Stock Portfolio Selection using Genetic Algorithm
|
|
- Derrick Jordan
- 6 years ago
- Views:
Transcription
1 Chapter 5. Stock Portfolio Selection using Genetic Algorithm In this study, a genetic algorithm is used for Stock Portfolio Selection. The shares of the companies are considered as stock in this work. In the first stage good quality of stocks are identified by stock ranking. In the second stage investment allocation in the selected good quality stocks is optimized using genetic algorithm. Hence by using genetic algorithm an optimal portfolio can be determined. This application provides a very feasible and useful tool to assist the investors in planning their investment strategy and constructing their portfolio. 5.1 Markowitz Portfolio Theory Modern portfolio theory (MPT) is a theory of finance which attempts to maximize portfolio expected return for a given amount of portfolio risk, or equivalently minimize risk for a given level of expected return, by carefully choosing the proportions of various assets. Although MPT is widely used in practice in the financial industry and several of its creators won a Nobel memorial prize for the theory, in recent years the basic assumptions of MPT have been widely challenged by fields such as behavioral economics. MPT is a mathematical formulation of the concept of diversification in investing, with the aim of selecting a collection of investment assets that has collectively lower risk than any individual asset. That this is possible can be seen intuitively because different types of assets often change in value in opposite ways. For example, to the extent prices in the stock market move differently from prices in the bond market, a collection of both types of assets can in theory face lower overall risk than either individually. But diversification lowers risk even if assets returns are not negatively correlated indeed, even if they are positively correlated. More technically, MPT models assets return as a normally distributed function (or more generally as an elliptically distributed random variable), define risk as the standard 159
2 deviation of return, and model a portfolio as a weighted combination of assets, so that the return of a portfolio is the weighted combination of the assets' returns. By combining different assets whose returns are not perfectly positively correlated, MPT seeks to reduce the total variance of the portfolio return. MPT also assumes that investors are rational and markets are efficient. MPT was developed in the 1950s through the early 1970s and was considered an important advance in the mathematical modeling of finance. Since then, many theoretical and practical criticisms have been leveled against it. These include the fact that financial returns do not follow a Gaussian distribution or indeed any symmetric distribution, and that correlations between asset classes are not fixed but can vary depending on external events (especially in crises). Further, there is growing evidence that investors are not rational and markets are not efficient. 5.2 Design and Implementation Genetic algorithms are probabilistic, robost and heuristic search algorithms premised on the evolutionary ideas of natural selection and genetic. The basic concept of genetic algorithms is designed to simulate the processes in natural system necessary in for evolution, specifically for those that follow the principle of survival of the fittest. They represent the intelligent exploitation of a random search within a defined search space to solve a problem. Genetic Algorithm is developed by John Holland and his students at Michigan University during Implementation of Problem The Complete Genetic Algorithm design has been prepared for the problem in C language and now it is applied to some real data. Total 32 listed Companies are considered as a data set. Financial indicators namely ROCE, LR and P/E Ratio are given as a input to stock ranking model. Here share is considered as stock for our problem. By applying Stock Ranking Model with the financial indicators ROCE, LR and P/E Ratio for 160
3 consecutively 5 years (2007 to 2011) as an input to genetic algorithm, rank of a particular company is obtained. After getting the rank and applying sorting to ranks we select top 10 companies as a input to genetic algorithm. Input Size: Top 10 companies as objects are taken as input to genetic algorithm. Hence the chromosome size will be 10. Encoding Scheme: Value Encoding has been applied to the problem. Fitness Function: The total value of the permutation if its weight is max capacity. Parent Selection: After finding the fitness value of each member of the population first Elitism is applied and few best chromosomes are selected and copied to the new generation and then Roulette wheel selection is applied to copy rest of the population. Crossover/ Mutation point: Whether to do the crossover or mutation is determined by generating a random number and comparing it with the user entered probability. Once it is decided to do crossover/mutation, the crossover/mutation points are also determined randomly, by generating a random number. Here one point crossover and exchange a position is used for crossover and mutation. 161
4 5.3 Stock Ranking Model The aim of this stage is to identify the quality of each stock so that investors can choose some good ones for investment by using stock ranking. In this study, total 32 companies are considered as shown in table 5.1. Some financial indicators of these listed companies are employed to determine and identify the quality of each stock. That is, the financial indicators of the companies are used as input variables while a score is given to rank the stocks. The output variable is stock rank. Through the study of Markowitz Portfolio Theory, three important financial indicators, Return On Capital Employed (ROCE), Price/Earnings ratio (P/E Ratio), and Liquidity Ratio are utilized in this study. The real data for the year 2007 to 2011 are utilized in this study. The real data is described in Annexure 1. The Stock Ranking Model is developed in C language. The definition of financial indicators are given as follows Financial Indicators ROCE = (Profit) / (Shareholder s Equity) * 100 % (5.1) P/E Ratio = (Stock Price) / ( Earning Per Share) * 100 % (5.2) Liquidity Ratio = (Current Assets) / (Current Liabilities) * 100 % (5.3) Table 5.1 Companies with its Sr.No. and Company Name Sr. No. Company Name 1 3I Infotech 2 Aarti Drugs 3 Ashok Leyland 4 Cipla 162
