PhD Topics in Fintech and Finance: 20 Ideas to Develop Into a Research Proposal

A finance scholar arrives at the first supervisor meeting with four interests: payment apps, investment decisions, market behaviour and taxes. Each could support a meaningful study, but each requires different evidence. Interviews with shop owners may reveal payment concerns, while trading data can help investigate market reactions. Tax research might depend on business records that owners are reluctant to share. Exploring PhD topics in fintech alongside investments, financial markets and taxation therefore starts with a practical question: what can you investigate convincingly? The strongest choice connects your interest with a clear problem, a defensible research question and evidence you can realistically obtain throughout the doctoral project.

Quick answer: A suitable finance PhD topic connects a specific financial problem, a defined population or market, an unanswered research question and obtainable data. Before selecting it, check what existing studies explain, what remains uncertain and whether your proposed method can produce evidence that addresses that uncertainty.

These are starting ideas, not verified research gaps or approved doctoral titles. Their suitability depends on your literature review, university expectations, supervisor feedback and access to evidence.

Table of contents

• Start with a financial decision worth explaining.
• PhD topics in fintech
• PhD topics in investment management
• PhD topics in financial markets
• PhD topics in taxation
• Compare topics before choosing one.
• Turn a broad interest into a research question.
• Check whether your proposed gap holds up.
• Build a topic outline with variables and evidence.
• Check data quality before committing.
• Avoid mistakes that weaken a finance proposal.
• Match the question with a suitable method.
• Questions to take to your supervisor
• Frequently asked questions

Start With a Financial Decision Worth Explaining

“Digital finance” and “investor behaviour” identify areas of interest. A researchable topic goes further by identifying a decision, the circumstances surrounding it and an outcome that needs explanation.

For example, a small retailer may accept digital payments but discourage customers from using them after a disputed transaction. The useful research problem concerns how that experience affects the retailer’s subsequent decisions.

Build your initial topic around four questions:

Who makes the decision?A specific group, such as small merchants, first-time investors or self-employed professionalsWhat influences it?Factors such as explanations, losses, administrative effort or perceived fairnessWhat outcome will you examine?Continued use, portfolio changes, filing behaviour or another clearly defined outcomeWhich evidence can support your explanation?Records, interviews, surveys, experiments or market observations

Choose the outcome carefully. Confidence in a financial service, intention to use it and actual usage are different measures. A study becomes clearer when it explains which one matters and why.

PhD Topics in Fintech

Useful PhD topics in fintech examine how technology changes financial decisions, service experiences and access. Studying an app’s popularity alone may offer limited explanatory value. Investigating what happens when a transaction fails, a loan is rejected, or consent is requested creates a more precise starting point.

1. Failed Digital Payments and Small-Merchant Trust

A payment failure can leave a merchant uncertain about whether the money will arrive, when the dispute will end and who should help.

Research question: How do payment failures and dispute-resolution experiences influence small merchants’ trust and continued acceptance of digital payments?

Possible data: Merchant interviews, transaction diaries, consented complaint records and surveys recording the frequency and resolution of payment problems.

Scope limit: Focus on one merchant category within a defined area. Separate technical failures from customer disputes instead of treating every unsuccessful payment as the same event.

2. Explainability in Digital Lending Decisions

Applicants may respond differently to a lending decision depending on whether they understand the explanation.

Research question: How does the clarity of a digital lending rejection explanation affect perceived fairness, trust and willingness to apply again?

Possible data: A scenario-based experiment comparing explanation formats, applicant interviews and survey responses.

Scope limit: Study one borrower group and one decision stage, such as rejected applications from small business owners. An experiment can examine reactions to explanations without access to a lender’s proprietary model.

3. Financial App Design and Borrowing Behaviour

An instalment amount may attract attention while the total repayment amount receives less consideration.

Research question: How does the presentation of borrowing costs influence users’ understanding of repayment obligations and their borrowing choices?

