For Researchers

Proposed Novel Contributions

This framework proposes three potential contributions to accounting theory and validation methodology (pending academic peer review), assessed via self-critique (adversarial review):

1. Moving Boundary Formalization (Novelty: 7/10)

Proposed Contribution: To our knowledge, the first application of discrete Reynolds Transport Theorem to accounting consolidation (pending peer review).

What's New: When Company A acquires Company B, B's equity is not "income" (operational source term) but boundary flux—an entity crossing the consolidation boundary. Prior work (Ellerman 1982, Liang 2001) formalized double-entry as graph flow but did not address moving boundaries.

Mathematical Form:


dE/dt = ∫(sources) dV + ∫(flux across ∂V) dA + ∫(boundary velocity · ρ) dA
         operational      static boundary      moving boundary (M&A)

Potential Impact (if validated): Could distinguish M&A equity changes from organic growth, potentially enabling automated detection of:

Undisclosed acquisitions (boundary flux without IFRS 3 disclosure)
Misclassified goodwill (boundary vs. operational)
Consolidation errors (NCI miscalculation)

Prior Art: Reynolds Transport Theorem standard in fluid dynamics (Aris 1962), population ecology (Gurtin & MacCamy 1979). Application to accounting appears novel per adversarial review.

Limitations: Formalization presented but implementation incomplete (~30% complete per adversarial review line 163-180). Proof-of-concept only.

Cite As:


@misc{chitnis2025moving,
  title={Moving Boundary Formalization for Accounting Consolidation},
  author={Nirvan Chitnis},
  year={2025},
  note={Section 4.3, Reynolds Transport for M\&A}
}

2. Standards-Aware Source Decomposition (Novelty: 6/10)

Contribution: Complete taxonomy of equity change source terms mapped to 91 IFRS/GAAP standards with XBRL tags.

What's New: Prior frameworks treat equity changes as undifferentiated "sources." We decompose into:

Profit/Loss (IAS 1) → us-gaap:NetIncomeLoss
OCI Components (IFRS 9, IAS 21, IAS 19) → 7 distinct XBRL tags
Owner Transactions (IAS 32) → Dividends, buybacks, contributions
Boundary Flux (IFRS 10) → NCI, acquisitions, disposals
Measurement (IFRS 13) → Fair value adjustments

Database: See docs/standards/STANDARDS_CROSSWALK.md for full 91-standard mapping.

Practical Impact: Enables:

Automated validation against specific standards (not just totals)
Regulatory compliance checking (IFRS vs. US GAAP)
Gap analysis (which standards lack XBRL coverage?)

Prior Art: XBRL taxonomies exist but don't map to conservation equations. This is the synthesis.

Limitations:

~70% complete per adversarial review
IFRS 17 (insurance), IFRS 16 (leases) partially mapped
US GAAP coverage weaker than IFRS

Cite As:


@misc{chitnis2025taxonomy,
  title={IFRS/GAAP Source Term Taxonomy for Equity Conservation},
  author={Nirvan Chitnis},
  year={2025},
  note={91 standards mapped, see docs/standards/}
}

3. Graph-Theoretic Double-Entry with Explicit Sources (Novelty: 4/10)

Contribution: Extends Ellerman (1982) and Liang (2001) graph-theoretic double-entry with explicit source terms.

What's New: Prior work showed double-entry satisfies Kirchhoff's Current Law (flow conservation on graphs). We add:

Source terms (not just flow)
Temporal dynamics (discrete continuity equation)
Entity graphs (not just account graphs)

Mathematical Form:


x_{t+1} = x_t + P·a_t + s_t
          prior   flows    sources

where P is incidence matrix (graph structure), a_t is flow vector (journal entries), s_t is source vector (IFRS/GAAP-defined changes).

Prior Art:

Ellerman (1982): Double-entry as category theory
Liang (2001): Accounting as graph dynamics
Ijiri (1989): Momentum accounting (precursor)

Limitations: Mathematical formalism adds rigor but implementation is standard graph algorithms (NetworkX). Main contribution is connecting existing graph theory to accounting standards.

Cite As:


@article{ellerman1982,
  title={The Mathematics of Double Entry Bookkeeping},
  author={Ellerman, David P},
  journal={Mathematics Magazine},
  volume={58},
  number={5},
  pages={226--233},
  year={1985}
}

@article{liang2001,
  title={Accounting as a Computational Process},
  author={Liang, Pierre-Jin},
  journal={Computational Economics},
  year={2001}
}

Reproducibility

Full Replication (4-6 hours)

Reproduce all 500-company validation results:


# 1. Clone repository
git clone https://github.com/nirvanchitnis-cmyk/accounting-conservation-framework
cd accounting-conservation-framework

# 2. Install dependencies
poetry install

# 3. Hydrate SEC EDGAR data (2 hours, 10GB download)
python scripts/hydrate_companyfacts.py --index SP500

# 4. Run validation pipeline (2 hours computation)
python scripts/run_empirical_validation_n500.py \
  --dataset data/cache/companyfacts/ \
  --output results/validation_n500.json

# 5. Compare to published results
diff results/validation_n500.json results/published/validation_n500.json

Expected Results:

Leverage identity pass rate: 72.9% ± 1.5%
Equity bridge pass rate: 54.9% ± 2.0%
AUC (ML audit risk): 0.949 ± 0.01

Data Checksums: See data/CHECKSUMS.md for SHA256 hashes of input data.

Randomness: ML model training uses random_state=42 for reproducibility.