5 5 Bajaj Telefims 6 Crisil 7 Blue Star 8 Fulford (India) 9 Gujarat Gas 10 JSW Steel 11 Sun Pharmaceutical Industries 12 Sonata Software 13 UltraTech Cement 14 Aptech 15 Amtek India 16 Apollo Tyres 17 Bharti Airtel 18 Cholamandalam I & FC 19 Crompton Greaves 20 Finolex Industries 21 Gabriel India 22 Kansai Nerolac Paints 23 Granules India 24 Graphite India 25 India Oil Corporation 26 Infomedia Kajaria Ceramics 28 Surya Roshni 29 Tata Steel 30 Uttam Galva Steel 31 UTV Software Communications 32 ACC 163
6 Status Here 8 statuses are designed representing different qualities in terms of different interval varying from 0 ( Extremely Poor ) to 7 ( Very Good ). The statuses for financial indicators are as shown in the status table 5.2 below. Table 5.2 Status Table VALUE STATUS - to -30 % 0-30 % to -10 % 1-10 % to +10 % 2 10 % to 30 % 3 30 % to 50 % 4 50 % to 70 % 5 70 % to 90 % 6 90 % to + 7 The output of the three financial indicators for each year is compared with the status table to get individual ranking of each financial indicator. After obtaining individual rank of each financial indicator for each year all are added to get rank. After getting the rank of each year, all the individual rank of all years are again added. After adding all the individual rank of all years an average is taken to get the final rank. Here the lowest final rank is 0 and highest final rank is 21. Hence in this way final stock rank of each and every company is obtained. After getting final stock rank of each and every company sorting of final ranks id done to get top 10 companies. 164
7 5.4 Stock Portfolio Selection using Knapsack Problem with Genetic Algorithm Knapsack Problem The knapsack problem is defined as follows: We are given a set of n items, each item j having an integer profit pj and an integer weight wj. The problem is to choose a subset of the items such that their overall profit is maximized, while the overall weight does not exceed a given capacity c. We may formulate the model as the following integer programming model: maximize n j =1 p j x j (5.4) subject to n w j j =1 x j C where x j 0,1, j = 1,2,, n where the binary decision variables x j are used to indicate whether item j is included in the knapsack or not. Without loss of generality it may be assumed that all profits and weights are positive, that all weights are smaller than the capacity c, and that the overall weight of the items exceeds c. Knapsack problem is one of the most intensively studied discrete programming problems. The reason for such interest basically derives from three facets. (a) (b) (c) It can be viewed as the simplest Integer Linear Programming problem It appears as a sub-problem in many more complex problems It may represent a great many practical situation. 165
8 In the previous stage, some good quality stocks can be revealed in terms of stock return ranking. These good qualities of stocks are used as an input to genetic algorithm. The three basic questions regarding investment decision occurs as follows:- In which company should I invest? How much money in which company should I invest? Steps of Genetic Algorithm for Stock Portfolio Selection:- 1) Generate initial random population. 2) Calculate the fitness value of each chromosome. 3) Fitness value = (Profit)*(Units) where Profit = ((Current share value Previous share value) / previous share value) 1 Unit = Rs Maximum Units = 100 Total Investment = Rs. 1 Lac 4) Apply Roulette wheel selection method to select good chromosomes. 5) Apply crossover and mutation to good chromosomes. 6) Again calculate fitness value of chromosomes. 7) Repeat from step 2 to 6 until a best chromosome is found. 166
9 After completing genetic algorithm the answers of all the three questions specified above are obtained Selection Criteria Encoding Technique : Value Encoding Number of Chromosomes : 30 Number of Companies : 10 Number of Units = 100 Maximum Investment = Rs. 1 Lac Minimum Crossover Probability :0.60 Maximum Crossover Probability : 0.95 Minimum Mutation Probability : Maximum Mutation Probability : 1.00 Selection Method: - Roulette Wheel Selection Crossover Method: - 1-point crossover Mutation Method:- Exchange a position 167
10 5.5 Computational Results Results of Stock Ranking Model Here the stock ranking model gives the output as top 10 companies with its Sr.No. Rank and Company Name in table 5.3. Now from the 32 companies, 18 companies are listed below as a top 10 companies. Below listed companies are considered for the investment for the investor. The investor can select any 10 companies from the below given 18 companies for the investment. Table 5.3 Top 10 companies with its Sr.No. Rank and Company Name Sr. No. Rank Company Name 2 21 Aarti Drugs 4 21 Cipla 5 21 Balaji Telefims 6 21 Crisil 7 21 Blue Star 8 21 Fulford (India) JSW Steel Sun Pharmaceutical Industries Ultratech Cement Apollo Tyres Crompton Greaves Gabriel India Kansai Nerolac Paints Graphite India India Oil Corporation Kajaria Ceramics Tata Steel ACC 168
11 5.5.2 Stock Portfolio Selection From the table 5.3 top 10 companies can be selected. Here A to J represents the top 10 companies from as shown in table 5.4. The description of these top 10 companies is as shown in table 5.4. Table 5.4 Top 10 companies with its company name Sr. No. Company Company Name 1 A Aarti Drugs 2 B Cipla 3 C Balaji Telefims 4 D Crisil 5 E Fulford (India) 6 F JSW Steel 7 G Sun Pharmaceutical Industries 8 H Crompton Greaves 9 I India Oil Corporation 10 J Tata Steel 169
12 Executing Genetic Algorithm By executing the Genetic Algorithm the Initial Random Population is generated which is shown below in table 5.5. Here companies are A to J. The values of A to J are represented in Units. 1 Unit is Rs Total represents the total investment in rupees (thousands) in the various companies A to J. Fitness represents the fitness value of the chromosome. The initial random population gives the output Best Chromosome, Maximum Fitness and Total investment as shown in table 5.5. Table 5.5 Initial Random Population generated by Genetic Algorithm A B C D E F G H I J Total Fitness
13 Best Chromosome = Maximum Fitness = at Total =
