This document summarizes a research paper that proposes new portfolio selection models using possibilistic Sharpe ratio to account for uncertainty in fuzzy environments. It defines possibilistic moments like mean, variance, skewness, and risk premium for fuzzy numbers. It then defines possibilistic Sharpe ratio as the ratio of possibilistic risk premium to standard deviation. New bi-objective and multi-objective portfolio models are presented that maximize possibilistic Sharpe ratio and skewness to allow for asymmetric returns. The models are solved using a genetic algorithm and tested on stock price data to demonstrate the approach.
A Fuzzy Mean-Variance-Skewness Portfolioselection Problem.inventionjournals
A fuzzy number is a normal and convex fuzzy subsetof the real line. In this paper, based on membership function, we redefine the concepts of mean and variance for fuzzy numbers. Furthermore, we propose the concept of skewness and prove some desirable properties. A fuzzy mean-variance-skewness portfolio se-lection model is formulated and two variations are given, which are transformed to nonlinear optimization models with polynomial ob-jective and constraint functions such that they can be solved analytically. Finally, we present some numerical examples to demonstrate the effectiveness of the proposed models
This paper proposes using a "shrinkage" estimator as an alternative to the traditional sample covariance matrix for portfolio optimization. The shrinkage estimator combines the sample covariance matrix with a structured "shrinkage target" using a shrinkage constant to minimize distance from the true covariance matrix. The paper finds this shrinkage estimator significantly increases the realized information ratio of active portfolio managers compared to the sample covariance matrix. An empirical study on historical stock return data confirms the shrinkage method leads to higher ex post information ratios in portfolio optimization. However, the shrinkage target assumes identical pairwise correlations that may not fully reflect market characteristics.
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.
The document provides an overview of hypothesis testing with one sample. It introduces key concepts such as the null and alternative hypotheses, types of errors, level of significance, test statistics, p-values, and the nature of hypothesis tests. Examples are provided to demonstrate how to state hypotheses based on a claim, identify types of errors, and determine if a test is left-tailed, right-tailed, or two-tailed. The document serves as an introduction for students to the basic framework and terminology of hypothesis testing with one sample.
This technical report explores using set-valued attributes for decision tree induction algorithms. Conventional algorithms use single-valued attributes, but the authors argue set-valued attributes can improve accuracy and speed. They describe modifying decision tree algorithms for splitting, pruning, and classification when attributes can have set values. Experiments show the proposed approach works well with only simple pre-pruning needed to limit excessive instance replication across tree branches. The set-valued approach is intended to better handle noise and variability in data values.
Are Evolutionary Algorithms Required to Solve Sudoku Problemscsandit
1. Evolutionary algorithms aim to iteratively improve solutions through random mutation and fitness evaluation, but can become trapped in local optima. The author developed a "greedy random" mutation approach that preferentially adds rather than removes values.
2. Experiments showed this greedy random mutation was sometimes more effective at solving harder Sudoku puzzles than traditional evolutionary algorithms. This implies the quality of random mutation can significantly impact evolutionary algorithm performance with Sudoku.
3. The greedy random mutation was integrated into the evolutionary algorithm lifecycle to balance exploration and exploitation. Candidates were assessed after a removal to concentrate entropy around boundary solutions.
To demonstrate our approaches we will use Sudoku puzzles, which are an excellent test bed for
evolutionary algorithms. The puzzles are accessible enough for people to enjoy. However the more complex
puzzles require thousands of iterations before an evolutionary algorithm finds a solution. If we were
attempting to compare evolutionary algorithms we could count their iterations to solution as an indicator
of relative efficiency. Evolutionary algorithms however include a process of random mutation for solution
candidates. We will show that by improving the random mutation behaviours we were able to solve
problems with minimal evolutionary optimisation. Experiments demonstrated the random mutation was at
times more effective at solving the harder problems than the evolutionary algorithms. This implies that the
quality of random mutation may have a significant impact on the performance of evolutionary algorithms
with Sudoku puzzles. Additionally this random mutation may hold promise for reuse in hybrid evolutionary
algorithm behaviours.
A Fuzzy Mean-Variance-Skewness Portfolioselection Problem.inventionjournals
A fuzzy number is a normal and convex fuzzy subsetof the real line. In this paper, based on membership function, we redefine the concepts of mean and variance for fuzzy numbers. Furthermore, we propose the concept of skewness and prove some desirable properties. A fuzzy mean-variance-skewness portfolio se-lection model is formulated and two variations are given, which are transformed to nonlinear optimization models with polynomial ob-jective and constraint functions such that they can be solved analytically. Finally, we present some numerical examples to demonstrate the effectiveness of the proposed models
This paper proposes using a "shrinkage" estimator as an alternative to the traditional sample covariance matrix for portfolio optimization. The shrinkage estimator combines the sample covariance matrix with a structured "shrinkage target" using a shrinkage constant to minimize distance from the true covariance matrix. The paper finds this shrinkage estimator significantly increases the realized information ratio of active portfolio managers compared to the sample covariance matrix. An empirical study on historical stock return data confirms the shrinkage method leads to higher ex post information ratios in portfolio optimization. However, the shrinkage target assumes identical pairwise correlations that may not fully reflect market characteristics.
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.
The document provides an overview of hypothesis testing with one sample. It introduces key concepts such as the null and alternative hypotheses, types of errors, level of significance, test statistics, p-values, and the nature of hypothesis tests. Examples are provided to demonstrate how to state hypotheses based on a claim, identify types of errors, and determine if a test is left-tailed, right-tailed, or two-tailed. The document serves as an introduction for students to the basic framework and terminology of hypothesis testing with one sample.
This technical report explores using set-valued attributes for decision tree induction algorithms. Conventional algorithms use single-valued attributes, but the authors argue set-valued attributes can improve accuracy and speed. They describe modifying decision tree algorithms for splitting, pruning, and classification when attributes can have set values. Experiments show the proposed approach works well with only simple pre-pruning needed to limit excessive instance replication across tree branches. The set-valued approach is intended to better handle noise and variability in data values.
Are Evolutionary Algorithms Required to Solve Sudoku Problemscsandit
1. Evolutionary algorithms aim to iteratively improve solutions through random mutation and fitness evaluation, but can become trapped in local optima. The author developed a "greedy random" mutation approach that preferentially adds rather than removes values.
2. Experiments showed this greedy random mutation was sometimes more effective at solving harder Sudoku puzzles than traditional evolutionary algorithms. This implies the quality of random mutation can significantly impact evolutionary algorithm performance with Sudoku.
3. The greedy random mutation was integrated into the evolutionary algorithm lifecycle to balance exploration and exploitation. Candidates were assessed after a removal to concentrate entropy around boundary solutions.
To demonstrate our approaches we will use Sudoku puzzles, which are an excellent test bed for
evolutionary algorithms. The puzzles are accessible enough for people to enjoy. However the more complex
puzzles require thousands of iterations before an evolutionary algorithm finds a solution. If we were
attempting to compare evolutionary algorithms we could count their iterations to solution as an indicator
of relative efficiency. Evolutionary algorithms however include a process of random mutation for solution
candidates. We will show that by improving the random mutation behaviours we were able to solve
problems with minimal evolutionary optimisation. Experiments demonstrated the random mutation was at
times more effective at solving the harder problems than the evolutionary algorithms. This implies that the
quality of random mutation may have a significant impact on the performance of evolutionary algorithms
with Sudoku puzzles. Additionally this random mutation may hold promise for reuse in hybrid evolutionary
algorithm behaviours.
This document outlines how to perform a chi-square test of independence using a contingency table. It explains that a contingency table displays the observed frequencies of two variables arranged in rows and columns. Expected frequencies are calculated for each cell assuming independence by multiplying the corresponding row and column totals and dividing by the sample size. A chi-square test can then determine if the observed frequencies differ significantly from the expected frequencies under independence.
Investment portfolio optimization with garch modelsEvans Tee
Since the introduction of the Markowitz mean-variance optimization model, several extensions have been made to improve optimality. This study examines the application of two models - the ARMA-GARCH model and the ARMA- DCC GARCH model - for the Mean-VaR optimization of funds managed by HFC Investment Limited. Weekly prices of the above mentioned funds from 2009 to 2012 were examined. The funds analyzed were the Equity Trust Fund, the Future Plan Fund and the Unit Trust Fund. The returns of the funds are modelled with the Autoregressive Moving Average (ARMA) whiles volatility was modelled with the univariate Generalized Autoregressive Conditional Heteroskedasti city (GARCH) as well as the multivariate Dynamic Conditional Correlation GARCH (DCC GARCH). This was based on the assumption of non-constant mean and volatility of fund returns. In this study the risk of a portfolio is measured using the value-at-risk. A single constrained Mean-VaR optimization problem was obtained based on the assumption that investors’ preference is solely based on risk and return. The optimization process was performed using the Lagrange Multiplier approach and the solution was obtained by the Kuhn-Tucker theorems. Conclusions which were drawn based on the results pointed to the fact that a more efficient portfolio is obtained when the value-at-risk (VaR) is modelled with a multivariate GARCH.
This document outlines how to perform hypothesis tests to compare the means of two independent samples. It discusses using a two-sample z-test when samples are large and normally distributed, and a two-sample t-test when samples are small. The key steps are to state the null and alternative hypotheses, calculate the test statistic, find the critical value, make a decision to reject or fail to reject the null hypothesis, and interpret the results. Examples are provided to demonstrate these tests.
Credit card fraud detection and concept drift adaptation with delayed supervi...Andrea Dal Pozzolo
This document discusses methods for credit card fraud detection in the presence of concept drift and delayed labeling. It formalizes the problem and proposes learning strategies to handle feedback from investigators and delayed labels separately. Experiments on real and artificial datasets show that aggregating classifiers trained on feedback and delayed samples outperforms approaches that do not distinguish between sample types or do not aggregate classifiers. Future work will focus on adaptive aggregation and addressing sample selection bias.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Advanced Statistical Manual for Ayurveda ResearchAyurdata
These slides covers more advanced statistical applications including that in data science.
