Dissertation Topics Accounting and Finance

A sharp dissertation topic can open doors to internships, conference slots, and first-job interviews in accounting and finance. As the TopicSuggestions team, we see how quickly the field shifts with new reporting standards, fintech tools, sustainability metrics, and data-driven auditing, and we know broad ideas get stronger when they align with clear methods and accessible data. Today we’ll come up with fresh, realistic ideas you can actually research this semester.

Accounting and Finance Dissertation Topic Ideas

Our thesis is simple: the best topics sit where a timely issue meets available datasets and a method you can execute confidently. We’ll map the list by themes—financial reporting and auditing, corporate finance and governance, banking and fintech, taxation, ESG and sustainability, and risk and analytics—so you can scan your area and grab a title you can pitch to your supervisor this week.

1. Accounting for Autonomous AI Agents as Quasi-Employees and Intangible Co-workers

We define recognition criteria for AI agent–generated revenues and costs when agents act autonomously across jurisdictions.
How do we capitalize or expense pre-training, fine-tuning, and prompt engineering as asset components.
Do we measure and amortize model drift as a form of economic depreciation.
How do we provision for agent-caused loss events and allocate liability across principals.

2. Tokenized Extended Producer Responsibility in Cost Systems for Circular Supply Chains

We build a measurement model for tokenized EPR obligations that fluctuate with real-time take-back and recycling rates.
Do we embed EPR token flows into standard costing, variance analysis, and transfer pricing for closed-loop materials.
How do we present reverse-logistics credits—contra-expense or other income—at segment level.
Can we design on-chain oracles that we can audit for fair valuation of EPR tokens.

3. Zero-Knowledge Revenue Assurance for SMEs Without Exposing Customer Data

We design audit evidence protocols using zero-knowledge proofs to verify existence and occurrence without revealing counterparties.
Do we calibrate materiality and assurance levels in ZKP-based attestations relative to traditional confirmations.
How do we recognize and amortize ZKP system implementation costs—intangible asset or period expense.
Can we standardize verifiable-computation attest reports for regulatory filings we can rely on.

4. Depreciation and Impairment of Digital Twins Linked to Physical Assets

We delineate the unit of account for a digital twin—per asset, subsystem, or use-case portfolio.
Do we capitalize continuous data ingestion and model updates or treat them as maintenance expense.
How do we test impairment when a twin diverges from physical performance or market relevance.
Can we isolate incremental cash flows from the twin to support valuation and useful life estimates.

5. Product-Level Margin Accounting for Climate Insets Versus Offsets

We operationalize allocation of farm-level or supplier-level insets to SKUs without double counting across scopes.
Do we recognize insets as inventory enhancements or as separate environmental intangibles on the balance sheet.
How do we set internal carbon transfer prices when insets and offsets co-exist in the same portfolio.
Can we audit MRV pipelines end-to-end to substantiate inset claims embedded in COGS.

6. Post-Quantum Cryptography Migration as Defensive Intangible and Auditability Challenge

We define recognition and amortization policies for post-quantum migration costs as defensive intangibles.
Do we model decommissioning obligations for legacy cryptography akin to asset-retirement obligations.
How do we test crypto-agility controls within ITGC and quantify their impact on audit risk.
Can we estimate expected loss from harvest-now-decrypt-later and provision accordingly.

7. Management Accounting for the Economic Value of Synthetic Data

We classify synthetic datasets as inventory, R&D, or intangible assets under varying use cases and monetization paths.
Do we build variance analysis that attributes model performance gains to specific synthetic data batches.
How do we trigger impairment when ground truth shifts, leakage is detected, or utility decays.
Can we set arm’s-length prices for intercompany transfers of synthetic data and document them.

8. Real-Time Micro-Metered Revenue Recognition for Spatial Computing Advertising

We specify performance obligations tied to gaze, dwell time, and occlusion-adjusted exposure in AR.
Do we recognize variable consideration in real time using edge telemetry, and how do we constrain it.
How do we design controls against synthetic gaze fraud and misattribution in micro-billing.
Can we audit privacy-preserving pipelines that feed revenue recognition at millisecond granularity.

