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This document summarizes research analyzing the credit default swap market using Cartesian genetic programming. The researchers evolved computational programs through CGP to model credit default swap pricing more accurately than the standard Duffie model. Experiments evolved different pricing equations in each run but frequently included components of buying price over selling price of bonds. The CGP-evolved models provided more accurate pricing than the Duffie approach according to error rates on test data.

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Improving Returns from the Markowitz Model using GA- AnEmpirical Validation o...

Portfolio optimization is the task of allocating the investors capital among
different assets in such a way that the returns are maximized while at the same time, the
risk is minimized. The traditional model followed for portfolio optimization is the
Markowitz model [1], [2],[3]. Markowitz model, considering the ideal case of linear
constraints, can be solved using quadratic programming, however, in real-life scenario, the
presence of nonlinear constraints such as limits on the number of assets in the portfolio, the
constraints on budgetary allocation to each asset class, transaction costs and limits to the
maximum weightage that can be assigned to each asset in the portfolio etc., this problem
becomes increasingly computationally difficult to solve, ie NP-hard. Hence, soft computing
based approaches seem best suited for solving such a problem. An attempt has been made in
this study to use soft computing technique (specifically, Genetic Algorithms), to overcome
this issue. In this study, Genetic Algorithm (GA) has been used to optimize the parameters
of the Markowitz model such that overall portfolio returns are maximized with the standard
deviation of the returns being minimized at the same time. The proposed system is validated
by testing its ability to generate optimal stock portfolios with high returns and low standard
deviations with the assets drawn from the stocks traded on the Bombay Stock Exchange
(BSE). Results show that the proposed system is able to generate much better portfolios
when compared to the traditional Markowitz model.

Decision Tree Learning

Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example

Using R for Analyzing Loans, Portfolios and Risk: From Academic Theory to Fi...

Dr. Sanjiv Das has held positions as at Citibank, Harvard University Professor and Program Director at the FDIC’s Center for Financial Research. His research relies heavily on R for analysis and decision-making. In this webinar, Dr. Das will present a mix of some of his more current and topical research that uses R-based models, and some pedagogical applications of R. He will present:
* An R-based model for optimizing loan modifications on distressed home loans, and the economics of these modifications.
* A goal-based portfolio optimization model for investors who use derivatives.
*Using network modeling tools in R to detect systemically risky financial institutions.
*Using R for web delivery of financial models and random generation of pedagogical problems.
Promising to be entertaining and enlightening, this webinar will emphasize the interplay of mathematical models, economic problems, and R.

Investment management chapter 4.1 optimal portfolio choice -b

- The document discusses efficient portfolios and the efficient frontier in the context of investing in different combinations of stocks.
- It explains that a portfolio with a higher expected return and lower volatility is more efficient. Investing solely in one stock, like Coca-Cola, is inefficient compared to a diversified portfolio.
- The correlation between stocks affects the volatility of a portfolio - lower correlation results in lower volatility. Short selling, more stocks, and including risk-free assets like Treasury bills can also affect the efficient frontier.
- The tangent portfolio, which generates the steepest line when combined with the risk-free rate, provides the best risk-return tradeoff. The efficient portfolio is a combination of the tangent

A Short Glimpse Intrododuction to Multi-Period Fuzzy Bond Imunization for Con...

A Short Glimpse Intrododuction to Multi-Period Fuzzy Bond Imunization for Con...NABIH IBRAHIM BAWAZIR

A Short Glimpse Intrododuction to Multi-Period Fuzzy Bond Imunization for Construct Active Bond Portofolio, this paper is made to fullfill Fixed-Income securities mid semester examDecision Tree and Bayesian Classification

Here there is detail about classification methods in Database mining and business intelligence that is "Decision tree" , "Bayesian classification".

Classification Using Decision tree

The document provides an introduction to classification techniques in machine learning. It defines classification as assigning objects to predefined categories based on their attributes. The goal is to build a model from a training set that can accurately classify previously unseen records. Decision trees are discussed as a popular classification technique that recursively splits data into more homogeneous subgroups based on attribute tests. The document outlines the process of building decision trees, including selecting splitting attributes, stopping criteria, and evaluating performance on a test set. Examples are provided to illustrate classification tasks and building a decision tree model.

L1 flashcards portfolio management (ss12)

The document discusses portfolio management concepts including diversification, types of investors, the portfolio management process, developing an investment policy statement, mutual funds, and measures of investment returns. It provides definitions and formulas for key concepts such as holding period return, money weighted return, annualizing returns, portfolio return, variance, standard deviation, beta, the capital asset pricing model, and the Sharpe ratio.

Improving Returns from the Markowitz Model using GA- AnEmpirical Validation o...

Portfolio optimization is the task of allocating the investors capital among
different assets in such a way that the returns are maximized while at the same time, the
risk is minimized. The traditional model followed for portfolio optimization is the
Markowitz model [1], [2],[3]. Markowitz model, considering the ideal case of linear
constraints, can be solved using quadratic programming, however, in real-life scenario, the
presence of nonlinear constraints such as limits on the number of assets in the portfolio, the
constraints on budgetary allocation to each asset class, transaction costs and limits to the
maximum weightage that can be assigned to each asset in the portfolio etc., this problem
becomes increasingly computationally difficult to solve, ie NP-hard. Hence, soft computing
based approaches seem best suited for solving such a problem. An attempt has been made in
this study to use soft computing technique (specifically, Genetic Algorithms), to overcome
this issue. In this study, Genetic Algorithm (GA) has been used to optimize the parameters
of the Markowitz model such that overall portfolio returns are maximized with the standard
deviation of the returns being minimized at the same time. The proposed system is validated
by testing its ability to generate optimal stock portfolios with high returns and low standard
deviations with the assets drawn from the stocks traded on the Bombay Stock Exchange
(BSE). Results show that the proposed system is able to generate much better portfolios
when compared to the traditional Markowitz model.

