt

Status

  • Issues: Enabled
  • Wiki: Enabled
  • Pages: Enabled

README

Algorithmic Rebound Trading in Small-Cap Biotech

MSc Thesis: Algorithm-based trading strategy identifying profitable rebound opportunities in small/mid-cap biotechs after Phase 3 clinical trial failures, systematically filtering insolvency-risk firms.

Website: https://digital-ai-finance.github.io/Prediction-Small-Cap-Biotech-Anomalies/

Overview

This repository contains the full codebase and thesis for an MSc research project that investigates whether small- and mid-cap biotech stocks ($50M--$10B market cap) exhibit exploitable abnormal returns following Phase 3 clinical trial failure announcements, and whether a composite insolvency filter can improve trading strategy performance by excluding firms at high risk of bankruptcy.

Research Questions

  1. Do small/mid-cap biotechs exhibit statistically significant abnormal returns after Phase 3 failure announcements?
  2. Can a composite insolvency filter effectively separate viable rebound candidates from insolvency-bound firms?
  3. Does an insolvency-filtered trading strategy generate positive risk-adjusted returns net of transaction costs?

Project Structure

src/biotech_rebound/
  config.py              # Global constants and parameters
  data/
    clinical_trials.py   # ClinicalTrials.gov API v2 client
    event_dates.py       # SEC EDGAR 8-K event date identification
    stock_prices.py      # Yahoo Finance price data + delisting detection
    delisting.py         # Shumway (1997) delisting return adjustments
    fundamentals.py      # Financial metrics (yfinance + SEC EDGAR XBRL)
    fama_french.py       # Fama-French 3- and 5-factor loading
    universe.py          # Universe construction and filtering
    openalex.py          # OpenAlex literature search
  analysis/
    event_study.py       # Market model, CAR, BHAR computation
    statistics.py        # Patell, BMP, Corrado, bootstrap, MHC
    insolvency.py        # Composite insolvency scoring (Z'', cash, pipeline)
    spreads.py           # Corwin-Schultz (2012) bid-ask spread estimator
    robustness.py        # Sensitivity, subsample, permutation, calendar-time
  strategy/
    signals.py           # Entry/exit signal generation
    backtest.py          # Walk-forward and expanding-window backtesting
    risk.py              # Position sizing (equal-weight, Kelly, risk parity)
    costs.py             # Transaction cost model (spread + impact + commission)
  visualization/
    plots.py             # Publication-quality matplotlib figures
    tables.py            # Summary and results tables

scripts/                 # End-to-end pipeline scripts (00-09 + run_all.py)
tests/                   # 122 unit tests (pytest)
thesis/                  # Quarto chapters (.qmd) + LaTeX template

Installation

python -m pip install -e ".[dev]"

Requires Python >= 3.10. No WRDS/CRSP access needed -- all data sources are freely available.

Running the Pipeline

python scripts/run_all.py

Individual steps can be run separately (scripts 01--09) or skipped with flags: 

python scripts/run_all.py --skip-fetch --skip-analysis

Data Sources

Source Purpose
ClinicalTrials.gov (v2 API) Phase 3 trial identification
SEC EDGAR (EFTS + XBRL) 8-K event dates, financial fundamentals
Yahoo Finance (yfinance) Stock prices, market cap, delisting detection
Kenneth French Data Library Fama-French 3- and 5-factor returns
OpenAlex Academic literature for systematic review

Testing

python -m pytest tests/ -v

122 tests covering all modules. Linting: python -m ruff check src/ scripts/ tests/.

Thesis

The thesis is rendered with Quarto to both HTML (GitHub Pages) and PDF (LaTeX).

quarto render thesis/ --to html   # Academic website
quarto render thesis/ --to pdf    # Formal thesis PDF

Disclaimer

This research is for academic purposes only and does not constitute financial advice. Past performance does not guarantee future results. The trading strategy described herein is a theoretical exercise and should not be used for actual investment decisions without extensive additional validation.

License

MIT