9. Shadow Ledgers for AI Decision Traceability in the Financial Close

We architect shadow ledgers that capture AI decision traces and reconcile them with primary books.
Do we treat explainability artifacts as audit evidence, and what retention and access policies do we adopt.
How do we model misstatement risk from AI interventions in estimates, accruals, and classifications.
Can we quantify close-cycle time reductions attributable to AI while preserving segregation of duties.

10. Accounting for Shared Low-Earth-Orbit Satellite Swarms and Ephemeral Mesh Services

We allocate depreciation, maintenance, and uptime risk across participants in shared satellite swarms.
Do we recognize revenue for mesh services delivered by constellations with dynamic topology and shifting control.
How do we provision for orbital debris mitigation and deorbiting as contingent liabilities.
Can we design transfer pricing for space-to-cloud capacity trades among operators we jointly manage.

11. Blockchain-enabled smart-contract accounting: How do automated contract executions alter audit materiality and evidence collection in micro-cap firms?

We ask: (1) How does the immutability and automation of smart contracts change auditors’ materiality judgments? (2) How do auditors gather persuasive evidence from on-chain events versus traditional ledgers? (3) What new control frameworks are required for small public firms using smart contracts? We outline how to work on it: we combine field interviews with auditors and CFOs of micro-cap firms, perform comparative case studies of firms before/after smart-contract adoption, and analyze audit files and on-chain transaction records; we triangulate with a Delphi panel to propose revised materiality heuristics.

12. Augmented reality (AR) financial reporting: What is the impact of AR-enhanced financial statements on investor comprehension and decision-making under cognitive load?

We ask: (1) Do AR visualizations of financial statements improve comprehension for retail vs institutional investors? (2) How does cognitive load moderate the AR effect on risk assessment? (3) Which disclosure designs (3D cash flows, layered footnotes) yield the best decision accuracy? We outline how to work on it: we design lab experiments with eye-tracking and decision-tasks, develop prototype AR disclosures, recruit retail and professional participants, and analyze treatment effects on accuracy, time-to-decision, and confidence using mixed-effects models.

13. Valuing biodiversity credits as green intangible assets: What frameworks and disclosures should firms adopt for biodiversity-related capitalized assets?

We ask: (1) How can firms measure and value biodiversity credits as intangible assets under IFRS/GAAP paradigms? (2) What disclosure gaps exist across industries that depend on biodiversity services? (3) How do market prices for biodiversity credits affect impairment testing and earnings volatility? We outline how to work on it: we build valuation models linking ecological metrics to market credit prices, conduct cross-industry disclosure content analysis, and run sensitivity and impairment simulations; we validate with interviews of sustainability officers and valuation experts.

14. AI-authored earnings guidance: How does disclosure generated or assisted by large language models affect analyst forecast accuracy and managerial opportunism?

We ask: (1) Are AI-assisted earnings guidances systematically more optimistic or conservative than human-authored guidance? (2) Do analysts update forecasts differently when guidance is AI-labeled vs unlabeled? (3) Can textual features reveal managerial opportunism in AI-assisted guidance? We outline how to work on it: we collect corporate guidance texts, use AI-detection and NLP sentiment/opacity measures, run event-study and analyst-revision regressions, and conduct an experimental vignette study where we vary provenance (AI vs human) to observe analyst reactions.

15. Cross-border tax compliance among digital nomads: What are the implications for corporate transfer pricing and employer withholding obligations?

We ask: (1) How does the rise of remote digital nomads employed by multinationals alter payroll withholding and permanent establishment risk? (2) What transfer-pricing adjustments are needed for value created by mobile employees? (3) How do tax authorities respond in enforcement and guidance? We outline how to work on it: we merge survey data of remote workers with administrative tax rulings, run case vignettes with tax practitioners, model firm-level transfer pricing scenarios with mobility shocks, and recommend policy adjustments based on simulated revenue and compliance outcomes.