The mode of presentation is that the concept is introduced first, followed by illustration and the use in a real context.
This document discusses using a hybrid modeling approach combining decision trees and logistic regression to develop a credit scoring model for retail loans. It presents a case study where this approach was tested on real loan data. Key points:
- A decision tree was first used to identify important borrower characteristics and assign initial weights. Logistic regression was then used to compute odds ratios and assign refined weights within each characteristic.
- This iterative process determined weights for both numeric factors like time at bank and non-numeric factors like occupation. Binning of numeric variables was done automatically using additional decision trees.
- When tested on loan application data, the hybrid model achieved a classification accuracy of around 80%, higher than single models. This approach provides an
This presentation discusses about following topics:
Types of Problems Solved Using Artificial Intelligence Algorithms
Problem categories
Classification Algorithms
Naive Bayes
Example: A person playing golf
Decision Tree
Random Forest
Logistic Regression
Support Vector Machine
Support Vector Machine
K Nearest Neighbors
Business Bankruptcy Prediction Based on Survival Analysis Approachijcsit
This document discusses business bankruptcy prediction models using survival analysis. It analyzes companies listed on the Taiwan Stock Exchange from 2003 to 2009. The study uses the Cox proportional hazards model to identify key financial ratios that predict business failure. The model includes profitability, leverage, efficiency, and valuation ratios as predictors. The accuracy of the proposed survival analysis model in classifying business failures is 87.93%. The document also discusses other statistical and machine learning techniques used for business bankruptcy prediction, such as logistic regression, neural networks, and hybrid models.
Interestingness Measures In Rule Mining: A ValuationIJERA Editor
In data mining it is normally desirable that discovered knowledge should possess characteristics such as accuracy, comprehensibility and interestingness. The vast majority of data mining algorithms generate patterns that are accurate and reliable but they might not be interesting. Interestingness measures are used to find the truly interesting rules which will help the user in decision making in exceptional state of affairs. A variety of interestingness measures for rule mining have been suggested by researchers in the field of data mining. In this paper we are going to carry out a valuation of these interestingness measures and classify them from numerous perspectives.
Okay, let's solve this step-by-step:
- zα/2 for a 90% CI is 1.645
- p from the pilot study is 20/30 = 0.667
- 1 - p is 1 - 0.667 = 0.333
- Desired margin of error E is 0.1
Plugging into the formula:
n = (1.645)2 * 0.667 * 0.333 / (0.1)2
n = (1.645)2 * 0.222 / 0.01
n = 96.75 ~ 97
The required sample size is 97
Slides of the paper http://arxiv.org/abs/1505.04637
source code is available at https://github.com/albahnsen/CostSensitiveClassification/blob/master/costcla/models/cost_tree.py#L15
Abstract:
Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings.
This document summarizes a presentation on maximizing profit from customer churn prediction models using cost-sensitive machine learning techniques. It discusses how traditional evaluation measures like accuracy do not account for different costs of prediction errors. It then covers cost-sensitive approaches like cost-proportionate sampling, Bayes minimum risk, and cost-sensitive decision trees. The results show these cost-sensitive methods improve savings over traditional models and sampling approaches when the business costs are incorporated into the predictive modeling.
The document introduces a fuzzy logic system for momentum analysis to improve high-frequency order execution. It proposes using fuzzy logic to analyze current market conditions and momentum to determine optimal order placement. The system uses fuzzy rules and membership functions to analyze market inputs and determine buy or sell signals. The system is shown to outperform traditional volume-based order execution strategies by increasing profits on both buy and sell orders.
This paper proposes a parallel evolutionary algorithm to solve single variable optimization problems. Specifically:
- It presents a genetic algorithm approach that runs in parallel using a master-slave model, where the master performs genetic operations and distributes individuals to slaves for evaluation.
- The algorithm is tested on single variable optimization problems to find minimum/maximum values.
- Experimental results show the parallel genetic algorithm is effective at finding optimal solutions to these problems and represents an efficient parallel approach for optimization.
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better solution participates more times in the position modernize procedure. The position update equation is inspired from the memetic search in artificial bee colony algorithm. The proposed strategy is named as Memetic Search in Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is tested over 8 benchmark optimization problems and three real world optimization problems. A comparative analysis has also been carried out among proposed MSDE and original DE. Results show that the anticipated algorithm go one better than the basic DE and its recent deviations in a good number of the experiments.
Improving Returns from the Markowitz Model using GA- AnEmpirical Validation o...idescitation
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.
In this paper, we introduce a universal framework for mean-distortion robust risk measurement and
portfolio optimization. We take accounts for the uncertainty based on Gelbrich distance and another uncertainty
set proposed by Delage & Ye. We also establish the model under the constraints of probabilistic safety
criteria and compare the different frontiers and the investment ratio to each asset. The empirical analysis in the
final part explores the impact of different parameters on the model results.
This paper studies an optimal investment and reinsurance problem for a jump-diffusion risk model
with short-selling constraint under the mean-variance criterion. Assume that the insurer
is allowed to purchase proportional reinsurance from the reinsurer and invest in a risk-free asset and a risky
asset whose price follows a geometric Brownian motion. In particular, both the insurance and reinsurance
premium are assumed to be calculated via the variance principle with different p
This document discusses using multiple regression analysis to predict real estate sale prices. Several independent variables are considered as predictors, including floor height, distance from elevator, ocean view, whether it is an end unit, and whether furniture is included. The analysis finds some variables like ocean view and floor height are statistically significant in predicting sale price, while others like the interaction between distance from elevator and ocean view are also important. The regression model provides insight into how real estate businesses can focus their resources based on which factors most influence prices.
This document outlines how to perform a chi-square test of independence using a contingency table. It explains that a contingency table displays the observed frequencies of two variables arranged in rows and columns. Expected frequencies are calculated for each cell assuming independence by multiplying the corresponding row and column totals and dividing by the sample size. A chi-square test can then determine if the observed frequencies differ significantly from the expected frequencies under independence.
Investment portfolio optimization with garch modelsEvans Tee
Since the introduction of the Markowitz mean-variance optimization model, several extensions have been made to improve optimality. This study examines the application of two models - the ARMA-GARCH model and the ARMA- DCC GARCH model - for the Mean-VaR optimization of funds managed by HFC Investment Limited. Weekly prices of the above mentioned funds from 2009 to 2012 were examined. The funds analyzed were the Equity Trust Fund, the Future Plan Fund and the Unit Trust Fund. The returns of the funds are modelled with the Autoregressive Moving Average (ARMA) whiles volatility was modelled with the univariate Generalized Autoregressive Conditional Heteroskedasti city (GARCH) as well as the multivariate Dynamic Conditional Correlation GARCH (DCC GARCH). This was based on the assumption of non-constant mean and volatility of fund returns. In this study the risk of a portfolio is measured using the value-at-risk. A single constrained Mean-VaR optimization problem was obtained based on the assumption that investors’ preference is solely based on risk and return. The optimization process was performed using the Lagrange Multiplier approach and the solution was obtained by the Kuhn-Tucker theorems. Conclusions which were drawn based on the results pointed to the fact that a more efficient portfolio is obtained when the value-at-risk (VaR) is modelled with a multivariate GARCH.
This document outlines how to perform hypothesis tests to compare the means of two independent samples. It discusses using a two-sample z-test when samples are large and normally distributed, and a two-sample t-test when samples are small. The key steps are to state the null and alternative hypotheses, calculate the test statistic, find the critical value, make a decision to reject or fail to reject the null hypothesis, and interpret the results. Examples are provided to demonstrate these tests.
Credit card fraud detection and concept drift adaptation with delayed supervi...Andrea Dal Pozzolo
This document discusses methods for credit card fraud detection in the presence of concept drift and delayed labeling. It formalizes the problem and proposes learning strategies to handle feedback from investigators and delayed labels separately. Experiments on real and artificial datasets show that aggregating classifiers trained on feedback and delayed samples outperforms approaches that do not distinguish between sample types or do not aggregate classifiers. Future work will focus on adaptive aggregation and addressing sample selection bias.
Basic of Decision Tree Learning. This slide includes definition of decision tree, basic example, basic construction of a decision tree, mathlab example
Advanced Statistical Manual for Ayurveda ResearchAyurdata
These slides covers more advanced statistical applications including that in data science.
The mode of presentation is that the concept is introduced first, followed by illustration and the use in a real context.
This document discusses using a hybrid modeling approach combining decision trees and logistic regression to develop a credit scoring model for retail loans. It presents a case study where this approach was tested on real loan data. Key points:
- A decision tree was first used to identify important borrower characteristics and assign initial weights. Logistic regression was then used to compute odds ratios and assign refined weights within each characteristic.
- This iterative process determined weights for both numeric factors like time at bank and non-numeric factors like occupation. Binning of numeric variables was done automatically using additional decision trees.
- When tested on loan application data, the hybrid model achieved a classification accuracy of around 80%, higher than single models. This approach provides an
This presentation discusses about following topics:
Types of Problems Solved Using Artificial Intelligence Algorithms
Problem categories
Classification Algorithms
Naive Bayes
Example: A person playing golf
Decision Tree
Random Forest
Logistic Regression
Support Vector Machine
Support Vector Machine
K Nearest Neighbors
Business Bankruptcy Prediction Based on Survival Analysis Approachijcsit
This document discusses business bankruptcy prediction models using survival analysis. It analyzes companies listed on the Taiwan Stock Exchange from 2003 to 2009. The study uses the Cox proportional hazards model to identify key financial ratios that predict business failure. The model includes profitability, leverage, efficiency, and valuation ratios as predictors. The accuracy of the proposed survival analysis model in classifying business failures is 87.93%. The document also discusses other statistical and machine learning techniques used for business bankruptcy prediction, such as logistic regression, neural networks, and hybrid models.
