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LM01 Alternative Investment Features, Methods, and Structures
LM01 Categories, Characteristics, and Compensation Structures of Alternative Investments
LM01 Corporate Structures and Ownership
LM01 Derivative Instrument and Derivative Market Features
LM01 Derivative Instrument and Derivative Market Features
LM01 Ethics and Trust in the Investment Profession
LM01 Ethics and Trust in the Investment Profession
LM01 Fixed-Income Instrument Features
LM01 Fixed-Income Securities: Defining Elements
LM01 Introduction to Financial Statement Analysis
LM01 Introduction to Financial Statement Analysis
LM01 Market Organization & Structure
LM01 Market Organization and Structure
LM01 Organizational Forms, Corporate Issuer Features, and Ownership
LM01 Portfolio Management Overview
LM01 Portfolio Management: An Overview
LM01 Rates and Returns
LM01 The Firm & Market Structures
LM01 Time Value of Money
LM01 Topics in Demand and Supply Analysis
LM02 Alternative Investment Performance and Returns
LM02 Analyzing Income Statements
LM02 Code of Ethics and Standards of Professional Conduct
LM02 Code of Ethics and Standards of Professional Conduct Profession
LM02 Financial Reporting Standards
LM02 Fixed Income Markets - Issuance Trading and Funding
LM02 Fixed-Income Cash Flows and Types
LM02 Forward Commitment and Contingent Claim Features and Instruments
LM02 Forward Commitment and Contingent Claim Features and Instruments
LM02 Introduction to Corporate Governance and Other ESG Considerations
LM02 Investors and Other Stakeholders
LM02 Organizing, Visualizing, and Describing Data
LM02 Performance Calculation and Appraisal of Alternative Investments
LM02 Portfolio Risk & Return: Part I
LM02 Portfolio Risk and Return Part I
LM02 Security Market Indexes
LM02 Security Market Indexes
LM02 The Firm and Market Structures
LM02 Time Value of Money in Finance
LM02 Understanding Business Cycles
LM03 Aggregate Output, Prices and Economic Growth
LM03 Analyzing Balance Sheets
LM03 Business Models & Risks
LM03 Corporate Governance: Conflicts, Mechanisms, Risks, and Benefits
LM03 Derivative Benefits, Risks, and Issuer and Investor Uses
LM03 Derivative Benefits, Risks, and Issuer and Investor Uses
LM03 Fiscal Policy
LM03 Fixed-Income Issuance and Trading
LM03 Guidance for Standards I-VII
LM03 Guidance for Standards I–VII
LM03 Introduction to Fixed Income Valuation
LM03 Investments in Private Capital: Equity and Debt
LM03 Market Efficiency
LM03 Market Efficiency
LM03 Portfolio Risk & Return: Part II
LM03 Portfolio Risk and Return Part II
LM03 Private Capital, Real Estate, Infrastructure, Natural Resources, and Hedge Funds
LM03 Probability Concepts
LM03 Statistical Measures of Asset Returns
LM03 Understanding Income Statements
LM04 An Introduction to Asset-Backed Securities
LM04 Analyzing Statements of Cash Flows I
LM04 Arbitrage, Replication, and the Cost of Carry in Pricing Derivatives
LM04 Arbitrage, Replication, and the Cost of Carry in Pricing Derivatives
LM04 Basics of Portfolio Planning & Construction
LM04 Basics of Portfolio Planning and Construction
LM04 Capital Investments
LM04 Common Probability Distributions
LM04 Fixed-Income Markets for Corporate Issuers
LM04 Introduction to the Global Investment Performance Standards (GIPS)
LM04 Introduction to the Global Investment Performance Standards (GIPS)
LM04 Monetary Policy
LM04 Overview of Equity Securities
LM04 Overview of Equity Securities
LM04 Probability Trees and Conditional Expectations
LM04 Real Estate and Infrastructure
LM04 Understanding Balance Sheets