16. Stablecoin reserve transparency and systemic liquidity in emerging markets: How does reserve disclosure affect local currency liquidity and bank funding costs?

We ask: (1) Does transparent proof-of-reserve reporting by stablecoin issuers reduce liquidity runs in local FX markets? (2) How do changes in stablecoin reserve compositions affect domestic bank funding spreads? (3) What policy designs (disclosure frequency, attestation type) best stabilize liquidity? We outline how to work on it: we collect blockchain reserve data, FX and bank-liquidity time series for selected emerging markets, implement event studies around reserve disclosure shocks, and run VAR models to estimate spillovers to funding costs.

17. Pricing sustainability-linked derivatives: Can contingent derivatives be structured to hedge corporate ESG target attainment risk?

We ask: (1) How do we price derivatives whose payoffs hinge on achievement of specified ESG metrics? (2) What calibration methods map firm-level ESG measurement error into derivative spreads? (3) How do such instruments affect corporate risk exposures and investment behaviors? We outline how to work on it: we develop structural pricing models embedding stochastic ESG trajectories, calibrate using historical ESG score volatility, simulate hedging effectiveness under measurement error scenarios, and present prototype contract terms for market pilot testing.

18. ERP-log network anomaly detection for revenue manipulation: Can unsupervised network analytics detect subtle internal revenue-fraud patterns that traditional audits miss?

We ask: (1) What network features in ERP event logs (user-account-transaction networks) signal revenue inflation? (2) How does an unsupervised anomaly detector compare to rule-based audit tests in precision/recall? (3) What are adoption barriers and auditor interpretability needs? We outline how to work on it: we obtain anonymized ERP logs (or construct realistic synthetic datasets), engineer bipartite and temporal network features, develop unsupervised detectors (graph autoencoders, isolation forests), benchmark against known fraud cases, and run auditor workshops to refine interpretability.

19. Machine-learning optimized tax incentives: What are the welfare and compliance implications of firm-level ML targeting for investment tax credits?

We ask: (1) Can ML-based targeting of tax incentives improve fiscal efficiency compared to uniform incentives? (2) How do firms respond strategically to ML-driven selection rules? (3) What are distributional and compliance consequences across firm sizes and regions? We outline how to work on it: we simulate a policymaker using administrative firm data to train treatment-allocation ML models, run policy counterfactual simulations with heterogeneous firm responses, and evaluate welfare, revenue, and compliance trade-offs; we validate with synthetic experiments and sensitivity analyses.

20. Digital inheritance in family firms: How does cryptographic key succession for founder-held crypto assets affect governance, financial reporting, and succession outcomes?

We ask: (1) How do family firms disclose and account for founder-held crypto assets and planned key-transfer mechanisms? (2) Does the design of key succession protocols correlate with firm continuity, valuation, or agency conflicts? (3) What governance mechanisms mitigate risks of irrevocable asset loss at founder death or incapacity? We outline how to work on it: we perform multiple case studies of family firms with crypto holdings, analyze disclosure language and valuation adjustments, survey family office and legal advisors about succession practices, and model scenario-based effects on firm value and control.

21. Accounting for programmable-money waterfalls in smart contracts

We, the TopicSuggestions team, propose research questions: How should revenue and liabilities be recognized when smart contracts autonomously enforce multi‑party payment waterfalls; How can auditors obtain sufficient evidence of execution and intent from on‑chain code; How should tax authorities treat on‑chain conditional transfers for income timing; What disclosure framework best captures code‑based contingent obligations? We outline how to work on this topic: We will map common waterfall patterns in DeFi and tokenized securities; We will develop recognition and measurement criteria by adapting IFRS/GAAP principles to executable code; We will test proposals using public blockchain data and controlled smart‑contract deployments; We will interview auditors, tax officials and developers to validate practicality.

