# Empirical Finance Paper: [Working title stating the question or finding]

> Manuscript template for an empirical corporate finance, asset pricing,
> banking or household finance paper written for a peer-reviewed finance
> journal. It follows the structure most finance journals expect
> (introduction with contribution, hypothesis development, data and sample
> construction, identification, results, robustness, conclusion and an
> Internet Appendix) rather than the IMRaD format used in the health
> sciences. Submission facts cited for the Journal of Finance and the
> Journal of Financial Economics were checked on the journals' official
> pages in October 2026. Replace every `[bracketed]` value and every row
> marked *Example*. This is a document format, not investment, financial or
> legal advice. Always follow the current submission guidelines of your
> target journal, which take precedence over this template.

<!--
Guidance: How to use this template
- Use it as the author team's working manuscript. Draft in Markdown, review
  in Markdown, then move the text into the journal's LaTeX or Word format
  for submission.
- Draft in this order: research question and one-sentence answer (section
  1.1), sample attrition table (section 3.3), main regression table
  (section 5), identification argument (section 4.2), introduction, and
  the abstract last.
- Delete Document control, Revision history and Contents before
  submission. Keep the Disclosure statement page.
- The example study in this template is fictional: the variables, the
  sample counts and the coefficients are invented to show the format. Do
  not cite them.
- Plan the replication package from day one with the Replication Package
  README (`academic/replication-package-readme`); journals now ask for
  code and data at acceptance.
-->

## Document control

<!-- Guidance: Internal tracking for the author team. Copy the target journal's limits here so that nobody has to look them up again. Remove before submission. -->

| Field | Value |
|---|---|
| Paper ID | [PAPER-2026-001] |
| Working title | [Working title] |
| Field | [Corporate finance / Asset pricing / Banking / Household finance / Market microstructure] |
| Target journal | [*Example:* Journal of Finance] |
| Abstract limit | [*Example:* 100 words for the Journal of Finance; check the journal page] |
| Page limit | [*Example:* 60 pages including appendices, references, tables and figures for the Journal of Finance; Internet Appendix excluded; check the journal page] |
| Review model | [Single-blind / Double-blind] |
| Code and data policy | [*Example:* JF Data and Code Sharing Policy (1 April 2024): disclosure plan at first submission] |
| Version | [0.1] |
| Status | [Draft / Internal review / Working paper / Submitted / Revise and resubmit / Accepted] |
| Owner (corresponding author) | [@corresponding-author] |
| Approvers / reviewers | [@co-authors], [@friendly-reader] |
| Last updated | YYYY-MM-DD |
| Classification | [Confidential draft / Public working paper] |
| Working paper series | [SSRN / NBER / institution series, or "not posted"] |

### Approvals

| Role | Name | Decision | Date |
|---|---|---|---|
| Corresponding author | [@name] | [Ready to submit] | YYYY-MM-DD |
| Co-author | [@name] | [Approved final version and author order] | YYYY-MM-DD |
| Co-author | [@name] | [Approved final version and disclosure statement] | YYYY-MM-DD |

## Revision history

<!-- Guidance: Record each internal round, each conference version and each journal round. Link referee responses to the round they answer. -->

| Version | Date | Author | Change summary |
|---|---|---|---|
| 0.1 | YYYY-MM-DD | [@author] | First full draft |
| 0.5 | YYYY-MM-DD | [@author] | *Example:* Conference version; added staggered difference-in-differences estimator |
| 1.0 | YYYY-MM-DD | [@author] | *Example:* First journal submission |
| 1.1 | YYYY-MM-DD | [@author] | *Example:* Revise and resubmit round 1; new placebo tests in section 6.2 |

## Contents

1. [Title page and abstract](#1-title-page-and-abstract)
2. [Disclosure statement](#2-disclosure-statement)
3. [Introduction](#3-introduction)
4. [Related literature and hypothesis development](#4-related-literature-and-hypothesis-development)
5. [Data and sample construction](#5-data-and-sample-construction)
6. [Empirical methodology](#6-empirical-methodology)
7. [Main results](#7-main-results)
8. [Robustness and alternative explanations](#8-robustness-and-alternative-explanations)
9. [Conclusion](#9-conclusion)
10. [References](#10-references)
11. [Tables and figures](#11-tables-and-figures)
12. [Internet Appendix](#12-internet-appendix)
13. [Data, code and AI-use statement](#13-data-code-and-ai-use-statement)
14. [Pre-submission checklist](#14-pre-submission-checklist)
15. [Glossary](#15-glossary)
16. [Standard and licence note](#16-standard-and-licence-note)