Interestingness Measures In Rule Mining: A ValuationIJERA Editor
In data mining it is normally desirable that discovered knowledge should possess characteristics such as accuracy, comprehensibility and interestingness. The vast majority of data mining algorithms generate patterns that are accurate and reliable but they might not be interesting. Interestingness measures are used to find the truly interesting rules which will help the user in decision making in exceptional state of affairs. A variety of interestingness measures for rule mining have been suggested by researchers in the field of data mining. In this paper we are going to carry out a valuation of these interestingness measures and classify them from numerous perspectives.
Okay, let's solve this step-by-step:
- zα/2 for a 90% CI is 1.645
- p from the pilot study is 20/30 = 0.667
- 1 - p is 1 - 0.667 = 0.333
- Desired margin of error E is 0.1
Plugging into the formula:
n = (1.645)2 * 0.667 * 0.333 / (0.1)2
n = (1.645)2 * 0.222 / 0.01
n = 96.75 ~ 97
The required sample size is 97
Slides of the paper http://arxiv.org/abs/1505.04637
source code is available at https://github.com/albahnsen/CostSensitiveClassification/blob/master/costcla/models/cost_tree.py#L15
Abstract:
Several real-world classification problems are example-dependent cost-sensitive in nature, where the costs due to misclassification vary between examples and not only within classes. However, standard classification methods do not take these costs into account, and assume a constant cost of misclassification errors. In previous works, some methods that take into account the financial costs into the training of different algorithms have been proposed, with the example-dependent cost-sensitive decision tree algorithm being the one that gives the highest savings. In this paper we propose a new framework of ensembles of example-dependent cost-sensitive decision-trees. The framework consists in creating different example-dependent cost-sensitive decision trees on random subsamples of the training set, and then combining them using three different combination approaches. Moreover, we propose two new cost-sensitive combination approaches; cost-sensitive weighted voting and cost-sensitive stacking, the latter being based on the cost-sensitive logistic regression method. Finally, using five different databases, from four real-world applications: credit card fraud detection, churn modeling, credit scoring and direct marketing, we evaluate the proposed method against state-of-the-art example-dependent cost-sensitive techniques, namely, cost-proportionate sampling, Bayes minimum risk and cost-sensitive decision trees. The results show that the proposed algorithms have better results for all databases, in the sense of higher savings.
This document summarizes a presentation on maximizing profit from customer churn prediction models using cost-sensitive machine learning techniques. It discusses how traditional evaluation measures like accuracy do not account for different costs of prediction errors. It then covers cost-sensitive approaches like cost-proportionate sampling, Bayes minimum risk, and cost-sensitive decision trees. The results show these cost-sensitive methods improve savings over traditional models and sampling approaches when the business costs are incorporated into the predictive modeling.
The document introduces a fuzzy logic system for momentum analysis to improve high-frequency order execution. It proposes using fuzzy logic to analyze current market conditions and momentum to determine optimal order placement. The system uses fuzzy rules and membership functions to analyze market inputs and determine buy or sell signals. The system is shown to outperform traditional volume-based order execution strategies by increasing profits on both buy and sell orders.
This paper proposes a parallel evolutionary algorithm to solve single variable optimization problems. Specifically:
- It presents a genetic algorithm approach that runs in parallel using a master-slave model, where the master performs genetic operations and distributes individuals to slaves for evaluation.
- The algorithm is tested on single variable optimization problems to find minimum/maximum values.
- Experimental results show the parallel genetic algorithm is effective at finding optimal solutions to these problems and represents an efficient parallel approach for optimization.
Analysis of Parameter using Fuzzy Genetic Algorithm in E-learning SystemHarshal Jain
The aim of this project is to analyze the parameter, for the inputs to find an optimization problem than the candidate solution we have. This will help us to find more accurate knowledge level of user, using Genetic Algorithm (GA). In this algorithm a population of candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem is evolved toward better solutions.
Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search heuristics in the vein of Particle Swarm Optimization at what time of testing over both yardstick and actual world problems. Nevertheless, DE, like other probabilistic optimization algorithms, from time to time exhibits precipitate convergence and stagnates at suboptimal position. In order to stay away from stagnation behavior while maintaining an excellent convergence speed, an innovative search strategy is introduced, named memetic search in DE. In the planned strategy, positions update equation customized as per a memetic search stratagem. In this strategy a better solution participates more times in the position modernize procedure. The position update equation is inspired from the memetic search in artificial bee colony algorithm. The proposed strategy is named as Memetic Search in Differential Evolution (MSDE). To prove efficiency and efficacy of MSDE, it is tested over 8 benchmark optimization problems and three real world optimization problems. A comparative analysis has also been carried out among proposed MSDE and original DE. Results show that the anticipated algorithm go one better than the basic DE and its recent deviations in a good number of the experiments.
Improving Returns from the Markowitz Model using GA- AnEmpirical Validation o...idescitation
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.
In this paper, we introduce a universal framework for mean-distortion robust risk measurement and
portfolio optimization. We take accounts for the uncertainty based on Gelbrich distance and another uncertainty
set proposed by Delage & Ye. We also establish the model under the constraints of probabilistic safety
criteria and compare the different frontiers and the investment ratio to each asset. The empirical analysis in the
final part explores the impact of different parameters on the model results.
This paper studies an optimal investment and reinsurance problem for a jump-diffusion risk model
with short-selling constraint under the mean-variance criterion. Assume that the insurer
is allowed to purchase proportional reinsurance from the reinsurer and invest in a risk-free asset and a risky
asset whose price follows a geometric Brownian motion. In particular, both the insurance and reinsurance
premium are assumed to be calculated via the variance principle with different p
This document discusses using multiple regression analysis to predict real estate sale prices. Several independent variables are considered as predictors, including floor height, distance from elevator, ocean view, whether it is an end unit, and whether furniture is included. The analysis finds some variables like ocean view and floor height are statistically significant in predicting sale price, while others like the interaction between distance from elevator and ocean view are also important. The regression model provides insight into how real estate businesses can focus their resources based on which factors most influence prices.
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/scoring-and-predicting-risk-preferences/
This study presents amethodology to determine risk scores of individ-uals, for a given financial risk preference survey. To this end, we use a regression-based iterative algorithm to determine the weights for survey questions in the scoring process. Next, we generate classification models to classify individuals into risk-averse and risk-seeking categories, using a subset of survey questions. We illustrate the methodology through a sample survey with 656 respondents. We find that the demographic (indirect) questions can be almost as successful as risk-related (direct) questions in predicting risk preference classes of respon-dents. Using a decision-tree based classification model, we discuss how one can generate actionable business rules based on the findings.
Scoring and predicting risk preferencesGurdal Ertek
This study presents a methodology to determine risk scores of individuals, for a given financial risk preference survey. To this end, we use a regression based iterative algorithm to determine the weights for survey questions in the scoring process. Next, we generate classification models to classify individuals into risk-averse and risk-seeking categories, using a subset of survey questions. We illustrate the methodology through a sample survey with 656 respondents. We find that the demographic (indirect) questions can be almost as successful as risk-related (direct) questions in predicting risk preference classes of respondents. Using a decision-tree based classification model, we discuss how one can
generate actionable business rules based on the findings.
http://research.sabanciuniv.edu.
This document discusses methods for clustering time series data in a way that allows the cluster structure to change over time. It begins by introducing the problem and defining relevant terms. It then provides spectral clustering as a preliminary benchmark approach before exploring an alternative method using triangular potentials within a graphical model framework. The document presents the proposed method and provides illustrative examples and discussion of extensions.
ENTROPY-COST RATIO MAXIMIZATION MODEL FOR EFFICIENT STOCK PORTFOLIO SELECTION...cscpconf
This paper introduces a new stock portfolio selection model in non-stochastic environment.Following the principle of maximum entropy, a new entropy-cost ratio function is introduced as
the objective function. The uncertain returns, risks and ividends of the securities are considered as interval numbers. Along with the objective function, eight different types of constraints are used in the model to convert it into a pragmatic one. Three different models have been proposed by defining the future inancial market optimistically, pessimistically and in hecombined form to model the portfolio selection problem. To illustrate the effectiveness and tractability of the proposed models, these are tested on a set of data from Bombay Stock Exchange (BSE). The solution has been done by genetic algorithm.
This document discusses estimating asset price volatility using generalized autoregressive conditional heteroskedastic (GARCH) models. It begins with an introduction to modeling stock return volatility and the assumptions of non-constant variance. It then presents the GARCH model for estimating variance in the univariate case. Next, it discusses estimating the GARCH model parameters using maximum likelihood estimation. Finally, it discusses extending the GARCH model to the multivariate case to simultaneously estimate the volatilities and correlations of a portfolio of stocks.
MODELING THE AUTOREGRESSIVE CAPITAL ASSET PRICING MODEL FOR TOP 10 SELECTED...IAEME Publication
Systematic risk is the uncertainty inherent to the entire market or entire market segment and Unsystematic risk is the type of uncertainty that comes with the company or industry we invest. It can be reduced through diversification. The study generalized for selecting of non -linear capital asset pricing model for top securities in BSE and made an attempt to identify the marketable and non-marketable risk of investors of top companies. The analysis was conducted at different stages. They are Vector auto regression of systematic and unsystematic risk.