22. Valuation and financial reporting of personal data as an intangible asset for SMEs

We, the TopicSuggestions team, propose research questions: Under what circumstances can SMEs recognize collected personal data as an asset rather than an expense; Which measurement bases (cost, income, market) best capture the value of datasets; How should impairment and depreciation be modeled given regulatory and technological obsolescence; How do privacy laws (GDPR/CCPA) affect recognition and disclosure? We outline how to work on this topic: We will construct valuation templates (discounted cash flows from data monetization, proxy markets, replacement cost); We will run firm‑level case studies and surveys of SME data practices; We will simulate shocks (regulatory fines, data breach) to test impairment rules; We will propose a practical disclosure checklist.

23. Integrating satellite and remote‑sensing environmental metrics into corporate impairment and risk models

We, the TopicSuggestions team, propose research questions: How do satellite‑derived indicators (drought, deforestation, flood risk) inform asset impairment and provisioning decisions; Can remote sensing be quantitatively linked to expected credit loss models for exposed industries; What thresholds in imagery data should trigger disclosure or write‑downs? We outline how to work on this topic: We will merge high‑resolution environmental datasets with firm‑level fixed‑asset locations and financials; We will estimate panel models linking remote signals to subsequent cash‑flow deterioration; We will develop algorithmic triggers for impairment and validate with event studies and insurer loss records.

24. Hybrid human‑AI auditor liability: attribution models and insurance pricing

We, the TopicSuggestions team, propose research questions: How can audit failures be causally attributed between human judgment and AI tooling; What legal and economic frameworks best allocate liability in hybrid audits; How should professional indemnity insurance be priced given AI‑related risk shifts? We outline how to work on this topic: We will design an attribution framework combining audit trail logging, counterfactual human‑only vs AI‑assisted outcomes, and expert elicitation; We will run controlled audit experiments with firms using different AI stacks; We will build actuarial models to price insurance under alternative liability allocations and propose regulatory safeguards.

25. Accounting for payments for ecosystem services (PES) within agricultural supply chains

We, the TopicSuggestions team, propose research questions: How should PES receipts to farmers be classified (revenue, government grant, other income) and recognized amid high volatility; How do PES contracts affect cost capitalization and fair value of biological assets; What disclosure and assurance practices are needed to prevent double‑counting of environmental benefits? We outline how to work on this topic: We will catalogue PES contract structures across jurisdictions; We will model cash‑flow profiles for recognition and impairment decisions; We will analyze buyer/supplier accounting through matched case studies and propose standardized reporting templates and third‑party assurance protocols.

26. Behavioral finance of gig‑economy rating inflation and its impact on alternative credit scoring

We, the TopicSuggestions team, propose research questions: To what extent do rating inflation and reciprocal rating strategies on gig platforms bias lenders’ credit assessments; How can alternative credit scoring models correct for platform‑specific social bias; What regulatory or design interventions reduce toxic rating dynamics without harming platform liquidity? We outline how to work on this topic: We will collect platform transaction and rating data across multiple marketplaces; We will estimate behavioral models of rating generation and incorporate corrections into credit models; We will field experimental interventions (anonymized ratings, delayed feedback) to evaluate impacts on underwriting and lending outcomes.

27. Valuing human capital options: accounting for noncompete clauses, talent mobility rights and retention instruments

We, the TopicSuggestions team, propose research questions: Can noncompetes, golden‑handcuff agreements and talent mobility clauses be modeled as financial options for firm valuation; How should firms measure and disclose the value and risk of human‑capital‑linked derivatives; What is the effect of treating such contracts as balance‑sheet items on M&A and investor decisions? We outline how to work on this topic: We will develop option‑pricing analogues tailored to employment contract features (vesting, enforceability, mobility rates); We will gather contract and turnover data from tech firms and calibrate models; We will simulate valuation effects and study market reactions to alternate accounting treatments.