Decision Tree Learning

Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example

Using R for Analyzing Loans, Portfolios and Risk: From Academic Theory to Fi...

Dr. Sanjiv Das has held positions as at Citibank, Harvard University Professor and Program Director at the FDIC’s Center for Financial Research. His research relies heavily on R for analysis and decision-making. In this webinar, Dr. Das will present a mix of some of his more current and topical research that uses R-based models, and some pedagogical applications of R. He will present:
* An R-based model for optimizing loan modifications on distressed home loans, and the economics of these modifications.
* A goal-based portfolio optimization model for investors who use derivatives.
*Using network modeling tools in R to detect systemically risky financial institutions.
*Using R for web delivery of financial models and random generation of pedagogical problems.
Promising to be entertaining and enlightening, this webinar will emphasize the interplay of mathematical models, economic problems, and R.

Investment management chapter 4.1 optimal portfolio choice -b

- The document discusses efficient portfolios and the efficient frontier in the context of investing in different combinations of stocks.
- It explains that a portfolio with a higher expected return and lower volatility is more efficient. Investing solely in one stock, like Coca-Cola, is inefficient compared to a diversified portfolio.
- The correlation between stocks affects the volatility of a portfolio - lower correlation results in lower volatility. Short selling, more stocks, and including risk-free assets like Treasury bills can also affect the efficient frontier.
- The tangent portfolio, which generates the steepest line when combined with the risk-free rate, provides the best risk-return tradeoff. The efficient portfolio is a combination of the tangent

A Short Glimpse Intrododuction to Multi-Period Fuzzy Bond Imunization for Con...

A Short Glimpse Intrododuction to Multi-Period Fuzzy Bond Imunization for Con...NABIH IBRAHIM BAWAZIR

A Short Glimpse Intrododuction to Multi-Period Fuzzy Bond Imunization for Construct Active Bond Portofolio, this paper is made to fullfill Fixed-Income securities mid semester examDecision Tree and Bayesian Classification

Here there is detail about classification methods in Database mining and business intelligence that is "Decision tree" , "Bayesian classification".

Classification Using Decision tree

The document provides an introduction to classification techniques in machine learning. It defines classification as assigning objects to predefined categories based on their attributes. The goal is to build a model from a training set that can accurately classify previously unseen records. Decision trees are discussed as a popular classification technique that recursively splits data into more homogeneous subgroups based on attribute tests. The document outlines the process of building decision trees, including selecting splitting attributes, stopping criteria, and evaluating performance on a test set. Examples are provided to illustrate classification tasks and building a decision tree model.

L1 flashcards portfolio management (ss12)

The document discusses portfolio management concepts including diversification, types of investors, the portfolio management process, developing an investment policy statement, mutual funds, and measures of investment returns. It provides definitions and formulas for key concepts such as holding period return, money weighted return, annualizing returns, portfolio return, variance, standard deviation, beta, the capital asset pricing model, and the Sharpe ratio.

L2 flash cards derivatives - ss 17

The document discusses various financial derivatives including synthetic instruments, options, interest rate derivatives, currency and equity swaps, credit default swaps, and credit derivative trading strategies. It provides formulas for pricing these instruments and outlines how their values are affected by various risk factors.

Mb2521002105

This document discusses using fuzzy logic and genetic algorithms to optimize investment portfolio selection. It begins with an overview of traditional portfolio management approaches and their limitations. It then introduces using fuzzy logic to better represent uncertain information from financial markets. Trapezoidal fuzzy numbers are proposed to model variables like expected asset returns. Genetic algorithms are described as an optimization tool inspired by natural selection, and their application to the portfolio selection problem is discussed. The selection process would use fuzzy logic to rank investments based on financial indicators. The genetic algorithm would then operate on a population of portfolio solutions to maximize expected returns based on the fuzzy representations of the variables.

Decision tree for Predictive Modeling

This document discusses decision trees and their use in predictive modeling. It provides an example of using a decision tree to predict credit ratings. The decision tree splits the data into nodes based on variables like checking accounts, savings accounts, and duration. Each node shows the percentage of good and bad credit ratings, with deeper nodes having higher percentages. Decision trees allow targeting subsets of a population that have higher response rates to improve outcomes.

Valuation of shares & bonds NOTES @ BECDOMS

This document discusses valuation of shares and bonds. It covers:
1) Calculating the intrinsic value of a security based on the required rate of return and determining if it is underpriced or overpriced compared to the current market price.
2) Calculating the expected rate of return of a security based on its current market price and determining if it is underpriced or overpriced compared to the required rate of return.
3) Methods for calculating the price and intrinsic value of bonds, preference shares, and ordinary shares. This includes determining the expected rate of return for each type of security.

Cost Of Capital

This document discusses the cost of capital and how to calculate it. It defines cost of capital as the rate of return a firm must earn on its investments to maintain its market value and attract funds. It then discusses how to calculate the costs of different sources of capital including long-term debt, preferred stock, common stock, and retained earnings. It explains how to calculate the weighted average cost of capital (WACC) and discusses weighting schemes. Finally, it discusses how to determine break points and calculate the weighted marginal cost of capital (WMCC), which can be used with the investment opportunities schedule to make financing decisions.

