Begin with official data and documentation. Record assumptions and verify figures before using them in an analysis.
Frame the question. Define the decision, audience, time horizon, and unit of analysis.
Find the source. Prefer filings, central banks, exchanges, and documented datasets.
Build the model. Separate assumptions, calculations, outputs, and sensitivity analysis.
Explain the result. State what drives the conclusion and where uncertainty remains.
FRED — economic and financial time series from the Federal Reserve Bank of St. Louis.
SEC EDGAR — company filings, including 10-Ks, 10-Qs, 8-Ks, proxy statements, and registration documents.
Yahoo Finance — quotes, charts, company summaries, and news. Verify high-stakes figures against filings or an authoritative source.
Nasdaq Market Activity — quotes, earnings information, dividends, IPO resources, and market activity.
Cboe Market Statistics — options, equities, and futures statistics.
Parkes Trading Room — Fisher’s Bloomberg-equipped teaching lab for market analysis and finance courses.
Time value of money: move cash flows to a common date before comparing them.
NPV and IRR: understand the reinvestment and scale assumptions behind each decision rule.
Cost of capital: match the discount rate to the risk and financing of the cash flows.
Financial statements: connect income, cash flow, and balance-sheet changes.
Valuation: separate operating assumptions, capital structure, and terminal value.
OpenStax Principles of Finance
Modeling principle: a clean model is transparent about assumptions and easy to audit. More detail is not always more insight.
Use consistent signs, explicit assumptions, clear units, and error checks. Avoid hard-coding the same input in multiple places.
Financial functions — Microsoft’s reference for NPV, IRR, XNPV, XIRR, PMT, RATE, and related functions.
Excel help & learning — formulas, tables, charts, PivotTables, data imports, and collaboration.
Valuation spreadsheets — Damodaran’s public models for valuation, corporate finance, and investment analysis.
Inputs are visually separate from formulas and outputs.
Dates, units, currency, and signs are consistent.
Cash-flow timing is explicit.
Key assumptions have sensitivity or scenario analysis.
Balance checks and reasonableness tests are visible.
Every external number has a source and access date.
Fisher Career Services — career exploration, résumés, interviews, internships, job searches, graduate school, and employer connections.
Financial Management Association — job postings, conferences, student chapters, and other finance opportunities.
CFA Institute career resources — investment-industry roles, professional skills, and career paths.
Bloomberg Market Concepts — self-paced introduction to markets and Bloomberg functionality; availability may depend on institutional access.
Be prepared to explain a model, dataset, or project you completed. State the question, your role, the method, the result, and what you learned.
Google Scholar — search articles and trace forward and backward citations.
SSRN — working papers and early-stage research in finance and related fields.
NBER Working Papers — current research in economics and finance.
Identify the research question and claimed contribution.
State the unit of observation, sample period, and main variables.
Translate the empirical design into a comparison in plain language.
List the assumptions required for a causal interpretation.
Interpret magnitude and economic importance, not only significance.
Decide which table, figure, or test most changes your confidence.
Ask one answerable question.
Find five closely related papers and map what each contributes.
Write the hypothesis and ideal test before downloading data.
Build a small, auditable dataset first.
Create descriptive statistics and plots before regression models.
Keep a research log and record every source.