LM04 Understanding Business Cycles
LM04 Working Capital and Liquidity.
LM05 Analyzing Statements of Cash Flows II
LM05 Capital Investments and Capital Allocation
LM05 Company Analysis: Past and Present
LM05 Fixed-Income Markets for Government Issuers
LM05 Introduction to Geopolitics
LM05 Introduction to Industry and Company Analysis
LM05 Monetary and Fiscal Policy
LM05 Natural Resources
LM05 Portfolio Mathematics
LM05 Pricing and Valuation of Forward Contracts and for an Underlying with Varying Maturities
LM05 Pricing and Valuation of Forward Contracts.
LM05 Sampling and Estimation
LM05 The Behavioral Biases of Individuals
LM05 The Behavioral Biases of Individuals
LM05 Understanding Cash Flow Statements
LM05 Understanding Fixed-Income Risk and Return
LM05 Working Capital & Liquidity
LM06 Analysis of Inventories
LM06 Capital Structure
LM06 Cost of Capital-Foundational Topics
LM06 Equity Valuation: Concepts and Basic Tools
LM06 Financial Analysis Techniques
LM06 Fixed-Income Bond Valuation: Prices and Yields
LM06 Fundamentals of Credit Analysis
LM06 Hedge Funds
LM06 Hypothesis Testing
LM06 Industry and Competitive Analysis
LM06 International Trade
LM06 Introduction to Geopolitics
LM06 Introduction to Risk Management
LM06 Introduction to Risk Management
LM06 Pricing and Valuation of Futures Contracts
LM06 Pricing and Valuation of Futures Contracts
LM06 Simulation Methods
LM07 Analysis of Long-Term Assets
LM07 Business Models
LM07 Capital Flows and the FX Market
LM07 Capital Structure
LM07 Company Analysis: Forecasting
LM07 Estimation and Inference
LM07 International Trade and Capital Flows
LM07 Introduction to Digital Assets
LM07 Introduction to Linear Regression
LM07 Inventories
LM07 Pricing and Valuation of Interest Rate and Other Swaps
LM07 Pricing and Valuation of Interest Rates and Other Swaps
LM07 Technical Analysis
LM07 Yield and Yield Spread Measures for Fixed-Rate Bonds.
LM08 Currency Exchange Rates
LM08 Equity Valuation: Concepts and Basic Tools
LM08 Exchange Rate Calculations
LM08 Fintech in Investment Management
LM08 Hypothesis Testing
LM08 Long Lived Assets
LM08 Measures of Leverage
LM08 Pricing and Valuation of Options
LM08 Pricing and Valuation of Options
LM08 Topics in Long-Term Liabilities and Equity
LM08 Yield and Yield Spread Measures for Floating-Rate Instruments
LM09 Analysis of Income Taxes
LM09 Income Taxes
LM09 Option Replication Using Put-Call Parity
LM09 Option Replication Using Put–Call Parity
LM09 Parametric and Non-Parametric Tests of Independence
LM09 The Term Structure of Interest Rates: Spot, Par, and Forward Curves
LM10 Financial Reporting Quality
LM10 Interest Rate Risk and Return
LM10 Non-current (Long-Term) Liabilities
LM10 Simple Linear Regression
LM10 Valuing a Derivative Using a One-Period Binomial Model
LM10 Valuing a Derivative Using a One-Period Binomial Model
LM11 Financial Analysis Techniques
LM11 Financial Reporting Quality
LM11 Introduction to Big Data Techniques
LM11 Yield-Based Bond Duration Measures and Properties
LM12 Applications of Financial Statement Analysis
LM12 Introduction to Financial Statement Modeling
LM12 Yield-Based Bond Convexity and Portfolio Properties
LM13 Curve-Based and Empirical Fixed-Income Risk Measures
LM14 Credit Risk
LM15 Credit Analysis for Government Issuers
LM16 Credit Analysis for Corporate Issuers
LM17 Fixed-Income Securitization
LM18 Asset-Backed Security (ABS) Instrument and Market Features
LM19 Mortgage-Backed Security (MBS) Instrument and Market Features
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Category: Essential Concepts for Level II

