CFA® Level I Exam Preparation

CFA Institute Code of Ethics overview

Standards I through VII: duties and conflicts

Scenario-based vignette reasoning approach

GIPS compliance and composite construction

2027 Ethics restructuring: one module per Standard

Time value of money: PV, FV, NPV, IRR

Annuities, perpetuities, and loan amortisation

Descriptive statistics and probability distributions

Expected value, variance, covariance, correlation

Hypothesis testing and confidence intervals

2027 update: financial data science and AI overview

Supply, demand, and market equilibrium

GDP, inflation, and business cycle phases

Monetary policy tools and interest rate effects

Fiscal policy and government spending impacts

Currency exchange rates and international trade

Income statement: revenue recognition and margins

Balance sheet: assets, liabilities, and equity

Cash flow statement: operating, investing, financing

Ratio analysis: liquidity, solvency, profitability

Earnings quality: adjustments and red flags

Inventory methods and depreciation assumptions

Capital structure: debt versus equity trade-offs

Weighted average cost of capital (WACC)

Dividend policy and shareholder value effects

ESG considerations in corporate decision-making

Business models and corporate governance basics

Equity market structure and index construction

Dividend Discount Model (DDM) and variants

Free Cash Flow to Equity (FCFE) valuation

Price multiples: P/E, P/B, P/S application

Market efficiency: forms and implications

2027 update: analyst workflow and cost of equity

Bond features: coupon, maturity, par value

Bond pricing and the price-yield relationship

Yield measures: current yield, YTM, yield spreads

Duration: Macaulay, modified, and effective

Convexity and bond price sensitivity

Credit risk and fixed-income market structure

Forwards and futures: pricing and payoff profiles

Options: calls, puts, and basic strategies

Swaps: interest rate and currency mechanics

Private equity, hedge funds, and real estate

Commodities, infrastructure, and diversification role

Investment policy statement (IPS) components

Risk-return trade-off and efficient frontier

Capital Asset Pricing Model (CAPM) and beta

Systematic versus unsystematic risk

Asset allocation and diversification principles

Full 180-question mock exam: sessions1 and 2

Time discipline: triage, elimination, no- blank rule

Topic-level performance diagnosis and gap mapping

Practical Skills Module (PSM) orientation and planning

2026 versus 2027 curriculum: transition checklist

Participants take on the role of junior investment analysts at a multi-asset fund preparing for a quarterly portfolio review. They receive a briefing pack containing a company's three-year financial statements, a macroeconomic outlook summary, a fixed-income portfolio with duration targets, and a set of ethics scenarios drawn from the Standards of Professional Conduct. Their task is to work through each component as they would in a real analyst role: assess earnings quality, value the equity using at least two models, evaluate the bond portfolio's interest rate

Teams work through the simulation in timed blocks, replicating the pressure of the actual exam environment while applying content from all 10 topic areas in a connected, realistic context. Each team presents their equity valuation rationale, fixed-income risk assessment, and ethics recommendations to a panel, receiving structured feedback on both technical accuracy and reasoning quality. The debrief identifies which topic areas produced the most errors, maps those gaps back to the official exam weight table, and feeds directly into each candidate's personalised

Participants work inside a structured digital environment that mirrors the self-study phase of CFA Level I preparation. They connect a question-bank dataset to an automated scoring and tagging workflow, categorising every practice question by topic area, difficulty level, and Learning Outcome Statement. Using spreadsheet-based tools and Python-style logic aligned with the 2027 Practical Skills Module, they build a tracker that automatically flags underperforming topic areas based on rolling accuracy thresholds. The environment is designed to replicate the kind of data-driven study management that separates high-pass-rate candidates from those who rely on unstructured revision.

Participants then use AI-assisted tools to generate additional practice questions for their three weakest topic areas, review the outputs for accuracy against the official LOS, and apply a quality-check rubric before adding questions to their personal drill set. The workflow concludes with an automated revision schedule generator that allocates remaining study hours across topics in proportion to exam weight and current accuracy gaps. Governance is maintained through a human-review step at each stage, ensuring AI-generated content is validated before use and that the final revision plan reflects the candidate's actual performance data rather than generic study guides.