Possible data: Controlled interface experiments, comprehension questions and interviews about how participants interpret cost information.

Scope limit: Compare a small number of design features, such as instalment prominence and total-cost visibility. Use hypothetical choices where real borrowing would introduce unnecessary risk.

4. Fraud Warnings and User Response

A warning can be visible without being understood. Its usefulness also depends on when it appears and what action it recommends.

Research question: Which fraud-warning formats improve users’ recognition of suspicious payment requests and willingness to pause a transaction?

Possible data: Simulated payment scenarios, response-time measurements, comprehension scores and participant interviews.

Scope limit: Examine one fraud scenario and a defined user group. Describe simulated responses accurately; they do not automatically demonstrate behaviour during a real fraud attempt.

5. Open Banking Consent and Customer Confidence

Customers may agree to share financial information without understanding the purpose, duration or extent of access.

Research question: How do consent explanations influence customers’ understanding, confidence and willingness to share financial data?

Possible data: Experiments using sample consent screens, surveys and interviews exploring perceived control over data sharing.

Scope limit: Select one jurisdiction and one service use case. Define what “open banking” means within the study’s setting before choosing participants or measures.

PhD Topics in Investment Management

PhD topics in investment management focus on how investors and advisers select, maintain and change portfolios. These questions often involve suitability, judgement and the difference between stated preferences and actual decisions.

1. Investor Reactions to Short-Term Portfolio Losses

Two investors facing similar losses may make very different decisions.

Research question: How do investment experience, financial knowledge and time horizon relate to investors’ decisions after short-term portfolio losses?

Possible data: Repeated investor surveys, consented portfolio records and hypothetical loss scenarios.

Scope limit: Study one investor segment and distinguish temporary price declines from realised losses. A single survey cannot reliably reconstruct every decision made during a market downturn.

2. Risk Profiling and Suitability of Investment Advice

A questionnaire score may not fully capture an investor’s financial circumstances or response to uncertainty.

Research question: Where do risk-profile assessments differ from investors’ stated objectives, financial capacity and observed portfolio choices?

Possible data: Anonymised risk questionnaires, adviser interviews, investor surveys and consented portfolio summaries.

Scope limit: Choose one advisory setting. Define suitability using a clear framework rather than assuming that every difference between a score and a portfolio indicates unsuitable advice.

3. ESG Preferences and Actual Portfolio Choices

An investor may express support for environmental, social and governance considerations but prioritise other factors when allocating money.

Research question: Under what conditions do stated ESG preferences translate into portfolio allocations?

Possible data: Choice experiments, investor interviews and consented holdings data.

Scope limit: Specify whether you are examining environmental concerns, governance concerns or a combined measure. Separate stated intentions from observed investments.

4. Passive Investing and Household Diversification

Holding several funds does not necessarily mean the underlying investments are diverse.

Research question: How is passive-fund participation associated with household portfolio diversification and overlapping exposure?

Possible data: Consented household holdings, fund portfolio information and surveys about investment selection.

Scope limit: Define diversification at the level of underlying assets, sectors or markets. Account for holdings overlap rather than counting the number of products alone.

5. Behavioural Biases in Retirement Investing

Retirement planning involves decisions whose consequences may remain distant for many years.

Research question: How do present bias and loss aversion relate to retirement contribution choices among early-career employees?

Possible data: Behavioural tasks, surveys, interviews and voluntarily shared contribution histories.

Scope limit: Focus on one employment group or retirement arrangement. Consider income constraints alongside psychological explanations; a low contribution may reflect limited resources.

PhD Topics in Financial Markets

PhD topics in financial markets examine trading, prices, liquidity and the movement of information. Their feasibility often depends on data frequency, timestamp accuracy and the ability to distinguish competing explanations.

1. Liquidity Changes Around Corporate Announcements

An announcement may change how easily participants can trade, even when its price effect is modest.

Research question: How does liquidity change before and after a defined category of corporate announcement?

