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Level II Schweser’s QuickSheet: CRITICAL CONCEPTS FOR THE 2019 CFA EXAM
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C r it ic a l C o n c e pt s f o r t h e 2019 l r ETHICAL AND PROFESSIONAL , STANDARDS I Professionalism I (A) Knowledge of the Law I (B) Independence and Objectivity I (C ) Misrepresentation I (D) Misconduct II II (A) II (B) III HI (A) HI (B) HI (C) HI (D) HI (E) IV IV (A) IV (B) IV (C) V v (A) V (B) V (C ) VI VI (A) VI (B) VI (C) VII VII (A) VII (B) Integrity o f Capital Markets Material Nonpublic Information Market Manipulation Duties to Clients Loyalty, Prudence, and Care Fair Dealing Suitability Performance Presentation Preservation o f Confidentiality Duties to Employers Loyalty Additional Compensation Arrangements Responsibilities o f Supervisors Investment Analysis, Recommendations, and Action Diligence and Reasonable Basis Communication with Clients and Prospective Clients Record Retention Conflicts o f Interest Disclosure o f Conflicts Priority o f Transactions Referral Fees Responsibilities as a CFA Institute Member or CFA Candidate Conduct in the CFA Program Reference to CFA Institute, CFA Designation, and CFA Program QUANTITATIVE METHODS Machine learning: Gives a computer the ability to improve its performance o f a task over time. Distributed ledger: A shared database with a consensus mechanism, ensuring identical copies. Simple Linear Regression Correlation: Ny = covXY (sx )( sy ) t-test for r (n —2 df): t = r>/n —2 V l-r 2 cov xy Estimated slope coefficient: CFA® E x a m M SR = RSS / k. • M SE = SSE / (n - k - 1). • Test statistical significance o f regression: F = M SR / M SE with k and n - k — 1 df (1-tail). Risk Types: Appropriate m ethod D istribution o f risk Sequential? Accommodates Correlated Variables? • Standard error o f estimate (SEE = >/MSE ). Smaller SEE means better fit. • Coefficient of determination (R 2 = RSS / SST). % o f variability o f Y explained by Xs; higher R 2 means better fit. Simulations Continuous Does not matter Yes Scenario analysis Discrete No Yes Decision trees Discrete Yes No Regression Analysis— Problems • Heteroskedasticity. Non-constant error variance. Detect with Breusch-Pagan test. Correct with White-corrected standard errors. • Autocorrelation. Correlation among error terms. Detect with Durbin-Watson test; positive autocorrelation if D W < dl.="" correct="" by="" adjusting="" standard="" errors="" using="" hansen="" method.="" •="" multicollinearity.="" high="" correlation="" among="" xs.="" detect="" if="" f-test="" significant,="" t-tests="" insignificant.="" correct="" by="" dropping="" x="" variables.="" m="" odel="" m="" isspecification="" •="" omitting="" a="" variable.="" •="" variable="" should="" be="" transformed.="" •="" incorrectly="" pooling="" data.="" •="" using="" lagged="" dependent="" vbl.="" as="" independent="" vbl.="" •="" forecasting="" the="" past.="" •="" measuring="" independent="" variables="" with="" error.="" effects="" o="" f="" m="" isspecification="" regression="" coefficients="" are="" biased="" and="" inconsistent,="" lack="" o="" f="" confidence="" in="" hypothesis="" tests="" o="" f="" the="" coefficients="" or="" in="" the="" model="" predictions.="" supervised="" machine="" learning:="" inputs,="" outputs="" are="" identified.="" relationships="" modeled="" from="" labeled="" data.="" unsupervised="" machine="" learning:="" algorithm="" itself="" seeks="" to="" describe="" the="" structure="" o="" f="" unlabeled="" data.="" linear="" trend="" model:="" yt="b" 0="" +="" b="" jt="" +=""