Skill UI

This guide details how to conduct rigorous, reproducible numerical simulations for mathematical finance research. It emphasizes that numerical experiments must strictly support a theoretical theorem (e.g., convergence rates, error bounds) rather than performing empirical data fitting. Learn how to properly specify schemes, report theoretical vs. empirical error rates, and maintain full reproducibility to meet high academic standards.

A comprehensive guide for authors responding to 'revise-and-resubmit' decisions in Mathematical Finance journals. It provides a structured approach to triage referee comments regarding proof gaps, assumption scope, novelty, martingale conditions, and financial relevance. Learn how to write a point-by-point response letter and make aligned, rigorous manuscript edits.

This guide details the rigorous review process for papers in Mathematical Finance, covering single-blind peer review standards, from editor screening to line-by-line proof verification by referees. Learn what reviewers prioritize—methodological novelty, proof correctness, and financial applicability—to avoid common submission pitfalls and improve your chances of acceptance.

A comprehensive guide for authors writing mathematical finance manuscripts. It specifies the rigorous standards for presenting figures, numerical tables, algorithms, and dependency maps, ensuring every visual exhibit supports a formal theorem or proposition. It advises on reporting numerical settings, avoiding empirical claims, and structuring evidence to aid theoretical comprehension rather than serving as standalone proof.

A detailed guide for researchers tackling advanced financial-mathematics problems. It outlines the stringent criteria for suitability in top-tier journals, emphasizing methodological novelty, mathematical rigor, and deep theoretical contribution (e.g., stochastic analysis, structural theorems) over routine data computation. Use this to refine and scope your research proposal.

A comprehensive guide for executing and reporting advanced analysis for high-level academic manuscripts, particularly in management science. It mandates rigorous verification across two lanes: the analytical lane (proving theorems and comparative statics) and the empirical lane (identifying and estimating causal effects using methods like DiD, IV, and RDD). It emphasizes adherence to Data-and-Code-Disclosure policies, ensuring all results are reproducible, verifiable, and meet high methodological standards.

This guide assists researchers in selecting and rigorously defending the appropriate methodology for Management Science (INFORMS) manuscripts. It covers both analytical modeling approaches (e.g., optimization, stochastic processes, game theory) and empirical designs (e.g., quasi-experiments, field trials). It ensures the chosen design not only matches the research question but also meets strict departmental standards and generates decision-relevant managerial insights.

A comprehensive preflight checklist for authors submitting manuscripts to Management Science (INFORMS). It systematically verifies adherence to crucial publishing standards, including double-anonymization, author-year formatting, abstract length constraints, fee/waiver eligibility, and the mandatory Code and Data Disclosure package using an AsCollected URL. Use this guide right before final submission to ensure maximum readiness.

This guide details best practices for generating self-contained, high-impact exhibits (tables and figures) for academic journals, particularly those with strict page limits like Management Science. It instructs users on structuring both theoretical proofs (analytical lane) and empirical results (e.g., regression coefficients, event-study plots) to ensure the findings are immediately readable and persuasive, even without reading the main text.

A comprehensive guide for structuring the theoretical core of academic papers in Management Science. It covers two main approaches: developing formal analytical models (assumptions, propositions, comparative statics) or generating robust empirical hypotheses based on explicit mechanisms. The goal is always to derive decision-relevant, cross-departmental insights.

A comprehensive guide for executing, evaluating, and reporting the empirical core of MIS Quarterly manuscripts. It covers specialized methods for Behavioral IS (PLS-SEM), Economics of IS (causal identification), Design Science (artifact evaluation), and qualitative studies, ensuring full transparency and replicability for top-tier academic submissions.

A comprehensive guide for structuring and defending the methodology section of an academic manuscript, particularly for MIS Quarterly submissions. It helps researchers choose the appropriate design (Behavioral, Design Science, Economics of IS, Organizational) and addresses critical methodological rigor, such as causal identification, common-method bias, and thorough evaluation planning.