Possible data: Announcement timestamps, trading volumes, bid–ask spreads and transaction or quote data where accessible.

Scope limit: Select one announcement type, exchange and observation period. Daily volume alone may not adequately measure every aspect of liquidity.

2. Retail Investor Attention and Price Volatility

Attention can rise because prices move, while increased attention may also accompany further trading.

Research question: What relationship exists between retail attention indicators and subsequent volatility in a defined group of stocks?

Possible data: Search-interest measures, publicly available discussion activity, trading volumes and price series.

Scope limit: Explain what your attention measure captures. Search activity is a proxy and does not directly identify who traded or why.

3. Market Response to Climate-Related Disclosures

The publication of a disclosure does not guarantee that it contains information investors consider new.

Research question: How do market responses differ between climate disclosures containing measurable commitments and those containing general statements?

Possible data: Corporate reports, disclosure dates, coded text and market-return data.

Scope limit: Use a transparent coding framework and investigate other announcements around the same date that could affect prices.

4. Derivatives Participation and Risk-Taking Behaviour

Derivatives can serve different purposes, including hedging and speculation.

Research question: How do retail derivatives participants describe their objectives, risk controls and responses to trading losses?

Possible data: Trader interviews, surveys, trading diaries and consented account records.

Scope limit: Focus on one instrument category. Aggregate market activity cannot establish the motives of individual traders.

5. Information Delays Across Connected Markets

Related instruments may respond to information at different speeds.

Research question: Which market leads price adjustment across a selected pair of connected financial instruments?

Possible data: Synchronised price series, trading calendars, timestamps and relevant announcement records.

Scope limit: Choose one instrument pair and address differences in trading hours. Apparent delays can result from mismatched timestamps or inactive trading periods.

PhD Topics in Taxation

PhD topics in taxation can investigate administrative burdens, understanding, uncertainty and compliance behaviour. A manageable study identifies the tax, taxpayer group, jurisdiction and period being examined.

1. Compliance Costs for Small Businesses

The burden of compliance includes time and effort as well as direct professional fees.

Research question: Which administrative tasks account for the largest compliance costs among small businesses dealing with a selected tax?

Possible data: Business-owner interviews, time-use diaries, bookkeeping records and professional-fee information shared with consent.

Scope limit: Study one tax and business category within a single jurisdiction. Separate routine compliance costs from penalties or exceptional disputes.

2. Digital Tax Services and Filing Behaviour

A digital service may simplify filing for some taxpayers while introducing difficulties for others.

Research question: How do usability, digital confidence and access to assistance relate to filing experiences among a defined taxpayer group?

Possible data: Taxpayer surveys, usability exercises, interviews and voluntarily shared filing histories.

Scope limit: Examine one filing process. Distinguish satisfaction with the service from filing accuracy and timeliness.

3. Tax Uncertainty and Business Investment Decisions

Uncertainty can arise from unclear interpretation, expected changes or unresolved disputes.

Research question: How does perceived uncertainty about a selected tax treatment influence the timing of business investment decisions?

Possible data: Manager interviews, investment plans, public company disclosures and surveys.

Scope limit: Define the source of uncertainty and select one sector. Consider demand, financing and other factors that may also influence investment timing.

4. Tax Literacy Among Self-Employed Professionals

Knowing tax terminology is different from applying it correctly to a practical situation.

Research question: Which gaps in tax understanding are associated with recordkeeping difficulties and reliance on professional assistance?

Possible data: Scenario-based knowledge assessments, interviews and surveys about recordkeeping practices.

Scope limit: Choose one professional group and jurisdiction. Have assessment questions reviewed against the rules applicable to the study period.

5. Perceived Fairness and Voluntary Compliance

Taxpayers may judge fairness through administrative treatment, distribution of burdens or their understanding of public spending.

Research question: How do different dimensions of perceived tax fairness relate to willingness to comply voluntarily?

Possible data: Anonymous surveys, interviews and carefully designed vignette experiments.