VaR Approximation Methods

The document evaluates alternative methods to approximating historical value at risk (HsVaR) that can significantly reduce computational requirements compared to the full revaluation method, while maintaining acceptable accuracy. It considers a sample portfolio and calculates HsVaR using full revaluation and approximation methods involving delta, delta-gamma, and delta-gamma-theta. The results show the approximation methods produce HsVaR values close to full revaluation, within 1-18%, while requiring far fewer valuations, resulting in up to 99.6% reduction in computations. This demonstrates the tradeoff between accuracy and reduced processing from using approximation methods.

Designing Alternate Solutions: How to Find Solutions That Meet Your Requirements

Here are the key financial performance metrics that are commonly used to evaluate projects and make investment decisions:
- Net Present Value (NPV) - The present value of the future net cash flows from an investment minus the cost of the investment. It takes the time value of money into account. A positive NPV means the projected returns from the investment exceed the required rate of return and it should be accepted.
- Internal Rate of Return (IRR) - The discount rate that makes the NPV of all cash flows from a particular project equal to zero. It measures the efficiency or profitability of an investment. The higher the IRR, the more desirable the investment. IRR should be compared to the required rate of return or cost

Optimization By Simulated Annealing

This document discusses using simulated annealing (SA) as an algorithm to optimize portfolios by finding optimal combinations of risk and return. SA is presented as an alternative to gradient search methods which tend to get stuck in local optima. The SA algorithm begins with a random portfolio and iteratively explores neighboring portfolios to search for better risk-return metrics. It allows for some probability of accepting worse portfolios to avoid local optima. Step-by-step instructions are provided for implementing an SA algorithm along with an example application to optimizing a credit default swap portfolio. The SA approach is shown to outperform a greedy search algorithm in finding superior risk-return portfolios.

Decision treeprimer 2

The document discusses risk aversion and expected utility theory as an approach to decision making under risk or uncertainty. It defines key concepts like risk attitude, certainty equivalent, and utility functions. Specifically, it introduces the exponential utility function as a commonly used function for modeling risk aversion. It also provides a process for assessing a decision maker's risk tolerance parameter R, which influences their risk attitude as modeled by the utility function. Finally, it provides an example application to a decision facing a company called Xanadu Traders.

Dynamic Pricing for Personal Unsecured Loans

An outline of the mathematics and technicalities of predicting personalized loan pricing -- i.e. the next step beyond pure risk-based pricing. Based largely on the work of Robert Phillips.

Top down attribution - Journal of Performance Measurement - par Christian Levecq

Top down attribution - Journal of Performance Measurement - par Christian LevecqAlban Jarry (Bibliothèque de Documents)

Auteur du document : Christian Levecq
Type d’auteur : Expert
Réglementation financière traitée : Modèle d'Attribution de Performance
Langue du document : Anglais
64920420 solution-ch10-charles-p-jones

This document provides an overview and summary of Chapter 10 from an investments textbook. The chapter covers common stock valuation, including discounted cash flow models like the dividend discount model and relative valuation techniques like the P/E ratio approach. It emphasizes that students should understand how to value stocks by discounting expected future dividends or earnings. The chapter also discusses determining a stock's required rate of return and intrinsic value, as well as how valuation is affected by growth rates, payout ratios, and interest rates. Worked examples and practice problems are provided to help students apply these valuation concepts.

Financial ratios

The document defines and provides formulas for various financial ratios used to evaluate companies, including liquidity ratios, leverage ratios, profitability ratios, return ratios, valuation ratios, and risk measures. It also defines financial terms related to raising capital, costs of capital, discounted cash flow analysis, and company valuation approaches.

Decision theory

The document discusses decision theory and decision trees. It introduces decision making under certainty, risk, and uncertainty. It defines elements related to decisions like goals, courses of action, states of nature, and payoffs. It also discusses concepts like expected monetary value, expected profit with perfect information, expected value of perfect information, and expected opportunity loss. Examples are provided to demonstrate calculating these metrics. Finally, it provides an overview of how to construct a decision tree, including defining the different node types and how to calculate values within the tree.

Neural Network Model

The objective was to develop a neural network model to predict loan defaults using variables like number of derogatory reports, number of delinquent credit lines, debt-to-income ratio, age of oldest credit line, and loan divided by value. The best model had a training profit of $25.48 million and testing profit of $27.48 million, outperforming logistic regression. Key changes were reducing variables and lowering the probability cutoff, which increased profits and lift. The neural network model was simpler, more consistent and predictable than complex models, with training and testing profits varying less than $150,000.

Supervised learning

Using Python library such as numpy, scipy and pandas to carry out supervised learning operations like Support vector machine, decision tree and K-nearest neighbor.

Decision tree

The document discusses decision trees, which are graphical representations of possible solutions to a decision based on certain conditions. It describes the basic components of decision trees, including decision nodes, chance nodes, and end nodes. It also covers different types of decision trees, algorithms for building decision trees like CART and C5.0, advantages like interpretability, and limitations like potential for overfitting. Finally, it provides examples of applications of decision trees in domains like business, healthcare, and engineering.

Statistical Learning on Credit Data

Firas Obeid builds statistical learning models to predict loan defaults using lending club data. Various statistical models are explored, including tree-based and neural network models. Model interpretation techniques are discussed to address the "blackbox" problem with complex models. The best-performing model is used to assign customers to risk buckets and develop a scorecard to aid in segmentation, pricing, and allocation of operational risk capital. The scorecard approach identifies customer risk levels and helps determine appropriate interest rates.

A predictive system for detection of bankruptcy using machine learning techni...