14 5.5.3 Results at different Crossover Probability and Mutation Probability For, Minimum Crossover Probability = 0.00 Maximum Crossover Probability = 1.00 Minimum Mutation Probability = Maximum Mutation Probability = 0.08 Results for 800 generations Best chromosome = Maximum Fitness = at Total = 62 Results for 1000 generations Best chromosome = Maximum Fitness = at Total = 64 Results for 1500 generations Best chromosome = Maximum Fitness = at Total = 75 Results for 2000 generations Best chromosome = Maximum Fitness = at Total =
15 Results for 3000 generations Best chromosome = Maximum Fitness = at Total = 82 Results for 4000 generations Best chromosome = Maximum Fitness = at Total = 82 Results for 5000 generations Best chromosome = Maximum Fitness = at Total = 82 From the above results it is found that at generations 800, 1000, 1500 and 2000 we are getting variations in Best chromosome, Maximum Fitness and Total investment. But at generations 3000, 4000 and 5000 we are getting the same Best chromosome, Maximum Fitness and Total investment. This concludes that at generations 800, 1000, 1500 and 2000 investor may not take decision for investment in various companies for portfolio management. Finally at generations 3000, 4000 and 5000 the investor can take decision for investment in various companies for portfolio management. 173
16 For, Minimum Crossover Probability = 0.40 Maximum Crossover Probability = 1.00 Minimum Mutation Probability = Maximum Mutation Probability = 0.50 Results for 800 generations Best chromosome = Maximum Fitness = at Total = 70 Results for 1000 generations Best chromosome = Maximum Fitness = at Total = 74 Results for 1500 generations Best chromosome = Maximum Fitness = at Total = 80 Results for 2000 generations Best chromosome = Maximum Fitness = at Total =
17 Results for 3000 generations Best chromosome = Maximum Fitness = at Total = 86 Results for 4000 generations Best chromosome = Maximum Fitness = at Total = 86 Results for 5000 generations Best chromosome = Maximum Fitness = at Total = 86 From the above results it is found that at generations 800, 1000, 1500 and 2000 we are getting variations in Best chromosome, Maximum Fitness and Total investment. But at generations 3000, 4000 and 5000 we are getting the same Best chromosome, Maximum Fitness and Total investment. This concludes that at generations 800, 1000, 1500 and 2000 investor may not take decision for investment in various companies for portfolio management. Finally at generations 3000, 4000 and 5000 the investor can take decision for investment in various companies for portfolio management. 175
18 For, Minimum Crossover Probability = 0.60 Maximum Crossover Probability = 0.95 Minimum Mutation Probability = Maximum Mutation Probability = 1.00 Results for 800 generations Best chromosome = Maximum Fitness = at Total = 90 Results for 1000 generations Best chromosome = Maximum Fitness = at Total = 90 Results for 1500 generations Best chromosome = Maximum Fitness = at Total = 94 Results for 2000 generations Best chromosome = Maximum Fitness = at Total =
19 Results for 3000 generations Best chromosome = Maximum Fitness = at Total = 96 Results for 4000 generations Best chromosome = Maximum Fitness = at Total = 96 Results for 5000 generations Best chromosome = Maximum Fitness = at Total = 96 From the above results it is found that at generations 800, 1000 and 1500 we are getting variations in Best chromosome, Maximum Fitness and Total investment. But at generations 2000, 3000, 4000 and 5000 we are getting the same Best chromosome, Maximum Fitness and Total investment. This concludes that at generations 800, 1000 and 1500 investor may not take decision for investment in various companies for portfolio management. Finally at generations 2000, 3000, 4000 and 5000 the investor can take decision for investment in various companies for portfolio management. From the above all results it has been concluded that for Minimum Crossover Probability = 0.60, Maximum Crossover Probability = 0.95, Minimum Mutation Probability = and Maximum Mutation Probability = 1.00 the genetic algorithm gives far better results for stock portfolio selection. 177
Modern Portfolio Theory -Markowitz Model
Modern Portfolio Theory -Markowitz Model Rahul Kumar Project Trainee, IDRBT 3 rd year student Integrated M.Sc. Mathematics & Computing IIT Kharagpur Email: rahulkumar641@gmail.com Project guide: Dr Mahil
More informationCSCI 1951-G Optimization Methods in Finance Part 00: Course Logistics Introduction to Finance Optimization Problems
CSCI 1951-G Optimization Methods in Finance Part 00: Course Logistics Introduction to Finance Optimization Problems January 26, 2018 1 / 24 Basic information All information is available in the syllabus
More informationAIRCURRENTS: PORTFOLIO OPTIMIZATION FOR REINSURERS
MARCH 12 AIRCURRENTS: PORTFOLIO OPTIMIZATION FOR REINSURERS EDITOR S NOTE: A previous AIRCurrent explored portfolio optimization techniques for primary insurance companies. In this article, Dr. SiewMun
More informationAn Investigation on Genetic Algorithm Parameters
An Investigation on Genetic Algorithm Parameters Siamak Sarmady School of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia [P-COM/(R), P-COM/] {sarmady@cs.usm.my, shaher11@yahoo.com} Abstract
More informationAppendix I List of Companies Selected
Appendix I List of Companies Selected 1. Aarti Industries Limited 2. Abhishek Industries Limited 3. ACC Limited 4. Acrysil Limited 5. ADF Foods Limited 6. Aditya Birla Nuvo Limited 7. Akar Tools Limited
More informationData based stock portfolio construction using Computational Intelligence
Data based stock portfolio construction using Computational Intelligence Asimina Dimara and Christos-Nikolaos Anagnostopoulos Data Economy workshop: How online data change economy and business Introduction
More informationPortfolio Analysis with Random Portfolios
pjb25 Portfolio Analysis with Random Portfolios Patrick Burns http://www.burns-stat.com stat.com September 2006 filename 1 1 Slide 1 pjb25 This was presented in London on 5 September 2006 at an event sponsored
More informationCIRCULAR. Circular No Circular Date Regulatory and Compliance. Derivatives. Category. Segment
CIRCULAR Circular No. 20190228-4 Circular Date 20190228 Category Regulatory and Compliance Segment Derivatives Subject Revised Combined Futures &Options Position Limits for Single Stock Derivatives. Attachments