Scope limit: Measure the dimensions separately. A stated willingness to comply is not direct evidence of actual compliance.

Compare Topics Before Choosing One

The following comparison helps identify what each project would require. Data access remains conditional on permissions, availability and research design.

Payment failures and merchant trustRecruit merchants; transaction records may require consentInterviews, diaries, surveysRecall errors and incomplete recordsLending explanations and applicant trustSimulated decisions can support primary researchExperiment, interviewsConnecting simulated responses with real behaviourESG preferences and portfolio choicesSurveys are feasible; actual holdings may be difficult to obtainChoice experiment, holdings analysisMeasuring the difference between intentions and actionsAnnouncement-related liquiditySuitable quote or transaction data may require a licenceEvent study, statistical analysisData detail and overlapping announcementsSmall-business tax compliance costsRequires cooperation from owners or advisersDiaries, interviews, cost analysisSensitive records and inconsistent estimatesDigital tax service usabilityParticipant recruitment and ethical approval may be neededUsability study, surveyMeasuring filing outcomes reliably

Before committing, attempt a small feasibility check: inspect a sample dataset, test recruitment through an approved route or review whether essential variables exist. This can reveal problems that a polished topic title conceals.

Turn a Broad Interest Into a Research Question

Consider the starting interest:

“Fintech adoption.”

This covers many services, users and decisions. Narrow it progressively:

1. Choose the service: Digital lending.
2. Identify the population: Small business owners.
3. Select the experience: Receiving a rejected loan application.
4. Identify the explanatory factor: Clarity of the rejection explanation.
5. Define the outcomes: Perceived fairness, trust and willingness to apply again.

The resulting question could be:

How do explanations of digital lending rejection decisions influence perceived fairness, trust and willingness to reapply among small business owners in a defined study area?

A working title might be:

Digital Lending Rejection Explanations and Small-Business Borrower Trust

The method must match the wording. An experiment can test responses to different explanations under controlled conditions. An observational survey may support conclusions about associations, with more limited claims about causation.

Check Whether Your Proposed Gap Holds Up

An interesting topic is a starting point. Its originality depends on what existing research has already established and what your study can add.

Compare relevant studies using a literature matrix:

PopulationWho participated, and which groups were excluded?LocationWhich institutional or market setting was examined?VariablesWhat explanations and outcomes were measured?MethodsWhat design, sampling approach and analytical methods were used?FindingsWhere do results agree, conflict or remain uncertain?LimitationsWhat evidence or interpretation remained unresolved?

A change of city alone does not establish a strong research gap. Explain why that setting could change the relationship being studied—for example, through differences in access, business practices or institutional conditions.

Likewise, adding another variable should have a theoretical purpose. Show why it could explain something previous models leave unresolved.

Write the proposed gap cautiously until the literature review supports it. “This study will examine an unresolved relationship” is more defensible than announcing that a topic is completely new.

Build a Topic Outline With Variables and Evidence

A supervisor needs more than an attractive title. A short outline should explain what you expect to observe, how you will measure it and what the result could contribute.

Take the lending-explanation idea from the fintech section. Instead of promising to investigate every aspect of borrower trust, you could prepare this outline:

Decision being studiedHow a small-business applicant responds to a lending rejectionFeature being comparedA brief generic explanation versus an explanation stating the main rejection reasonPrimary outcomePerceived procedural fairness, measured using suitable items identified through the literature reviewAdditional outcomeStated willingness to apply again, kept separate from actual reapplicationBackground informationPrevious borrowing experience, financial knowledge and business characteristicsPossible designRandom assignment to hypothetical rejection messages followed by comprehension and response questionsContribution to investigateWhether explanation clarity helps account for differences in perceived fairnessMain limitationResponses to hypothetical messages may differ from responses to a real rejected application

This is a provisional design, not a ready-made proposal. You still need to justify the measures, review earlier experiments and decide whether the participant group fits the research question.