28. Financial reporting and governance mechanisms in decentralized autonomous organizations (DAOs)

We, the TopicSuggestions team, propose research questions: What constitutes a set of financial statements for DAOs given tokenized treasuries, multisig governance and off‑chain obligations; Who holds fiduciary duties and how are they enforced; What auditing and assurance models are feasible for pseudonymous DAO participants? We outline how to work on this topic: We will map fund flows across representative DAOs, formalize a reporting taxonomy (treasury, vested tokens, liabilities); We will explore governance enforcement mechanisms and legal wrappers via interviews and legal analysis; We will prototype an assurance framework combining cryptographic proofs, on‑chain attestations and oracles.

29. Automating audit of sustainability‑linked loan covenant compliance using natural language processing

We, the TopicSuggestions team, propose research questions: How can NLP reliably extract, normalize and verify KPI narratives in borrower reports relative to SLL covenants; What error bounds are tolerable for automated screening vs. evidence‑level assurance; How does automated monitoring change lender pricing and covenant design? We outline how to work on this topic: We will build NLP pipelines for KPI extraction, trend detection and anomaly scoring using labeled covenant datasets; We will validate models against third‑party audits and stress‑test on multilingual disclosures; We will run lender experiments to measure changes in pricing and covenant tightness with automated monitoring.

30. Quantum‑resilient financial risk metrics: preparing portfolio models for cryptographic and algorithmic quantum shocks

We, the TopicSuggestions team, propose research questions: Which financial instruments are most exposed to near‑term quantum‑capable attacks (cryptographic breakage, optimization acceleration) and how should that exposure be measured; How should investors price a quantum‑risk premium; What hedging strategies and disclosure norms mitigate systemic quantum shocks? We outline how to work on this topic: We will threat‑model quantum capabilities against market infrastructure and instrument-level cryptography; We will extend conventional risk metrics to include quantum‑attack scenarios and simulate portfolio impacts; We will design hedges (insurance, diversified key‑management, rapid migration protocols) and propose disclosure and governance recommendations.

31. Accounting for Decentralized Autonomous Organizations (DAOs): Revenue Recognition, Governance Tokens, and Expense Attribution

We, the TopicSuggestions team, ask: How should revenue recognition principles be adapted for DAOs that receive pooled funds and distribute value through tokens? We ask: How do governance tokens and utility tokens qualify as equity, liabilities, or intangible assets across jurisdictions? We ask: How can expense attribution be fairly recorded when contributors are pseudonymous and work is trial-and-error? We outline: We will perform doctrinal analysis of IFRS and US GAAP, map common DAO operational models, collect case studies of 10 active DAOs, and design proposed accounting treatments. We will validate with expert interviews (accountants, blockchain lawyers) and simulate financial statement impacts using reconstructed transaction ledgers.

32. Valuing Biodiversity Credits for Financial Reporting: Measurement, Recognition, and Impairment

We, the TopicSuggestions team, ask: How can biodiversity credits be measured and recognized reliably on corporate balance sheets? We ask: What impairment model best captures ecological uncertainty and regulatory risk for biodiversity-linked assets? We ask: How should firms disclose measurement uncertainty and scenario analysis for biodiversity credits? We outline: We will review emerging biodiversity credit markets, construct a valuation framework combining option-like pricing with ecosystem service metrics, and apply the framework to firm-level case simulations. We will perform sensitivity and scenario analyses and propose disclosure checklists for auditors and reporting standards.

33. Real-Time API Banking Data Integration into SME Financial Statements and Credit Models

We, the TopicSuggestions team, ask: How does inclusion of real-time bank API data change the reliability and timeliness of SME financial statements? We ask: What modifications to audit procedures are necessary when auditors rely on continuous bank feeds? We ask: How do credit scoring models perform when updated with intraday cashflow patterns vs monthly statements? We outline: We will partner with SMEs and fintechs to obtain anonymized API cashflow streams, construct hybrid accounting ledgers that reconcile periodic accruals with real-time cash, and backtest credit models on historical API data to measure predictive improvement. We will interview auditors about control implications and design recommended audit procedures.