## 1. Title page and abstract

<!-- Guidance: The title page carries the title, authors, affiliations, contact details, acknowledgements footnote and abstract. A good finance title names the economic mechanism or the finding, not the dataset. Write the abstract last, count its words, and make sure every number in it appears in the paper. -->

| Field | Value |
|---|---|
| Title | [*Example:* Losing the Analyst: Coverage Terminations and Corporate Investment] |
| Authors and affiliations | [Author A (Institution); Author B (Institution)] |
| Corresponding author | [Name, postal address, email] |
| Date of this version | YYYY-MM-DD |
| JEL codes | [*Example:* G24, G31, G14] |
| Keywords | [3 to 6 terms not repeated from the title] |
| Abstract word count | [*Example:* 76 of 100] |

**Acknowledgements footnote:** [Thank discussants, seminar audiences and
research assistants by name. List funding sources with grant numbers.
State that errors are the authors' own.]

**Abstract:** [State the question, the setting and the identification
source, the main finding with its economic magnitude, and one sentence on
the mechanism. *Example:* We study how losing sell-side analyst coverage
affects corporate investment. Using broker closures and mergers as
exogenous shocks to coverage, we find that treated firms reduce capital
expenditure by 7.75 percent of the sample mean relative to matched controls. The effect is
concentrated in firms with high information asymmetry and is absent when
the departing analyst covered the firm for less than one year. The
evidence suggests that analysts relax financing constraints by reducing
information frictions.]

A strong abstract answers four questions in under 100 words: what you ask,
how you identify it, what you find and how large the effect is, and why it
matters. Avoid adjectives such as "novel" or "important"; let the
magnitude carry the claim.

## 2. Disclosure statement

<!-- Guidance: Several finance journals require a disclosure page. The Journal of Finance asks for it on the page immediately after the title page, with a separate entry for every author by name, including an explicit statement when an author has nothing to disclose. Submissions without it are returned. Check your journal's rule. -->

| Author | Funding and paid relationships | Data provider relationships | Other potential conflicts | Statement |
|---|---|---|---|---|
| [Author A] | [Grant name and number; consulting for named firms in the past three years] | [*Example:* None] | [*Example:* None] | [I have nothing to disclose / Disclosures listed] |
| [Author B] | [*Example:* Paid speaker for an industry association, YYYY] | [*Example:* Data obtained under a non-disclosure agreement with a data vendor] | [None] | [Disclosures listed] |

Write one entry per author even when nothing is disclosed. Include
relationships with any party that has a financial interest in the paper's
findings: data vendors, regulators, consulting clients, asset managers,
and expert witness work in related litigation.

## 3. Introduction

<!-- Guidance: The introduction is the paper for most readers. Within the first two pages state the question, why it matters, how you answer it, what you find and how large the effect is, and what is new relative to the two or three closest papers. Common pitfall: three pages of literature before the reader learns the result. -->

### 3.1 Question and answer

[State the research question in one sentence and the answer in one
sentence. *Example:* Does sell-side analyst coverage affect how much firms
invest? Yes: firms that lose coverage for reasons unrelated to their
prospects cut investment, and the cut is largest where information
asymmetry is highest.]

### 3.2 Motivation

[Explain why the question matters for theory, for practice or for policy.
Give one fact that shows the economic stakes, with its source.]

### 3.3 Empirical approach in brief

[Describe the setting, the source of variation and why it is plausibly
exogenous, in plain language. Name the main threat to identification and
say how you address it.]

### 3.4 Main findings and magnitudes

[Give the headline estimate, its standard error or confidence interval,
and its economic magnitude relative to the sample mean or standard
deviation. Then list the mechanism and heterogeneity results.]

### 3.5 Contribution

<!-- Guidance: Name the closest papers and say precisely what you add: a new setting, a cleaner identification, a new mechanism, a reconciled conflict. Two or three paragraphs, not a survey. -->

| Closest paper | What it shows | What this paper adds |
|---|---|---|
| [Author (Year)] | [Their finding in your words] | [*Example:* Uses a different source of exogenous variation and studies real investment rather than liquidity] |
| [Author (Year)] | [Their finding] | [*Example:* Separates the information channel from the monitoring channel] |

### 3.6 Roadmap

[One short paragraph: "Section 4 develops the hypotheses. Section 5
describes the data..." Many journals now consider this optional; keep it
short.]

