Finance Internship Report Topics

We at TopicSuggestions see that finance internship reports often shape final grades and even first-round job offers. We have worked with students and supervisors across FP&A, markets, banking, risk, fintech, and sustainability, and we know the hardest part is turning day-to-day tasks into a focused, evidence-based report. We will share practical, supervisor-friendly topics you can complete with real data and standard methods, aligned with common assessment rubrics.

Good Internship Report Ideas on Finance

We will group ideas by area (corporate finance, markets, banking, risk, fintech, ESG), note typical data sources and key metrics, and flag the expected deliverable (memo, dashboard, model) so you can match scope to your timeline. Today we will come up with some ideas for you.

1. We examine personal AI co-workers and the re-optimization of task boundaries in hybrid teams

– We ask how personal AIs shift the marginal productivity of generalists versus specialists across industries.
– We investigate how task decomposability and verification costs reshape wage dispersion and internal labor markets.
– We study how contract design, monitoring technologies, and misattribution risk affect surplus division and incentives.
– We evaluate how learning-by-doing and model drift alter firm-specific human capital and turnover.

2. We explore informal insurance and credit within digital fandom economies

– We ask how fan-driven crowdfunding, tipping, and mutual aid smooth creators’ and consumers’ consumption over shocks.
– We examine how network structure and platform design mediate default risk, repayment norms, and enforcement.
– We study whether participation in fandom finance substitutes for or complements access to formal credit.
– We assess cross-border frictions, currency conversion, and platform policy on emergent shadow financial systems.

3. We analyze attention budgets as household public goods

– We ask how couples and families bargain over notifications, feeds, and recommendation filters as scarce attention resources.
– We study the welfare effects of attention reallocation between market work, care, and leisure under different norms.
– We evaluate how markets for attention-management tools price segmentation, privacy, and intra-household externalities.
– We examine how shocks to attention supply (e.g., do-not-disturb mandates) propagate to labor supply and consumption.

4. We investigate the carbon externalities of virality across compute-intensive platforms

– We ask how a unit increase in virality translates to energy demand across video, streaming, AI inference, and crypto layers.
– We study the incidence of carbon costs along creator–platform–infrastructure supply chains under various pricing regimes.
– We evaluate policy instruments (carbon labels, dynamic throttling, peak-load pricing) for mitigating emissions without stifling innovation.
– We examine relocation of data centers and content production in response to regional carbon pricing and grid composition.

5. We study proto-markets for AI prompts and “prompt IP”

– We ask how property rights, versioning, and provenance shape price discovery and entry in prompt marketplaces.
– We examine reputational capital, rating systems, and disclosure strategies under adverse selection and learning spillovers.
– We evaluate how copyright doctrines and open-source licenses affect innovation and market concentration.
– We study cross-platform interoperability and multi-homing costs for creators and buyers.

6. We assess eclipse tourism as a natural experiment for rural infrastructure spillovers

– We ask how temporary demand spikes for connectivity, lodging, and transport alter long-run investment trajectories.
– We examine persistence in broadband quality, mobile coverage, and road improvements after the shock dissipates.
– We study distributional effects on local businesses, housing affordability, and seasonal labor markets.
– We evaluate optimal contracting between municipalities and private providers under uncertainty about repeat events.

7. We evaluate soulbound labor credentials and cross-border hiring frictions

– We ask whether non-transferable digital credentials reduce adverse selection relative to traditional signaling.
– We examine privacy–discrimination trade-offs, employer screening intensity, and wage premia for verified skills.
– We study compliance burdens across jurisdictions and their impact on global talent flows and remote work.
– We evaluate governance failures, revocation risks, and dispute resolution in credential issuance.

8. We analyze default climate futures embedded in contracts and their asset-pricing effects

– We ask how “climate scenario clauses” in long-dated contracts shift risk premia in infrastructure, insurance, and muni bonds.
– We examine heterogeneity in legal enforceability and how it propagates through reinsurance and project finance.
– We study coordination failures when counterparties anchor to divergent scenario baselines.
– We evaluate how disclosure mandates alter term structures of climate risk and capital allocation.