More informationEvolution of Strategies with Different Representation Schemes. in a Spatial Iterated Prisoner s Dilemma Game
Submitted to IEEE Transactions on Computational Intelligence and AI in Games (Final) Evolution of Strategies with Different Representation Schemes in a Spatial Iterated Prisoner s Dilemma Game Hisao Ishibuchi,
More informationAnt colony optimization approach to portfolio optimization
2012 International Conference on Economics, Business and Marketing Management IPEDR vol.29 (2012) (2012) IACSIT Press, Singapore Ant colony optimization approach to portfolio optimization Kambiz Forqandoost
More informationPortfolio Optimization by Heuristic Algorithms. Collether John. A thesis submitted for the degree of PhD in Computing and Electronic Systems
1 Portfolio Optimization by Heuristic Algorithms Collether John A thesis submitted for the degree of PhD in Computing and Electronic Systems School of Computer Science and Electronic Engineering University
More informationBesting Dollar Cost Averaging Using A Genetic Algorithm A Master of Science Thesis Proposal For Applied Physics and Computer Science
Besting Dollar Cost Averaging Using A Genetic Algorithm A Master of Science Thesis Proposal For Applied Physics and Computer Science By James Maxlow Christopher Newport University October, 2003 Approved
More informationCOMPARISON BETWEEN SINGLE AND MULTI OBJECTIVE GENETIC ALGORITHM APPROACH FOR OPTIMAL STOCK PORTFOLIO SELECTION
COMPARISON BETWEEN SINGLE AND MULTI OBJECTIVE GENETIC ALGORITHM APPROACH FOR OPTIMAL STOCK PORTFOLIO SELECTION Nejc Cvörnjek Faculty of Mechanical Engineering, University of Maribor, Slovenia and Faculty
More informationOptimal Step-Function Approximation of Load Duration Curve Using Evolutionary Programming (EP)
12 Optimal Step-Function Approximation of Load Duration Curve Using Evolutionary Programming (EP) Eda Azuin Othman Abstract This paper proposes Evolutionary Programming (EP) to determine optimal step-function
More informationMulti-Objective Optimization Model using Constraint-Based Genetic Algorithms for Thailand Pavement Management
Multi-Objective Optimization Model using Constraint-Based Genetic Algorithms for Thailand Pavement Management Pannapa HERABAT Assistant Professor School of Civil Engineering Asian Institute of Technology
More informationInternational Finance. Estimation Error. Campbell R. Harvey Duke University, NBER and Investment Strategy Advisor, Man Group, plc.
International Finance Estimation Error Campbell R. Harvey Duke University, NBER and Investment Strategy Advisor, Man Group, plc February 17, 2017 Motivation The Markowitz Mean Variance Efficiency is the
More informationTheoretical Aspects Concerning the Use of the Markowitz Model in the Management of Financial Instruments Portfolios
Theoretical Aspects Concerning the Use of the Markowitz Model in the Management of Financial Instruments Portfolios Lecturer Mădălina - Gabriela ANGHEL, PhD Student madalinagabriela_anghel@yahoo.com Artifex
More information1 Shapley-Shubik Model
1 Shapley-Shubik Model There is a set of buyers B and a set of sellers S each selling one unit of a good (could be divisible or not). Let v ij 0 be the monetary value that buyer j B assigns to seller i
More informationModeling Tax Evasion with Genetic Algorithms
Modeling Tax Evasion with Genetic Algorithms Geoff Warner 1 Sanith Wijesinghe 1 Uma Marques 1 Una-May O Reilly 2 Erik Hemberg 2 Osama Badar 2 1 The MITRE Corporation McLean, VA, USA 2 Computer Science
More informationMaximizing of Portfolio Performance
Maximizing of Portfolio Performance PEKÁR Juraj, BREZINA Ivan, ČIČKOVÁ Zuzana Department of Operations Research and Econometrics, University of Economics, Bratislava, Slovakia Outline Problem of portfolio
More informationMS-E2114 Investment Science Lecture 5: Mean-variance portfolio theory
MS-E2114 Investment Science Lecture 5: Mean-variance portfolio theory A. Salo, T. Seeve Systems Analysis Laboratory Department of System Analysis and Mathematics Aalto University, School of Science Overview
More informationApplications of Linear Programming
Applications of Linear Programming lecturer: András London University of Szeged Institute of Informatics Department of Computational Optimization Lecture 8 The portfolio selection problem The portfolio
More informationTrading Code 249A0009 DPAccount IN ClientID ECNStatus YES POA Status YES PAN No AHXPA2319N O.COM
Tel No. 040-23312454 Fax No. 040-23431505, CIN: U67120TG1995PLC019877 SEBI Reg. Nos. NSE: INB / INF/ INE 230770138 BSE: INB 010770130/ INF 010770131 MCX-SX: INB/INF 260770137 INE 260770138 DP ID s IN 300394
More informationMotif Capital Horizon Models: A robust asset allocation framework
Motif Capital Horizon Models: A robust asset allocation framework Executive Summary By some estimates, over 93% of the variation in a portfolio s returns can be attributed to the allocation to broad asset
More informationLecture 10: The knapsack problem
Optimization Methods in Finance (EPFL, Fall 2010) Lecture 10: The knapsack problem 24.11.2010 Lecturer: Prof. Friedrich Eisenbrand Scribe: Anu Harjula The knapsack problem The Knapsack problem is a problem
More informationA Genetic Algorithm for the Calibration of a Micro- Simulation Model Omar Baqueiro Espinosa
A Genetic Algorithm for the Calibration of a Micro- Simulation Model Omar Baqueiro Espinosa Abstract: This paper describes the process followed to calibrate a microsimulation model for the Altmark region
More informationA Heuristic Crossover for Portfolio Selection
Applied Mathematical Sciences, Vol. 8, 2014, no. 65, 3215-3227 HIKARI Ltd, www.m-hikari.com http://dx.doi.org/10.12988/ams.2014.43203 A Heuristic Crossover for Portfolio Selection Joseph Ackora-Prah Department
More informationMBA 7020 Sample Final Exam
Descriptive Measures, Confidence Intervals MBA 7020 Sample Final Exam Given the following sample of weight measurements (in pounds) of 25 children aged 4, answer the following questions(1 through 3): 45,
More informationEvolutionary Approach to Portfolio Optimization
Evolutionary Approach to Portfolio Optimization Jerzy J. Korczak 1, Piotr Lipiński 2 1 Louis Pasteur University, LSIIT, CNRS, Strasbourg, France e-mail: jjk@dpt-info.u-strasbg.fr 2 Louis Pasteur University,
More informationWhich Investment Option Would You Choose?
