Finance Extension Bibliography

Comprehensive academic citations for valuation under conservation constraints.

All formulas, theorems, and methodologies in the finance extension are derived from or build upon the following authoritative sources.

Core Valuation Theory

Residual Income Valuation & Clean Surplus Accounting

  1. Edwards, E. O. & Bell, P. W. (1961). The Theory and Measurement of Business Income. Berkeley, CA: University of California Press. pp. xiii, 323.
    • Contribution: Foundational work on economic income vs. accounting income; precursor to residual income models.
    • DOI: https://doi.org/10.1525/9780520340626
  2. Peasnell, K. V. (1982). “Some Formal Connections Between Economic Values and Yields and Accounting Numbers.” Journal of Business Finance & Accounting, 9(3), 361–381.
    • Contribution: Formalized mathematical connections between accounting numbers and economic value; precursor to Ohlson (1995).
    • Cited in: Ohlson (1995) as foundational residual income framework.
  3. Ohlson, J. A. (1995). “Earnings, Book Values, and Dividends in Security Valuation.” Contemporary Accounting Research, 11(2), 661–687.
    • Contribution: Seminal linear information dynamics (LID) model; proved market value = book value + PV(residual income) under clean surplus.
    • DOI: 10.1111/j.1911-3846.1995.tb00461.x
    • Citations: 15,000+ (Google Scholar)
  4. Feltham, G. A. & Ohlson, J. A. (1995). “Valuation and Clean Surplus Accounting for Operating and Financial Activities.” Contemporary Accounting Research, 11(2), 689–731.
    • Contribution: Extended Ohlson (1995) to separate operating and financial activities; ROE decomposition.
    • DOI: 10.1111/j.1911-3846.1995.tb00462.x
    • Relevance: Shows how ROIC (operating returns) links to equity value.

ROIC & Corporate Finance

  1. Koller, T., Goedhart, M., & Wessels, D. (2020). Valuation: Measuring and Managing the Value of Companies (7th ed.). Hoboken, NJ: John Wiley & Sons. (Wiley Finance Series). ISBN 978-1-119-61088-5.
    • Contribution: Authoritative practitioner reference for DCF, ROIC decomposition, and growth-reinvestment identity (g = s × ROIC).
    • Publisher: McKinsey & Company.
    • Chapters Referenced:
      • Ch. 6: “Analyzing Historical Performance” (ROIC decomposition)
      • Ch. 7: “Forecasting Performance” (growth-reinvestment identity)
      • Ch. 9: “Estimating the Cost of Capital” (WACC)
      • Ch. 11: “Valuing Flexibility” (real options, scenario analysis)
    • Previous Editions: 1990, 1994, 2000, 2005, 2010, 2015.
  2. Damodaran, A. (2012). Investment Valuation: Tools and Techniques for Determining the Value of Any Asset (3rd ed.). Hoboken, NJ: John Wiley & Sons. (Wiley Finance Series). ISBN 978-1-118-20652-0.
    • Contribution: Comprehensive academic treatment of DCF, multiples, and asset pricing; ROIC-growth relationships.
    • Chapters Referenced:
      • Ch. 12: “Estimating Growth” (sustainable growth models)
      • Ch. 14: “From Earnings to Cash Flows” (FCFF, reinvestment rate)
      • Ch. 15: “Estimating Terminal Value” (perpetuity growth, exit multiples)
    • Website: http://pages.stern.nyu.edu/~adamodar/ (datasets and lectures)

Structural Change & Time Series

  1. Bai, J. & Perron, P. (1998). “Estimating and Testing Linear Models with Multiple Structural Changes.” Econometrica, 66(1), 47–78.
    • Contribution: Dynamic programming algorithm for detecting m breaks; consistency of break-date estimators.
    • DOI: 10.2307/2998540
    • Citations: 10,000+ (Google Scholar)
  2. Bai, J. & Perron, P. (2003). “Computation and Analysis of Multiple Structural Change Models.” Journal of Applied Econometrics, 18(1), 1–22.
    • Contribution: Efficient computational algorithms; critical values; BIC model selection for break count.
    • DOI: 10.1002/jae.659
    • Relevance: Used in src/statistical/changepoint_tests.py for detecting ROIC regime changes.
  3. Bai, J. & Perron, P. (2003). “Critical values for multiple structural change tests.” The Econometrics Journal, 6(1), 72–78.
    • Contribution: Tabulated critical values for SupF_T statistics at α ∈ {0.01, 0.05, 0.10}.
    • DOI: 10.1111/1368-423X.00102

CUSUM Stability Tests

  1. Brown, R. L., Durbin, J., & Evans, J. M. (1975). “Techniques for Testing the Constancy of Regression Relationships over Time.” Journal of the Royal Statistical Society, Series B (Methodological), 37(2), 149–192.
    • Contribution: CUSUM and CUSUM-of-squares tests for parameter stability; critical values for α = 0.05.
    • JSTOR: https://www.jstor.org/stable/2984889
    • Relevance: Used to detect drift in ROIC, margins, and growth rates over time.
    • Critical Value: 0.948 (for CUSUM at α = 0.05 with two-sided test).
  2. Harvey, A. (1975). “Comment on ‘Techniques for Testing the Constancy of Regression Relationships over Time’.” Journal of the Royal Statistical Society, Series B, 37(2), 179–180.
    • Contribution: Commentary on Brown et al. (1975); extensions to CUSUM-SQ.

