UNIVERSAL PORTFOLIO GENERATED BY IDEMPOTENT MATRIX AND SOME PROBABILITY DISTRIBUTION LIM KIAN HENG MASTER OF MATHEMATICAL SCIENCES
|
|
- Noel McGee
- 5 years ago
- Views:
Transcription
1 UNIVERSAL PORTFOLIO GENERATED BY IDEMPOTENT MATRIX AND SOME PROBABILITY DISTRIBUTION LIM KIAN HENG MASTER OF MATHEMATICAL SCIENCES FACULTY OF ENGINEERING AND SCIENCE UNIVERSITI TUNKU ABDUL RAHMAN APRIL 2015
2 ii UNIVERSAL PORTFOLIO GENERATED BY IDEMPOTENT MATRIX AND SOME PROBABILITY DISTRIBUTION By LIM KIAN HENG A project report submitted to the Department of Mathematical and Actuarial Sciences, Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, in partial fulfillment of the requirements for the degree of Master in Mathematical Sciences in April 2015
3 iii DECLARATION I Lim Kian Heng hereby declare that this project report is based on my original work except for quotations and citations which have been duly acknowledge. I also declare that it has not been previously or concurrently submitted for any other degree at UTAR or other institutions. (LIM KIAN HENG) Date
4 iv APPROVAL FOR SUBMISSION I certify that this project report UNIVERSAL PORTFOLIO GENERATED BY IDEMPOTENT MATRIX AND SOME PROBABILITY DISTRIBUTION was prepared by LIM KIAN HENG has met the requirements for the award of Master of Mathematical Sciences at Universiti Tunku Abdul Rahman. Approved by, Signature : Supervisor : Date :
5 v ABSTRACT UNIVERSAL PORTFOLIO GENERATED BY IDEMPOTENT MATRIX AND SOME PROBABILITY DISTRIBUTION Lim Kian Heng Universal portfolio is a robust trading strategy. Two methods were used to generate the universal portfolios in order to obtain higher return in this thesis. First method universal portfolio which generated by the symmetric idempotent matrix. The second method universal portfolio generated by probability distribution such as Gamma, Beta, Lognormal, Gaussian and Inverse Gaussian distributions. By empirical study, the best value of u in symmetric idempotent matrix to generate highest return from the universal portfolio after a certain period is studied. Both results are used to compare with the CSD universal portfolio which studied by Tan and Lim (2011) to find out a better method to generate the universal portfolio.
6 vi ACKNOWLEDGEMENTS I would like to convey my appreciation to those who have supported me for this project. Firstly thanks to my supervisor Dr Tan Choon Peng who provided guidance to me since the very beginning till end. He teach and advise me patiently so that I can understand further about this topic, I couldn t finish this project without his invaluable guidance and advice. Secondly I would like to thank you University Tunku Abdul Rahman to provide me a delightful study environment and group of dedicated lecturers during my study. Furthermore, thank you very much indeed to families and friends with their assistant to me during this research.
7 vii TABLE OF CONTENT Page DECLARATION ABSTRACT ACKNOWLEDGEMENTS LIST OF TABLES iii iv v viii CHAPTER 1 INTRODUCTION Universal portfolio Objective Problem definition 3 2 LITERATURE REVIEW 4 3 AN ADDITIVE-UPDATE UNIVERSAL PORTFOLIO Chi-square Divergence (CSD) Universal Portfolios Research by Tan and Lim(2011) The MAHALANOBIS Additive-Update Universal Portfolios 12 4 LOW ORDER UNIVERSAL PORTFOLIOS Introduction to Low Order Universal Portfolios The Finite Order Universal Portfolios Low Order Universal Portfolios Order 1 Universal Portfolio Order 2 Universal Portfolio Order 3 Universal Portfolio The Order Universal Portfolio Wealth Function 21 5 RESEARCH METHODOLOGY Introduction Universal Portfolios generated by idempotent Matrix Idempotent matrix Idempotent matrix construction 27
8 viii 5.3 The Universal Portfolio Generated by Five Special Distribution Order 1 Universal Portfolio Order 2 Universal Portfolio Order 3 Universal Portfolio The Five Special Distributions 44 6 COMPUTATIONAL RESULT ANALYSIS Result of Universal Portfolio generated by idempotent matrix Performance of Finite Order Universal Portfolio 56 7 CONCLUSIONS 73 APPENDICES 75 REFERENCES 76
9 ix LIST OF TABLES TABLE TITLE PAGE Value of (max), and, where 11 (0.3333, , ) for the CSD universal portfolios The 5 probability distributions and its corresponding probability 44 density functions The First four moments of the selected probability distributions Values of the principal and the generated by 47 idempotent matrix for Data Set A 6.2 Values of the principal and the generated by 48 idempotent matrix for Data Set B 6.3 Values of the principal and the generated by 49 idempotent matrix for Data Set C 6.4 Values of the principal and the generated by 50 idempotent matrix for Data Set D 6.5 Values of the principal and the generated by 51 idempotent matrix for Data Set E
10 x TABLE TITLE PAGE 6.6 Values of the principal and the generated by 52 idempotent matrix for Data Set F 6.7 Values of the principal and the generated by 53 idempotent matrix for Data Set G 6.8 Values of the principal and the generated by 54 idempotent matrix for Data Set H 6.9 The return after 500 days for the finite order universal portfolio 58 generated by Gaussian distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when µ = The return after 500 days for the finite order universal portfolio 58 generated by Gaussian distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when µ = The return after 500 days for the finite order universal portfolio 59 generated by Gaussian distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when σ = 1