Bankruptcy is a legal procedure that claims a person or organization as a debtor. It is essential to
ascertain the risk of bankruptcy at initial stages to prevent financial losses. In this perspective, different
soft computing techniques can be employed to ascertain bankruptcy. This study proposes a bankruptcy
prediction system to categorize the companies based on extent of risk. The prediction system acts as a
decision support tool for detection of bankruptcy

Machine_Learning.pptx

This document provides an overview of machine learning and logistic regression. It discusses key concepts in machine learning like representation, evaluation, and optimization. It also discusses different machine learning algorithms like decision trees, neural networks, and support vector machines. The document then focuses on logistic regression, explaining concepts like maximum likelihood estimation, concordance, and confusion matrices which are used to evaluate logistic regression models. It provides an example of using logistic regression for a banking customer classification problem to predict defaults.

Case2_Best_Model_Final

- The objective is to build a model to predict whether loan applicants will default on their home loans for Trojan Financial Services using 10,000 loan applications received per month. Each defaulted loan costs the company around $20,000 on average.
- The best JMP model uses "Bad" as the Y-axis to predict which loans will default. It includes the variables Reason[DebtCon], Derog, Delinq, Debtinc, Log Age, and Loan/Value and provides a profit of over $2.4 million in training data and over $2.5 million in testing data.
- This model is better than the baseline because it provides Trojan Financial between $24-25 million

Data mining approaches and methods

This slide is about the data mining tasks and techniques.This slide consist of all the major mining task algorithms with their respective examples.

L2 flash cards derivatives - ss 17

The document discusses various financial derivatives including synthetic instruments, options, interest rate derivatives, currency and equity swaps, credit default swaps, and credit derivative trading strategies. It provides formulas for pricing these instruments and outlines how their values are affected by various risk factors.

Mb2521002105

This document discusses using fuzzy logic and genetic algorithms to optimize investment portfolio selection. It begins with an overview of traditional portfolio management approaches and their limitations. It then introduces using fuzzy logic to better represent uncertain information from financial markets. Trapezoidal fuzzy numbers are proposed to model variables like expected asset returns. Genetic algorithms are described as an optimization tool inspired by natural selection, and their application to the portfolio selection problem is discussed. The selection process would use fuzzy logic to rank investments based on financial indicators. The genetic algorithm would then operate on a population of portfolio solutions to maximize expected returns based on the fuzzy representations of the variables.

Decision tree for Predictive Modeling

This document discusses decision trees and their use in predictive modeling. It provides an example of using a decision tree to predict credit ratings. The decision tree splits the data into nodes based on variables like checking accounts, savings accounts, and duration. Each node shows the percentage of good and bad credit ratings, with deeper nodes having higher percentages. Decision trees allow targeting subsets of a population that have higher response rates to improve outcomes.

Valuation of shares & bonds NOTES @ BECDOMS

This document discusses valuation of shares and bonds. It covers:
1) Calculating the intrinsic value of a security based on the required rate of return and determining if it is underpriced or overpriced compared to the current market price.
2) Calculating the expected rate of return of a security based on its current market price and determining if it is underpriced or overpriced compared to the required rate of return.
3) Methods for calculating the price and intrinsic value of bonds, preference shares, and ordinary shares. This includes determining the expected rate of return for each type of security.

Cost Of Capital

This document discusses the cost of capital and how to calculate it. It defines cost of capital as the rate of return a firm must earn on its investments to maintain its market value and attract funds. It then discusses how to calculate the costs of different sources of capital including long-term debt, preferred stock, common stock, and retained earnings. It explains how to calculate the weighted average cost of capital (WACC) and discusses weighting schemes. Finally, it discusses how to determine break points and calculate the weighted marginal cost of capital (WMCC), which can be used with the investment opportunities schedule to make financing decisions.

VaR Approximation Methods

The document evaluates alternative methods to approximating historical value at risk (HsVaR) that can significantly reduce computational requirements compared to the full revaluation method, while maintaining acceptable accuracy. It considers a sample portfolio and calculates HsVaR using full revaluation and approximation methods involving delta, delta-gamma, and delta-gamma-theta. The results show the approximation methods produce HsVaR values close to full revaluation, within 1-18%, while requiring far fewer valuations, resulting in up to 99.6% reduction in computations. This demonstrates the tradeoff between accuracy and reduced processing from using approximation methods.

Designing Alternate Solutions: How to Find Solutions That Meet Your Requirements

Here are the key financial performance metrics that are commonly used to evaluate projects and make investment decisions:
- Net Present Value (NPV) - The present value of the future net cash flows from an investment minus the cost of the investment. It takes the time value of money into account. A positive NPV means the projected returns from the investment exceed the required rate of return and it should be accepted.
- Internal Rate of Return (IRR) - The discount rate that makes the NPV of all cash flows from a particular project equal to zero. It measures the efficiency or profitability of an investment. The higher the IRR, the more desirable the investment. IRR should be compared to the required rate of return or cost

Optimization By Simulated Annealing

This document discusses using simulated annealing (SA) as an algorithm to optimize portfolios by finding optimal combinations of risk and return. SA is presented as an alternative to gradient search methods which tend to get stuck in local optima. The SA algorithm begins with a random portfolio and iteratively explores neighboring portfolios to search for better risk-return metrics. It allows for some probability of accepting worse portfolios to avoid local optima. Step-by-step instructions are provided for implementing an SA algorithm along with an example application to optimizing a credit default swap portfolio. The SA approach is shown to outperform a greedy search algorithm in finding superior risk-return portfolios.

Decision treeprimer 2

The document discusses risk aversion and expected utility theory as an approach to decision making under risk or uncertainty. It defines key concepts like risk attitude, certainty equivalent, and utility functions. Specifically, it introduces the exponential utility function as a commonly used function for modeling risk aversion. It also provides a process for assessing a decision maker's risk tolerance parameter R, which influences their risk attitude as modeled by the utility function. Finally, it provides an example application to a decision facing a company called Xanadu Traders.