Does the capital assets pricing model (capm) predicts stock market returns in...Alexander Decker
This document examines whether the Capital Asset Pricing Model (CAPM) can predict stock returns in Ghana using data from selected stocks on the Ghana Stock Exchange from 2006-2010. The results found no statistically significant relationship between actual and predicted returns, indicating CAPM with constant beta cannot explain differences in returns. It was also found that some stocks were on average undervalued while one was overvalued over the period studied. The conclusion is that the standard CAPM model cannot statistically explain the observed differences in actual and estimated returns of the selected Ghanaian stocks.
This document discusses using institutional ownership data to predict future stock returns. It analyzes institutional ownership data from the US between 2004-2014 using machine learning algorithms. A support vector machine was able to classify stocks into 3 bins of future 4-quarter returns with 37.3% accuracy, significantly better than chance. The study aims to identify which institutional ownership features best predict returns and whether their combination improves predictions over individual features.
VOLATILITY FORECASTING - A PERFORMANCE MEASURE OF GARCH TECHNIQUES WITH DIFFE...ijscmcj
Volatility Forecasting is an interesting challenging topic in current financial instruments as it is directly associated with profits. There are many risks and rewards directly associated with volatility. Hence forecasting volatility becomes most dispensable topic in finance. The GARCH distributions play an important role in the risk measurement and option pricing. The min motive of this paper is to measure the performance of GARCH techniques for forecasting volatility by using different distribution model. We have used 9 variations in distribution models that are used to forecast the volatility of a stock entity. The different GARCH distribution models observed in this paper are Std, Norm, SNorm, GED, SSTD, SGED, NIG, GHYP and JSU. Volatility is forecasted for 10 days in advance and values are compared with the actual values to find out the best distribution model for volatility forecast. From the results obtain it has been observed that GARCH with GED distribution models has outperformed all models.
The International Journal of Soft Computing, Mathematics and Control (IJSCMC) is a Quarterly peer-reviewed and refereed open access journal that publishes articles which contribute new results in all areas of Soft Computing, Pure, Applied and Numerical Mathematics and Control. The focus of this new journal is on all theoretical and numerical methods on soft computing, mathematics and control theory with applications in science and industry. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on latest topics of soft computing, pure, applied and numerical mathematics and control engineering, and establishing new collaborations in these areas.
Authors are solicited to contribute to this journal by submitting articles that illustrate new algorithms, theorems, modeling results, research results, projects, surveying works and industrial experiences that describe significant advances in Soft Computing, Mathematics and Control Engineering
VOLATILITY FORECASTING - A PERFORMANCE MEASURE OF GARCH TECHNIQUES WITH DIFFE...ijscmcj
Volatility Forecasting is an interesting challenging topic in current financial instruments as it is directly
associated with profits. There are many risks and rewards directly associated with volatility. Hence
forecasting volatility becomes most dispensable topic in finance. The GARCH distributions play an important
role in the risk measurement and option pricing. The min motive of this paper is to measure the performance
of GARCH techniques for forecasting volatility by using different distribution model. We have used 9
variations in distribution models that are used to forecast the volatility of a stock entity. The different GARCH
distribution models observed in this paper are Std, Norm, SNorm, GED, SSTD, SGED, NIG, GHYP and JSU.
Volatility is forecasted for 10 days in advance and values are compared with the actual values to find out the
best distribution model for volatility forecast. From the results obtain it has been observed that GARCH with
GED distribution models has outperformed all models.
Volatility Forecasting - A Performance Measure of Garch Techniques With Diffe...ijscmcj
Volatility Forecasting is an interesting challengingtopicin current financial instruments as it is directly associated with profits. There are many risks and rewards directly associated with volatility. Hence forecasting volatility becomes most dispensable topic in finance. The GARCH distributionsplay an import ant role in the risk measurement a nd option pricing. T heminmotiveof this paper is tomeasure the performance of GARCH techniques for forecasting volatility by using different distribution model. We have used 9 variations in distribution models that are used to forecast t he volatility of a stock entity. Thedifferent GARCH
distribution models observed in this paper are Std, Norm, SNorm,GED, SSTD, SGED, NIG, GHYP and JSU.Volatility is forecasted for 10 days in dvance andvalues are compared with the actual values to find out the best distribution model for volatility forecast. From the results obtain it has been observed that GARCH withGED distribution models has outperformed all models
Review Parameters Model Building & Interpretation and Model Tunin.docxcarlstromcurtis
Review Parameters: Model Building & Interpretation and Model Tuning
1. Model Building
a. Assessments and Rationale of Various Models Employed to Predict Loan Defaults
The z-score formula model was employed by Altman (1968) while envisaging bankruptcy. The model was utilized to forecast the likelihood that an organization may fall into bankruptcy in a period of two years. In addition, the Z-score model was instrumental in predicting corporate defaults. The model makes use of various organizational income and balance sheet data to weigh the financial soundness of a firm. The Z-score involves a Linear combination of five general financial ratios which are assessed through coefficients. The author employed the statistical technique of discriminant examination of data set sourced from publically listed manufacturers. A research study by Alexander (2012) made use of symmetric binary alternative models, otherwise referred to as conditional probability models. The study sought to establish the asymmetric binary options models subject to the extreme value theory in better explicating bankruptcy.
In their research study on the likelihood of default models examining Russian banks, Anatoly et al. (2014) made use of binary alternative models in predicting the likelihood of default. The study established that preface specialist clustering or mechanical clustering enhances the prediction capacity of the models. Rajan et al. (2010) accentuated the statistical default models as well as inducements. They postulated that purely numerical models disregard the concept that an alteration in the inducements of agents who produce the data may alter the very nature of data. The study attempted to appraise statistical models that unpretentiously pool resources on historical figures devoid of modeling the behavior of driving forces that generates these data. Goodhart (2011) sought to assess the likelihood of small businesses to default on loans. Making use of data on business loan assortment, the study established the particular lender, loan, and borrower characteristics as well as modifications in the economic environments that lead to a rise in the probability of default. The results of the study form the basis for the scoring model. Focusing on modeling default possibility, Singhee & Rutenbar (2010) found the risk as the uncertainty revolving around an enterprise’s capacity to service its obligations and debts.
Using the logistic model to forecast the probability of bank loan defaults, Adam et al. (2012) employed a data set with demographic information on borrowers. The authors attempted to establish the risk factors linked to borrowers are attributable to default. The identified risk factors included marital status, gender, occupation, age, and loan duration. Cababrese (2012) employed three accepted data mining algorithms, naïve Bayesian classifiers, artificial neural network decision trees coupled with a logical regression model to formulate a prediction m ...
This document discusses estimating stochastic relative risk aversion from interest rates. It first introduces a model for deriving relative risk aversion from interest rates using a time inhomogeneous single factor short rate model. It then details the estimation methodology used, which calibrates the model to US LIBOR data to estimate a time series for the market price of risk and ex-ante bond Sharpe ratio. This allows deducing a stochastic process for relative risk aversion under a power utility function. Estimated mean relative risk aversion is 49.89. The document then introduces modifying a Real Business Cycle model to allow time-varying relative risk aversion, finding it better matches empirical consumption volatility than a baseline model.
This paper examines the "variance premium", which is the difference between the squared VIX index and expected realized variance. The authors show that the variance premium captures attitudes toward economic uncertainty and predicts future stock returns over short horizons. They develop a generalized long-run risks model that generates a time-varying variance premium consistent with market return and risk-free rate levels. The model requires extensions to match the large size, volatility and skewness of the variance premium and its short-term return predictability. Calibrating the model to cash flow and asset pricing targets allows it to generate the variance premium and its return predictability features.
This document provides an overview of the development of bond immunization theory and fuzzy bond immunization strategies over several decades. It discusses early works in 1952 by Redington on bond immunization and Markowitz on portfolio selection. Later developments include using fuzzy numbers to model interest rates and utility functions. The document outlines an approach to designing active bond management strategies in a fuzzy environment using Sengupta's methodology to evaluate portfolio return and risk. It proposes constructing fuzzy return-risk maps to help decision makers choose the best interest rate forecasts and durations.
Similar to POSSIBILISTIC SHARPE RATIO BASED NOVICE PORTFOLIO SELECTION MODELS (20)
ANALYSIS OF LAND SURFACE DEFORMATION GRADIENT BY DINSAR cscpconf
The progressive development of Synthetic Aperture Radar (SAR) systems diversify the exploitation of the generated images by these systems in different applications of geoscience. Detection and monitoring surface deformations, procreated by various phenomena had benefited from this evolution and had been realized by interferometry (InSAR) and differential interferometry (DInSAR) techniques. Nevertheless, spatial and temporal decorrelations of the interferometric couples used, limit strongly the precision of analysis results by these techniques. In this context, we propose, in this work, a methodological approach of surface deformation detection and analysis by differential interferograms to show the limits of this technique according to noise quality and level. The detectability model is generated from the deformation signatures, by simulating a linear fault merged to the images couples of ERS1 / ERS2 sensors acquired in a region of the Algerian south.
4D AUTOMATIC LIP-READING FOR SPEAKER'S FACE IDENTIFCATIONcscpconf
A novel based a trajectory-guided, concatenating approach for synthesizing high-quality image real sample renders video is proposed . The lips reading automated is seeking for modeled the closest real image sample sequence preserve in the library under the data video to the HMM predicted trajectory. The object trajectory is modeled obtained by projecting the face patterns into an KDA feature space is estimated. The approach for speaker's face identification by using synthesise the identity surface of a subject face from a small sample of patterns which sparsely each the view sphere. An KDA algorithm use to the Lip-reading image is discrimination, after that work consisted of in the low dimensional for the fundamental lip features vector is reduced by using the 2D-DCT.The mouth of the set area dimensionality is ordered by a normally reduction base on the PCA to obtain the Eigen lips approach, their proposed approach by[33]. The subjective performance results of the cost function under the automatic lips reading modeled , which wasn’t illustrate the superior performance of the
method.
