Finance & Accounting Dissertation Examples

Go to the limitations passage on page 33. Most limitations sections are an apology in list form: small sample, one country, self-reported data, and no indication of what any of it costs the argument. This one names the consequence every time.

The first: her design is cross-sectional, and she says why that is not a stylistic choice — there is no time-series dimension because consistent firm-level biodiversity data separate from broader ESG scores does not yet exist. So the analysis "cannot rule out omitted variable bias or reverse causality", and the fix is named too: panel data, instrumental variables, or a quasi-experimental design.

The second is better still. Her regional results hold up statistically, but the mechanism behind them has not been tested, and she says her own explanation for it is "theoretically plausible but requires further decomposition". Writing that sentence about your own favourite idea is harder than it looks.

It is also a useful model for writing about something the data barely covers yet. She is measuring biodiversity dependence through ENCORE, a dataset she notes "remains under development", across more than 10,000 listed firms — and the caution shows in the claims rather than only in the caveats.

The Master's example, and the one to read if the doctoral theses are making you despair. Five chapters, 122 pages, one clearly bounded question: what does decarbonizing the global power sector cost, and what does it return?

Its most copyable feature is the smallest. At the end of chapter 1 there is a roadmap paragraph saying what each remaining chapter does: chapter 2 reviews the scientific consensus and the existing finance literature, chapter 3 details data sources and the methodological framework, chapter 4 presents results across financing scenarios, chapter 5 discusses implementation barriers and the role of climate clubs. Four sentences, and a reader knows the shape of the whole document before reading any of it. Examiners read a lot of theses; a roadmap buys goodwill in the first few pages, and almost every supervisor asks for one without showing an example.

Read this one for the problem it has rather than the answer it reaches: two datasets that do not line up, and a decision made in the open.

Open it at "Methodological Innovations in Measuring Financial Literacy" (page 12). He is joining the National Financial Capability Study to COMPUSTAT. The NFCS is run "every three years", so it exists for 2009, 2012, 2015, 2018 and 2021; COMPUSTAT is annual and his sample runs 2009 to 2024. Merge them and two years in three have a hole where the literacy measure should be.

He names the problem — "due to variations in survey participation, missing data is a recurring challenge" — then uses Fully Conditional Specification to impute the gaps, and, the part worth copying, reports the results both ways: Panel A on the observed survey data, Panel B on the imputed series, side by side in the tables. The reader can see what the imputation did rather than taking his word for it.

At 103 pages it is also the longest of the professional doctorates here, and the closest in feel to a traditional thesis if that is the shape you have been asked for.

Read this for a single artefact: the literature summary table, Table B.1, running from page 188. Its columns are Paper, Setting, Dataset and Source, Method, Explanatory variables, Outcome variables, and the column most students omit, Sign of effect, which records that droughts raise intimate partner violence where floods show a null effect, and notes for each study what moderated it.

A table like that does something prose cannot: it makes studies comparable, and it makes a gap visible rather than asserted. When you can see that every prior paper used one dataset and one method, the case for your own design writes itself. Build one early whatever your field; it also stops you rereading the same twelve papers because you cannot remember what they found. The topic pairing is instructive too: development economics, weather shocks and gender-based violence in one dissertation shows how an essays-format thesis holds together on a question rather than a dataset.

Open it at "Literature Review, Regulatory Rules, and Hypotheses" (page 131), the best answer here to "synthesize the literature, don't summarize it." Xiong is writing about trading halts. He opens: "The effect of trading halts is debatable." Then the first school and its authors: halts ease market tensions, reduce volatility, protect liquidity providers. Then the opposing school: halts merely delay volatility and can create a magnet effect dragging prices toward the suspension threshold.

Then: "There can be a middle ground between the above two schools": Harris, who splits volatility into informed and uninformed trading and shows the halt helps for one and harms the other. Position, counter-position, resolution, with the hypotheses built on the unresolved part. Copy the three-move structure directly; it works in any field with a live disagreement. Also the cleanest example here of the three-essay format.

The theory entry. If your dissertation is a formal model rather than an empirical study, this is the shape: a baseline model, its results, and then "Appendix A. Extensions and Robustness" (page 59), where the model is extended rather than the regressions re-run.

The extension is instructive in itself: the main model covers the takedown half of a notice-and-takedown copyright regime, and the appendix adds the counter-notice procedure as a settlement game: the owner sues, offers to drop the suit if the platform closes, the creator accepts or refuses, and the creator responds only if the threat to litigate is credible, meaning the owner's expected gain must exceed litigation costs. Decision nodes are color-coded by player, with nature's moves marked separately. Two things to take: robustness in a theory paper means showing your result survives a richer model, not a different standard error; and a labelled game tree is worth several pages of prose.

The model here for explaining how you built your sample, which is the paragraph most students write as one sentence and lose marks for.

Open it at "Data and Methodology" (page 12). She starts from the Public Plans Data, over 3,700 observations covering 2001 to 2018. Then she cuts it, and says why each time. The years 2001 and 2002 go, and the sentence doing it is worth stealing: "To obtain a balanced data set, where each plan is observed during the same time-period, 2001 and 2002 data were excluded." That leaves 2,800 observations across 2003 to 2018. The District of Columbia goes "because 16 years of plan data were not reported". Federal employee plans are out because the source does not track them.

Then she does the thing that makes a sample real to a reader: she describes its shape. About 3.5 retirement plans per state; one each for Hawaii, Idaho, Mississippi, Oregon and Wyoming; fifteen for California and ten for Texas. You finish the page able to picture the dataset, which is the whole job of that section.

It is also a good example of a practitioner question — a working professional asking which lever actually closes a funding gap — answered with public data rather than proprietary access.

The model for structural estimation, and more generally for interpreting a number instead of reporting it. Zahur estimates a conduct parameter for LNG sellers and gets 0.72 to 0.90. He does not stop there.

He tells you that 1 means firms behave exactly as Cournot competition predicts and 0 means fully competitive, and therefore that 0.72-0.90 means sellers are close to Cournot but somewhat more competitive, and that fully competitive conduct is implausible because the estimate is nowhere near zero. Three sentences turning a coefficient into a finding. Most results chapters report significance and move on, leaving the examiner to work out what the magnitude means. Note also how he handles model simplicity: he runs a deliberately parsimonious specification, says so, and reports that adding covariates does not measurably change the fit. Defending a simple model on evidence beats apologizing for it.

Thirty-three pages, seven numbered sections, no chapters. If you have been told a doctorate must be four hundred pages, read this one first: it is built like a journal article, and it was examined as a doctorate.

Open it at "III. Description of Data and Empirical Testing Methodology" (page 7) for the thing it does best, which is justifying every variable from the research question rather than from availability. She wants to know whether market risk drives macroeconomic stability, so she picks the two variables the Federal Reserve is actually mandated to manage — "the macro variables that stem directly from the Federal Reserve's 'dual mandate' to ensure low unemployment and price stability" — and then splits inflation in two, survey-based CPI against the breakeven rate that reflects bond market expectations.

The detail worth copying is on the same page. Her VIX series runs from January 1990, but the breakeven inflation data only start in January 2003, and instead of quietly using the shorter window she says why: earlier estimates "suffered from serious liquidity constraints of TIPS", with three citations. A reader never has to wonder whether she noticed.