11 xi TABLE TITLE PAGE 6.12 The return after 500 days for the finite order universal portfolio 59 generated by the Gaussian distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when σ = The return after 500 days for the finite order universal portfolio 60 generated by the Lognormal distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when µ = The return after 500 days for the finite order universal portfolio 60 generated by the Lognormal distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when µ = The return after 500 days for the finite order universal portfolio 61 generated by the Lognormal distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when σ = 1
12 xii TABLE TITLE PAGE 6.16 The return after 500 days for the finite order universal portfolio 61 generated by the Lognormal distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when σ = The return after 500 days for the finite order universal portfolio 62 generated by the Inverse Gaussian distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when = The return after 500 days for the finite order universal portfolio 62 generated by the Inverse Gaussian distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when = The return after 500 days for the finite order universal portfolio 63 generated by the Inverse Gaussian distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when = 1
13 xiii TABLE TITLE PAGE 6.20 The return after 500 days for the finite order universal portfolio 63 generated by the Inverse Gaussian distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when = The return after 500 days for the finite order universal portfolio 64 generated by the Beta distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when The return after 500 days for the finite order universal portfolio 64 generated by the Beta distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when The return after 500 days for the finite order universal portfolio 65 generated by the Beta distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when The return after 500 days for the finite order universal portfolio 65 generated by the Beta distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when 10
14 xiv TABLE TITLE PAGE 6.25 The return after 500 days for the finite order universal portfolio 66 generated by the Gamma distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when The return after 500 days for the finite order universal portfolio 66 generated by the Gamma distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when The return after 500 days for the finite order universal portfolio 67 generated by the Gamma distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when The return after 500 days for the finite order universal portfolio 67 generated by the Gamma distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when 10
15 1 CHAPTER 1 INTRODUCTION 1.1 Universal portfolio Universal portfolio is a robust investment strategy. This portfolio suggested that investor to distribute investment principal into m numbers of shares with portfolio vector and price relative vector accordingly on the n-th trading day. Where, and is the ratio of the closing price to the opening price for the m shares respectively on n-th trading day At the end of first trading day, the portfolio vector price relative vector, the total principal of investor and the S 1 = =. At the end of second trading day, the portfolio vector the price relative vector, the total principal of investor and S 2 = = =. By applying the same method, at the end of n trading day, the portfolio vector and the price relative vector, the total principal of investor S n = =. This project, we carried out a research on the universal portfolio which portfolio vector is generated by the symmetric Idempotent matrix and some probability
16 2 distributions. We used these methods to generate the universal portfolio which maximise the profit by a best parameter u value selected. First method, universal portfolio which portfolio vector generate by using the symmetric idempotent matrix via the Mahalanobis formula The symmetric idempotent matrix is a special matrix where the matrix multiplied by itself, the result is itself, that is x =. In this research, we consider only the symmetric idempotent matrix. We will study the best value of parameter u in symmetric idempotent matrix M(u) for the universal portfolio which generate maximum profit after certain trade period. The second method we used 5 common probability distributions to generate the universal portfolio where the portfolio vector is given by In this research, we study only universal portfolios of order v = 1, 2, 3 due to the limitation of computational time and memory. Gamma distribution, Beta distribution, Lognormal distribution, Gaussian distribution and Inverse Gaussian distribution were used in this research to generate the universal portfolio, where each distribution has 2 parameters. We will study how the parameter affected the return of investment. 1.2 Objective (i) To establish the Mahalanobis universal portfolio which generated by symmetric Idempotent matrix ( non-negative definite matrix). (ii) To establish universal portfolio which generated by Gamma distribution, Beta distribution, Lognormal distribution, Gaussian distribution and Inverse Gaussian distribution
17 3 (iii) To achieved higher wealth of investor by selected best parameter value through the above (i) and (ii) method. (iv) To compare the both result in (iii) with CSD universal portfolio which studied by Tan and Lim (2011) 1.3 Problem definition (i) How to develop a symmetric idempotent matrix? (ii) Can the Mahalanobis universal portfolio generated by symmetric idempotent matrix (non negative definite matrix)? (iii) How to generate universal portfolio by using Gamma distribution, Beta distribution, Lognormal distribution, Gaussian distribution and Inverse Gaussian distribution? (iv) How good/bad is the wealth of investor by using these methods?