## 4. Related literature and hypothesis development

<!-- Guidance: Develop each hypothesis from an economic argument, not from "prior studies find". State competing predictions where theory disagrees; a test that can reject one theory in favour of another is more valuable than a test with only one possible answer. -->

### 4.1 Literature streams

[Group the literature into two to four streams and state the open
question each leaves. Use the Literature Review template
(`academic/literature-review`) to organise the search if the field is
new to you.]

### 4.2 Hypotheses

| ID | Hypothesis | Economic argument | Predicted sign | Test in section |
|---|---|---|---|---|
| H1 *(Example)* | Loss of analyst coverage reduces corporate investment | Coverage lowers information asymmetry and the cost of external finance | Negative | 7.1 |
| H2 *(Example)* | The effect is larger for firms with high information asymmetry | The marginal information value of an analyst is greater when other signals are scarce | Negative, larger in magnitude | 7.2 |
| H3 *(Example)* | The effect is weaker when the departing analyst had short tenure | A new analyst produces little firm-specific information | Closer to zero | 7.3 |
| [H4] | [Hypothesis] | [Argument] | [Sign] | [Section] |

Distinguish confirmatory hypotheses fixed before looking at the outcome
data from exploratory analyses added later. If you pre-registered any part
of the design, cite the registration and link the Pre-Analysis Plan
(`academic/pre-analysis-plan`).

## 5. Data and sample construction

<!-- Guidance: A referee must be able to rebuild your sample from this section and the replication package alone. Name every data source, the version or download date, every filter in the order applied and the observation count after each step. -->

### 5.1 Data sources

| ID | Source | Content used | Frequency and period | Access and version | Licence or restriction |
|---|---|---|---|---|---|
| DS-001 *(Example)* | CRSP (via WRDS) | Daily and monthly stock returns, prices, shares outstanding | Daily, 1995-2024 | Subscription; downloaded YYYY-MM-DD | Licensed, not redistributable |
| DS-002 *(Example)* | Compustat North America (via WRDS) | Annual fundamentals: capital expenditure, total assets, cash flow | Annual, 1995-2024 | Subscription; downloaded YYYY-MM-DD | Licensed, not redistributable |
| DS-003 *(Example)* | I/B/E/S detail history | Analyst identifiers, broker identifiers, estimate dates | Event level | Subscription | Licensed, not redistributable |
| DS-004 *(Example)* | Hand-collected broker closure and merger dates | Event dates from press releases and regulatory filings | Event level | Authors' collection | Released with the replication package |
| [DS-005] | [Source] | [Content] | [Period] | [Access] | [Restriction] |

Name commercial databases by product name only. State which identifiers
link the files (for example CUSIP, PERMNO, GVKEY, analyst code) and how
you resolved many-to-one links.

### 5.2 Variable definitions

Put the full definitions in an appendix table (section 11.3) and summarise
the key variables here: the outcome, the treatment, and the main controls.
Define each variable with its database item names so that a reader can
recompute it, state the timing (fiscal year end, lag structure) and say how
you treated extreme values.

*Example:* Investment is capital expenditure in year t divided by total
assets at the end of year t minus 1. All continuous variables are
winsorised at the 1st and 99th percentiles by year.

### 5.3 Sample attrition

<!-- Guidance: The attrition table answers the first question every referee asks: where did the observations go? Recompute each running total. -->

| Step | Filter | Observations removed | Observations remaining |
|---|---|---|---|
| 0 *(Example)* | Firm-years in Compustat with a CRSP link, 1995-2024 | | 48,250 |
| 1 *(Example)* | Remove financial firms and utilities (SIC 6000-6999, 4900-4999) | 9,830 | 38,420 |
| 2 *(Example)* | Remove firm-years with missing or non-positive total assets | 2,115 | 36,305 |
| 3 *(Example)* | Remove firm-years without analyst coverage in year t minus 1 | 4,005 | 32,300 |
| Final *(Example)* | Estimation sample | | 32,300 firm-years |

### 5.4 Descriptive statistics

[Report N, mean, standard deviation, 25th percentile, median and 75th
percentile for every variable used in the regressions, for the full
sample and separately for treated and control groups. Add a column with
the normalised difference or the t-statistic of the difference in means.]