Dynamic Pricing for Personal Unsecured Loans

An outline of the mathematics and technicalities of predicting personalized loan pricing -- i.e. the next step beyond pure risk-based pricing. Based largely on the work of Robert Phillips.

Top down attribution - Journal of Performance Measurement - par Christian Levecq

Top down attribution - Journal of Performance Measurement - par Christian LevecqAlban Jarry (Bibliothèque de Documents)

Auteur du document : Christian Levecq
Type d’auteur : Expert
Réglementation financière traitée : Modèle d'Attribution de Performance
Langue du document : Anglais
64920420 solution-ch10-charles-p-jones

This document provides an overview and summary of Chapter 10 from an investments textbook. The chapter covers common stock valuation, including discounted cash flow models like the dividend discount model and relative valuation techniques like the P/E ratio approach. It emphasizes that students should understand how to value stocks by discounting expected future dividends or earnings. The chapter also discusses determining a stock's required rate of return and intrinsic value, as well as how valuation is affected by growth rates, payout ratios, and interest rates. Worked examples and practice problems are provided to help students apply these valuation concepts.

Financial ratios

The document defines and provides formulas for various financial ratios used to evaluate companies, including liquidity ratios, leverage ratios, profitability ratios, return ratios, valuation ratios, and risk measures. It also defines financial terms related to raising capital, costs of capital, discounted cash flow analysis, and company valuation approaches.

L2 flash cards derivatives - ss 17

L2 flash cards derivatives - ss 17

Mb2521002105

Mb2521002105

Decision tree for Predictive Modeling

Decision tree for Predictive Modeling

Valuation of shares & bonds NOTES @ BECDOMS

Valuation of shares & bonds NOTES @ BECDOMS

Cost Of Capital

Cost Of Capital

VaR Approximation Methods

VaR Approximation Methods

Designing Alternate Solutions: How to Find Solutions That Meet Your Requirements

Designing Alternate Solutions: How to Find Solutions That Meet Your Requirements

Optimization By Simulated Annealing

Optimization By Simulated Annealing

Decision treeprimer 2

Decision treeprimer 2

Dynamic Pricing for Personal Unsecured Loans

Dynamic Pricing for Personal Unsecured Loans

Top down attribution - Journal of Performance Measurement - par Christian Levecq

Top down attribution - Journal of Performance Measurement - par Christian Levecq

64920420 solution-ch10-charles-p-jones

64920420 solution-ch10-charles-p-jones

Financial ratios

Financial ratios

Decision theory

The document discusses decision theory and decision trees. It introduces decision making under certainty, risk, and uncertainty. It defines elements related to decisions like goals, courses of action, states of nature, and payoffs. It also discusses concepts like expected monetary value, expected profit with perfect information, expected value of perfect information, and expected opportunity loss. Examples are provided to demonstrate calculating these metrics. Finally, it provides an overview of how to construct a decision tree, including defining the different node types and how to calculate values within the tree.

Neural Network Model

The objective was to develop a neural network model to predict loan defaults using variables like number of derogatory reports, number of delinquent credit lines, debt-to-income ratio, age of oldest credit line, and loan divided by value. The best model had a training profit of $25.48 million and testing profit of $27.48 million, outperforming logistic regression. Key changes were reducing variables and lowering the probability cutoff, which increased profits and lift. The neural network model was simpler, more consistent and predictable than complex models, with training and testing profits varying less than $150,000.

Supervised learning

Using Python library such as numpy, scipy and pandas to carry out supervised learning operations like Support vector machine, decision tree and K-nearest neighbor.

Decision tree

The document discusses decision trees, which are graphical representations of possible solutions to a decision based on certain conditions. It describes the basic components of decision trees, including decision nodes, chance nodes, and end nodes. It also covers different types of decision trees, algorithms for building decision trees like CART and C5.0, advantages like interpretability, and limitations like potential for overfitting. Finally, it provides examples of applications of decision trees in domains like business, healthcare, and engineering.

Statistical Learning on Credit Data

Firas Obeid builds statistical learning models to predict loan defaults using lending club data. Various statistical models are explored, including tree-based and neural network models. Model interpretation techniques are discussed to address the "blackbox" problem with complex models. The best-performing model is used to assign customers to risk buckets and develop a scorecard to aid in segmentation, pricing, and allocation of operational risk capital. The scorecard approach identifies customer risk levels and helps determine appropriate interest rates.

A predictive system for detection of bankruptcy using machine learning techni...

Bankruptcy is a legal procedure that claims a person or organization as a debtor. It is essential to
ascertain the risk of bankruptcy at initial stages to prevent financial losses. In this perspective, different
soft computing techniques can be employed to ascertain bankruptcy. This study proposes a bankruptcy
prediction system to categorize the companies based on extent of risk. The prediction system acts as a
decision support tool for detection of bankruptcy

Machine_Learning.pptx

This document provides an overview of machine learning and logistic regression. It discusses key concepts in machine learning like representation, evaluation, and optimization. It also discusses different machine learning algorithms like decision trees, neural networks, and support vector machines. The document then focuses on logistic regression, explaining concepts like maximum likelihood estimation, concordance, and confusion matrices which are used to evaluate logistic regression models. It provides an example of using logistic regression for a banking customer classification problem to predict defaults.

Case2_Best_Model_Final

- The objective is to build a model to predict whether loan applicants will default on their home loans for Trojan Financial Services using 10,000 loan applications received per month. Each defaulted loan costs the company around $20,000 on average.
- The best JMP model uses "Bad" as the Y-axis to predict which loans will default. It includes the variables Reason[DebtCon], Derog, Delinq, Debtinc, Log Age, and Loan/Value and provides a profit of over $2.4 million in training data and over $2.5 million in testing data.
- This model is better than the baseline because it provides Trojan Financial between $24-25 million

Data mining approaches and methods

This slide is about the data mining tasks and techniques.This slide consist of all the major mining task algorithms with their respective examples.