18 4 CHAPTER 2 LITERATURE REVIEW Many past researchers in the world carried out research on universal portfolios. Thomas M. Cover (1991) was the earliest researcher who study this topic. He uses sequential portfolio selection procedure for stock market investment. Throughout his research he didn t made any statistical assumption on the behaviour of the stock market. This is the initial idea of universal portfolio. In the universal portfolios research of Thomas M. Cover and Erik Ordentlich (1996), they used a -weighted side information in the sequential investment algorithm. As a result, they establish a close connection between universal data compression and universal investment. Avrim Blum and Adam Kalai (1999) study the universal portfolios with and without transaction cost. They found that the proportion of total wealth generate by a stock is same at the beginning of each trade period. They also discovered the constant rebalanced portfolio (CRP) investment strategy.
19 5 Alexei A. Gaivoronski and Fabio Stella (2000) used the BCRP (best constant rebalanced portfolio) and SCRP (successive constant rebalanced portfolio) to generate the universal portfolio. The advantages of these portfolios are less demanding in computationally compare to other previously known portfolios. The stock data they used in NYSE (New York Stock Exchange) show that their portfolio included the consideration of possible nonstationary market behaviour. Suleyman S. Kozat and Andrew C. Singer (2006) carried out research on universal portfolios with side information and switching. He study further in the best constant rebalanced portfolios, he manage to establish a less complexity portfolio algorithm and show that significant gains on historical stock pairs can be achieves with this portfolio algorithm Patrick O Sullivan and David Edelman (2011) study and introduced the adaptive universal portfolios. Their result can achieve greater early growth compare to Cover s universal portfolio which relatively poor in the early growth. Tan and Lim (2011) carried out research on additive-update universal portfolio.they use the chi-square divergence (CSD) distance measure to minimizing and maximizing the same objective functions in order to generate the universal portfolios. Later on, Tan and Lim (2012) focusing on the Mahalanobis squared divergence (or quadratic divergence) universal portfolio, by using the Mahalanobis squared divergence method they derived many group of additive-update universal portfolio. They used symmetric,
20 6 positive definite matrices to generate these groups of Mahalanobis squared divergence additive-update universal portfolio. They manage to obtain sufficient bound for valid parametric values. They also show that the Mahalanobis additive-update universal portfolio can achieve a wealth higher than that of the best constant rebalanced portfolio (BCRP) with a sufficient condition. In year 2013, in order to solve the long implementation time and huge memory problem, Tan (2013) introduced universal portfolio with performance bounds which generated by distribution. His finite order universal portfolio advantage in much lesser time and memory for implementation due to less past data require for storage and computation.