9. We probe the night-time economy under noise externality constraints and silent logistics

– We ask how noise caps, robot deliveries, and off-peak pricing jointly define efficiency frontiers for urban logistics.
– We examine neighborhood-level distributional effects on employment, small retailers, and nighttime amenities.
– We study technology choice under multi-objective regulation (noise, safety, emissions) and platform competition.
– We evaluate welfare impacts of dynamic noise permits and quiet-zone auctions.

10. We investigate battery depreciation as a hidden inflation component in electrified household bundles

– We ask how to measure effective price changes when durability and degradation profiles dominate ownership costs.
– We examine substitution patterns between owning, leasing, and second-life markets for batteries and devices.
– We study inequality in exposure to degradation-driven costs across income groups and climates.
– We evaluate CPI methodologies and propose hedonic adjustments for energy storage and EV components.

11. Intern-led Treasury Process Improvements: What measurable efficiency gains do short-term intern projects deliver in corporate treasury operations?

We, TopicSuggestions, frame research questions: 1) Which treasury KPIs (cash conversion cycle, reconciliation time, payment error rate) improve after intern-led process changes? 2) What organizational factors mediate realized gains? 3) How sustainable are improvements three to six months post-internship? We propose a pre-post case study design across 6–10 mid-sized firms, collect KPI time series, conduct structured interviews with treasury staff, and use difference-in-differences and thematic analysis to separate intern impact from secular trends.

12. Trading Desk Short-term Rotations: How do intern rotations affect execution quality and desk learning curves?

We, TopicSuggestions, ask: 1) Does the presence of rotating interns alter fill rates, slippage, or trade latency? 2) How quickly do interns contribute positively to desk execution? 3) What supervision structures accelerate beneficial effects? We recommend instrumenting trading logs to tag intern-involved trades, estimate learning-curve models (nonlinear mixed effects), compare supervised vs unsupervised periods, and run interviews to triangulate behavioral mechanisms.

13. Internship-driven ESG Data Harmonization: Can short internships materially improve ESG data quality for investment decisions?

We, TopicSuggestions, pose: 1) By how much do intern-led harmonization projects reduce missingness and increase consistency in ESG datasets? 2) Do these improvements change portfolio ESG scores or risk exposures? 3) What validation protocols maximize intern output reliability? We advise assembling baseline ESG feeds, assigning harmonization tasks to interns with clear SOPs, measuring data-quality metrics (completeness, inter-rater reliability), and backtesting portfolio tilt changes pre/post harmonization.

14. Decision Aids for Intern Analysts: Do structured decision tools reduce cognitive biases in investment memo recommendations?

We, TopicSuggestions, question: 1) Which biases (anchoring, overconfidence, confirmation) are most reduced by checklists and scoring templates? 2) Do decision aids improve recommendation accuracy and consistency? 3) Are effects persistent after training ends? We recommend an RCT among intern cohorts using standardized valuation tasks, compare qualitative memo outcomes and quantitative forecast errors, and analyze carryover using follow-up assessments.

15. Valuing Short-term Intern Projects with Real Options: How can we quantify the expected value and continuation option of intern-generated projects?

We, TopicSuggestions, inquire: 1) How to map intern project deliverables into cash-flow proxies and volatility estimates for option valuation? 2) What thresholds justify converting projects into funded initiatives? 3) How sensitive are option values to supervision intensity? We propose building a template to estimate immediate cost-savings or revenue impact, model the project as a real option (Black‑Scholes or binomial for managerial flexibility), and validate on historical intern projects with follow-up outcomes.

16. Carbon Footprint Comparison: What is the relative environmental impact of remote versus on-site finance internships?

We, TopicSuggestions, ask: 1) How do travel, office energy, and digital-work emissions compare per intern-week? 2) What internship designs (hybrid schedules, local hubs) minimize footprint while preserving learning outcomes? 3) How does intern behavior (commuting mode, device usage) affect total emissions? We suggest conducting a life-cycle assessment using surveys, energy-consumption sampling, and travel logs across matched cohorts, then run scenario analyses for program redesign.