Give Each Variable a Clear Meaning

Words such as trust, literacy and uncertainty sound precise until you try to measure them. Trust in a provider’s competence may differ from trust in its treatment of customers. Tax literacy may mean recognising terminology, applying a rule to a scenario or maintaining accurate records.

Write a plain-language definition of each main concept before choosing survey questions. Then identify established measures where appropriate and assess whether they fit your population and setting. A convenient questionnaire is not automatically a suitable instrument.

Explain the Relationship You Expect to Investigate

A proposal should give a reason for connecting its variables. For example, a clearer rejection message might improve understanding without improving perceived fairness. That possibility helps distinguish two outcomes that could otherwise be treated as interchangeable.

Use the literature to explain the proposed relationship and plausible alternatives. Let the research question guide your choice of theory; selecting a familiar theory first and forcing every variable into it can leave the actual problem unexplained.

Check Data Quality Before Committing

“Data is available” is only the beginning of a feasibility assessment. You also need to know whether it has the detail, coverage and consistency your analysis requires.

For PhD topics in financial markets, inspect a sample of the records. Check whether announcement times are available, prices are adjusted consistently, and your sample includes companies that later stopped trading. A dataset containing only surviving firms can distort a historical comparison.

For PhD topics in investment management, establish what portfolio information participants can actually provide. Current holdings alone cannot reconstruct previous decisions, deposits or withdrawals. Revise the question if the investment history you need is missing.

For PhD topics in taxation, check whether owners can distinguish professional fees, staff time and exceptional dispute costs. A short diary may capture ongoing effort more clearly than asking someone to estimate a full year from memory, although the diary period must still suit your question.

For PhD topics in fintech, define what counts as a failure or complaint. A declined transaction, delayed confirmation and disputed payment may involve different experiences. Combining them without explanation can make the findings difficult to interpret.

Before collecting data, record:

• The variables you need and how each will be obtained.
• The population and period the evidence actually covers.
• Access conditions, permissions and any costs.
• Missing information that could prevent the planned analysis.
• A fallback question that alternative evidence could answer.

Choose sample size to suit the design and analysis. There is no single respondent count that makes every finance PhD study adequate. Discuss the statistical or qualitative justification with your supervisor before recruitment.

Avoid Mistakes That Weaken a Finance Proposal

Treating a Topic List as Proof of Originality

A title can be newly worded while its research question has already been studied. Use the ideas here to begin a literature search, then explain the specific unresolved issue your project addresses.

Using Causal Language for an Association

If a one-time survey finds that tax uncertainty and postponed investment occur together, it does not by itself establish that uncertainty caused the delay. Match words such as “effect” and “influence” to a design that can support the claim, and discuss competing explanations.

Measuring Everything in One Questionnaire

A long survey covering trust, literacy, satisfaction, risk, adoption and compliance can become several disconnected projects. Identify the primary outcome and retain additional variables only when they have a clear role in answering the question.

Choosing a Method Before Defining the Problem

An advanced analytical technique does not compensate for an unclear question. Decide what you need to explain, identify suitable evidence and then choose the analysis. The method should help answer the problem rather than become the reason for inventing one.

Assuming a New Location Is Enough

A different city can matter when its institutional or economic conditions provide a reason to expect different findings. Explain that reason and investigate it. Changing the place name in an existing title is not, by itself, a doctoral contribution.

Match the Question With a Suitable Method

Surveys: Measure Patterns Across Participants

Surveys can examine reported experiences, attitudes and relationships between variables.

They require clear measures, a defensible sampling approach and enough suitable responses for the planned analysis. Convenience samples may be practical, but their limits should be reflected in the conclusions.

Interviews: Understand Decisions and Experiences

Interviews are useful when the question concerns how people interpret a rejection, navigate filing difficulties or respond to investment losses.

Ask participants to describe specific experiences. Detailed accounts often provide more useful evidence than broad questions about whether they “trust finance technology.”