21 7 CHAPTER 3 ADDITIVE UPDATE UNIVERSAL PORTFOLIO 3.1 Chi-square Divergence (CSD) Universal Portfolios The sequence portfolio vectors of chi-square divergence (CSD) universal portfolio is given by where is a real number so that for all and The xi-parametric family chi-square divergence (CSD) universal portfolios can be define by Equation (3.1) for real number so that for and Please take note that xi-parametric family can only defined with some bounded values and not all (real number). By maximizing and minimize the objective functions using the chi-square divergence distance measure we can generate the xi-parametric family of chi-square divergence (CSD) universal portfolios. The logarithms which applied below are all with base e. Proposition Let the objective functions and
22 8 where is the chi-square divergence distance measured (Kullback-Leibler distance measure) and. In order to simplify both the objective functions in (3.2) and (3.3), we let approximate to then, the maximum of the objective function is achieved at given by (3.2) and similarly, the minimum of is also achieved at given by (3.3) where is replaced by. Proof. We introduce the Lagrange multiplier to maximize the objective functions for portfolio vector is where and When we applied the m partial derivatives and let it to be zero for the maximum objective function,
23 9 Solving (3.8), we get summing over, we obtain which results in (3.2). Also with the same approach it can be shown that the minimum of achieved at in (3.2). Proposition A sufficient condition for for all and all positive integers n where defined in (3.2) is that where is the relative stock price of stock i on the trading day and is given. Proof. For in (3.2), for and we have, thus
24 10 Please note that is true and valid for all and any satisfying (3.11) will imply that (3.12) is satisfied as well. Remark Any real number satisfy the equation (3.11) will generate a xiparametric family of chi-square divergence (CSD) universal portfolios. Please note that if the minimum and the maximum price relatives are 0.92 and 1.00 respectively, then the condition in (3.12) says that = That mean a parametric family of chi-square divergence (CSD) universal portfolios can be generated for range Research by Tan and Lim Tan and Lim (2011) had carried out the research of chi-square divergence (CSD) universal portfolios for different data sets. These data sets are Set A, Set B and Set C where Set A consist of listed company in Kuala Lumpur Stock Exchange(KLSE) which is Malayan Banking, Genting and Amway (M) Holdings. Set B consist of Public Bank, Sunrise and YTL Corporation. Set C consist of Hong Leong Bank, RHB Capital and YTL Corporation. The trading period is chosen from January 1, 2003 to November 30, 2004, total 500 trading days. The starting principal is assumed to be 1. The final principal after n trading days is as shown in the table below [ ]. The initial portfolio verctor (0.3333, , ) for Tan and Lim research on Set A, Set B and Set C. The maximum returns, vector portfolio, the value of where is maximum and the range are shown in Table
25 11 Table Value of (max), and, where (0.3333, , ) for the CSD universal portfolios. Data Set A B C Normal range of determined by (3.12) (max) = at = = (0.2106, , ) (max) = at = = (0.0000, , ) (max) = at = = (0.0000, , ) Extended range of (max) = at = = (0.2106, , ) (max) = at = = (0.0000, , ) (max) = at = = (0.0000, , )
26 12 MAHALANOBIS UNIVERSAL PORTFOLIO 3.3 The MAHALANOBIS Additive-Update Universal Portfolios The Mahalanobis Additive-Update Universal Portfolio has three parameters, that is positive definite symmetric matrix A, the initial portfolio vector and a scalar parameter. The sequence portfolio vector of Mahalanobis Additive-Update Universal Portfolio is given by formula as follow:, where the initial portfolio vector is given, and real number so that all value in 0 for n=0,1,2,3.. Matrix A is a positive definite symmetric matrix and =. Proposition The sequence portfolio vector of Mahalanobis Additive-Update Universal Portfolio can be replace by formula as shown below:, where is given by
27 13 Matrix E = Matrix = ), matrix is symmetric and the sum of row of matrix is zero. Proof Given by, where E= let Then by comparing both formula we should get. Identify the i-th row in the above equation then we get. Comparing the coefficients of we get,.
28 14 Proposition Given a portfolio vector define under equation, the sequence portfolio vector to be a portfolio, it is necessary and sufficient that: or, for i=1,2..,m, where represent the i-th element of vector. Proof Given that portfolio vector and, since, then, we get. (since ) if > 0, then. if < 0, then. The wealth of investor At the end of n trading day, the portfolio vector price relative vector, the total principal of investor and the S n = =.
29 15 CHAPTER 4 LOW ORDER UNIVERSAL PORTFOLIOS 4.1 Introduction to Low Order Universal Portfolios Thomas M. Cover and Erik Ordentlich (1996) had presented a sequential investment algorithm, the -weighted universal portfolio with side information, which achieves, to first order in the exponent, the same wealth as the best side-information dependent investment strategy determined in hindsight from observed market and side-information outcomes. Based on this concept, Tan (2013) adopted this approach and introduced the theory of finite and moving order universal portfolios generated by some special probability distributions. In this chapter, it is our main objective to study the empirical performance of low order universal portfolios. The advantage of low order universal portfolio is that it can help in saving a lot of computational time and computer memory when performing calculation compared to the method which is used in Cover-Ordentlich universal portfolio. For this reason, low order universal portfolio is a better approach than the method in Cover- Ordentlich universal portfolio to generate the total wealth achieved in investment..