Partial Replication (30 minutes)

Test framework on single company:


python scripts/demo.py --ticker AAPL --frequency quarterly

Expected output: 4 quarters validated, pass/fail for each test.

Theoretical Foundations

Mathematical Structure

The framework rests on three mathematical pillars:

Graph Theory (Kirchhoff 1847, Ellerman 1982)

- Accounts as nodes

- Journal entries as directed edges

- Balance sheet as graph snapshot

Discrete Conservation (Finite-difference PDEs)

- Continuity equation: ∂ρ/∂t + ∇·J = s

- Discrete analog: Δx = flows + sources

- Source terms from IFRS/GAAP

Reynolds Transport (Moving boundaries)

- Control volume with moving boundary

- Leibniz integral rule for moving domains

- Applied to M&A consolidation

Formal Proofs

The framework provides rigorous mathematical proofs in logical sequence:

Theorem 1: Kirchhoff's Law - Why A = L + E holds (double-entry → graph conservation)
Minimal Example - 2-period equity bridge walkthrough ($1,000 → $1,150)
Theorem 3: Equity Bridge Closure - Complete 51-term source taxonomy with method-invariance
Theorem 4: Multi-Entity Continuity - Consolidation framework (parent-subsidiary groups)
Advanced: Continuous RTT - Reynolds Transport Theorem (continuous PDE formulation)

Recommended reading sequence: Theorem 1 → Hello World → Theorem 3 → Theorem 4

Limitations & Future Work

Known Limitations

From adversarial review:

Reynolds Transport Implementation (30% complete)

- Formalization presented

- Validator code incomplete

- M&A detection heuristic-based

Source Term Coverage (70% complete)

- IFRS 17 (insurance contracts): partial

- IFRS 16 (leases): partial

- US GAAP ASC 842, 944: incomplete

Test Coverage (77%)

- Validators well-tested

- XBRL parsers under-tested

- Ground truth APIs stubbed

Scalability (tested to 2,000 filings)

- Memory: O(n × accounts)

- Time: O(n × log n) with graph algorithms

- Not tested beyond 10K filings

Future Research Directions

Theoretical:

Extend to blockchain accounting (distributed ledgers)
Formalize non-accounting conserved quantities (contracts, obligations)
Connection to information theory (entropy-based validation)

Empirical:

Test on international markets (FTSE 100, DAX, Nikkei)
Longitudinal study (10-year panel)
Event study: Does framework predict audit failures?

Practical:

Real-time streaming validation (not batch)
Interactive debugging tools for auditors
Integration with ERP systems (SAP, Oracle)

Open Questions for Collaboration

We welcome collaboration on:

1. Standards Completeness

Question: Are there IFRS/GAAP source terms we missed?

How to contribute: Review docs/standards/STANDARDS_CROSSWALK.md, identify gaps, open issue or PR.

2. Alternative Formalizations

Question: Could category theory (Ellerman) provide cleaner formalization than graph theory?

Context: Current approach uses directed graphs. Category theory morphisms might be more general. See [issue #TBD].

3. Machine Learning Integration

Question: Can ML models learn source term mappings from data, rather than hand-coding XBRL tags?

Potential: Transfer learning from BERT-like models on financial disclosures.

4. Regulatory Adoption

Question: What would SEC/PCAOB require for this to become official validation standard?

Status: Preliminary discussions with [redacted]. Seeking academic partners for peer review.

Peer Review & Validation

Self-Critique

We conducted adversarial self-review (see ADVERSARIAL_REVIEW.md):

Novelty scoring (0-10 scale)
Implementation gaps documented
Overclaims identified and removed

External Review (Invited)

We invite peer review from:

Accounting academics: Is standards taxonomy complete/correct?
Graph theorists: Is formalization rigorous?
Auditors: Does this solve real problems?
Regulators: Could this become validation standard?

Contact: Submit issues via GitHub or email author.

Citation

If you use this framework:


@misc{accounting-conservation-2025,
  title={Accounting Conservation Framework: Mathematical Foundation for Audit Validation},
  author={Nirvan Chitnis},
  year={2025},
  url={https://github.com/nirvanchitnis-cmyk/accounting-conservation-framework},
  note={Validated on 500 S\&P 500 companies (2,000 filings), AUC 0.949}
}

For specific contributions:

Moving boundaries: Cite as "Chitnis 2025, Section 4.3"
Source taxonomy: Cite as "Chitnis 2025, Standards Crosswalk"
Graph formalization: Cite Ellerman 1982 + Liang 2001 + Chitnis 2025

See RELATED_WORK.md for comprehensive literature review and comparison to:

Prior accounting formalizations
Audit automation frameworks
XBRL validation tools

Status: Preprint (not peer-reviewed). Seeking journal venue. Suggestions welcome.

Last Updated: 2025-01-05

Proposed Novel Contributions

1. Moving Boundary Formalization (Novelty: 7/10)

2. Standards-Aware Source Decomposition (Novelty: 6/10)

3. Graph-Theoretic Double-Entry with Explicit Sources (Novelty: 4/10)

Reproducibility

Full Replication (4-6 hours)

Partial Replication (30 minutes)

Theoretical Foundations

Mathematical Structure

Formal Proofs

Limitations & Future Work

Known Limitations

Future Research Directions

Open Questions for Collaboration

1. Standards Completeness

2. Alternative Formalizations

3. Machine Learning Integration

4. Regulatory Adoption

Peer Review & Validation

Self-Critique

External Review (Invited)

Citation

Related Work