M.Sc Quantitative Methods for Valuation Sep 2024

Description

Quantitative Methods for Valuation

September 2024 Examination

 

 

1. Slope coefficients play an important role in the analysis of Predictive Models. How far do you agree with the statement? Give reasons in support of your answer. (10 Marks)

Ans 1.

Introduction

In the realm of quantitative methods for valuation, predictive models are crucial tools that enable analysts to forecast future values based on historical data. Among the various components of these models, slope coefficients hold a pivotal role, particularly in regression analysis. These coefficients represent the relationship between the dependent variable and one or more independent variables, essentially quantifying the impact of the independent variable(s) on the dependent variable. The accurate interpretation and estimation of slope coefficients are fundamental to the effectiveness of predictive models, influencing their reliability and validity. This essay explores the significance of slope coefficients in predictive modeling, examining their role in ensuring accurate predictions, their impact on decision-making processes

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2. One of the criteria for good regression results, is how well the regression line fits the sample data. In this regard what is the role of SEE. Explain with example. (10 Marks)

Introduction

In regression analysis, the quality of a model is often evaluated by how well the regression line fits the sample data. A well-fitting model provides accurate predictions and reliable insights, making it crucial for decision-making processes. One of the key metrics used to assess the fit of a regression line is the Standard Error of the Estimate (SEE). SEE measures the accuracy of predictions by quantifying the average distance between the observed data points and the regression line. This essay

 

3a. Support Vehicle Machine is a powerful technique used for classifications. Explain the algorithm with two features.  (5 Marks)

Ans 3a.

Introduction

Support Vector Machine (SVM) is a powerful supervised learning algorithm widely used for classification tasks. It works by finding the optimal hyperplane that best separates the data into different classes. SVM is particularly effective in high-dimensional spaces and is robust against overfitting, especially in cases where the number of dimensions exceeds the number of samples. This essay will explain the SVM algorithm using an example with two features, focusing on how it

 

1. An alternative to soft margin is to administer a non-linear SVM protocol. How this is different from the standard SVM used in general cases? (5 Marks)

Ans 3b.

Introduction

In traditional SVM, the algorithm assumes that the data is linearly separable, meaning that a straight line (or hyperplane in higher dimensions) can separate the data points into their respective classes. However, real-world data is often non-linear and cannot be separated by a straight line. In such cases, non-linear SVM protocols are applied. These protocols use kernel functions to map the data into a higher-dimensional space where a linear separation becomes possibl