MOVING FROM WATERFALL TO AGILE PROCESS IN SOFTWARE ENGINEERING CAPSTONE PROJE...cscpconf
Universities offer software engineering capstone course to simulate a real world-working environment in which students can work in a team for a fixed period to deliver a quality product. The objective of the paper is to report on our experience in moving from Waterfall process to Agile process in conducting the software engineering capstone project. We present the capstone course designs for both Waterfall driven and Agile driven methodologies that highlight the structure, deliverables and assessment plans.To evaluate the improvement, we conducted a survey for two different sections taught by two different instructors to evaluate students’ experience in moving from traditional Waterfall model to Agile like process. Twentyeight students filled the survey. The survey consisted of eight multiple-choice questions and an open-ended question to collect feedback from students. The survey results show that students were able to attain hands one experience, which simulate a real world-working environment. The results also show that the Agile approach helped students to have overall better design and avoid mistakes they have made in the initial design completed in of the first phase of the capstone project. In addition, they were able to decide on their team capabilities, training needs and thus learn the required technologies earlier which is reflected on the final product quality
PROMOTING STUDENT ENGAGEMENT USING SOCIAL MEDIA TECHNOLOGIEScscpconf
This document discusses using social media technologies to promote student engagement in a software project management course. It describes the course and objectives of enhancing communication. It discusses using Facebook for 4 years, then switching to WhatsApp based on student feedback, and finally introducing Slack to enable personalized team communication. Surveys found students engaged and satisfied with all three tools, though less familiar with Slack. The conclusion is that social media promotes engagement but familiarity with the tool also impacts satisfaction.
A SURVEY ON QUESTION ANSWERING SYSTEMS: THE ADVANCES OF FUZZY LOGICcscpconf
In real world computing environment with using a computer to answer questions has been a human dream since the beginning of the digital era, Question-answering systems are referred to as intelligent systems, that can be used to provide responses for the questions being asked by the user based on certain facts or rules stored in the knowledge base it can generate answers of questions asked in natural , and the first main idea of fuzzy logic was to working on the problem of computer understanding of natural language, so this survey paper provides an overview on what Question-Answering is and its system architecture and the possible relationship and
different with fuzzy logic, as well as the previous related research with respect to approaches that were followed. At the end, the survey provides an analytical discussion of the proposed QA models, along or combined with fuzzy logic and their main contributions and limitations.
DYNAMIC PHONE WARPING – A METHOD TO MEASURE THE DISTANCE BETWEEN PRONUNCIATIONS cscpconf
Human beings generate different speech waveforms while speaking the same word at different times. Also, different human beings have different accents and generate significantly varying speech waveforms for the same word. There is a need to measure the distances between various words which facilitate preparation of pronunciation dictionaries. A new algorithm called Dynamic Phone Warping (DPW) is presented in this paper. It uses dynamic programming technique for global alignment and shortest distance measurements. The DPW algorithm can be used to enhance the pronunciation dictionaries of the well-known languages like English or to build pronunciation dictionaries to the less known sparse languages. The precision measurement experiments show 88.9% accuracy.
INTELLIGENT ELECTRONIC ASSESSMENT FOR SUBJECTIVE EXAMS cscpconf
In education, the use of electronic (E) examination systems is not a novel idea, as Eexamination systems have been used to conduct objective assessments for the last few years. This research deals with randomly designed E-examinations and proposes an E-assessment system that can be used for subjective questions. This system assesses answers to subjective questions by finding a matching ratio for the keywords in instructor and student answers. The matching ratio is achieved based on semantic and document similarity. The assessment system is composed of four modules: preprocessing, keyword expansion, matching, and grading. A survey and case study were used in the research design to validate the proposed system. The examination assessment system will help instructors to save time, costs, and resources, while increasing efficiency and improving the productivity of exam setting and assessments.
TWO DISCRETE BINARY VERSIONS OF AFRICAN BUFFALO OPTIMIZATION METAHEURISTICcscpconf
African Buffalo Optimization (ABO) is one of the most recent swarms intelligence based metaheuristics. ABO algorithm is inspired by the buffalo’s behavior and lifestyle. Unfortunately, the standard ABO algorithm is proposed only for continuous optimization problems. In this paper, the authors propose two discrete binary ABO algorithms to deal with binary optimization problems. In the first version (called SBABO) they use the sigmoid function and probability model to generate binary solutions. In the second version (called LBABO) they use some logical operator to operate the binary solutions. Computational results on two knapsack problems (KP and MKP) instances show the effectiveness of the proposed algorithm and their ability to achieve good and promising solutions.
DETECTION OF ALGORITHMICALLY GENERATED MALICIOUS DOMAINcscpconf
In recent years, many malware writers have relied on Dynamic Domain Name Services (DDNS) to maintain their Command and Control (C&C) network infrastructure to ensure a persistence presence on a compromised host. Amongst the various DDNS techniques, Domain Generation Algorithm (DGA) is often perceived as the most difficult to detect using traditional methods. This paper presents an approach for detecting DGA using frequency analysis of the character distribution and the weighted scores of the domain names. The approach’s feasibility is demonstrated using a range of legitimate domains and a number of malicious algorithmicallygenerated domain names. Findings from this study show that domain names made up of English characters “a-z” achieving a weighted score of < 45 are often associated with DGA. When a weighted score of < 45 is applied to the Alexa one million list of domain names, only 15% of the domain names were treated as non-human generated.
GLOBAL MUSIC ASSET ASSURANCE DIGITAL CURRENCY: A DRM SOLUTION FOR STREAMING C...cscpconf
The document proposes a blockchain-based digital currency and streaming platform called GoMAA to address issues of piracy in the online music streaming industry. Key points:
- GoMAA would use a digital token on the iMediaStreams blockchain to enable secure dissemination and tracking of streamed content. Content owners could control access and track consumption of released content.
- Original media files would be converted to a Secure Portable Streaming (SPS) format, embedding watermarks and smart contract data to indicate ownership and enable validation on the blockchain.
- A browser plugin would provide wallets for fans to collect GoMAA tokens as rewards for consuming content, incentivizing participation and addressing royalty discrepancies by recording
IMPORTANCE OF VERB SUFFIX MAPPING IN DISCOURSE TRANSLATION SYSTEMcscpconf
This document discusses the importance of verb suffix mapping in discourse translation from English to Telugu. It explains that after anaphora resolution, the verbs must be changed to agree with the gender, number, and person features of the subject or anaphoric pronoun. Verbs in Telugu inflect based on these features, while verbs in English only inflect based on number and person. Several examples are provided that demonstrate how the Telugu verb changes based on whether the subject or pronoun is masculine, feminine, neuter, singular or plural. Proper verb suffix mapping is essential for generating natural and coherent translations while preserving the context and meaning of the original discourse.
EXACT SOLUTIONS OF A FAMILY OF HIGHER-DIMENSIONAL SPACE-TIME FRACTIONAL KDV-T...cscpconf
In this paper, based on the definition of conformable fractional derivative, the functional
variable method (FVM) is proposed to seek the exact traveling wave solutions of two higherdimensional
space-time fractional KdV-type equations in mathematical physics, namely the
(3+1)-dimensional space–time fractional Zakharov-Kuznetsov (ZK) equation and the (2+1)-
dimensional space–time fractional Generalized Zakharov-Kuznetsov-Benjamin-Bona-Mahony
(GZK-BBM) equation. Some new solutions are procured and depicted. These solutions, which
contain kink-shaped, singular kink, bell-shaped soliton, singular soliton and periodic wave
solutions, have many potential applications in mathematical physics and engineering. The
simplicity and reliability of the proposed method is verified.
AUTOMATED PENETRATION TESTING: AN OVERVIEWcscpconf
The document discusses automated penetration testing and provides an overview. It compares manual and automated penetration testing, noting that automated testing allows for faster, more standardized and repeatable tests but has limitations in developing new exploits. It also reviews some current automated penetration testing methodologies and tools, including those using HTTP/TCP/IP attacks, linking common scanning tools, a Python-based tool targeting databases, and one using POMDPs for multi-step penetration test planning under uncertainty. The document concludes that automated testing is more efficient than manual for known vulnerabilities but cannot replace manual testing for discovering new exploits.
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
VALIDATION METHOD OF FUZZY ASSOCIATION RULES BASED ON FUZZY FORMAL CONCEPT AN...cscpconf
The document proposes a new validation method for fuzzy association rules based on three steps: (1) applying the EFAR-PN algorithm to extract a generic base of non-redundant fuzzy association rules using fuzzy formal concept analysis, (2) categorizing the extracted rules into groups, and (3) evaluating the relevance of the rules using structural equation modeling, specifically partial least squares. The method aims to address issues with existing fuzzy association rule extraction algorithms such as large numbers of extracted rules, redundancy, and difficulties with manual validation.
PROBABILITY BASED CLUSTER EXPANSION OVERSAMPLING TECHNIQUE FOR IMBALANCED DATAcscpconf
In many applications of data mining, class imbalance is noticed when examples in one class are
overrepresented. Traditional classifiers result in poor accuracy of the minority class due to the
class imbalance. Further, the presence of within class imbalance where classes are composed of
multiple sub-concepts with different number of examples also affect the performance of
classifier. In this paper, we propose an oversampling technique that handles between class and
within class imbalance simultaneously and also takes into consideration the generalization
ability in data space. The proposed method is based on two steps- performing Model Based
Clustering with respect to classes to identify the sub-concepts; and then computing the
separating hyperplane based on equal posterior probability between the classes. The proposed
method is tested on 10 publicly available data sets and the result shows that the proposed
method is statistically superior to other existing oversampling methods.