17. Intern-generated Alternative Data for Small-cap Liquidity Forecasting: Can intern teams collect alt-data that improves liquidity predictions for illiquid equities?

We, TopicSuggestions, probe: 1) Which low-cost alt-data types (local store visits, supplier call logs, web-scrapes) correlate with short-term volume and quoted spreads? 2) How does intern-collected data compare to commercial feeds in predictive power? 3) What quality-control protocols are necessary? We recommend deploying intern field-collection pilots, engineer features, train nowcasting models (LASSO, random forest), and backtest improvements to liquidity forecasts and execution cost estimates.

18. Internship-to-Employee Pipeline Modeling: Which internship signals best predict conversion into high-performing, long-term hires?

We, TopicSuggestions, investigate: 1) Which observable intern behaviors (task completion velocity, independent initiative, analytic accuracy) predict subsequent performance ratings? 2) How do mentorship and project complexity moderate predictive strength? 3) What lead-time is optimal for retention interventions? We advise compiling longitudinal HR datasets linking intern metrics to post-hire outcomes, apply survival analysis and classification models, and validate with cross-cohort holdouts.

19. Shadow Credit Risk from Temporary Underwriting Delegation: Do short-term intern contributions introduce unrecorded credit risks in loan pipelines?

We, TopicSuggestions, ask: 1) What error types and rates occur when interns assist underwriting (documentation, covenants, cashflow adjustments)? 2) How do these errors translate into measurable credit risk exposure? 3) Which controls most effectively mitigate risk without eliminating intern learning? We propose auditing underwriting files with independent reviewers, quantify error-induced PD/LGD shifts via scenario analysis, and test control interventions (checklists, co-sign thresholds).

20. Task-allocation Optimization for Internship Programs: Can we apply portfolio theory to allocate intern tasks to maximize combined learning and enterprise value?

We, TopicSuggestions, question: 1) How to define expected “returns” (learning gain, project value) and covariances between tasks? 2) Which optimization frameworks (mean-variance, multi-objective, integer programming) best balance constraints (time, supervision)? 3) How robust are allocations to misestimation of task returns? We recommend designing pilot programs that score tasks on standardized metrics, estimate task correlations from prior cohorts, run optimization experiments, and perform sensitivity and robustness checks.

21. Assessing the impact of intern-led micro-trading strategies on asset liquidity during after-hours

We ask: How do concentrated small-volume trades executed by interns affect bid-ask spreads and depth in after-hours trading? We ask: Do intern micro-trades create transient price pressure that reverts during regular hours? We ask: Which asset characteristics (volatility, market cap, liquidity) amplify these effects? We outline how to work on this: We collect timestamped trade and order-book data from a partnered trading desk or use high-frequency exchanges’ anonymized logs, label trades executed by interns or simulate intern-sized orders, compute liquidity metrics (spread, depth, price impact), run event-study and difference-in-differences analyses, and validate with agent-based simulations; we incorporate compliance and anonymization safeguards.

22. Quantifying cognitive load of finance interns via eye-tracking and its effect on spreadsheet error rates

We ask: How does measurable cognitive load during spreadsheet tasks correlate with the frequency and severity of financial modeling errors? We ask: Which task types (reconciliation, forecasting, complex formula construction) produce the highest error rates under cognitive strain? We outline how to work on this: We recruit interns for controlled lab sessions, collect eye-tracking, pupillometry, and keystroke logs while they complete standardized finance tasks, annotate spreadsheet errors, apply mixed-effects regression to link physiological load to errors, and propose workflow or UI interventions to reduce mistakes.

23. Using internship project timelines to predict early-career attrition risk in investment banking

We ask: Can features of intern project assignments (task variety, mentorship intensity, task completion speed) predict whether interns accept full-time offers and their two-year retention? We ask: Which timeline and performance indicators provide the strongest signal for early attrition? We outline how to work on this: We assemble HR and project management data across cohorts, engineer temporal features (time-to-first-deliverable, rework rate, mentorship touchpoints), train survival and classification models to predict attrition, validate with holdout cohorts, and run qualitative follow-ups to interpret causal mechanisms.