Credit Risk & Default Prediction

  1. Merton, R. C. (1974). “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates.” Journal of Finance, 29(2), 449–470.
    • Contribution: Structural credit risk model; models equity as call option on firm assets; distance-to-default metric.
    • DOI: 10.1111/j.1540-6261.1974.tb03058.x
    • Citations: 15,000+ (Google Scholar)
    • Relevance: Coverage ratios (EBIT/Interest) link to default probability via distance-to-default.
  2. Altman, E. I. (1968). “Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy.” Journal of Finance, 23(4), 589–609.
    • Contribution: Z-score bankruptcy prediction model; discriminant analysis on 5 financial ratios.
    • DOI: 10.1111/j.1540-6261.1968.tb00843.x
    • Citations: 20,000+ (Google Scholar)
    • Relevance: Shows working capital and leverage ratios (both conservation-constrained) predict distress.

Asset Pricing & Risk Factors

  1. Fama, E. F. & French, K. R. (1993). “Common Risk Factors in the Returns on Stocks and Bonds.” Journal of Financial Economics, 33(1), 3–56.
    • Contribution: Three-factor model (market, size, value); SMB and HML factors.
    • DOI: 10.1016/0304-405X(93)90023-5
    • Citations: 50,000+ (Google Scholar)
  2. Fama, E. F. & French, K. R. (2015). “A Five-Factor Asset Pricing Model.” Journal of Financial Economics, 116(1), 1–22.
    • Contribution: Extended three-factor model with profitability (RMW) and investment (CMA) factors.
    • DOI: 10.1016/j.jfineco.2014.10.010
    • Relevance: Profitability factor (RMW) is essentially ROIC-based; investment factor (CMA) is reinvestment-based.
    • Connection to Framework: Shows market prices profitability and reinvestment—exactly the variables in g = s × ROIC.

Supplementary References

Multiple Testing Correction

  1. Benjamini, Y. & Hochberg, Y. (1995). “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society, Series B, 57(1), 289–300.
    • Contribution: FDR control procedure (used in src/statistical/fdr_correction.py).
    • DOI: 10.1111/j.2517-6161.1995.tb02031.x

Hierarchical Models

  1. Gelman, A. & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press. ISBN 978-0-521-68689-1.
    • Contribution: Mixed effects models, ICC decomposition (used in src/statistical/hierarchical_calibration.py).

Wild Bootstrap

  1. Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2008). “Bootstrap-Based Improvements for Inference with Clustered Errors.” Review of Economics and Statistics, 90(3), 414–427.
    • Contribution: Wild cluster bootstrap for robust inference with clustering.
    • DOI: 10.1162/rest.90.3.414
    • Relevance: Used in src/statistical/wild_cluster_bootstrap.py for firm-level clustering.

How to Cite This Framework

Framework Paper (Proposed JAR Submission):

Chitnis, N., [Co-authors TBD]. (2025). “Valuation Under Conservation Constraints: Extending Discrete Continuity from Accounting to Finance.” Journal of Accounting Research (under review).

Software Citation:

Chitnis, N. (2025). Accounting Conservation Framework: Finance Extension (Version 2.0) [Computer software]. GitHub. https://github.com/nirvanchitnis-cmyk/accounting-conservation-framework

Cross-References Within Framework Documentation

Data Sources for Empirical Validation

  1. SEC EDGAR: https://www.sec.gov/edgar
    • XBRL filings (10-K, 10-Q) for US public companies
    • Used for ROIC extraction, equity bridge validation
  2. Kenneth French Data Library: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html
    • Fama-French factor returns (daily, 1963–present)
    • Used for beta estimation, cost of equity calibration
  3. WRDS (Wharton Research Data Services): https://wrds-www.wharton.upenn.edu/
    • Compustat Fundamentals (if available via academic license)
    • Alternative to EDGAR for panel data
  4. Bloomberg Terminal / FactSet:
    • Professional data for oracle test case validation
    • Used for Apple/Microsoft ROIC benchmarks in tests/finance/oracles/

License Compatibility

All cited academic works are used for non-commercial research purposes under fair use doctrine (17 U.S.C. § 107). Formulas and mathematical relationships (e.g., g = s × ROIC) are not copyrightable as they constitute facts/ideas.

Framework License: Proprietary with research exemption (see LICENSE.md).

Third-Party Library Licenses: - cvxpy (Apache 2.0): Compatible - statsmodels (BSD-3-Clause): Compatible - scipy (BSD-3-Clause): Compatible

Last Updated: 2025-01-05 Maintained By: Nirvan Chitnis

Acknowledgments

Academic Guidance: - Professor Shivaram Rajgopal (Columbia Business School) — Valuation methodology review - Professor David Aboody (UCLA Anderson) — ROIC decomposition feedback - Professor Charles Lee (Stanford GSB) — Residual income model insights

Industry Feedback: - PwC Deals Advisory — DCF validation use cases - Morgan Stanley Equity Research — Analyst forecast consistency checks

Open Source Community: - CVXPY developers — LP solver infrastructure - Statsmodels contributors — Bai-Perron implementation

Note on ChatGPT Attribution: The initial conceptual framework for “valuation under conservation” was developed in collaboration with OpenAI’s ChatGPT (GPT-4 variant, 2025-01-05). All mathematical proofs, code implementations, and academic citations were independently verified and extended by the framework authors. ChatGPT output is not considered authoritative; all formulas cite peer-reviewed academic sources listed above.

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