CHAPTER 9 Investment Management: Concepts and Strategies Elements of risk Which Investment Option Would You Choose? FIXED INCOME SECURITIES FIXED-INCOME SECURITY RETURN Where does it come from? FIXED-INCOME
More informationRandom variables The binomial distribution The normal distribution Sampling distributions. Distributions. Patrick Breheny.
Distributions September 17 Random variables Anything that can be measured or categorized is called a variable If the value that a variable takes on is subject to variability, then it the variable is a
More informationHow quantitative methods influence and shape finance industry
How quantitative methods influence and shape finance industry Marek Musiela UNSW December 2017 Non-quantitative talk about the role quantitative methods play in finance industry. Focus on investment banking,
More informationContinuing Education Course #287 Engineering Methods in Microsoft Excel Part 2: Applied Optimization
1 of 6 Continuing Education Course #287 Engineering Methods in Microsoft Excel Part 2: Applied Optimization 1. Which of the following is NOT an element of an optimization formulation? a. Objective function
More informationRandom Search Techniques for Optimal Bidding in Auction Markets
Random Search Techniques for Optimal Bidding in Auction Markets Shahram Tabandeh and Hannah Michalska Abstract Evolutionary algorithms based on stochastic programming are proposed for learning of the optimum
More informationCHAPTER 4 RELATIONSHIP BETWEEN FUTURES PRICE AND OPEN INTEREST. This chapter probes the relationship between the price of the futures
CHAPTER 4 RELATIONSHIP BETWEEN FUTURES PRICE AND OPEN INTEREST This chapter probes the relationship between the price of the futures contract in the derivatives market with the open interest in that futures
More informationHDFC Ltd As on September 30, 2015 Reliance Gas Transportation Infrastructure Ltd Absolute Return Power Finance Corporation Ltd. 2.
September, 2015 THE LINKED INSURANCE PRODUCTS DO NOT OFFER ANY LIQUIDITY DURING THE FIRST FIVE YEARS OF THE CONTRACT. THE POLICYHOLDER WILL NOT BE ABLE TO SURRENDER/WITHDRAW THE MONIES INVESTED IN LINKED.
More informationProfitability Optimization of Construction Project Using Genetic Algorithm Cash Flow Model
American Journal of Civil Engineering and Architecture, 2016, Vol. 4, No. 1, 17-27 Available online at http://pubs.sciepub.com/ajcea/4/1/3 Science and Education Publishing DOI:10.12691/ajcea-4-1-3 Profitability
More informationJournal of Computational and Applied Mathematics. The mean-absolute deviation portfolio selection problem with interval-valued returns
Journal of Computational and Applied Mathematics 235 (2011) 4149 4157 Contents lists available at ScienceDirect Journal of Computational and Applied Mathematics journal homepage: www.elsevier.com/locate/cam
More informationPrediction scheme of stock price using multiagent
Prediction scheme of stock price using multiagent system E. Kits&Y Katsuno School ofnformatics and Sciences, Nagoya University, Japan. Abstract This paper describes the prediction scheme of stock price
More informationEnhancing the Practical Usefulness of a Markowitz Optimal Portfolio by Controlling a Market Factor in Correlation between Stocks
Enhancing the Practical Usefulness of a Markowitz Optimal Portfolio by Controlling a Market Factor in Correlation between Stocks Cheoljun Eom 1, Taisei Kaizoji 2**, Yong H. Kim 3, and Jong Won Park 4 1.