CHARACTER AND IMAGE RECOGNITION FOR DATA CATALOGING IN ECOLOGICAL RESEARCHcscpconf
Data collection is an essential, but manpower intensive procedure in ecological research. An
algorithm was developed by the author which incorporated two important computer vision
techniques to automate data cataloging for butterfly measurements. Optical Character
Recognition is used for character recognition and Contour Detection is used for imageprocessing.
Proper pre-processing is first done on the images to improve accuracy. Although
there are limitations to Tesseract’s detection of certain fonts, overall, it can successfully identify
words of basic fonts. Contour detection is an advanced technique that can be utilized to
measure an image. Shapes and mathematical calculations are crucial in determining the precise
location of the points on which to draw the body and forewing lines of the butterfly. Overall,
92% accuracy were achieved by the program for the set of butterflies measured.
SOCIAL MEDIA ANALYTICS FOR SENTIMENT ANALYSIS AND EVENT DETECTION IN SMART CI...cscpconf
Smart cities utilize Internet of Things (IoT) devices and sensors to enhance the quality of the city
services including energy, transportation, health, and much more. They generate massive
volumes of structured and unstructured data on a daily basis. Also, social networks, such as
Twitter, Facebook, and Google+, are becoming a new source of real-time information in smart
cities. Social network users are acting as social sensors. These datasets so large and complex
are difficult to manage with conventional data management tools and methods. To become
valuable, this massive amount of data, known as 'big data,' needs to be processed and
comprehended to hold the promise of supporting a broad range of urban and smart cities
functions, including among others transportation, water, and energy consumption, pollution
surveillance, and smart city governance. In this work, we investigate how social media analytics
help to analyze smart city data collected from various social media sources, such as Twitter and
Facebook, to detect various events taking place in a smart city and identify the importance of
events and concerns of citizens regarding some events. A case scenario analyses the opinions of
users concerning the traffic in three largest cities in the UAE
SOCIAL NETWORK HATE SPEECH DETECTION FOR AMHARIC LANGUAGEcscpconf
The anonymity of social networks makes it attractive for hate speech to mask their criminal
activities online posing a challenge to the world and in particular Ethiopia. With this everincreasing
volume of social media data, hate speech identification becomes a challenge in
aggravating conflict between citizens of nations. The high rate of production, has become
difficult to collect, store and analyze such big data using traditional detection methods. This
paper proposed the application of apache spark in hate speech detection to reduce the
challenges. Authors developed an apache spark based model to classify Amharic Facebook
posts and comments into hate and not hate. Authors employed Random forest and Naïve Bayes
for learning and Word2Vec and TF-IDF for feature selection. Tested by 10-fold crossvalidation,
the model based on word2vec embedding performed best with 79.83%accuracy. The
proposed method achieve a promising result with unique feature of spark for big data.
GENERAL REGRESSION NEURAL NETWORK BASED POS TAGGING FOR NEPALI TEXTcscpconf
This article presents Part of Speech tagging for Nepali text using General Regression Neural
Network (GRNN). The corpus is divided into two parts viz. training and testing. The network is
trained and validated on both training and testing data. It is observed that 96.13% words are
correctly being tagged on training set whereas 74.38% words are tagged correctly on testing
data set using GRNN. The result is compared with the traditional Viterbi algorithm based on
Hidden Markov Model. Viterbi algorithm yields 97.2% and 40% classification accuracies on
training and testing data sets respectively. GRNN based POS Tagger is more consistent than the
traditional Viterbi decoding technique.
Communicating effectively and consistently with students can help them feel at ease during their learning experience and provide the instructor with a communication trail to track the course's progress. This workshop will take you through constructing an engaging course container to facilitate effective communication.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
2. 34 Computer Science & Information Technology (CS & IT)
Since the Sharpe ratio has been derived in 1966 by William Sharpe [24], it has been one of the
most referred risk/return measures used in finance, and much of this popularity can be attributed
to its simplicity. The ratio's credibility has been boosted further when Professor Sharpe won a
Nobel Memorial Prize in Economic Sciences in 1990. The Sharpe ratio is defined as the ratio
between the risk premium and the standard deviation. It is a risk-adjusted measure of return that is
often used to evaluate the performance of a portfolio. The ratio helps to make the performance of
one portfolio comparable to that of another portfolio by making an adjustment for risk. The idea
of the ratio is to see how much additional return you are receiving for the additional volatility of
holding the risky asset over a risk-free asset - the higher the better. However, the problem with
Sharpe ratio is the presence of standard deviation in the formula. The issue with this formula lies
in its application to investments or securities that do not have normally distributed returns.
Nevertheless, consideration of positively skewed returns can solve this problem.
In most of the research works on portfolio selection, the common assumptions are that the
investor have enough historical data and that the situation of asset markets in future can be
reflected with certainty by asset data in past. However, it cannot always be made with certainty.
The usual feature of financial environment is uncertainty. Mostly, it is realized as risk uncertainty
and is modelled by stochastic approaches. However, the term uncertainty has the second aspect-
vagueness (imprecision or ambiguity) which can be modelled by fuzzy methodology. In this
respect, to tackle the uncertainty in financial market, fuzzy, stochastic-fuzzy and fuzzy-stochastic
methodologies are extensively used in portfolio modelling. By incurring fuzzy approaches
quantitative analysis, qualitative analysis, experts’ knowledge and investors’ subjective opinions
can be better integrated into a portfolio selection model. Authors like Konno and Suzuki [25],
Leon et al. [26], Vercher [27], Bhattacharyya et al. [12] and others use fuzzy numbers to embody
uncertain returns of the securities and they define the portfolio selection as a mathematical
programming problem in order to select the best alternative. In possibilistic portfolio selection
models, two types of approaches are noticed. The return of a security is considered either as a
possibilistic variable or as a fuzzy number. In the later case, the possibilistic moments of the
fuzzy numbers are considered. Possibilistic portfolio models integrate the past security data and
experts’ judgment to catch variations of stock markets more plausibly. Tanaka and Guo [28]
propose two kinds of portfolio selection models by utilizing fuzzy probabilities and exponential
possibility distributions, respectively. Inuiguchi and Tanino [29] introduce a possibilistic
programming approach to the portfolio selection problem under the minimax regret criterion. Lai
et al. [30], Wang and Zhu [31] and Giove et al. [32] construct interval-programming models for
portfolio selection. Ida [33] investigates portfolio selection problem with interval and fuzzy
coefficients, two kinds of efficient solutions are introduced: possibly efficient solution as an
optimistic solution, necessity efficient solution as a pessimistic solution. Carlsson et al. [34]
introduce a possibilistic approach for selecting portfolios with the highest utility value under the
assumption that the returns of assets are trapezoidal fuzzy numbers. Fang et al. [35] propose a
portfolio-rebalancing model with transaction costs based on fuzzy decision theory. Wang et al.
[36] and Zhang and Wang [37] discuss the general weighted possibilistic portfolio selection
problems. Moreover, Lacagnina and Pecorella [38] develop a multistage stochastic soft
constraints fuzzy program with recourse in order to capture both uncertainty and imprecision as
well as to solve a portfolio management problem. Lin et al. [39] propose a systematic approach
by incorporating fuzzy set theory in conjunction with portfolio matrices to assist managers in
reaching a better understanding of the overall competitiveness of their business portfolios. Huang
[40] presents two portfolio selection models with fuzzy returns by criteria of chance represented
by credibility measure. Fei [41] studies the optimal consumption and portfolio choice with
ambiguity and anticipation. Zhang et al. [42] assume that the rates of return of assets can be
expressed by possibility distribution. They propose two types of portfolio selection models based
on upper and lower possibilistic means and possibilistic variances and introduce the notions of
lower and upper possibilistic efficient portfolios. Li and Xu [43] deal with a possibilistic portfolio
selection problem with interval center values. Parra et al. [44] introduce vague goals for return
3. Computer Science & Information Technology (CS & IT) 35
rate, risk and liquidity based on expected intervals. Terol et al. [45] formulate a fuzzy
compromise programming to the mean-variance portfolio selection problem. Huang [46] proposes
a mean-semivariance model for describing the asymmetry of fuzzy returns. Huang [47] extends
the risk definition of variance and chance to a random fuzzy environment and formulates
optimization models where security returns are fuzzy random variables.
In this paper, we have defined the possibilistic Sharpe ratio as the ratio between the possibilistic
risk premium and possibilistic standard deviation. First, we have considered a bi-objective
optimize problem that maximizes the possibilistic Sharpe ratio as well as the possibilistic
skewness of the portfolio. As the Sharpe ratio prefers symmetric distribution, we maximize the
skewness to get rid of this problem and to give more preference to positively skewed returns. We
also have proposed six more models that represent different scenarios.
The construction of the paper is as follows. In section 2, the possibilistic mean, standard
deviation, skewness and risk premium of fuzzy numbers are derived. The outcomes are used to
define the possibilistic Sharpe ratio. In section 3, the portfolio selection models are modelled. In
section 4, a multiple objective genetic algorithm is discussed. In section 5, an example is provided
to illustrate the feasibility and effectiveness of the proposed model using stock price data in the
form of triangular fuzzy numbers extracted from Bombay Stock exchange (BSE). Finally, in
section 6, some conclusions are specified.
2. POSSIBILISTIC MOMENTS AND POSSIBILISTIC RISK AVERSION
Possibility theory is introduced by Zadeh [48] as an alternative to probability theory in the
treatment of uncertainty. Fuzzy numbers represent a significant class of possibility distribution.
The operations with fuzzy numbers can be done by Zadeh’s extension principle. Different authors
have dealt with the notion of possibilistic moments of fuzzy numbers in different times. Dubois
and Prade [49] propose the concept of interval-valued expectation of fuzzy numbers. Calsson and
Fuller [50] define the possibilistic mean and variance of a fuzzy number where Fuller and
Majlender [51] define the weighted possibilistic mean and variance of a fuzzy number. Liu and
Liu [52] propose a definition of expected value of a fuzzy variable based on the possibilistic
notion of credibility measure. Saeidifar and Pasha [53] define possibilistic moments of fuzzy
numbers. Georgescu [54] proposes risk aversion by possibility theory and find out the risk
premium by defining a new notion of possibilistic variance.