Machine Learning Approach.pptx

The document provides an overview of various machine learning algorithms including:
- K-means clustering which groups data into a specified number of clusters based on euclidean distance.
- Linear regression which finds the relationship between variables to predict outcomes.
- Logistic regression which predicts probabilities of categorical outcomes.
- Decision trees which use splitting criteria like gini impurity or information gain to create rules for classifications.
- Random forests which create an ensemble of decision trees to improve accuracy and handle large datasets.
Business applications and key concepts behind each algorithm are discussed along with advantages, disadvantages and evaluation metrics.

Credit risk models

This document discusses three main approaches to modeling credit risk: structural, reduced form, and incomplete information. It provides details on the structural approach using the Merton and first passage models and the reduced form approach using a Poisson process for default. It also discusses extending these models to value bank loans, specifically comparing the structural KMV model and reduced form CreditRisk+ model. The critiques note limitations like non-observability of variables, lack of dynamics, and potential underestimation of risk.

The Cost of Capital

This document discusses the importance of correctly calculating a firm's cost of capital and the various components that make up its cost of capital. It explains that a firm uses its cost of capital to determine if investments are profitable. If the cost of capital is miscalculated, the firm could invest in too many unprofitable projects or not enough profitable ones.
The document then outlines the various sources of long-term capital firms use, including long-term debt, preferred stock, and common equity. It discusses methods for calculating the costs of each component, such as using current market yields or the CAPM model. The weighted average cost of capital (WACC) is calculated using the costs of each component weighted by the firm's target

Proficiency comparison ofladtree

Predicting the Credit Defaulter is a perilous task of Financial Industries like Banks. Ascertainingnon payer
before giving loan is a significant and conflict-ridden task of the Banker. Classification techniques
are the better choice for predictive analysis like finding the claimant, whether he/she is an unpretentious
customer or a cheat. Defining the outstanding classifier is a risky assignment for any industrialist like a
banker. This allow computer science researchers to drill down efficient research works through evaluating
different classifiers and finding out the best classifier for such predictive problems. This research
work investigates the productivity of LADTree Classifier and REPTree Classifier for the credit risk prediction
and compares their fitness through various measures. German credit dataset has been taken and used
to predict the credit risk with a help of open source machine learning tool.

Week14_Business Simulation Modeling MSBA.pptx

Simulation of business methods and its development with latest technology

Credit Risk Evaluation Model

Credit risks are calculated based on the borrowers’ overall ability to repay. Our objective was to use optimization in order to create a tool that approves or rejects loans to borrowers. We also used optimization to establish how much interest rate/credit will be extended to borrowers who were approved for a loan.

Tutorial Knowledge Discovery

This document discusses knowledge discovery and data mining. It defines knowledge discovery as the process of automatically searching large volumes of data for patterns that can be considered knowledge. Data mining is defined as one step in the knowledge discovery process and involves using computational methods to discover patterns in large datasets. The document outlines common data mining tasks such as predictive tasks, descriptive tasks, and anomaly detection. It also discusses evaluating data mining algorithms, including assessing the performance of a single algorithm and comparing the performance of multiple algorithms.

presentation of stock valuation

This document discusses various methods for valuing common stock, including:
1. The discounted cash flow model, which values a stock based on the present value of its expected future cash flows.
2. The dividend discount model (DDM), which values a stock based on the present value of its expected future dividends. Constant and variable growth DDM are discussed.
3. Other valuation methods like the free cash flow model, P/E ratio approach, and price-to-sales ratio are also presented. The document concludes that the best estimate of a stock's value is usually the present value of its estimated future dividends.

Credit iconip

The document proposes using several machine learning algorithms, including multilayer perceptron neural networks, logistic regression, support vector machines, AdaBoostM1, and Hidden Layer Learning Vector Quantization (HLVQ-C), to improve personal credit scoring accuracy. The algorithms were tested on a large dataset from a Portuguese bank containing over 400,000 entries. HLVQ-C achieved the most accurate results, outperforming traditional linear methods. The document introduces a "usefulness" measure to evaluate classifiers based on earnings from correctly denying credit to risky applicants and losses from misclassifications.

Credit iconip

The document presents a study comparing various machine learning algorithms for personal credit scoring, including logistic regression, multilayer perceptron, support vector machines, AdaBoostM1, and Hidden Layer Learning Vector Quantization (HLVQ-C). The models were tested on datasets from a Portuguese bank. HLVQ-C achieved the highest accuracy on both datasets and the best performance on a proposed "usefulness" metric, making it the most effective model for credit scoring applications according to this research.

Fraud Detection by Stacking Cost-Sensitive Decision Trees

Worldwide, billions of euros are lost every year due to credit card fraud. Increasingly, fraud has diversified to different digital channels, including mobile and online payments, creating new challenges as innovative new fraud patterns emerge. Hence, it remains challenging to find effective methods of mitigating fraud. Existing solutions include simple if-then rules and classical machine learning algorithms. Credit card fraud is by definition an example-dependent and cost-sensitive classification problem, in which the costs due to is classification vary between examples and not only within classes, i.e., misclassifying a fraudulent transaction may have a financial impact ranging from a few to thousands of euros. In this paper, we propose an extension to the cost-sensitive decision trees algorithm, by creating an ensemble of such trees, and combining them using a stacking approach with a cost-sensitive logistic regression. We compare our method with standard machine learning algorithms and state-of-the-art cost-sensitive classification methods using a real credit card fraud dataset provided by a large European card processing company. The results show that our method achieves savings of up to 73.3%, more than 2 percentage points more than a single cost-sensitive decision tree.