More informationComputing Optimal Randomized Resource Allocations for Massive Security Games
Computing Optimal Randomized Resource Allocations for Massive Security Games Christopher Kiekintveld, Manish Jain, Jason Tsai, James Pita, Fernando Ordonez, Milind Tambe The Problem The LAX canine problems
More informationA Study on Importance of Portfolio - Combination of Risky Assets And Risk Free Assets
IOSR Journal of Business and Management (IOSR-JBM) e-issn: 2278-487X, p-issn: 2319-7668 PP 17-22 www.iosrjournals.org A Study on Importance of Portfolio - Combination of Risky Assets And Risk Free Assets
More informationColumn generation to solve planning problems
Column generation to solve planning problems ALGORITMe Han Hoogeveen 1 Continuous Knapsack problem We are given n items with integral weight a j ; integral value c j. B is a given integer. Goal: Find a
More informationFE670 Algorithmic Trading Strategies. Stevens Institute of Technology
FE670 Algorithmic Trading Strategies Lecture 4. Cross-Sectional Models and Trading Strategies Steve Yang Stevens Institute of Technology 09/26/2013 Outline 1 Cross-Sectional Methods for Evaluation of Factor
More informationOptimizing DSM Program Portfolios
Optimizing DSM Program Portfolios William B, Kallock, Summit Blue Consulting, Hinesburg, VT Daniel Violette, Summit Blue Consulting, Boulder, CO Abstract One of the most fundamental questions in DSM program
More informationChapter 2 Uncertainty Analysis and Sampling Techniques
Chapter 2 Uncertainty Analysis and Sampling Techniques The probabilistic or stochastic modeling (Fig. 2.) iterative loop in the stochastic optimization procedure (Fig..4 in Chap. ) involves:. Specifying
More informationNeural Network Prediction of Stock Price Trend Based on RS with Entropy Discretization
2017 International Conference on Materials, Energy, Civil Engineering and Computer (MATECC 2017) Neural Network Prediction of Stock Price Trend Based on RS with Entropy Discretization Huang Haiqing1,a,
More information1. Introduction 2. Model Formulation 3. Solution Approach 4. Case Study and Findings 5. On-going Research
1. Introduction 2. Model Formulation 3. Solution Approach 4. Case Study and Findings 5. On-going Research Natural disasters have caused: Huge amount of economical loss Fatal injuries Through effective
More informationResource Dedication Problem in a Multi-Project Environment*
1 Resource Dedication Problem in a Multi-Project Environment* Umut Be³ikci 1, Ümit Bilge 1 and Gündüz Ulusoy 2 1 Bogaziçi University, Turkey umut.besikci, bilge@boun.edu.tr 2 Sabanc University, Turkey
More informationAN EQUITABLE APPROACH TO THE PAYMENT SCHEDULING PROBLEM IN PROJECT MANAGEMENT
AN EQUITABLE APPROACH TO THE PAYMENT SCHEDULING PROBLEM IN PROJECT MANAGEMENT Gündüz Ulusoy Manufacturing Systems Engineering Faculty of Engineering and Natural Sciences Sabancı University Orhanlı,Tuzla,
More informationCSCI 1951-G Optimization Methods in Finance Part 07: Portfolio Optimization
CSCI 1951-G Optimization Methods in Finance Part 07: Portfolio Optimization March 9 16, 2018 1 / 19 The portfolio optimization problem How to best allocate our money to n risky assets S 1,..., S n with
More informationA Genetic Algorithm improving tariff variables reclassification for risk segmentation in Motor Third Party Liability Insurance.
A Genetic Algorithm improving tariff variables reclassification for risk segmentation in Motor Third Party Liability Insurance. Alberto Busetto, Andrea Costa RAS Insurance, Italy SAS European Users Group
More informationRisk Aversion, Stochastic Dominance, and Rules of Thumb: Concept and Application
Risk Aversion, Stochastic Dominance, and Rules of Thumb: Concept and Application Vivek H. Dehejia Carleton University and CESifo Email: vdehejia@ccs.carleton.ca January 14, 2008 JEL classification code:
More informationAnswers to Concepts in Review
Answers to Concepts in Review 1. A portfolio is simply a collection of investment vehicles assembled to meet a common investment goal. An efficient portfolio is a portfolio offering the highest expected
More informationLog-Robust Portfolio Management
Log-Robust Portfolio Management Dr. Aurélie Thiele Lehigh University Joint work with Elcin Cetinkaya and Ban Kawas Research partially supported by the National Science Foundation Grant CMMI-0757983 Dr.
More informationReturn on Capital Employed-A Tool for Analyzing Profitability of Companies
Return on Capital Employed-A Tool for Analyzing Profitability of Companies DR. JEET SINGH*, DR. PREETI YADAV** Head, Department of Management, Moradabad Institute of Technology, Moradabad (U.P.)* Assistant
More informationMarkowitz portfolio theory
Markowitz portfolio theory Farhad Amu, Marcus Millegård February 9, 2009 1 Introduction Optimizing a portfolio is a major area in nance. The objective is to maximize the yield and simultaneously minimize
More informationAUTOMATED PORTFOLIO ANALYSIS AS ON OVERVIEW ASSET TYPES THAT YOU HAVE INVESTED IN MARKET CAPITALIZATION
AUTOMATED PORTFOLIO ANALYSIS AS ON 31-01-2017 OVERVIEW Overall your complete portfolio has grown. The net worth as per the holdings available with us as on 31-01-2017 has increased by 13.15% to Rs. 1.2
More informationVelocityShares Equal Risk Weight ETF (ERW) Please refer to Important Disclosures and the Glossary of Terms section at the end of this material.