24. Evaluating carbon-offset portfolio decisions by intern teams compared to senior managers

We ask: How do intern-led recommendations for corporate carbon-offset portfolios differ from those of senior sustainability teams in terms of cost, additionality, and co-benefits? We ask: Do interns prioritize short-term metrics differently, and how does that affect long-term environmental and financial outcomes? We outline how to work on this: We run parallel case-study assignments where intern teams and senior teams construct offset portfolios using the same budget and constraints, evaluate portfolios on carbon permanence, verification standards, risk and co-benefit scores, and perform lifecycle cost-benefit and scenario analyses; we then assess decision rationales with structured interviews.

25. Algorithmic bias in intern-designed credit scoring models using limited data

We ask: When interns build credit models with small or biased datasets, what fairness and generalization issues emerge compared to professional models? We ask: Which simple mitigation techniques (regularization, reweighing, synthetic augmentation) are most effective in the internship-constrained setting? We outline how to work on this: We host a data-challenge where intern groups develop scoring models on deliberately limited datasets, measure performance and fairness metrics across demographics, test mitigation strategies, and compare to benchmark models; we complement with simulations to generalize findings and craft best-practice training materials.

26. Effectiveness of peer mentorship among intern cohorts on short-term trading performance in simulated markets

We ask: Does structured peer-mentorship improve interns’ trading rule discovery and portfolio returns in market-simulated tasks? We ask: What mentorship formats (one-to-one, small-group, rotating) yield the greatest improvement in learning and risk management? We outline how to work on this: We design a randomized experiment within a simulated trading platform, assign mentorship treatments to cohorts, log strategy evolution and P&L trajectories, apply reinforcement learning diagnostics and statistical comparisons, and survey participants to capture perceived learning and collaboration effects.

27. Analyzing social media sentiment contribution from intern tasks to corporate investor relations outcomes

We ask: Can content produced or curated by interns (press drafts, social posts) measurably alter short-term social sentiment metrics and subsequent investor attention? We ask: Which content features or timing strategies maximize positive engagement while minimizing disclosure risk? We outline how to work on this: We coordinate with an IR team to A/B test intern-created vs. standard posts under controlled conditions, collect social media sentiment, engagement, and search trends, link to short-term flows or trading volume where possible, and analyze via time-series regressions and text-embeddings; we ensure compliance review of all external communications.

28. Measuring the value of hands-on risk management tasks assigned to interns on firm-wide stress test results

We ask: Do intern-driven scenario analyses and sensitivity checks uncover tail risks that materially change stress test outcomes? We ask: What categories of intern-assigned tasks most often produce high-value risk insights? We outline how to work on this: We map intern assignments against risk-model inputs, track contributions that alter stress-test parameters, perform counterfactual stress simulations with and without intern inputs, and quantify change in capital metrics; we supplement with interviews to identify workflow improvements for incorporating intern findings.

29. Internship rotation patterns as predictors of cross-functional financial innovation adoption

We ask: Are firms with interns who rotate across trading, risk, and product teams more likely to adopt cross-functional financial innovations (new products, analytics pipelines)? We ask: Which rotation structures best facilitate knowledge transfer and experimentation? We outline how to work on this: We collect internship program designs across firms, code rotation patterns and post-internship innovation adoption events, run panel regressions controlling for firm size and R&D investment, and conduct network analyses of intern-driven idea propagation; we propose guidelines for rotation design to accelerate innovation diffusion.

30. Real-time compliance violation detection from intern communications using NLP and its impact on regulatory reporting

We ask: Can an NLP pipeline flag potential regulatory or disclosure risks in interns’ real-time communications (chat, email, draft documents) without generating excessive false positives? We ask: How does early detection change the timeliness and accuracy of regulatory reporting? We outline how to work on this: We develop a privacy-preserving NLP classifier trained on redacted historical violations, deploy in a monitored pilot to score intern communications, measure precision/recall and operational response times, and model downstream impacts on reporting accuracy and remediation costs; we incorporate human-in-the-loop tuning and strict data governance.