34. Quantum-Resilient Cryptographic Assets and Their Accounting: Recognition, Measurement, and Control Risks

We, the TopicSuggestions team, ask: How should firms account for cryptographic assets (tokens, private keys) when quantum-computing threats necessitate re-keying or migration? We ask: When does re-encryption or migration qualify as an intangible asset remeasurement vs an impairment? We ask: What internal control and disclosure obligations arise from quantum-resilience upgrades? We outline: We will survey cryptography migration strategies, model economic impacts of forced migrations on token recoverability, and propose accounting rules for recognition and subsequent measurement. We will develop audit-control matrices and conduct interviews with custodians and cybersecurity experts.

35. Central Bank Digital Currency (CBDC) Implementation and Bank Liquidity Reporting: Effects on Regulatory Ratios and Financial Statements

We, the TopicSuggestions team, ask: How will wholesale and retail CBDC issuance and circulation affect bank reserve reporting and liquidity coverage ratios? We ask: How should banks present CBDC holdings, tokenized liabilities, and related settlement exposures in financial statements? We ask: What stress-testing frameworks capture CBDC-induced run dynamics? We outline: We will model CBDC scenarios (partial substitution, flight-to-CBDC, two-tier intermediation) in bank balance-sheet simulations, analyze regulatory reporting implications, and propose disclosure templates. We will also survey central bank payment design choices to map accounting consequences.

36. Accounting for Algorithmic Tax Attribution in Gig-Economy Platforms: Measurement of Withholding, Permanent Establishment, and Profit Allocation

We, the TopicSuggestions team, ask: How should platforms recognize tax-related liabilities when algorithmic matching determines cross-border service delivery and revenue attribution? We ask: What metrics should determine withholding obligations and permanent establishment risk for gig workers operating across jurisdictions? We ask: How can firms model uncertain future tax audits and provisions? We outline: We will analyze platform transaction logs, construct allocation algorithms linking trip/contract data to tax jurisdictions, and simulate contingent tax provisioning under competing tax rules. We will interview tax authorities and propose an accounting decision tree for platforms.

37. Machine-Readable Audit Trails with Homomorphic Encryption: Feasibility for Continuous Audit without Privacy Loss

We, the TopicSuggestions team, ask: Can homomorphic encryption enable auditors to perform continuous verification of financial transactions without accessing raw sensitive data? We ask: What are the computational, accuracy, and control-traceability trade-offs of homomorphic proofs in audit tasks? We ask: How should audit standards incorporate cryptographic evidence and chain-of-custody metadata? We outline: We will build a prototype pipeline encrypting transaction streams with homomorphic schemes, implement standard audit queries (reconciliations, ratio checks) on encrypted data, measure performance and false-positive rates, and draft procedural guidance for auditors and regulators.

38. Dynamic Pension Accounting under AI-Simulated Longevity Scenarios: Integrating Behavioral Mortality and Healthcare Advances

We, the TopicSuggestions team, ask: How do AI-generated stochastic longevity scenarios (incorporating healthcare tech adoption and behavioral change) alter pension liability valuation? We ask: What discounting and risk-adjustment approaches best reflect model uncertainty and scenario correlation? We ask: How should plan sponsors disclose model-dependency and hedging strategies? We outline: We will train generative models on mortality, health-adoption, and socioeconomic data to produce scenario ensembles, integrate these into actuarial valuation models, and quantify sensitivity of pension obligations. We will recommend disclosure metrics and hedging benchmarks for trustees.

39. Behavioral Finance of Microtransaction Patterns: Intraday Consumer Purchase Spikes as Predictors of Retailer Cashflow Volatility and Credit Risk

We, the TopicSuggestions team, ask: How do microtransaction clustering patterns (in-app purchases, micropayments) predict short-term cashflow volatility and merchant credit stress? We ask: Can intraday consumer behavioral signals enhance early-warning models of retailer insolvency? We ask: What accounting recognition and revenue smoothing adjustments best capture high-frequency microtransaction revenue streams? We outline: We will obtain high-frequency sales datasets from digital retailers, extract clustering and sentiment-correlated features, and build machine-learning early-warning credit models. We will test accounting smoothing policies via counterfactual cashflow reconstructions and advise on disclosure of volatility metrics.