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Fraud Detection by Stacking Cost-Sensitive Decision Trees

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一比一原版宾夕法尼亚大学毕业证(UPenn毕业证书)学历如何办理

挂科购买【微信号:176555708】【挂科购买(UPenn毕业证书)】【微信号:176555708】《成绩单、外壳、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
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三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！

Monthly Market Risk Update: June 2024 [SlideShare]

Markets rallied in May, with all three major U.S. equity indices up for the month, said Sam Millette, director of fixed income, in his latest Market Risk Update.
For more market updates, subscribe to The Independent Market Observer at https://blog.commonwealth.com/independent-market-observer.

China's Investment Leader - Dr. Alyce SU

In World Expo 2010 Shanghai – the most visited Expo in the World History
https://www.britannica.com/event/Expo-Shanghai-2010
China’s official organizer of the Expo, CCPIT (China Council for the Promotion of International Trade https://en.ccpit.org/) has chosen Dr. Alyce Su as the Cover Person with Cover Story, in the Expo’s official magazine distributed throughout the Expo, showcasing China’s New Generation of Leaders to the World.

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“Amidst Tempered Optimism” Main economic trends in May 2024 based on the results of the New Monthly Enterprises Survey, #NRES
On 12 June 2024 the Institute for Economic Research and Policy Consulting (IER) held an online event “Economic Trends from a Business Perspective (May 2024)”.
During the event, the results of the 25-th monthly survey of business executives “Ukrainian Business during the war”, which was conducted in May 2024, were presented.
The field stage of the 25-th wave lasted from May 20 to May 31, 2024. In May, 532 companies were surveyed.
The enterprise managers compared the work results in May 2024 with April, assessed the indicators at the time of the survey (May 2024), and gave forecasts for the next two, three, or six months, depending on the question. In certain issues (where indicated), the work results were compared with the pre-war period (before February 24, 2022).
✅ More survey results in the presentation.
✅ Video presentation: https://youtu.be/4ZvsSKd1MzE
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Fabular Frames and the Four Ratio Problem

Digital, interactive art showing the struggle of a society in providing for its present population while also saving planetary resources for future generations. Spread across several frames, the art is actually the rendering of real and speculative data. The stereographic projections change shape in response to prompts and provocations. Visitors interact with the model through speculative statements about how to increase savings across communities, regions, ecosystems and environments. Their fabulations combined with random noise, i.e. factors beyond control, have a dramatic effect on the societal transition. Things get better. Things get worse. The aim is to give visitors a new grasp and feel of the ongoing struggles in democracies around the world.
Stunning art in the small multiples format brings out the spatiotemporal nature of societal transitions, against backdrop issues such as energy, housing, waste, farmland and forest. In each frame we see hopeful and frightful interplays between spending and saving. Problems emerge when one of the two parts of the existential anaglyph rapidly shrinks like Arctic ice, as factors cross thresholds. Ecological wealth and intergenerational equity areFour at stake. Not enough spending could mean economic stress, social unrest and political conflict. Not enough saving and there will be climate breakdown and ‘bankruptcy’. So where does speculative design start and the gambling and betting end? Behind each fabular frame is a four ratio problem. Each ratio reflects the level of sacrifice and self-restraint a society is willing to accept, against promises of prosperity and freedom. Some values seem to stabilise a frame while others cause collapse. Get the ratios right and we can have it all. Get them wrong and things get more desperate.

Enhanced metrics to measure the Regulatory impact

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How to Invest in Cryptocurrency for Beginners: A Complete Guide

Cryptocurrency is digital money that operates independently of a central authority, utilizing cryptography for security. Unlike traditional currencies issued by governments (fiat currencies), cryptocurrencies are decentralized and typically operate on a technology called blockchain. Each cryptocurrency transaction is recorded on a public ledger, ensuring transparency and security.
Cryptocurrencies can be used for various purposes, including online purchases, investment opportunities, and as a means of transferring value globally without the need for intermediaries like banks.