31. Intern-sourced microliquidity signals: can intern trade-desks detect intraday liquidity pockets missed by algorithms?

We propose to test whether interns stationed on trading desks identify short-lived liquidity pockets that systematic algos ignore. Research questions: Can we quantify intern-detected microliquidity events and their predictive value for short-term execution improvement? Do human observations reduce slippage relative to algorithmic benchmarks? How persistent is the edge across market regimes? We will collect timestamped intern trade notes, match them to market-tape snapshots, and run event studies and execution-cost regressions. We will use mixed-effects models to control for desk, instrument, and intern experience, and run backtests of simple human-signal overlays on existing execution algorithms.

32. Narrative drift in pitchbooks: how intern edits alter deal valuation language and investor perception

We examine whether wording changes made by interns in pitchbooks systematically shift valuation framing and investor responses. Research questions: Do intern edits introduce optimism bias or conservatism into deal narratives? How do those edits affect investor meeting outcomes and term-sheet pricing? We will perform textual analysis comparing draft versions with final pitchbooks, annotate edits by type, and correlate linguistic shifts (sentiment, modality, certainty) with meeting-level outcomes and pricing. We will run human-AI blind evaluations to separate stylistic vs. substantive effects.

33. Shadow compliance learning: can intern onboarding detect hidden AML/CF vulnerabilities faster than automated scans?

We investigate whether newly onboarded interns detect anti-money-laundering (AML) and compliance process gaps through fresh reviews that automated systems miss. Research questions: Are interns more likely to flag procedural inconsistencies or suspicious patterns overlooked by rule-based systems? What training elements accelerate intern detection rates? We will design a staged audit where interns and automated tools independently review anonymized transaction subsets, measure true/false positive rates, and model learning curves. We will supplement with qualitative interviews to map cognitive heuristics used by interns.

34. Intern rotation effects on firm-wide risk sensitivity: does short-term cross-department exposure reduce siloed risk blind spots?

We test whether rotating interns through multiple finance functions increases cross-sectional risk awareness and improves escalation of emerging risks. Research questions: Does intern rotation reduce unreported near-misses or near-term operational losses? How durable is the rotation effect after interns leave? We will implement a randomized rotation schedule for cohorts, track incident reporting, and compare against control cohorts with static placements. We will use difference-in-differences and survival analysis on incident occurrence and corrective-action timing.

35. Micro-cost of intern-induced operational errors: assessing hidden expense lines in back-office workflows

We quantify the direct and indirect costs that small intern errors generate in reconciliation, settlement, and reporting processes. Research questions: What is the average propagation multiplier of a single intern error across downstream operations? Which process steps create the largest amplification of cost and risk? We will instrument workflows to tag intern-originated entries, follow error cascades, and compute time-to-resolution, rework hours, and monetary impact. We will build a causal model to identify chokepoints where training or automation yields highest ROI.

36. Intern social networks and deal origination: do intern referral patterns predict new-target pipelines?

We explore whether the informal social connections interns bring or form correlate with future deal leads and proprietary opportunities. Research questions: Can intern-originated introductions be statistically linked to higher deal conversion rates? Do diverse intern networks increase cross-border or sectoral deal flow? We will map intern LinkedIn and internal CRM touchpoints, code introductions, and trace subsequent pipeline entries. We will apply network centrality metrics and survival models to link intern-network features to deal outcomes.

37. Measuring training effectiveness via pre/post alpha: can short internship modules measurably improve junior contribution to portfolio performance?

We assess whether targeted training modules during internships produce measurable improvements in trade idea quality and small-signal alpha. Research questions: Which curriculum elements (quantitative, fundamental, risk management) most increase intern contribution to realized alpha? How persistent are improvements after program end? We will run randomized controlled trials assigning interns to different training tracks, collect idea submissions with timestamps and backtest their performance, and use hierarchical modeling to decompose skill vs. luck.