40. Climate-Linked Contingent Liabilities: Modeling Legal and Physical Risk Interaction for Financial Statement Provisioning

We, the TopicSuggestions team, ask: How should firms model and provision for contingent liabilities arising from combined physical climate risks (e.g., flooding) and emergent climate litigation? We ask: What probability-weighted approaches reconcile legal uncertainty with asset-level physical exposure for provisioning under IFRS/US GAAP? We ask: How can firms disclose scenario-based ranges without violating legal privilege or prejudicing litigation? We outline: We will integrate geospatial physical-risk models with legal case-outcome probability models, perform Monte Carlo provisioning simulations across industries, and propose a tiered disclosure framework balancing transparency and legal prudence.

41. Accounting for Tokenized Carbon Offsets on Corporate Financial Statements

We propose a novel examination of how firms should recognize, measure and disclose tokenized carbon offsets that are traded or retired on blockchain platforms (to our knowledge a novel, underexplored accounting area).
We ask: How do tokenization, fractional ownership and smart-contract retirement affect recognition as inventory, intangible asset, or liability?; How should firms measure fair value and impairment for tokenized offsets given market illiquidity and double-counting risks?; What disclosure framework best captures transferability, permanence, and verification of tokenized offsets for users of financial statements?
We will work by mapping current carbon-credit accounting to token features, conducting case studies of early adopters, running scenario fair-value models under different verification regimes, and interviewing auditors/regulators to draft a practical disclosure checklist.

42. Behavioral Finance Effects of Large-Language-Model-Generated Investment Narratives

We investigate how investor decisions differ when reading investment reports, sell-side notes, or ESG narratives generated by advanced LLMs versus human analysts.
We ask: To what extent do LLM-generated narratives induce overconfidence, herding, or anchoring compared with human-authored narratives?; How does the perceived credibility and perceived expertise affect pricing and trading volume?; What mitigation (disclosures/labels) reduces bias introduced by synthetic narratives?
We will work by designing controlled lab experiments with retail and institutional participants, deploying A/B field tests with simulated advisor platforms, analyzing trade responses, and using eye-tracking and qualitative debriefs to unpack cognitive mechanisms.

43. Treasury Accounting and Governance for Decentralized Autonomous Organizations (DAOs)

We study the accounting, valuation and governance implications when organizational treasuries operate via multisig wallets, on-chain stablecoins and automated payout protocols—a relatively unexplored institutional form.
We ask: How should DAO-controlled assets and obligations be classified and measured in financial statements?; How do smart-contract governance rules affect recognition of contingent liabilities and related-party transactions?; What audit and internal-control models suit decentralized treasuries?
We will work by tracing on-chain flows for a sample of DAOs, interviewing DAO treasurers and auditors, proposing accounting treatments aligned with existing frameworks, and simulating governance shock scenarios to assess disclosure needs.

44. Revenue Recognition for Programmable Smart-Contract Subscription Micro-payments

We examine revenue-recognition challenges for firms that receive continuous, micro-scale payments via smart contracts (metered usage collected on-chain), which breaks traditional periodic invoicing assumptions.
We ask: How should revenue be measured and allocated when payment is continuous, conditional, and reversible by smart-contract events?; What constitutes a performance obligation when services are delivered via autonomous agents?; How can firms reconcile on-chain receipts with GAAP/IFRS timing and presentation requirements?
We will work by modeling contract terms into IFRS/GAAP revenue frameworks, collecting telemetry from firms piloting micro-payment streams, building accounting recognition algorithms, and testing them on synthetic and real transaction logs.