## 6. Empirical methodology

<!-- Guidance: Write the estimating equation, define every symbol, state the fixed effects and the clustering, and argue identification in words. The question is always: what unobserved factor could produce the same pattern, and why does your design rule it out? -->

### 6.1 Baseline specification

$$
y_{i,t} = \alpha_i + \gamma_t + \beta \, \text{Treat}_{i} \times \text{Post}_{i,t} + X_{i,t-1}' \delta + \varepsilon_{i,t}
$$

| Symbol | Meaning |
|---|---|
| $y_{i,t}$ | Outcome for firm $i$ in year $t$ (*Example:* capital expenditure over lagged assets) |
| $\alpha_i$, $\gamma_t$ | Firm and year fixed effects |
| $\text{Treat}_i \times \text{Post}_{i,t}$ | Equals one for treated firm $i$ in years after its own coverage shock (treatment dates are staggered, so Post is firm-specific); zero for control firms |
| $X_{i,t-1}$ | Lagged controls (*Example:* size, Tobin's q, cash flow, leverage) |
| $\beta$ | Coefficient of interest |
| $\varepsilon_{i,t}$ | Error term; standard errors clustered by [firm / firm and year] |

### 6.2 Identification

[State the identifying assumption in one sentence, for example parallel
trends in investment between treated and control firms absent the shock.
Then list the threats and your response to each.]

| Threat ID | Threat to identification | Response | Section |
|---|---|---|---|
| ID-001 *(Example)* | Brokers close coverage of firms with poor prospects | Use only terminations caused by broker closures and mergers, which drop whole coverage portfolios | 6.2 |
| ID-002 *(Example)* | Pre-existing differences in investment trends | Event-time plot of coefficients for years minus 3 to plus 3 | 7.1, Figure 1 |
| ID-003 *(Example)* | Staggered treatment timing biases two-way fixed effects | Report a heterogeneity-robust staggered difference-in-differences estimator | 8.1 |
| ID-004 *(Example)* | Contemporaneous shocks to the same industry | Industry-by-year fixed effects | 7.1, column (3) |

### 6.3 Inference

State the clustering level and why it matches the level at which
treatment varies or errors are correlated. Panel data in finance often
have both firm and time correlation; Petersen (2009) discusses the
choice. When the number of clusters is small, report wild cluster
bootstrap p-values. For multiple outcomes or many subgroups, say how you
control the false discovery rate.

### 6.4 Matching or weighting (if used)

[Describe the matching variables, the algorithm, the caliper and the
balance diagnostics. Report balance before and after matching in a
table.]

```mermaid
flowchart LR
  A["Raw data: CRSP, Compustat, I/B/E/S"] --> B["Linked firm-year panel"]
  B --> C["Filters and attrition (5.3)"]
  C --> D["Identify coverage shocks"]
  D --> E["Matched treated and control firms"]
  E --> F["Baseline regression (6.1)"]
  F --> G["Mechanism and heterogeneity (7.2, 7.3)"]
  F --> H["Robustness (8)"]
```

## 7. Main results

<!-- Guidance: Lead with the table that answers the question. Interpret each coefficient in economic terms, not only its sign and star. Report the magnitude relative to the mean or a one-standard-deviation change. Avoid relying on significance stars alone; report standard errors. -->

### 7.1 Baseline result

[Describe Table 3. *Example:* The coefficient on Treat x Post in column 3
is minus 0.0062 (standard error 0.0021). The mean investment rate in the
sample is 0.0800, so the estimate implies a decline of 7.75 percent of the
mean (0.0062 divided by 0.0800).]

| Column | Fixed effects | Controls | $\hat{\beta}$ | Standard error | N | Within $R^2$ |
|---|---|---|---|---|---|---|
| (1) *(Example)* | Firm, year | No | -0.0071 | 0.0023 | 32,300 | 0.012 |
| (2) *(Example)* | Firm, year | Yes | -0.0065 | 0.0022 | 32,300 | 0.041 |
| (3) *(Example)* | Firm, industry by year | Yes | -0.0062 | 0.0021 | 32,300 | 0.048 |

### 7.2 Cross-sectional heterogeneity

[Report the test of H2: split by a pre-determined measure of information
asymmetry, or interact the treatment with it. State the difference between
groups and its standard error, not only the two separate estimates.]

### 7.3 Mechanism

[Show evidence for the channel and against rival channels. *Example:*
treated firms see a rise in the cost of new debt and a drop in equity
issuance, consistent with the financing-friction channel.]