VelocityShares Equal Risk Weight ETF (ERW) Please refer to Important Disclosures and the Glossary of Terms section at the end of this material. Glossary of Terms Beta: A measure of a stocks risk relative
More informationONLY FOR TRADERS PERFORMANCE
ONLY FOR TRADERS PERFORMANCE Assumption One Lot of Rs 600000 notional value, Two Lots Rs 1200000 notional value Assumption for Stock Options Rs 25000 or Rs 50000 investment per recommendation Assumption
More informationEmpirical Study on Corporate Governance Performance Index with Reference to Selected Corporate Sectors
International Journal of Managerial Studies and Research (IJMSR) Volume 6, Issue 1, January 2018, PP 1-7 ISSN 2349-0330 (Print) & ISSN 2349-0349 (Online) http://dx.doi.org/10.20431/2349-0349.0601001 www.arcjournals.org
More informationA Study on the Risk Regulation of Financial Investment Market Based on Quantitative
80 Journal of Advanced Statistics, Vol. 3, No. 4, December 2018 https://dx.doi.org/10.22606/jas.2018.34004 A Study on the Risk Regulation of Financial Investment Market Based on Quantitative Xinfeng Li
More informationHomework 1 posted, due Friday, September 30, 2 PM. Independence of random variables: We say that a collection of random variables
Generating Functions Tuesday, September 20, 2011 2:00 PM Homework 1 posted, due Friday, September 30, 2 PM. Independence of random variables: We say that a collection of random variables Is independent
More informationResearch Article The Effect of Exit Strategy on Optimal Portfolio Selection with Birandom Returns
Applied Mathematics Volume 2013, Article ID 236579, 6 pages http://dx.doi.org/10.1155/2013/236579 Research Article The Effect of Exit Strategy on Optimal Portfolio Selection with Birandom Returns Guohua
More informationSTARRY GOLD ACADEMY , , Page 1
ICAN KNOWLEDGE LEVEL QUANTITATIVE TECHNIQUE IN BUSINESS MOCK EXAMINATION QUESTIONS FOR NOVEMBER 2016 DIET. INSTRUCTION: ATTEMPT ALL QUESTIONS IN THIS SECTION OBJECTIVE QUESTIONS Given the following sample
More informationEvaluating a Hybrid Encoding and Three Crossover Operators on the Constrained Portfolio Selection Problem
Evaluating a Hybrid Encoding and Three Crossover Operators on the Constrained Portfolio Selection Problem Felix Streichert, Holger Ulmer, and Andreas Zell Centre for Bioinformatics Tübingen (ZBIT) University
More informationDate. Place. Signature of Applicant
Date Place 1 Signature of Applicant ANNEXURE I NIFTY SERIES Sr.# Script Name Quantity Amount Sr.# Script Name Quantity Amount 1 ACC Ltd. 2 Ambuja Cements Ltd. 3 Asian Paints Ltd. 4 Axis Bank Ltd. 5 Bajaj
More informationApplications of Quantum Annealing in Computational Finance. Dr. Phil Goddard Head of Research, 1QBit D-Wave User Conference, Santa Fe, Sept.
Applications of Quantum Annealing in Computational Finance Dr. Phil Goddard Head of Research, 1QBit D-Wave User Conference, Santa Fe, Sept. 2016 Outline Where s my Babel Fish? Quantum-Ready Applications
More informationBudget Management In GSP (2018)
Budget Management In GSP (2018) Yahoo! March 18, 2018 Miguel March 18, 2018 1 / 26 Today s Presentation: Budget Management Strategies in Repeated auctions, Balseiro, Kim, and Mahdian, WWW2017 Learning
More informationCHAPTER 5 RELATIONSHIP BETWEEN THE FUTURES PRICE AND COST OF CARRY. The cost of carry model was used in the third chapter and the relationship
CHAPTER 5 RELATIONSHIP BETWEEN THE FUTURES PRICE AND COST OF CARRY The cost of carry model was used in the third chapter and the relationship between the cost of carry and the risk free return (represented
More informationTime and Cost Optimization Techniques in Construction Project Management
Time and Cost Optimization Techniques in Construction Project Management Mr.Bhushan V 1. Tatar and Prof.Rahul S.Patil 2 1. INTRODUCTION In the field of Construction the term project refers as a temporary
More informationSimulation Wrap-up, Statistics COS 323
Simulation Wrap-up, Statistics COS 323 Today Simulation Re-cap Statistics Variance and confidence intervals for simulations Simulation wrap-up FYI: No class or office hours Thursday Simulation wrap-up
More informationHeuristic Methods in Finance
Heuristic Methods in Finance Enrico Schumann and David Ardia 1 Heuristic optimization methods and their application to finance are discussed. Two illustrations of these methods are presented: the selection
More informationSession 8: The Markowitz problem p. 1
Session 8: The Markowitz problem Susan Thomas http://www.igidr.ac.in/ susant susant@mayin.org IGIDR Bombay Session 8: The Markowitz problem p. 1 Portfolio optimisation Session 8: The Markowitz problem
More informationUPDATED IAA EDUCATION SYLLABUS
II. UPDATED IAA EDUCATION SYLLABUS A. Supporting Learning Areas 1. STATISTICS Aim: To enable students to apply core statistical techniques to actuarial applications in insurance, pensions and emerging
More informationCHAPTER 5: ANSWERS TO CONCEPTS IN REVIEW
CHAPTER 5: ANSWERS TO CONCEPTS IN REVIEW 5.1 A portfolio is simply a collection of investment vehicles assembled to meet a common investment goal. An efficient portfolio is a portfolio offering the highest
More informationCS 331: Artificial Intelligence Game Theory I. Prisoner s Dilemma
CS 331: Artificial Intelligence Game Theory I 1 Prisoner s Dilemma You and your partner have both been caught red handed near the scene of a burglary. Both of you have been brought to the police station,
More informationThe Markowitz framework
IGIDR, Bombay 4 May, 2011 Goals What is a portfolio? Asset classes that define an Indian portfolio, and their markets. Inputs to portfolio optimisation: measuring returns and risk of a portfolio Optimisation
More informationThe Fundamental Law of Mismanagement
The Fundamental Law of Mismanagement Richard Michaud, Robert Michaud, David Esch New Frontier Advisors Boston, MA 02110 Presented to: INSIGHTS 2016 fi360 National Conference April 6-8, 2016 San Diego,