45. Portfolio Risk Measurement under Climate Tipping-Point Regime Shifts

We propose new financial risk metrics that incorporate abrupt, non-linear regime shifts caused by climate tipping points (e.g., permafrost carbon release, collapse of major ocean currents) into asset pricing and VaR frameworks.
We ask: How do climate-induced regime shifts alter asset correlations, tail dependence and expected shortfall?; How should portfolio optimization incorporate low-probability, high-impact climate thresholds?; What stress-testing protocols reveal hidden climate-concentration risks?
We will work by integrating climate-econometric tipping models with asset-return simulations, calibrating to paleoclimate and climate-model outputs, and developing portfolio-level stress scenarios and optimization heuristics for risk managers.

46. Auditability and Forensic Accounting Approaches to Deepfaked Financial Disclosures

We assess how deepfake technologies that synthesize executive video/audio and manipulated supporting documents threaten audit evidence and propose forensic controls.
We ask: Which audit procedures are most effective at detecting multimedia deepfakes associated with financial disclosures?; How should auditors change evidence-collection and corroboration standards in the presence of synthetic documents?; What legal-evidentiary chains are required to admit digital forensic findings in enforcement actions?
We will work by creating a taxonomy of deepfake threats, training ML detectors on fabricated financial communications, piloting forensic workflows with audit firms, and proposing updates to audit standards for admissible digital evidence.

47. Valuation of Human Capital Using Workplace-Sensor and Productivity Telemetry

We explore methods to convert anonymized wearables, badge-access, and productivity telemetry into measurable components for human-capital valuation and disclosure while preserving privacy.
We ask: Which sensor-derived metrics reliably predict future firm-level productivity and are therefore candidate intangible asset drivers?; How should firms aggregate and monetize human-capital signals under impairment testing?; What privacy-preserving techniques permit valuation without re-identifying employees?
We will work by partnering with firms willing to share anonymized telemetry, applying panel regressions and machine-learning prediction models for productivity outcomes, testing differential valuation models, and designing differential-privacy protocols for reporting.

48. Real-Time Tax Withholding via Permissioned Blockchain: Effects on Corporate Liquidity and Tax Planning

We examine the operational and financial consequences when payroll and transactional taxes are withheld and remitted in near real-time through government-operated permissioned ledgers.
We ask: How does instantaneous tax remittance alter short-term cash management, working capital requirements, and intra-day liquidity strategies?; What are the behavioral responses of firms that lose float from delayed remittances?; How should tax accounting and deferred-tax constructs adapt to continuous remittance regimes?
We will work by building cash-flow simulation models under different remittance latencies, interviewing CFOs and tax authorities participating in pilots, and estimating macro-liquidity effects using firm-level cash holdings data.

49. Accounting and Risk Allocation for Blended Finance Instruments Used by Sovereign Wealth Funds in Emerging Markets

We analyze how sovereign wealth funds (SWFs) should account for blended finance structures—where public, philanthropic, and private tranches combine to fund development—focusing on risk-sharing, guarantees, and contingent returns.
We ask: How should SWFs recognize guarantees, first-loss tranches and performance-based grants within consolidated financial statements?; What valuation techniques capture the developmental additionality versus market-rate expectations?; How do blended instruments affect sovereign balance-sheet transparency and fiscal risk?
We will work by collecting a cross-country sample of SWF blended transactions, mapping contractual rights and contingent exposures, constructing valuation models for layered cash flows, and recommending disclosure templates for fiscal transparency.

50. Interaction of ESG Rating Volatility and Contingent Convertible Debt (CoCo) Pricing and Triggers

We investigate how short-term volatility in ESG ratings—and the possibility of sudden downgrades—affects the pricing, trigger design and investor behavior in CoCo instruments used by banks and corporates.
We ask: Do transient ESG-rating shocks materially alter trigger activation risk and hence CoCo spreads?; How should contract triggers be designed to avoid procyclical outcomes when ESG signals are noisy?; What hedging strategies mitigate ESG-trigger tail risk?
We will work by assembling a dataset of CoCo issuances, firm-level ESG rating time series, and market prices; running event studies around ESG revisions; estimating structural models linking ESG signal volatility to trigger probabilities; and simulating alternative trigger specifications for pricing implications.