### 7.4 Economic significance

Translate the estimate into units a reader cares about: currency
amounts, percentage of the mean, or the effect of a typical shock.
State the conversion explicitly so that the reader can check it.

## 8. Robustness and alternative explanations

<!-- Guidance: Organise robustness by the concern it addresses, not by the order in which you ran the tests. Keep the main paper for tests that a referee would otherwise demand; move the rest to the Internet Appendix. -->

### 8.1 Robustness tests

| Test ID | Concern addressed | Change to the baseline | Result | Location |
|---|---|---|---|---|
| RB-001 *(Example)* | Staggered treatment bias | Heterogeneity-robust estimator | Similar magnitude | Table 6 |
| RB-002 *(Example)* | Outliers | Winsorise at 5th and 95th percentiles | Similar | Internet Appendix Table IA.2 |
| RB-003 *(Example)* | Alternative outcome definition | Investment including research and development | Larger in magnitude | Internet Appendix Table IA.3 |
| RB-004 *(Example)* | Placebo timing | Shift event dates two years earlier | No effect | Table 7 |
| [RB-005] | [Concern] | [Change] | [Result] | [Location] |

Review the table before submission with three questions. First, does every
test map to a threat named in 6.2 or to a rival story in 8.2? A test that
answers no stated concern belongs in the Internet Appendix or nowhere.
Second, is the sample the same as in the baseline? If a test loses
observations, report the baseline on the reduced sample next to it so the
reader can separate the effect of the specification from the effect of the
sample. Third, do you report magnitudes, not only "similar" or "significant"?
Give the coefficient and its standard error for every row in the table that
appears in the paper. A common error is to describe a coefficient that
halves as "robust" because it remains significant; say how much the
estimate moves and why.

### 8.2 Alternative explanations

[For each rival story, state what it predicts that your story does not,
and show the test.]

### 8.3 Limitations

[State what the design cannot rule out, external validity limits (period,
country, firm size) and measurement limits. A frank limitations paragraph
reduces referee friction.]

## 9. Conclusion

<!-- Guidance: Restate the question, the answer and the magnitude in one paragraph. Then one paragraph on implications and one on open questions. Do not introduce new results here. -->

[Write the conclusion in three short paragraphs: findings, implications
for theory and practice, and directions for future research.]

## 10. References

<!-- Guidance: Use the journal's reference style. Cite datasets as well as papers. Check that every in-text citation is in the list and the reverse. -->

[Reference list in the journal's style.]

Method references commonly cited in empirical finance papers, listed here
as a starting point:

- Petersen, Mitchell A. (2009). Estimating standard errors in finance
  panel data sets: comparing approaches. *Review of Financial Studies*
  22(1).
- MacKinlay, A. Craig (1997). Event studies in economics and finance.
  *Journal of Economic Literature* 35(1), 13-39.
- [Data citations: provider, product name, version or download date.]

## 11. Tables and figures

<!-- Guidance: Every table must be readable on its own: a title that states the finding, a caption that defines the sample, the variables, the fixed effects, the clustering and the significance convention. -->

### 11.1 Table conventions

| Convention | Rule for this paper |
|---|---|
| Numbering | [Tables 1 to n in order of first mention; Internet Appendix tables IA.1 to IA.n] |
| Standard errors | [In parentheses below the coefficient; clustering level stated in the caption] |
| Significance markers | [*Example:* \*, \*\*, \*\*\* for 10, 5 and 1 percent, or none, as the journal prefers] |
| Decimal places | [*Example:* Coefficients to four decimal places; descriptive statistics to three] |
| Units | [State units for every variable in the appendix definitions table] |

### 11.2 Planned tables and figures

| Exhibit | Content | Status |
|---|---|---|
| Table 1 *(Example)* | Sample attrition | Done |
| Table 2 *(Example)* | Descriptive statistics, treated versus control | Done |
| Table 3 *(Example)* | Baseline regression | Done |
| Table 4 *(Example)* | Heterogeneity by information asymmetry | In progress |
| Figure 1 *(Example)* | Event-time coefficients, years minus 3 to plus 3 | Done |
| [Exhibit] | [Content] | [Status] |

### 11.3 Variable definitions appendix

| Variable | Definition | Source items | Source |
|---|---|---|---|
| Investment *(Example)* | Capital expenditure in year t over total assets at end of year t minus 1 | CAPX, AT | DS-002 |
| Treat x Post *(Example)* | One for treated firms from the first fiscal year ending after the shock | Event dates | DS-004 |
| [Variable] | [Definition] | [Items] | [Source] |

