Skill UI

This guide assists authors in preparing research materials for submission to the Journal of Financial Intermediation (JFI), adhering to Elsevier Option C. It provides detailed instructions on managing, depositing, citing, and linking research data, code, and algorithms—whether the data is public, proprietary, or restricted (e.g., central bank registers). The goal is to ensure methodological rigor and reproducibility through the Data Statement.

This guide provides a comprehensive strategic overview of the Journal of Financial Intermediation (JFI)'s unique submission process. It details the reality of active desk-rejection, the single-blind review format, and the expectations for engaging with a single expert referee. It is designed to help authors calibrate their manuscript to maximize acceptance chances at highly selective academic venues.

This guide provides rigorous conventions for constructing self-contained tables and figures for submissions to high-tier finance journals (e.g., JFI). It focuses on structuring exhibits—such as sample construction, event studies, and fixed-effect regressions—to make complex econometric findings accessible and persuasive to banking and intermediation specialists. It guides the visual narrative, ensuring every exhibit is self-sufficient and methodologically transparent.

This skill guides researchers in assessing whether a manuscript's core contribution centers on banking, financial intermediation, or market frictions. It helps determine the research's scope, guiding decisions on whether the paper is theory-led (models of friction) or empirics-led (causal designs on credit data). Use this to maximize publication chances in specialized finance journals.

This workflow router guides authors through the complex lifecycle of submitting a paper to the Journal of Financial Intermediation (JFI). It coordinates the entire academic writing process, determining the optimal sequence of skills based on whether the contribution is theory-led or empirics-led. It is designed to help authors structure their research, focusing on methodological rigor (e.g., Gate B checks) before drafting the final prose.

This guide provides best practices for empirical design and microstructure measurement in financial markets. It covers cleaning high-frequency TAQ/order-book data, constructing robust liquidity, spread, and price-impact measures, and meticulously documenting all sample filters and calculation assumptions. Essential for academic research where measurement rigor and transparency are paramount.

A comprehensive guide for authors writing empirical papers (especially in finance/economics). It teaches the critical skill of separating load-bearing claims (must be in the main text) from exhaustive supporting evidence (best placed in the Internet Appendix). It details how to build a fully reproducible package, including data lineage, codebooks, and documenting proprietary data access paths.

A comprehensive guide to positioning research contributions for top-tier financial microstructure journals, such as the Journal of Financial Markets (JFM). It teaches authors how to articulate their novel findings by anchoring them within established academic lineages (e.g., Glosten-Milgrom) and explicitly identifying the gap relative to 3-5 closest prior works. Focus must be placed on market mechanics and trading-process advances, rather than general financial outcomes.

A comprehensive guide for researchers preparing manuscripts for top-tier financial journals. It teaches advanced strategies for conducting a pre-submission pre-mortem, anticipating specific econometric objections (e.g., endogeneity, measurement artifacts), and systematically triaging reviewer feedback (fatal, fixable, defensible). Ideal for improving research robustness and maximizing acceptance chances.

A detailed guide for constructing a comprehensive robustness ledger in empirical financial research, particularly for microstructural data. It structures checks to address common academic threats, such as measurement bias, sample selection, microstructure noise (e.g., bid-ask bounce), and flawed inference, ensuring findings are methodologically sound and highly defensible in top-tier journals.

This guide provides rigorous best practices for authors presenting empirical evidence in microstructure finance papers. It ensures that tables and figures are self-contained, verifiable, and immediately convey the core mechanism and headline results to a skeptical academic referee, moving beyond simple descriptive statistics.

This guide helps academics determine if their research contribution is fundamentally about market microstructure—such as order flow, liquidity, price impact, or trading mechanisms—making it suitable for the Journal of Financial Markets (JFM). It provides a rigorous 'mechanism question' test to distinguish between microstructural findings and broader corporate or asset pricing issues.