More informationMarkowitz portfolio theory. May 4, 2017
Markowitz portfolio theory Elona Wallengren Robin S. Sigurdson May 4, 2017 1 Introduction A portfolio is the set of assets that an investor chooses to invest in. Choosing the optimal portfolio is a complex
More informationApplied Corporate Finance. Unit 2
Applied Corporate Finance Unit 2 Calculating the Hurdle Rate Definition of Risk Risk vs Return Hurdle Rate Choosing a risk return model CAPM Risk Free Rate Equity Risk Premium Beta First Principles Maximize
More informationCHAPTER II LITERATURE STUDY
CHAPTER II LITERATURE STUDY 2.1. Risk Management Monetary crisis that strike Indonesia during 1998 and 1999 has caused bad impact to numerous government s and commercial s bank. Most of those banks eventually
More informationSocially-Optimal Design of Crowdsourcing Platforms with Reputation Update Errors
Socially-Optimal Design of Crowdsourcing Platforms with Reputation Update Errors 1 Yuanzhang Xiao, Yu Zhang, and Mihaela van der Schaar Abstract Crowdsourcing systems (e.g. Yahoo! Answers and Amazon Mechanical
More informationOn the Effectiveness of a NSGA-II Local Search Approach Customized for Portfolio Optimization
On the Effectiveness of a NSGA-II Local Search Approach Customized for Portfolio Optimization Kalyanmoy Deb a, Ralph Steuer b, Rajat Tewari c and Rahul Tewari d a Indian Institute of Technology Kanpur,
More informationFINANCIAL OPERATIONS RESEARCH: Mean Absolute Deviation And Portfolio Indexing
[1] FINANCIAL OPERATIONS RESEARCH: Mean Absolute Deviation And Portfolio Indexing David Galica Tony Rauchberger Luca Balestrieri A thesis submitted in partial fulfillment of the requirements for the degree
More informationUC Berkeley Haas School of Business Game Theory (EMBA 296 & EWMBA 211) Summer 2016
UC Berkeley Haas School of Business Game Theory (EMBA 296 & EWMBA 211) Summer 2016 More on strategic games and extensive games with perfect information Block 2 Jun 11, 2017 Auctions results Histogram of
More informationMeasuring and managing market risk June 2003
Page 1 of 8 Measuring and managing market risk June 2003 Investment management is largely concerned with risk management. In the management of the Petroleum Fund, considerable emphasis is therefore placed
More informationMONTHLY UPDATE MARCH 2015
MONTHLY UPDATE MARCH 2015 Highest NAV Guarantee Fund as on 31 st March 2015 Fund Objective : To Generate Returns from Hybrid asset Allocation Portfolio over 10 year Term of Fund SFIN CODE : ULIF04001/09/10HighestNAV101
More informationQuantum Mutual Fund. Factsheets for the month of July, 2010
Quantum Mutual Fund Factsheets for the month of July, 2010 Quantum Long Term Equity Fund An Open ended Equity Scheme 5 Star Rated by Value Research~ Fact Sheet as on 31st July 2010 Net Asset Value as on
More informationFinancial Analysis The Price of Risk. Skema Business School. Portfolio Management 1.
Financial Analysis The Price of Risk bertrand.groslambert@skema.edu Skema Business School Portfolio Management Course Outline Introduction (lecture ) Presentation of portfolio management Chap.2,3,5 Introduction
More informationTechnical Guide. Issue: forecasting a successful outcome with cash flow modelling. To us there are no foreign markets. TM
Technical Guide To us there are no foreign markets. TM The are a unique investment solution, providing a powerful tool for managing volatility and risk that can complement any wealth strategy. Our volatility-led
More informationGRAMMATICAL EVOLUTION. Peter Černo
GRAMMATICAL EVOLUTION Peter Černo Grammatical Evolution (GE) Is an evolutionary algorithm that can evolve programs. Representation: linear genome + predefined grammar. Each individual: variable-length
More informationA Novel Iron Loss Reduction Technique for Distribution Transformers Based on a Combined Genetic Algorithm Neural Network Approach
16 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS PART C: APPLICATIONS AND REVIEWS, VOL. 31, NO. 1, FEBRUARY 2001 A Novel Iron Loss Reduction Technique for Distribution Transformers Based on a Combined
More information(AA12) QUANTITATIVE METHODS FOR BUSINESS
All Rights Reserved ASSOCIATION OF ACCOUNTING TECHNICIANS OF SRI LANKA AA1 EXAMINATION - JULY 2016 (AA12) QUANTITATIVE METHODS FOR BUSINESS Instructions to candidates (Please Read Carefully): (1) Time
More informationMarkov Decision Processes
Markov Decision Processes Robert Platt Northeastern University Some images and slides are used from: 1. CS188 UC Berkeley 2. AIMA 3. Chris Amato Stochastic domains So far, we have studied search Can use
More informationFinancial Mathematics III Theory summary
Financial Mathematics III Theory summary Table of Contents Lecture 1... 7 1. State the objective of modern portfolio theory... 7 2. Define the return of an asset... 7 3. How is expected return defined?...
More informationDecision Support Models 2012/2013
Risk Analysis Decision Support Models 2012/2013 Bibliography: Goodwin, P. and Wright, G. (2003) Decision Analysis for Management Judgment, John Wiley and Sons (chapter 7) Clemen, R.T. and Reilly, T. (2003).
More informationNATIONWIDE ASSET ALLOCATION INVESTMENT PROCESS
Nationwide Funds A Nationwide White Paper NATIONWIDE ASSET ALLOCATION INVESTMENT PROCESS May 2017 INTRODUCTION In the market decline of 2008, the S&P 500 Index lost more than 37%, numerous equity strategies
More informationJ B GUPTA CLASSES , Copyright: Dr JB Gupta. Chapter 4 RISK AND RETURN.
J B GUPTA CLASSES 98184931932, drjaibhagwan@gmail.com, www.jbguptaclasses.com Copyright: Dr JB Gupta Chapter 4 RISK AND RETURN Chapter Index Systematic and Unsystematic Risk Capital Asset Pricing Model
More informationOptimization of a Real Estate Portfolio with Contingent Portfolio Programming
Mat-2.108 Independent research projects in applied mathematics Optimization of a Real Estate Portfolio with Contingent Portfolio Programming 3 March, 2005 HELSINKI UNIVERSITY OF TECHNOLOGY System Analysis
More information