“I want to study interest rates, but what can I measure?” If you’re exploring PhD research topics in financial economics, start with that question. Interest rates could lead you to study the bond market’s reaction on an RBI announcement day, the time banks take to revise deposit rates, or the cost of borrowing for an NBFC. Each project needs a different dataset. Before you commit, try building a small sample. Even a few rows can show whether you have the dates, definitions and observations your proposal will need.
Quick answer — Choose a sample, a time period and one measurable outcome. For example, study listed Indian banks from 2022 to 2025 and measure the days between each RBI repo rate change and the first revision to a standard one-year retail deposit rate. Pull a few dated rate notices before committing to the topic.
Table of contents
- Financial economics topics
- Banking and finance topics
- Accounting and finance topics
- Business economics topics
- Turn a topic into a research question.
- #faqs
- #conclusion
PhD Research Topics in Financial Economics
1. RBI Rate Announcements and Bond Market Reactions
Research question — How do five-year and ten-year Indian government bond yields move on RBI policy announcement days?
Data and method — Pair RBI Monetary Policy Committee announcement dates with daily government securities yields from the RBI Database on Indian Economy. Compare yield changes in basis points over a short event window. If you can obtain pre-meeting expectations, test whether surprises explain larger moves.
2. Global Rate Shocks and Indian Sector Returns
Research question — Do Indian IT and banking stocks respond differently to unexpected US monetary policy tightening?
Data and method — Match Federal Reserve decision dates and a published monetary policy surprise series with historical Nifty IT and Nifty Bank index levels. Use the first Indian trading session after each announcement and estimate sector returns relative to the broader market.
3. Stablecoin Market Stress and Short-Term US Treasury Yields
Research question — Are large redemption episodes for dollar-linked stablecoins associated with changes in three-month US Treasury bill yields?
Data and method — Combine token supply or redemption data, issuer reserve disclosures and daily Treasury bill rates from FRED. Record the share of reserves in Treasury bills and flag overlapping macroeconomic announcements. The BIS 2026 report explains why reserve composition matters when studying how stablecoin flows relate to bill demand. BIS Annual Economic Report 2026
PhD Topics in Banking and Finance
Use the RBI’s June 2026 Financial Stability Report to understand sector-level risks. Build the institution-level sample from bank and NBFC disclosures, checking which variables each lender reports consistently. RBI Financial Stability Report, June 2026
4. Deposit Rate Changes Across Public and Private Banks
Research question — After a repo rate change, how large is the first revision to one-year retail deposit rates at public and private banks?
Data and method — Collect dated rate cards from bank archives and pair them with RBI policy decisions. Compare changes in basis points using the same deposit amount band and customer category across lenders.
5. Unsecured Retail Lending and Loan Delinquencies
Research question — Is faster growth in unsecured retail lending associated with higher delinquency ratios in subsequent quarters?
Data and method — Start with quarterly investor presentations and credit-rating reports for lenders that disclose comparable segment data. Estimate a lender-level panel with lagged loan growth. Match each delinquency measure to its loan segment and track any changes in the days-past-due definition.
6. Funding Costs and Loan Growth at Indian NBFCs
Research question — Do NBFCs that rely more heavily on market borrowing expand lending more slowly when funding costs rise?
Data and method — Extract finance costs, interest-bearing liabilities and gross loan balances from quarterly filings. Estimate funding cost consistently, for example by dividing annualised finance costs by average interest-bearing liabilities. Compare lenders with similar portfolios and test whether their funding mix changes the relationship.
PhD Topics in Accounting and Finance
7. Climate Disclosure Comparability in Carbon-Intensive Industries
Research question — How comparable are Scope 1 and Scope 2 emissions disclosures among cement producers reporting under IFRS S2 or the same jurisdictional implementation of it?
Data and method — Use annual and sustainability reports to code reporting boundaries, methods and intensity denominators. Compare firms under the same framework version and document any transition relief each firm applies. The ISSB’s implementation amendments help identify where disclosure choices may differ. IFRS S2 implementation amendments
8. Audit Fees and Reporting Delays
Research question — Are higher audit fees associated with longer audit reporting lags among listed Indian manufacturers?