Definition 2.1 [54] Let A% be a fuzzy number. Also let its α-level set 1 2[ A] [a ( ),a ( )]=α
α α% be
such that 1 2a ( ) a ( ).≠α α The central value of [ A]α% is defined as
a ( )2
2 1a ( )12 1
1 1
C([ A] ) xdx ( a ( ) a ( )).
a ( ) a ( ) 2
= = +
− ∫
αα
α
α α
α α
%
Definition 2.2 [50] The expected value of a fuzzy number A% is defined by
1
2 10
E( A) [ a ( ) a ( )]d .= +∫ α α α α%
Definition 2.3 [50] The variance of a fuzzy number A% is defined by
4. 36 Computer Science & Information Technology (CS & IT)
1 1
2 2
1 1 2 2
0 0
1
2 2
1 2
0
Var( A) POS[A a ( )][a ( ) E( A)] d POS[A a ( )][a ( ) E( A)] d
= ([a ( ) E( A)] [a ( ) E( A)] )d
α α α α α α
α α α α
= ≤ − + ≥ −
− + −
∫ ∫
∫
% % % % %
% %
Definition 2.4 The skewness of a fuzzy number A% is defined by
3
3
M ( A)
Skew( A) ,
( Var( A))
=
%
%
%
where,
1 1
3 3
3 1 1 2 2
0 0
1
3 3
1 2
0
M ( A) POS[ A a ( )][a ( ) E( A)] d POS[ A a ( )][a ( ) E( A)] d
= ([a ( ) E( A)] [a ( ) E( A)] )d .
= ≤ − + ≥ −
− + −
∫ ∫
∫
α α α α α α
α α α α
% % % % %
% %
Definition 2.5 [54] The possibilistic risk premium A A, u=ρ ρ% % associated with the fuzzy
number A% and the utility function u is defined by,
Au( E( A) ) E(u( A)).− =ρ %
% %
Let us assume that the utility function u is twice differentiable, strictly concave and increasing.
Then the possibilistic risk premium A
ρ % has the form
A
1 u ( E( A))
V( A) ,
2 u ( E( A))
′′
≈ −
′
ρ %
%
%
%
where,
a ( )1 2
2
2 10 a ( )1
1
2 2
2 1 2 1
0
1
2 2
2 2 1 1 2 1
0
1
V( A) 2 ( x E( A)) dx d
a ( ) a ( )
2
= (a ( ) E( A)) (a ( ) E( A)) (a ( ) E( A))(a ( ) E( A)) d
3
2
= (a ( ) a ( )a ( ) a ( )) d 3E( A) (a ( ) a ( )) d
3
α
α
α α
α α
α α α α α α
α α α α α α α α α
= −
−
− + − + − −
+ + − +
∫ ∫
∫
∫
% %
% % % %
%
1 1
2
0 0
1
2 2 2
2 2 1 1
0
3E ( A) d
2
= (a ( ) a ( )a ( ) a ( )) d E ( A).
3
α α α
α α α α α α
+
+ + −
∫ ∫
∫
%
%
Depending on different forms of the utility function, the risk premium will have different forms.
5. Computer Science & Information Technology (CS & IT) 37
Exponential utility of the form ( ) x
u x 1 e= − κ
is unique in exhibiting constant absolute risk
aversion (CARA) and has been used successfully in portfolio selection problem. In this literature,
we consider the utility function as u(x) = 1 − e− 2x
. Then we have,
2x
2x
u ( E( A)) 4e
2;
u ( E( A)) 2e
−
−
′′ −
= = −
′
%
%
so that, A V( A).≈ρ %
%
Definition 2.6 We define the possibilistic Sharpe ratio (PSR) of a fuzzy number A% as,
A V( A)
PSR( A) .
Var( A) Var( A)
= =
ρ % %
%
% %
Example 2.7 Let A ( a,b,c )=% be a triangular fuzzy number. Then we have,
2 2 2
3 3 3 2 2 2
2 2 2 3
2 2 2
A
2 2 2
2 2
a 4b c
E( A) ,
6
1
Var( A) [ a +b +c ab bc ca],
18
[19(a c ) 8b 42b(a c ) (12b 15ac)(a c) 60abc]
Skew(A) ,
10 2( a +b +c ab bc ca)
1
[a +2b +c 2ab 2bc],
36
a +2b +c 2ab 2bc
PSR
6 2 a +b +c
+ +
=
= − − −
+ − − + + − + +
=
− − −
= − −
− −
=
ρ%
%
%
%
2
.
ab bc ca− − −
The results are obtained by definitions 2.2, 2.3, 2.4, 2.5 and 2.6.
3. POSSIBILISTIC PORTFOLIO SECTION MODEL
Let for i = 1, 2, …, n,
xi = the portion of the total capital invested in security i;
ip =% fuzzy number representing the closing price of the ith
security at present;
'
ip =% fuzzy number representing the estimated closing price of the ith
security for the next year;
di = fuzzy number representing the estimated dividend of the ith
security for the next year;
ir =% fuzzy number representing the return of the ith
security
'
i i i
i
p + d p
= .
p
−
As per discussion in the Introduction, we propose the following portfolio selection model in fuzzy
environment.
6. 38 Computer Science & Information Technology (CS & IT)
1 1 2 2 n n
1 1 2 2 n n
1 1 2 2 n n
n
i
i 1
i
Maximize PSR[r x r x .... r x ]
Maximize Skew[r x r x .... r x ]
subject to
E[r x r x .... r x ]( 3.1)
x 1
x 0,i 1,2,...,n.
α
=
+ + +
+ + +
+ + + ≥
=
≥ =
∑
% % %
% % %
% % %
The constraint 1 1 2 2 n nE[r x r x .... r x ] α+ + + ≥% % % ensures that the expected return of the portfolio is
no less than a minimum desired value α. The second constraint
n
i
i 1
x 1
=
=
∑ is the well-known
capital budget constraint on the assets. The last constraint [ ]ix 0 i≥ ∀ ensures that no short selling
is allowed in the portfolio here.
Note: The following models (3.2), (3.3), (3.4), (3.5), (3.6), (3.7) can also be considered by the
investors depending on their priorities.
1 1 2 2 n n
1 1 2 2 n n
1 1 2 2 n n
n
i
i 1
i
Maximize PSR[r x r x .... r x ]
Maximize E[r x r x .... r x ]
subject to
Skew[r x r x .... r x ](3.2)
x 1
x 0,i 1,2,...,n.
β
=
+ + +
+ + +
+ + + ≥
=
≥ =
∑
% % %
% % %
% % %
1 1 2 2 n n
1 1 2 2 n n
1 1 2 2 n n
n
i
i 1
i
Maximize Skew[r x r x .... r x ]
subject to
E[r x r x .... r x ]
PSR[r x r x .... r x ]( 3.3 )
x 1
x 0,i 1,2,...,n.
α
γ
=
+ + +
+ + + ≥
+ + + ≥
=
≥ =
∑
% % %
% % %
% % %
1 1 2 2 n n
1 1 2 2 n n
1 1 2 2 n n
n
i
i 1
i
Maximize PSR[r x r x .... r x ]
subject to
E[r x r x .... r x ]
Skew[r x r x .... r x ](3.4 )
x 1
x 0,i 1,2,...,n.
α
β
=
+ + +
+ + + ≥
+ + + ≥
=
≥ =
∑
% % %
% % %
% % %
7. Computer Science & Information Technology (CS & IT) 39
1 1 2 2 n n
1 1 2 2 n n
1 1 2 2 n n
n
i
i 1
i
Maximize E[r x r x .... r x ]
subject to
PSR[r x r x .... r x ]
Skew[r x r x .... r x ]( 3.5 )
x 1
x 0,i 1,2,...,n.
γ
β
=
+ + +
+ + + ≥
+ + + ≥
=
≥ =
∑
% % %
% % %
% % %
and
1 1 2 2 n n
1 1 2 2 n n
1 1 2 2 n n
n
i
i 1
i
Maximize PSR[r x r x .... r x ]
Maximize E[r x r x .... r x ]
Maximize Skew[r x r x .... r x ]
subject to( 3.6 )
x 1
x 0,i 1,2,...,n.
=
+ + +
+ + +
+ + +
=
≥ =
∑
% % %
% % %
% % %
where the values of α, β, γ would be specified by the investors according to their needs.
Theorem 3.1 Suppose i i i ir = (a , b , c ), [i=1,2,...,n]% are independent triangular fuzzy numbers.
Then the model (3.1) generates the multi-objective programming problem model (3.8).
2 2 2n n n n n
i i i i i i i i i i
i = 1 i = 1 i = 1 i = 1 i = 1
n n n
i i i i i i
i = 1 i = 1 i = 1
1
M aximize a x + b x + c x a x b x
6 2
b x c x c x
( 3.7 )
−
− −
∑ ∑ ∑ ∑ ∑
∑ ∑ ∑
1
2 2
n n n2
i i i i i i
i = 1 i = 1 i = 1
2n n n n n
i i i i i i i i i i
i = 1 i = 1 i = 1 i = 1 i = 1
a x a x + 2 b x
+ c x 2 a x b x 2 b x c x
−
− −
∑ ∑ ∑
∑ ∑ ∑ ∑ ∑
2 2 2
n n n n n
i i i i i i i i i i
i = 1 i = 1 i = 1 i = 1 i = 1
n n n
i i i i i i
i = 1 i = 1 i = 1
1
M aximize a x + b x + c x a x b x
10 2
b x c x c x
−
− −
∑ ∑ ∑ ∑ ∑
∑ ∑ ∑
3
3 3n n n2
i i i i i i
i = 1 i = 1 i = 1
3 2n n n n
i i i i i i i i
i = 1 i = 1 i = 1 i = 1
a x 19 a x c x
8 b x 42 b x a x c x
− +
− − +
∑ ∑ ∑
∑ ∑ ∑ ∑
2
2
n n n n n
i i i i i i i i i i
i = 1 i = 1 i = 1 i = 1 i = 1
n n
i i i i
i = 1 i = 1
12 b x 15 a x c x a x c x
+ 60 a x b x
+
− +
∑ ∑ ∑ ∑ ∑
∑ ∑
n
i i
i = 1
n n n n
i i i i i i i i
i = 1 i = 1 i = 1 i 1
c x
subject to
1
a x + 4 b x + c x , x 1, x 0 ,i 1,2 ,...,n.