38. Behavioral signatures in intern-generated research: do novice analysts reveal systematic biases that forecast junior trader errors?

We analyze intern research reports to identify consistent behavioral biases (confirmatory search, anchoring, overconfidence) and test whether these biases predict execution or recommendation errors when those interns move to trading roles. Research questions: Which textual or decision-pattern features in intern reports forecast later trading mistakes? Can bias-correction feedback reduce future error rates? We will apply NLP to research corpora to extract bias proxies, validate against annotated examples, and run predictive models linking early biases to later operational or trading incidents.

39. Intern contributions to alternative-data pipelines: can curated intern curation improve alpha from niche data sources?

We investigate whether interns assigned to curate and label alternative datasets (satellite imagery, scraped job postings, app-usage logs) can materially enhance signal quality and downstream predictive performance. Research questions: What labeling protocols by interns maximize signal-to-noise ratio? Do intern-curated features outperform fully automated feature engineering for small-cap or illiquid assets? We will set up labeling tasks with validation sets, measure inter-annotator agreement, and embed curated features into predictive models to evaluate incremental R-squared and hit-rate improvements.

40. Internship-driven governance feedback loops: can structured intern reporting accelerate policy improvement in finance firms?

We study whether instituting formalized feedback channels for interns produces measurable governance improvements and policy updates within a firm. Research questions: Does systematic intern feedback shorten policy-update cycles and reduce recurrence of minor compliance breaches? Which reporting formats (anonymous, graded, mentor-mediated) yield highest uptake by senior management? We will pilot multiple feedback mechanisms across offices, track policy-change logs and incident recurrence, and use regression discontinuity to estimate causal impacts on governance quality metrics.

41. Intern-driven ESG Audit Adjustments: How do intern-prepared sustainability disclosures change corporate ESG scores?

We propose to examine whether and how sustainability disclosures drafted or adjusted by finance interns materially affect third-party ESG ratings. Research questions: 1) Do intern-authored sections correlate with measurable shifts in ESG scores? 2) Which types of intern edits (quantitative vs. narrative) produce the largest rating changes? 3) Are rating changes transient or persistent across reporting cycles? Overview: We will collect paired draft/final disclosure versions from internship cohorts (with permissions), apply diff analysis and NLP to classify edits, link edits to subsequent ESG provider score changes, and run robustness checks with matched firms and supervisor-fixed effects.

42. Small-firm Treasury Behavior Under Intern Rotation: Do rotating interns increase short-term cash inefficiencies?

We investigate whether frequent intern rotation in small corporate treasuries leads to measurable cash management inefficiencies. Research questions: 1) Does the presence of rotating interns correlate with elevated idle cash or suboptimal sweep timings? 2) Which treasury tasks assigned to interns (e.g., forecasting, payments) most strongly predict inefficiency? 3) Can structured handover protocols mitigate any negative effects? Overview: We will obtain high-frequency bank ledger data from SMEs that employ interns, label periods with intern activity, estimate cash efficiency metrics, and implement difference-in-differences around intern start/exit dates while interviewing treasury managers about protocols.

43. Behavioral Signaling in Intern Pitchbooks: How do interns’ compensation expectations surface in client-facing materials and affect deal outcomes?

We study whether subtle language or formatting choices by interns in pitchbooks reveal compensation expectations and whether such signals influence buyer/seller behavior. Research questions: 1) Can linguistic markers in intern-created pitchbooks predict their stated compensation demands? 2) Do clients perceive and react to these markers in negotiations or pricing? 3) Does supervision reduce signal leakage? Overview: We will combine surveys of interns about expectations, NLP analysis of pitchbook drafts, and experimental vignettes with corporate clients to measure perceived quality and pricing implications.