Data and method — Collect statutory audit fees and signed auditor reports from annual filings. Count days from fiscal year-end to the auditor’s signature. A firm-year panel can account for firm size, losses and complexity, all of which may influence both fees and timing.
9. Cash Flow Disclosure and Borrowing Costs
Research question — Do listed firms that explain the drivers of operating cash flow in more detail face lower borrowing costs the following year?
Data and method — Score narrative disclosures using a consistent rubric, then use interest expense divided by average interest-bearing debt as a borrowing-cost proxy. Check whether the association holds after accounting for leverage, profitability and credit quality where available.
PhD Topics in Business Economics
The OECD’s 2026 financing scoreboard offers context on SME credit conditions and payment delays. Its 2026 digitalisation survey helps frame what AI adoption looks like in practice, including the tools firms use and the barriers they face. OECD SME Financing Scoreboard · OECD Digitalisation Survey
10. AI Adoption and Labour Productivity in Small Firms
Research question — Does labour productivity change after small service firms adopt AI for recurring tasks such as invoice processing?
Data and method — Survey firms on adoption dates and seek monthly output and staff-hour records. Measure completed transactions per staff hour. If you have enough pre-adoption observations and a credible comparison group, compare changes across adopters and similar firms.
11. Late Customer Payments and SME Hiring
Research question — Are longer customer payment delays associated with slower hiring among small manufacturers over the next six months?
Data and method — Combine invoice ageing records or structured owner surveys with payroll headcounts. Measure days beyond the agreed payment date and track net employment changes. Collect order-volume data to help account for demand shifts that may affect both payments and hiring.
12. Working Capital Constraints and Export Entry
Research question — Are credit-constrained manufacturers less likely to record their first export sale within two years?
Data and method — Survey firms without prior export sales about rejected or discouraged credit applications, then follow up to record export entry. Estimate entry probability while accounting for size, sector and prior productivity. Check whether you can retain enough firms through follow-up.
How to Turn a Topic Into a PhD Research Question
Broad topic — Monetary policy and banking.
Research question — How quickly did listed Indian banks adjust one-year retail deposit rates after RBI repo rate changes between 2022 and 2025?
Make one row per bank and policy event. Record the bank, announcement date, applicable deposit category, pre-announcement rate and effective date of the first revision. Decide how long to observe each event, especially when another policy decision falls inside the observation window. Keep records of banks that made no change during the window so they are represented in the analysis.
Pilot the design with several banks and events. Check for dated historical notices, consistent coverage and enough observed revisions to compare banks. If publication records only identify the month of revision, consider measuring monthly pass-through instead of reporting a day-level estimate.
FAQs
Which PhD topic can I study with public data?
Start with an RBI announcement and bond-yield study or an audit reporting lag study. Download a sample of the series or filings before deciding how many years to cover.
Can I use RBI data for a PhD in financial economics?
Yes. RBI policy releases and economic time series can support work on interest rates and banking. Check the series frequency and whether the dataset provides the level of detail your question requires.
Where can I find bank-level disclosures?
Look at investor-relations archives, annual reports and exchange filings. Search quarterly presentations for loan-segment data and dated deposit notices for rate changes.
How can I study sustainability reporting?
Choose one industry, one reporting framework and a small set of disclosures. Pilot your scoring rules on a few firms and record how you handle missing values and different reporting boundaries.
How do I choose an empirical method?
Use an event study for a dated market reaction, panel regression for repeated firm observations, or duration analysis for time to a rate change. Match causal claims to what your design can establish.
How narrow should a PhD proposal topic be?
Define the sample, period, outcome and main comparison in one sentence. Your pilot should show whether the data can support that question or whether the scope needs adjusting.
Conclusion
Shortlist two topics and build a small dataset for each before finalising either. Bring your draft question, proposed sample and any gaps you found to a PhD synopsis assistance review.
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