6
α
=
≥ = ≥ =
∑
∑ ∑ ∑ ∑
8. 40 Computer Science & Information Technology (CS & IT)
Proof: Since i i i ir = (a , b , c )% are triangular fuzzy numbers, by extension Principle of Zadeh it
follows that
n n n
1 1 2 2 n n i i i i i i
i = 1 i = 1 i = 1
r x r x .... r x = a x , b x , c x ,
+ + +
∑ ∑ ∑% % % which is also a fuzzy
number. Combining this with the results obtained in example 2.7, we are with the theorem.
4. MULTIPLE OBJECTIVE GENETIC ALGORITHM
The proposed portfolio selection model (3.7) is solved by using Multiple Objective Genetic
Algorithm (MOGA). The MOGA proposed by Bhattacharyya et al. [55] is followed.
The following are followed for the development of the MOGA for the proposed model (3.7).
Representation: An n-dimensional real vector X = {x1, x2, …, xn} is used to represent a solution
where each ix [0,1], i =1, 2,..., n.∈
Initialization: L such solutions X1, X2, …, XL are randomly generated such that each of them
satisfies the constraints of the model. This solution set is the set P.
Cross Over and Mutation: Crossover operator is mainly responsible for the search of new strings.
Crossover operates on two parent solutions at a time and generates offspring solutions by
recombining both parent solution features. After selection of chromosomes for new population,
the crossover operator is applied. Here the arithmetic cross over is used. Mutation is the unary
operation by which genes present in a chromosome are changed. Here the usual mutation
procedure is followed.
Proposed multi-objective genetic algorithm has the following two important components:
(a) Consider a population P of feasible solutions of (3.7) of size L. We like to partition P into
subsets F1, F2, ..., Fk, such that every subset contain non-dominated solutions, but every solutions
of Fi are not dominated by any solution of Fi+1, for i = 1,2,..., k-1. Let the number of solutions of
P which dominate x is nx and the set of solutions of P that are dominated by x is Sx. Note that, as
there are two objective functions, these require O(2L2
) computations.
(b) To determine the distance of a solution from other solutions of a subset first sort the
subset according to each objective function values in ascending order of magnitude. For both
objective functions, the boundary solutions are assigned an infinite distance value (a large value).
All other intermediate solutions are assigned a distance value for the objective, equal to the
absolute normalized difference in the objective values of two adjacent solutions. The overall
distance of a solution from others is calculated as the sum of individual distance values
corresponding to each objective.
For detailed discussions on ‘the division of P(T) into disjoint subsets having non-dominated
solutions’ and ‘distance of a solution of subset F from other solutions’, Roy et al. [56] can be
consulted.
Since two independent sorting of at most L solutions (in case the subset contains all the solutions
of the population) are involved, the above algorithm has O(2Llog L) computational complexity.
9. Computer Science & Information Technology (CS & IT) 41
Using the above two operations proposed multi-objective genetic algorithm is formulated as:
1. Set probability of crossover Pc and probability of mutation Pm.
2. Set iteration counter T =1.
3. Generate initial population set of solution P(T) of size L.
4. Select solution P(T) for crossover and mutation.
5. Made crossover and mutation on selected solution and get the child set C(T).
6. Set 1P P(T ) C(T ).= U
7. Divide P1 into disjoint subsets having non-dominated solutions. Let these sets be F1, F2, ..., Fk.
8. Let 2 1 2 nP F F ... F .= U U U Select maximum integer L such that 2O( P ) L.≤
9. If O(P2) < L sort solutions of Fn+1 in descending order of their distance from other solutions of
the subset. Then select first L - O(P2) solutions from Fn+1 and add with P2.
10. Set T = T + 1 and P(T) = P2.
11. Go to step-4 if termination condition does not hold.
12. Output: P(T)
13. End algorithm.
Since in the above algorithm computational complexity of step-7 is O(2L2
), step-9 is O(2NlogN)
and other steps are ≤ O(N), so overall time complexity of the algorithm is O(2N2
).
In this MOGA, selection of new population after crossover and mutation on old population is
done by creating a mating pool by combining the parent and offspring population and among
them best L solutions are taken as solutions of new population. By this way, elitism is introduced
in the algorithm.
5. CASE STUDY: BOMBAY STOCK EXCHANGE
In this section we apply our portfolio selection model on the data set extracted from Bombay
stock exchange (BSE). Bombay Stock Exchange is the oldest stock exchange in Asia with a rich
heritage of over 133 years of existence. What is now popularly known as BSE was established as
"The Native Share & Stock Brokers' Association" in 1875. It is the first stock exchange in India
which obtained permanent recognition (in 1956) from the Government of India under the
Securities Contracts (Regulation) Act (SCRA) 1956. Today, BSE is the world's number 1
exchange in terms of the number of listed companies and the world's 5th in handling of
transactions through its electronic trading system. The companies listed on BSE command a total
market capitalization of USD Trillion 1.06 as of July, 2009. The BSE Index, SENSEX, is India's
first and most popular stock market benchmark index. Sensex is tracked worldwide. It constitutes
30 stocks representing 12 major sectors. It is constructed on a 'free-float' methodology, and is
sensitive to market movements and market realities. Apart from the SENSEX, BSE offers 23
indices, including 13 sectoral indices.
We have taken monthly share price data for sixty months (March 2003- February 2008) of just
five companies which are included in BSE index. Though any number of stocks can be
considered, we have taken only five stocks to reduce the complexity. The Table 5.1 shows the
companies name along with their return in the form of trapezoidal uncertain numbers.
10. 42 Computer Science & Information Technology (CS & IT)
Table 5.1 Stocks and their returns
Company Return ( ir% )
Reliance Energy (RE) (-0.008, 0.031,0.067)
Larsen and Toubro (LT) (-0.003, 0.043, 0.087)
Tata Steel (TS) (0.009, 0.030, 0.052)
Bharat Heavy Electricals Limited (BH) (-0.002, 0.036, 0.083)
State Bank if India (SB) (-0.010, 0.033, 0.079)
With respect to the above data, we consider the following portfolio selection model:
1 1 2 2 3 3 4 4 5 5
1 1 2 2 3 3 4 4 5 5
1 1 2 2 3 3 4 4 5 5
1 2 3 4 5
1 2 3 4 5
Maximize PSR[r x r x r x r x r x ]
Maximize Skew[r x r x r x r x r x ]
subject to
( 5.1)
E[r x r x r x r x r x ] 0.04
x ,x ,x ,x ,x 0
x +x +x +x +x 1
+ + + +
+ + + +
+ + + + ≥
≥
=
% % % % %
% % % % %
% % % % %
We apply theorem 3.1 to convert model (5.1) into the deterministic model (3.7). To solve it, the
proposed MOGA is used. The cross over and mutation probabilities are chosen as 0.6 and 0.2
respectively. The number of iterations is 100. The solution is obtained as shown in Table 5.2.
Table 5.2 Portfolio
x1 x2 x3 x4 x5
0.3936170 0.00000 0.00000 0.6000000 0.00638296
Table 5.2 shows that the investor should invest 39.36%, 60% and 0.64% of the total money in the
1st
, 4th
and 5th
stocks. The portfolio is explained via the pie chart given in Figure 5.2.
Figure 5.2 Portfolio
11. Computer Science & Information Technology (CS & IT) 43
6. CONCLUSIONS
In this paper, a new framework of fuzzy portfolio selection is introduced. Instead of following the
return-risk framework, this work concentrates on the risk-aversion nature of the investors and set
up a possibilistic Sharpe ratio –skewness portfolio selection problem. To do so, the possibilistic
Sharpe ratio is defined. As the Sharpe ratio prefers symmetric distribution, we consider skewness
to get rid of this drawback. The model is tested on a data set collected from BSE.
In near future, we will apply these portfolio selection models and solution method to other asset
allocation problems, combinational optimization models and multi-period problems to find
optimal investment strategy under complex market situations. Some other algorithms such as
ACO (ant colony optimization), PSO (particle swarm optimization), VEGA (vector evaluation
genetic algorithm), NEGA (Nondominated sorting genetic algorithm), NPGA (Niched Pareto
genetic algorithm) and PAES (Pareto archived evolution strategy) may be employed to solve the
problem, especially when the data set is significantly large.
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AUTHORS
Dr. Rupak Bhattacharyya is presently working as an Associate Professor in Mathematics
and HOD in the Department of Applied Science & Humanities of Global Institute of
Management & Technology, Krishnagar, West Bengal, India. Dr. Bhattacharyya received
his PhD degree from National Institute of Technology, Durgapur. The Operational
Research Society of India confers the Professor M N Gopalan Award on Dr.
Bhattacharyya for the best Doctoral Thesis in Operational Research of 2011. He has more
than eight years of teaching and research experience. He has more than 20 International
publications. Details on Dr. Bhattacharyya can be viewed from: https://sites.google.com/site/rupakmath.