## 12. Internet Appendix

<!-- Guidance: The Internet Appendix holds supporting material that referees need and readers may want. The Journal of Finance asks for it at the end of the same PDF with the title "Internet Appendix" on its first page; it does not count toward the page limit, but the paper must stand on its own without it. Check your journal's rule. -->

| Section | Content |
|---|---|
| IA.A *(Example)* | Details of event identification and sources for each broker closure |
| IA.B *(Example)* | Additional robustness tables IA.1 to IA.8 |
| IA.C *(Example)* | Proofs or model derivations |
| [IA.D] | [Content] |

## 13. Data, code and AI-use statement

<!-- Guidance: Journals increasingly require a disclosure plan at first submission and the code and data at acceptance. Decide now what you can share, what you cannot (licensed data), and how a replicator gets access. -->

### 13.1 Data and code availability

[State where the code will be deposited, which data are included, which
are licensed and how a replicator obtains them, and whether a pseudo
dataset will be provided for restricted data. Build the full README with
the Replication Package README (`academic/replication-package-readme`).]

Journal facts checked in October 2026 (check the current policy before
submission):

| Journal | Requirement summarised in our words |
|---|---|
| Journal of Finance | Data and Code Sharing Policy of 1 April 2024: a disclosure plan at initial submission; code, computation details, log files and permitted data within about 30 days of conditional acceptance; pseudo data where real data cannot be shared |
| Journal of Financial Economics | Accepted empirical, simulation or experimental papers share data, programs and run instructions in Mendeley Data or a domain repository; a pseudo dataset where data are proprietary |
| [Other journal] | [Requirement] |

### 13.2 AI-use statement

[State whether and how AI tools were used: language editing, code
assistance, data classification or generation. Authors remain accountable
for all content. If AI generated or classified data, record the model,
version or date stamp and the prompts in the replication package. The
Journal of Finance states that AI tools may assist but may not be listed as
authors, and asks for open-source or clearly time-stamped models and
documented prompts when AI produces data or classifications.]

## 14. Pre-submission checklist

<!-- Guidance: Run this the week before submission, and again before each resubmission. -->

- [ ] Abstract within the word limit; every number in it appears in the text.
- [ ] Disclosure statement page present, one entry per author.
- [ ] Page count within the limit, counting what the journal counts.
- [ ] Every table stands alone with a full caption.
- [ ] Attrition table running totals recomputed.
- [ ] Every hypothesis in section 4.2 is tested and reported, including null results.
- [ ] Economic magnitudes stated for each main estimate.
- [ ] Clustering level stated and justified.
- [ ] Internet Appendix placed and titled as the journal requires.
- [ ] Code runs end to end from raw data on a clean machine.
- [ ] Data and code disclosure plan included where required.
- [ ] Reference list complete; datasets cited.
- [ ] Peer feedback collected with the Peer Review template (`academic/peer-review`).

## 15. Glossary

| Term | Meaning in this template |
|---|---|
| Identification | The argument that the estimated coefficient reflects the causal effect of interest, not a confounding factor |
| Difference-in-differences | Comparison of the change in outcome for treated units with the change for control units over the same period |
| Fixed effects | Unit or time intercepts that absorb time-invariant or common shocks |
| Clustering | Allowing regression errors to be correlated within groups when computing standard errors |
| Winsorising | Replacing extreme values beyond chosen percentiles with the percentile value |
| Internet Appendix | Online supplementary material published with the paper |
| Pseudo data | Fictional data with the same structure as restricted data, used to test that code runs |
| Economic magnitude | The size of an effect in meaningful units, as opposed to its statistical significance |

Related templates: Research Paper (IMRaD) (`academic/research-paper-imrad`)
for experimental and health-science formats, Event Study
(`academic/event-study`) for short-window market reaction studies,
Dissertation Proposal (`academic/dissertation-proposal`) for the thesis
stage, and Conference Abstract (`academic/conference-abstract`) for
finance conference submissions.

## 16. Standard and licence note

Structure aligned with common practice for empirical finance manuscripts
and with the public submission pages of the Journal of Finance (American
Finance Association, afajof.org) and the data and code sharing policy of
the Journal of Financial Economics, as checked in October 2026. Journal
requirements are summarised in original words and are not the official
wording; always read the current author guidelines of your target
journal. All other text and the fictional example study are original;
example numbers are illustrative. Template licence: MIT.