INVESTMENT & PORTFOLIO MANAGEMENT FIN3IPM TUTORIAL ANSWERS TUTORIAL 1: INTRODUCTION CHAPTER 1: QUESTION 1 a The process of investment concerns the purchase of assets which will provide a future return to allow for future consumption or further investment. Individuals have to make choices between current and future consumption and because their pattern of income does not always match their pattern of consumption‚ they are required to make investments. Throughout an individual’s life
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Chapter 13: Chi-Square Applications SHORT ANSWER 1. When samples of size n are drawn from a normal population‚ the chi-square distribution is the sampling distribution of = ____________________‚ where s2 and are the sample and population variances‚ respectively. ANS: PTS: 1 OBJ: Section 13.2 2. Find the chi-square value for each of the right-tail areas below‚ given that the degrees of freedom are 7: A) 0.95 ____________________ B) 0.01 ____________________ C) 0.025 ____________________
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(RA ‚ RB ) −0.0322 = = −1. σ (RA )σ (RB ) 0.14 × 0.23 The assets are perfectly negatively correlated. Consider portfolio P formed from assets A and B such that you invest α fraction of your wealth into A and (1 − α) fraction into B. The variance of such portfolio is σ (RP )2 = = = = α2 σ (RA )2 + (1 − α)2 σ (RB )2 + 2α(1 − α)Cov (RA ‚ RB ) α2 σ (RA )2 + (1 − α)2 σ (RB )2 + 2α(1 − α)σ (RA )σ (RB )ρ(RA ‚ RB ) α2 σ (RA )2 + (1 − α)2 σ (RB )2 − 2α(1 − α)σ (RA )σ (RB ) [ασ (RA ) − (1
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Bivariate analysis Contingency table In this case‚ we use contingency table to analyze the relationship between 2 qualitative variables. And this test works by comparing expected and observed frequencies with x2 distribution. Correlation coefficient When we need to test the relationship between 2 quantitative variables‚ we use correlation coefficient and it measured by standardized covariance measure and investigates linear dependence. Before doing this‚ it is better to first make a scatterplot
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A statistical theory that states that given a sufficiently large sample size from a population with a finite level of variance‚ the mean of all samples from the same population will be approximately equal to the mean of the population. Furthermore‚ all of the samples will follow an approximate normal distribution pattern‚ with all variances being approximately equal to the variance of the population divided by each sample’s size. Using the central limit theorem allows you to find probabilities for
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Error 0.022666 Median 0.0607 Median 0.0511 Median 0.0108 Mode -0.5085 Mode -0.8652 Mode -0.3641 Standard Deviation 0.305747 Standard Deviation 0.489717 Standard Deviation 0.22552 Sample Variance 0.093481 Sample Variance 0.239822 Sample Variance 0.050859
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8%)] = – 4.4% 18% – 8% = 10% αC = 17% – [8% + 0.7 × (16% – 8%)] = 3.4% 17% – 8% = 9% αD = 12% – [8% + 1.0 × (16% – 8%)] = – 4.0% 12% – 8% = 4% Stocks A and C have positive alphas‚ whereas stocks B and D have negative alphas. The residual variances are: 2(eA ) = 582 = 3‚364 2(eB) = 712 = 5‚041 2(eC) = 602 = 3‚600 2(eD) = 552 = 3‚025 b. To construct the optimal risky portfolio‚ we first determine the optimal active portfolio. Using the Treynor-Black technique‚ we construct the active
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Remington’s Steakhouse Project Brian Jones Research Methods & Applications Dr. Jones August 25‚ 2011 Table of Contents Table of Contents 2 List of Tables 3 Introduction 4 The Research Objectives 4 The Research Questions 5 Literature Review 6 Answers to Research Questions 8 Recommendations to Remington’s 15 References 18 Annotated Bibliography 19 Appendix(ces) 22 List of Tables Table 1 Demographic Description of the Average Remington’s Patron9 Table 2 Reported Income by Remington’s
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required Beta Distribution • used to describe probabilistic time estimates. In special interest in network analysis is the average or expected time for each activity and the variance of each activity time. The expected time of analysis is a weighted average of the three estimates: te- expexted time ∂2 - variance of each activity time ta + 4tm +tp te= ----------------------- 6 The expected duration of a path is equal to the sum of the expected times of the
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weights to the observations we can set s i 1 i u m 2 n 2 n i where m i 1 i 1 5 ARCH(m) Model AutoRegressive Conditional Heteroskedasticity In an ARCH(m) model we also assign some weight to the long-run variance rate‚ VL: s VL i 1 i u m 2 n 2 n i where m i 1 i 1 6 ARCH(m) Model AutoRegressive Conditional Heteroskedasticity Robert Fry Engle is an American economist and the winner of the 2003 Nobel Memorial
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