44. Interns as Unofficial Credit Analysts: Do intern-prepared credit memos systematically bias SME lending decisions?

We examine whether credit memos prepared by interns introduce systematic biases into SME credit adjudication. Research questions: 1) Are intern memos more optimistic/pessimistic than analyst-authored memos for comparable borrowers? 2) What features (experience, templates, supervision) mediate bias? 3) Do loans initially supported by intern memos show different performance ex-post? Overview: We will collect internal credit memos labeled by preparer role, code sentiment and risk factors, match borrower characteristics, and link to loan performance while controlling for loan officer fixed effects.

45. Tokenization Internship Projects: How do intern-built tokenized asset prototypes affect in-house risk models at incumbent banks?

We explore whether prototype tokenization projects led by interns change how banks model liquidity and custody risk. Research questions: 1) Do intern prototypes lead to measurable updates in internal risk parameter estimates? 2) Which prototype features (custody architecture, transfer latency) trigger model revisions? 3) Are model changes sustained or rolled back after pilot termination? Overview: We will run case studies across banks piloting tokenization with interns, obtain model update logs, interview risk managers, and simulate counterfactual risk metrics pre/post-prototype.

46. Internship Portfolios: Can short-term intern-managed model portfolios predict future fund manager style drift?

We test whether investment decisions taken by interns during supervised rotations anticipate subsequent style drift among portfolio managers. Research questions: 1) Do intern trade baskets reveal early shifts in manager style (e.g., value to growth)? 2) What is the predictive horizon from intern trades to manager-level drift? 3) Does supervisor oversight dampen predictive power? Overview: We will gather intern trade records within asset managers, classify trades into style vectors, compute predictive regressions of manager holdings drift, and control for market regime and mandate changes.

47. Cryptoeconomic Internship Tasks and Market Microstructure: Do intern-maintained validator sets affect small-chain stability?

We analyze whether validator or node maintenance tasks performed by interns on small blockchains affect short-term chain stability and forks. Research questions: 1) Does intern operational error correlate with higher short-term variance in block times or fork rates? 2) Which operational tasks (key rotation, monitoring) are most error-prone? 3) Can checklists designed by interns reduce instability? Overview: We will partner with small-chain operators to log maintainer actions, timestamp chain metrics around intern shifts, perform event studies, and run randomized checklist interventions.

48. Intern-authored Tax Heuristics in MNCs: Do heuristic rules developed by interns persist in transfer-pricing practice and create taxable profit shifts?

We investigate whether simple tax heuristics or spreadsheet shortcuts created by interns propagate into persistent transfer-pricing practices that alter taxable income allocations. Research questions: 1) How often do intern heuristics become embedded in long-run transfer-pricing models? 2) Do such heuristics bias profit allocation toward or away from high-tax jurisdictions? 3) What governance mechanisms prevent heuristic entrenchment? Overview: We will audit historical model versions, interview tax teams, trace formula changes authored by interns, and simulate taxable profit impacts under counterfactual removal of heuristics.

49. ESG-Linked Derivative Templates Built by Interns: Do templated clauses change counterparty negotiation outcomes in sustainability derivatives?

We assess whether standardized derivative contract language drafted by interns affects negotiation concessions and final clause stringency in ESG-linked derivatives. Research questions: 1) Are intern-drafted templates systematically more or less stringent than market averages? 2) Do counterparties exploit perceived junior authorship to extract concessions? 3) Does legal review neutralize template-origin effects? Overview: We will collect draft templates, track negotiation rounds, code clause strictness, run lab experiments with practitioners exposed to templates labeled by author seniority, and analyze final contract outcomes.

50. Intern-Facilitated Retail Robo-Advisor Tests: Do intern-run A/B tests introduce survivorship bias into algorithmic performance assessments?

We probe whether A/B experiments on robo-advisors managed by interns create selection artifacts that bias performance evaluation. Research questions: 1) Do intern-managed cohorts systematically differ in retention, leading to biased backtests? 2) Which operational practices (sample selection, logging) most contribute to survivorship bias? 3) How should experiment design be altered to eliminate intern-induced bias? Overview: We will analyze experiment logs, compare intern-run and manager-run A/B tests, quantify bias in performance metrics, and propose protocol adjustments with validation through re-runs or simulation.