Essential Concept 1: Ethical Responsibilities Required by the Code and Standards

I. Professionalism A. Knowledge of the law Understand and comply with all applicable laws, rules and regulation. In a case of a conflict, comply with the stricter law. Do not knowingly participate in any violation. Disassociate from such activity. B. Independence and objectivity Use reasonable care and judgment. Maintain independence and objectivity. Do not offer, solicit or accept gifts; however, small token gifts are ok. C. Misrepresentation Do not omit important facts. Avoid plagiarism (the practice of taking someone else’s work or ideas and passing them off as one’s own). Do not guarantee investment performance. D. Misconduct Do not lie,… Read More

101 concepts level II

Essential Concept 2: Standard Error of Estimate, Coefficient of Determination, Confidence Interval for a Regression Coefficient

Standard error of estimate (SEE) measures how well a given linear regression model captures the relationship between the dependent and the independent variables.   The lower the SEE, the better the fit of the regression line. Also, note that the sum of the squared error terms can be written as SSE. Coefficient of determination (R2) measures the fraction of the total variation in the dependent variable that is explained by the independent variable.   Total variation = Unexplained variation + Explained variation     In a regression with one independent variable, R2 is the square of the correlation between the… Read More

101 concepts level II

Essential Concept 3: Analysis of Variance (ANOVA)

Analysis of variance is a statistical procedure for dividing the variability of a variable into components that can be attributed to different sources. We use ANOVA to determine the usefulness of the independent variable or variables in explaining variation in the dependent variable. ANOVA table Source of variation Degrees of freedom Sum of squares Mean sum of squares Regression (explained variation) k RSS  MSR=RSS/k Error (unexplained variation) n – 2 SSE  MSE=SSE/(n-k-1) Total variation n – 1 SST n represents the number of observations and k represents the number of independent variables. With one independent variable, k = 1. Hence,… Read More

101 concepts level II

Essential Concept 4: Confidence Interval of Regression Coefficient, Predicted Value of the Dependent Variable (Y)

The confidence interval for a regression coefficient is given by:     where:   is the critical t value for a given level of significance and degrees of freedom   is the standard error of the correlation coefficient To calculate the predicted value of the dependent variable Y we use a three-step process: Calculate estimates of the regression coefficients Assume values for the independent variables Use the regression equation:

101 concepts level II

Essential Concept 5: Problems in Regression Analysis

Problem Effect Solution Heteroskedasticity: variance of error term is not constant. Test using BP test BP = nR F-test is unreliable. Standard error underestimated. t-stat overstated. Robust standard errors Generalized least squares Serial correlation: error terms are correlated. Test using DW stat. DW 2(1 – r) F-stat too high. Standard error underestimated. t-stat overstated. Hansen method Modify the regression equation Multicollinearity: two or more independent variables are highly correlated. Inflated SE’s; t-stats of coefficients artificially small High R Omit one or more of the independent variables

101 concepts level II

Essential Concept 6: Linear vs Log-Linear Trend Models

When the dependent variable changes at a constant amount with time, a linear trend model is used. The linear trend equation is given by   When the dependent variable changes at a constant rate (grows exponentially), a log-linear trend model is used. The log-liner trend equation is given by ln   A limitation of trend models is that by nature they tend to exhibit serial correlation in errors, due to which they are not useful. The Durban-Watson statistic is used to test for serial correlation. If this statistic differs significantly from 2, then we can conclude the presence of serial… Read More

101 concepts level II

Essential Concept 7: Autoregressive (AR) Models

An autoregressive time series model is a linear model that predicts its current value using its most recent past value as the independent variable. An AR model of order p, denoted by AR(p) uses p lags of a time series to predict its current value. The chain rule of forecasting is used to predict successive forecasts. The one-period ahead forecast of  from an AR(1) model is  can be used to forecast the two-period ahead value : 

101 concepts level II

Essential Concept 8: Supervised Machine Learning Algorithms

In penalized regression the regression coefficients are chosen to minimize sum of squared residuals plus a penalty term that increases with the number of included variables. So, a feature must make a sufficient contribution to the model fit to offset the penalty from including it. Because of this penalty, the model remains parsimonious and only the most important variables for explaining Y remain in the model. A popular type of penalized regression is LASSO. Support vector machine (SVM) is a linear classifier that aims to seek the optimal hyperplane – the one that separates the two sets of data points… Read More

101 concepts level II

Essential Concept 9: Unsupervised Machine Learning Algorithms

Principal components analysis (PCA) is used to reduce highly correlated features into a few uncorrelated composite variables. A composite variable is a variable that combines two or more variables that are statistically strongly related to each other. K-means algorithm repeatedly partitions observations into k non-overlapping clusters. The number of clusters k, is a hyperparameter whose value must be set by the researcher before learning begins. Each cluster is characterized by its centroid and each observation is assigned to the cluster with the centroid to which that observation is closest. Hierarchical clustering algorithms create intermediate rounds of clusters in increasing or… Read More

101 concepts level II

Essential Concept 10: Data Prep & Wrangling

Structured data For structured data, data preparation and wrangling involve data cleansing and data preprocessing. Data cleansing involves resolving: Incompleteness errors: Data is missing. Invalidity errors: Data is outside a meaningful range. Inaccuracy errors: Data is not a measure of true value. Inconsistency errors: Data conflicts with the corresponding data points or reality. Non-uniformity errors: Data is not present in an identical format. Duplication errors: Duplicate observations are present. Data preprocessing involves performing the following transformations: Extraction: A new variable is extracted from the current variable for ease of analyzing and using for training the ML model. Aggregation: Two or… Read More

101 concepts level II
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