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Financial Analysis, Planning and Forecasting Theory and Application

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## Financial Analysis, Planning and Forecasting Theory and Application

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**Financial Analysis, Planning and ForecastingTheory and**Application Chapter 3 Discriminant Analysis and Factor Analysis: Theory and Method By Alice C. Lee San Francisco State University John C. Lee J.P. Morgan Chase Cheng F. Lee Rutgers University**Outline**• 3.1 Introduction • 3.2 Important concepts of linear algebra Linear combination and its distribution Vectors, matrices, and their operations Linear-equation system and its solution • 3.3 Two-group discriminant analysis • 3.4 k-group discriminant analysis • 3.5 Factor analysis and principal-component analysis Factor score Factor loadings • 3.6 Summary • Appendix 3A. Discriminant analysis and dummy regression analysis • Appendix 3B. Principal-component analysis**3.2 Important concepts of linear algebra**• Linear combination and its distribution • Vectors, matrices, and their operations • Linear-equation system and its solution**3.2 Important concepts of linear algebra**(3.1) (3.1′) (3.2a) (3.2b)**3.2 Important concepts of linear algebra**(3.2b′) (3.2b′′)**3.2 Important concepts of linear algebra**(3.2b′′′)**3.2 Important concepts of linear algebra**Step 1: Multiply A’ by B Step 2: Multiply C by A Linear Equation System and its Solution**3.2 Important concepts of linear algebra**(3.5) (3.6)**3.2 Important concepts of linear algebra**(3.7) (3.8)**3.2 Important concepts of linear algebra**Note to instructor: The numbers with red circle are different from those in the text.**Extra Example to show how simultaneous equation system can**be solve by matrix inversion method The simultaneous equation (a) can be written as matrix form as equation (b)Then we can solve this equation system by matrix inversion.**We know**Please note that this is one of three methods can be used to solve simultaneous equation system. Other two methods are substitution method and Cramer rule method. These two methods have been taught in high school algebra. In practice, matrix method is the best method to solve large equation systems, such as portfolio analysis (see Chapter 7).**3.3 Two-group Discriminant Analysis**where B = DD′, between-group variance; C = Within-group variance; A = Coefficient vector representing the coefficients of Eq. (3.8); E = Ratio of the weighted between-group variance to the pooled within variance. (3.12)**3.3 Two-group discriminant analysis**TABLE 3.1 Roster of liquidity and leverage ratios For two groups with two predictors and a “dummy” criterion variable Y.**3.3 Two-group discriminant analysis**(3.13) (3.14) Var(x1i)a1 + Cov(x1i , x2i) a2 = Cov(x1i , yi) (3.15a) Cov(x1i , x2i) a1 + Var(x2i)a2 = Cov(x2i , yi) (3.15b)**3.4 k-group discriminant analysis**(3.17) (3.18)**3.4 k-group discriminant analysis**(3.20a) (3.20b) (3.20c) (3.20r)**3.4 k-group discriminant analysis**(3.21a) (3.21b) Where = Prior probability of being classified as bankrupt, = Prior probability of being classified as non-bankrupt, = Conditional probability of being classified as non- bankrupt when, in fact, the firm is bankrupt, = Conditional probability of being classified as bankrupt when, in fact, the firm is non-bankrupt, = Cost of classifying a bankrupt firm as non-bankrupt, = Cost of classifying a non-bankrupt firm as bankrupt.**3.5 Factor analysis and principal-component analysis**• Factor score • Factor loadings**3.5 Factor analysis and principal-component analysis**(3.22) (3.23) (3.24)**3.6 Summary**• In this chapter, method and theory of both discriminant analysis and factor analysis needed for determining useful financial ratios, predicting corporate bankruptcy, determining bond rating, and analyzing the relationship between bankruptcy avoidance and merger are discussed in detail. Important concepts of linear algebra-linear combination and matrix operations- required to understand both discriminant and factor analysis are discussed.**Appendix 3A. Discriminant analysis and dummy regression**analysis (3.A.1) (3.A.2) where**Appendix 3A. Discriminant analysis and dummy regression**analysis (3.A.3) (3.A.2a)**Appendix 3A. Discriminant analysis and dummy regression**analysis**Appendix 3A. Discriminant analysis and dummy regression**analysis (3.A.4) (3.A.5)**Appendix 3A. Discriminant analysis and dummy regression**analysis (3.A.6) (3.A.7) (3.A.8)**Appendix 3A. Discriminant analysis and dummy regression**analysis BA = ECA. (3.A.9) (1 + E)BA = E(B + C)A or (3.A.10)**Appendix 3A. Discriminant analysis and dummy regression**analysis (3.A.11) (3.A.12) (3.A.l’) (3.A.13)**Appendix 3A. Discriminant analysis and dummy regression**analysis (3.A.l4a) (3.A.l4b) (3.A.l5)**Appendix 3A. Discriminant analysis and dummy regression**analysis (3.A.l6) .**Appendix 3B. Principal-component analysis**（3.B.1） （3.B.2） （3.B.3）