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- 1. Analyzing the Credit Default Swap Market Using Cartesian Genetic Programming based on the paper by L. Zangeneh and P. J. Bentley from University College London Karol Grzegorczyk 20-06-2013
- 2. Derivatives A derivative is a financial instrument which derives its value from the value of underlying entities Classification by the relationship with underlying asset: • • • Options Swaps Forwards Classification by the type of underlying asset: • • • Equity derivatives Credit derivatives Commodity derivatives
- 3. Credit Default Swap Default means to fail to do something, such as pay a debt, that you legally have to do There is a risk of default. The risk that debtor won't met their legal obligations according to the contract The lender can buy insurance in case of default. This insurance is called Credit Default Swap. In case of the event of default the buyer of the CDS (the loan lender) receives compensation (usually the value of the loan) CDS Spread is an amount of money that the buyer of the CDS regularly pays to the seller
- 4. loan lender & CDS buyer loan interest borrower CDS spread CDS seller payment in case of a default
- 5. CDS trading CDS contract can be treated as an investment instead of as an insurance! Investors can buy CDS contracts referencing some debt without actually owning this debt! It is called 'Naked CDS'. The value of a contract fluctuates based on the increasing or decreasing probability that a reference entity will have a credit event. Increased probability of such an event would make the contract worth more for the buyer of protection, and worth less for the seller. The opposite occurs if the probability of a credit event decreases. An investor with a negative view of some company's credit can buy protection for a relatively small periodic fee and receive a big payoff if the company defaults on its bonds or has some other credit event! Arbitrage is the practice of taking advantage of a price difference between two or more markets.
- 6. CDS pricing To be able to invest in CDS it is needed to model corporate default risk There are several approaches to modeling default risk. One of the approaches is the Duffie model, a.k.a. no-arbitrage model. It is defined by a simple equation: CDSspread = RiskFreeRate - DebtReturn RiskFreeRate - the interest that an investor would expect from an absolutely risk-free investment over a given period of time DebtReturn - measure of a company's performance based on the amount of debt it borrowed (bond yield)
- 7. Problem statement Duffie theory assumes that there is no risk free arbitrage. In practice relationship presented on the previous slide does not hold exactly. There is some possibility of an arbitrage. It is called arbitrage channel or arbitrage gap. Arbitrage gap is a problem. It would be desirable to to reduce it, to build the CDS pricing model more close to reality. What is the exact relationship between CDS spread, debt return and risk free rate?
- 8. Stochastic optimization Problem optimization is a search for the best solution among candidate solutions based on the quality of the candidate solution. To find optimal solution it is needed to do four things: • • • • Provide one or more initial candidate solutions. Assess the quality of a candidate solutions. Make a copy of a candidate solution. Tweak a candidate solution, which produces a randomly slightly different candidate solution. In order to invoke those functions on a single candidate solution it is needed to have the representation of the candidate solution. Often the representation is referred to as the data structure used to define the candidate solution (a vector, a tree, etc.)
- 9. Evolutionary Computation dictionary individual a candidate solution child and parent a child is the Tweaked copy of a candidate solution (its parent) population set of candidate solutions fitness quality fitness landscape quality function mutation plain tweaking recombination or crossover a special Tweak which takes two parents, swaps sections of them, and (usually) produces two children. This is often thought as “sexual” breeding. breeding producing one or more children from a population of parents through an iterated process of selection and Tweaking (typically mutation or recombination) genotype or genome an individual’s data structure, as used during breeding chromosome a genotype in the form of a fixed-length vector gene a particular slot position in a chromosome phenotype how the individual operates during fitness assessment
- 10. The representation of an individual In order to tackle a problem using a EA, candidate solutions must be encoded in a suitable form. For example in the traditional GA solutions are represented by binary bit strings ('chromosomes'). Other form of representation can be: • • • • • • a vector (e.g. a fixed-length vector of real-valued numbers) an arbitrary-length list of objects an unordered set or collection of objects a tree a forest (set of trees) a graph
- 11. Genetic Programming Evolutionary algorithm-based methodology using stochastic methods to search for and optimize small computer programs or other computational devices. In order to optimize a computer program, it needs to be evaluated as suboptimal rather than simply right or wrong. Computer programs are represented by trees or lists, that are formed from basic functions or CPU operations GP’s initialization and Tweaking operators are particularly concerned with maintaining closure, that is, producing valid individuals from previous ones The Lisp family of languages is particularly suitable for tree representation of computer programs.
- 12. Evolutionary operations applied for GP initialization - randomly build trees by selecting from a function set; function define how many children they require. fitness evaluation - run program and see how it do -> data is needed! mutation - rarely used, e.g. pick a random subtree and replace it with a randomly-generated subtree crossover - Using subtree crossover. In each individual, select a random subtree (which can possibly be the root). Then swap those two subtrees
- 13. Example program: sin(cos( x − sin x ) + x x ) in Lisp: (sin (+ (cos (−x (sin x))) (∗ x (sqrt x)) ) ) graph representation
- 14. Cartesian Genetic Programming Cartesian Genetic Programming (CGP) represents computational structures as directed acyclic graphs which are represented as a two-dimensional grid of computational nodes which are encoded in the form of a string of integers. When representing graph as a grid some genes become 'non-coding' genes. Integers represents the program primitives and how they are connected together. Program that results from the decoding of a genotype is called a phenotype. Fitness report is the CGP output.
- 15. CGP encoding and decoding example 001 100 131 201 044 254 2573 0 1 2 3 + Add the data presented to inputs - Subtract the data presented to inputs * Multiply data presented to inputs / Divide data presented to inputs (protected) y2 = x0 + x1 y5 = x0 * x1 y7 = x0 * x1 * ((x0 - x0) - x1) = - x0x12 y3 = 0
- 16. Analyzing CDS market using CGP Goal: find an algorithm/program/equation that calculate CDS spread Only fundamental operators were selected as a function set: add, subtract, multiplication, division, power, square root. Data divided into training (400 data points) and test (1000 data points) datasets. Experiments were performed for different mutation rates and the number of nodes Fitness is calculated for each datapoint by defining the error rate Error = | CGPoutput - Data|
- 18. Experiments Results CGP has evolved completely different equation in each run, but some components were repeated frequently: • • x2 / x3 x2 - x3 Where: x2 - buying price of bond x3 - selling price of bond In general created CDS pricing model is much more accurate compared to the standard Duffi approach.
- 19. Bibliography 1. "Analyzing the Credit Default Swap Market Using Cartesian Genetic Programming", L. Zangeneh, P.J. Bentley, Parallel Problem Solving from Nature, PPSN XI, 2010 2. "Essentials of Metaheuristics, A Set of Undergraduate Lecture Notes", S. Luke, Department of Computer Science, George Mason University, 2012 3. http://en.wikipedia.org/wiki/Credit_default_swap 4. http://www.investopedia.com/video/play/credit-default-swaps/ 5. Book: "Cartesian genetic programming", J. F. Miller, Springer, 2011 6. Tutorial: "Cartesian Genetic Programming", J. F. Miller, S. L. Harding, GECCO, 2012
- 20. Thank you