Experiments: Compare Responses to Defined Conditions

Experiments can compare lending explanations, fraud warnings or borrowing-cost displays.

They help isolate the feature being examined, but participants’ responses in a controlled setting may differ from real decisions involving money, urgency or personal consequences.

Market Datasets: Examine Prices, Trading and Timing

Market research may use event studies, time-series analysis or other methods suited to the question.

Check licensing, missing observations, trading hours, timestamp consistency and changes in sample composition. Confirm that the dataset supports the chosen measure before finalising the topic.

Policy and Document Analysis: Examine Rules and Their Interpretation

Taxation and disclosure research may involve legislation, official guidance, consultation documents and corporate reports.

Define the jurisdiction and period precisely. Document analysis can establish what a rule or disclosure says; claims about its effects usually require additional evidence.

For every method, plan ethical approval where required, informed consent, anonymisation and secure handling of sensitive information. Never assume that publicly accessible content is unrestricted for every research use.

Questions to Take to Your Supervisor

Bring a one-page outline covering your proposed question, population, evidence, method and expected contribution. Use the meeting to resolve decisions such as:

• Is the scope suitable for doctoral research?
• Can I obtain enough reliable evidence?
• What contribution could the study make?
• What should I remove to keep it manageable?
• Does the method support the claims I want to make?
• What alternative evidence could I use if access fails?

Leave with a narrower project and a clear next task, such as testing a measure, reviewing a disputed finding or checking dataset availability.

Frequently Asked Questions

1. How do I narrow PhD topics in fintech?

Choose one service, user group, decision and outcome. For example, narrow digital lending to how rejected small-business applicants respond to different explanation formats. Then check whether previous research leaves a meaningful question unresolved and whether you can obtain suitable evidence.

2. Can investment research use secondary data?

Yes. Some PhD topics in investment management can use fund information, company disclosures, market prices or existing household datasets. Confirm that the data contains the variables your question requires. Public market data, for example, may not reveal an individual investor’s motives or complete portfolio.

3. How do financial markets and investment topics differ?

Financial markets research commonly examines prices, liquidity, trading and information transmission. Investment management research commonly examines portfolio selection, diversification, suitability and investor decisions. They can overlap, but the main outcome determines the project’s focus.

4. How can I limit a taxation topic to one jurisdiction?

State the jurisdiction, selected tax, taxpayer group and observation period in your proposal. A study of compliance costs among a defined category of small businesses is more manageable than a comparison of entire national tax systems.

5. What if company or investor data is unavailable?

Consider an alternative design using interviews, experiments, public disclosures or accessible datasets. Revise the research question to fit the evidence. Do not retain a question that requires private records while substituting data that cannot answer it.

6. How do I assess originality before finalising a topic?

Review the relevant literature systematically and compare questions, populations, measures, methods and findings. Identify what your study could explain beyond existing work. Originality may come from testing a mechanism, resolving conflicting results or improving measurement, provided the contribution is clearly justified.

7. Are these finance PhD topics guaranteed to be original?

No. These are suggestions for developing research questions. Originality must be assessed against the relevant literature. A proposed contribution may involve explaining a mechanism, resolving inconsistent findings or improving measurement, but the topic title alone does not demonstrate it.

8. How many respondents do I need for a finance PhD study?

The number depends on your design, planned analysis, participant access and research objectives. Quantitative studies need an appropriate sample-size justification; qualitative studies need a reasoned approach to participant selection and the depth of evidence. Avoid choosing a number solely because another thesis used it.

Conclusion: Shortlist Three Ideas and Test Their Feasibility

Choosing among PhD topics in fintech, investment management, financial markets and taxation becomes easier when you compare actual research questions. Shortlist three ideas and write down the population, evidence, method and possible contribution for each. Check data access before becoming attached to a title, then discuss the strongest option with your supervisor. If you need support, enquire with ThesisLikho for topic refinement and research planning guidance that helps you develop, understand and defend your own proposed study.