Most MBS topic lists you find online are titles and nothing else. A title is the easy part — what gets a proposal rejected is a topic whose data does not exist, or exists only behind a permission you will never get. Every topic below names where the data actually comes from, how you would analyse it, and how hard it is to finish inside one semester.
The MBS dissertation carries 100 marks alongside fourteen core courses and two specialisation courses, and you are encouraged to write within your chosen specialisation. That constraint is useful: it narrows the field before you start. Pick from the group matching your specialisation first.
These are starting points, not submissions. Your supervisor and department have the final say on scope, and the concept note you take to them should be your own narrowing of one of these.
Every topic above is still too broad to submit. Narrowing means fixing four things: the population, the period, the unit of analysis and the outcome variable. "Determinants of capital structure in Nepali commercial banks" becomes submittable when it reads "Determinants of capital structure in Nepali commercial banks, 2015-2024: a panel analysis of 18 listed banks" — because now your supervisor can see exactly what you will and will not do.
Do the feasibility check before you fall in love with a topic. Open the data source named above and confirm with your own eyes that the specific series you need exists for the specific years you want. A surprising number of MBS proposals are approved on a data source that turns out to be incomplete for the period proposed, and the student discovers this in month four.
Then write the statement of the problem last, not first. It is much easier to explain why a question matters once you already know you can answer it.
Data that requires permission you have not secured. If the topic needs hospital, bank or government records, the proposal should say who you have already spoken to. "Permission will be sought" is the phrase that gets sent back.
A sample size with no derivation. Naming a figure without showing how you arrived at it invites the first question in your viva, and it is a question you will not be able to answer afterwards.
An objective that no result could answer. Read your objectives and your proposed analysis side by side — if a stated objective has no test behind it, either drop the objective or add the test.
A topic identical to one completed in your department within the last three years. Check the department library before you commit; this is fifteen minutes that saves a rewrite.
NEPSE-listed company data is the most accessible secondary data in Nepal, which makes these the most commonly completed MBS topics — and the most commonly duplicated. Narrow by sector and period to avoid writing the thesis your senior wrote.
Bank balance sheets are published quarterly and standardised by NRB reporting requirements, so the panel builds itself. The question stays live because Nepali banks carry capital ratios shaped by regulation rather than pure market choice.
Nepali firms pay unusually high bonus-share dividends, so the relationship behaves differently from textbook Western findings — which gives you something to actually discuss rather than confirm.
Manufacturing is under-researched relative to banking in Nepali MBS theses, so your literature gap is genuine rather than manufactured.
Development banks sit between commercial banks and microfinance and are rarely studied on their own, despite a distinct risk profile.
Hydropower is the sector where capital budgeting decisions genuinely bind, and where practice diverges most from textbook NPV discipline.
The regulatory cap on spread gives you a clean before-and-after policy setting, which is rarer in Nepali data than you would expect.
Marketing topics are the most likely to fail on data. Primary survey data is collectable but slow; be honest in your proposal about sampling frame and response rate rather than promising 400 responses you will not get.
eSewa and Khalti adoption moved fast enough that the behavioural question is current, and the population is reachable for a student survey.
A category with genuine multi-brand competition, high purchase frequency and a population that can actually answer questions about it from memory.
SERVQUAL is well-worn, but domestic aviation in Nepal has a service-reliability dimension that generic applications miss.
Common enough that you must narrow it — pick one platform and one product category, or your findings say nothing.
Distribution, not advertising, is where Nepali FMCG competition is actually decided, and almost nobody studies it.
The Indian-Chinese-Western comparison is specific to the Nepali market and gives a real contrast to test.
These depend entirely on getting organisational access. Secure written permission from one organisation before you submit the proposal — a thesis blocked at data collection is the single most common MBS failure.
Turnover in Nepali banking is high and openly discussed, so respondents answer honestly and HR departments often cooperate.
The post-2020 shift is settled enough to study but recent enough to be unwritten in Nepali literature.
MFIs train heavily because field staff turnover is high, so there is real variation to measure.
A genuine workforce issue with validated instruments available, so you are not inventing a scale.
Family ownership dominates Nepali business and produces culture dynamics the standard literature does not describe.
Public enterprise appraisal is procedurally rigid, which makes perceived fairness an unusually sharp variable.
The strongest MBS theses in this group use NRB published data as a backbone and add a small primary component — this gives you volume without depending entirely on survey response rates.
Policy-relevant, well funded in the literature internationally, and under-evidenced at the Nepali district level.
Repayment is the outcome MFIs track most carefully, so records exist and the question matters commercially.
Remittance is the largest single force in the Nepali household economy and endlessly researchable at new angles.
Recent cooperative collapses make this topical, and members are willing to talk.
A macro-finance question with entirely public data, suitable if you prefer econometrics over fieldwork.
Banks publish both digital transaction volumes and cost-to-income ratios, so the relationship is testable without primary data.
Three, ranked, with a two-line feasibility note on each. Supervisors respond far better to a shortlist with reasoning than to a single fixed idea or an open request for suggestions. It also means a rejection of your first choice does not cost you a week.
The curriculum encourages you to write within your selected specialisation, and departments generally hold to that. If you have a strong reason to cross over, raise it with your supervisor before drafting rather than after.
Yes, and for finance and banking topics it is usually preferred — NEPSE and NRB data are published, verifiable and large enough for real analysis. What examiners object to is secondary data used thinly, with no hypothesis and no statistical test behind it.
Plan for one full semester of real work, and start the proposal in the semester before. Students who begin at the start of the final semester are almost always compressed, and it shows in the analysis chapter, which is where marks are actually won.
No. We help with topic feasibility, proposal structure, methodology, sample size derivation and data analysis, and we review drafts you have written. The research and the writing stay yours — read our academic integrity policy for where we draw the line.
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