30 The Finite Order Universal Portfolios Suppose Y1, Y2,..., Y m are mutually independent random variables with probability density functions f ( y ), f ( y ),..., f ( y ) respectively. Then the joint probability density Y1 1 Y2 2 Ym m function of Y1, Y2,..., Y m is: f ( y, y,, y ) f ( y ), f ( y ),..., f ( y ) (4.1) 1 2 m Y1 1 Y2 2 Ym m for, where D is defined by: 1 2 D y, y,, y : f (y ) 0, for all i 1, 2,..., m (4.2) m Y i i Let denote the price-relative vector on day n. Then the inner product for is well defined for an m-stock market. Let v be a fixed positive integer. Then the order v universal portfolio generated by the m independent random variables is sequence given by: for ;. Notice that we have to assume that the moments are positive for ;.
31 Low Order Universal Portfolios In universal portfolio (4.3), the order of v is the number of days of the past stock-price information to be taken into account in calculating the next-day portfolio. We introduced a low order universal portfolio because it can save a lot of computational time and computer memory if the order v is small, say By doing so, we are able to generate results effectively and efficiently. Therefore, we will investigate the universal portfolios with orders 1, 2, 3 which is generated by some special probability distributions. The specific formula of (4.3) for each will be obtained in the following sub-sections. Also, we need the moments of each probability distribution to help in our calculation as shown in the formula (4.3) Order 1 Universal Portfolio From (4.3), the portfolio proportion for stock k on day n+1 for the order 1 universal portfolio is given by:
32 18 where the normalizing constant, We note that by the independence of, Order 2 Universal Portfolio From (4.3), the order 2 portfolio proportion for stock k on day n+1 is given by:
33 19 where the normalizing constant, We note that by the independence of,, where the values of are distinct.
34 Order 3 Universal Portfolio From (4.3), the order 3 portfolio proportion for stock k on day n+1 is given by: for k = 1, 2,, m, where the normalizing constant, We note that by the independence of,
35 21 where the values of are distinct. 4.4 The Order Universal Portfolio Wealth Function The wealth function can be calculated recursively as follows: where. From (4.3), the wealth increase on day n+1, namely can be evaluated as follows for the order v universal portfolio:
36 22 From (4.4), for the special case of the order 1 universal portfolio for 3 stocks, the wealth increase on day n+1 is given by: where from equation (4.5),
37 23 CHAPTER 5 RESEARCH METHODOLOGY 5.1 Introduction For the first part we used the symmetric idempotent matrix (non-negative definite matrix) to generate the universal portfolio. The second part universal portfolio is generate by some probability distributions. Each of the data set contain of 3 or 5 stocks. For the first part, by using the Mahalanobis universal portfolio formula, the symmetric idempotent matrix is used to generate the universal portfolio. The Mahalanobis universal portfolio formula as follow: where all elements in the matrix is given by where is the i-th row, j-th column element in the idempotent matrix, is the sum of i-th row of idempotent matrix,
38 24 is the sum of j-th column of idempotent matrix, is the sum of all elements in the idempotent matrix. The second part we use some probability distributions to generate the portfolio. Let v be a positive integer, function be m random variables having a joint probability density defined over B, where Let the measure have a p.d.f.. The universal portfolio which generated by is defined as follow: for. The case we consider here, the random variables are mutually independent from the probability density function, namely has the probability density function for where is a parameter. So the joint probability density function is given as follow: where is defined over.
39 25 The numerator of : now where is the number of in the product, for and is the moment of.
40 26 The denominator of : Therefore universal portfolio of order v which generated by as follow: where where, for is the number of in the product
41 Universal Portfolios generated by idempotent Matrix Idempotent matrix Idempotent matrix is a very special matrix where the matrix multiplied by itself, the result is itself. That is idempotent matrix x idempotent matrix = idempotent matrix. Some characteristics of idempotent matrix as follow: 1. Idempotent matrix is a non-negative definite matrix and its eigenvalues are either 0 or Sum of the matrix diagonal is equal to the rank of the matrix. 3. A full rank idempotent matrix must be identity matrix. Example: 4. A zero rank idempotent matrix must be zero matrix. Example: 5. Idempotent matrix must be a square matrix Idempotent matrix construction Base on the idempotent matrix s characteristic 2, 3, 4 and 5, we assume a 2x2 rank 1 symmetric idempotent matrix as follow: M(u) = Since M(u) x M(u) = M(u), then. By matrix multiplication, we can obtain simultaneous equations as follow:,.
42 28 Solved these simultaneous equation we get, hence a 2x2 rank 1 symmetric idempotent matrix as follow: M(u) =, where 0 Assume a 3x3 rank 2 symmetric idempotent matrix as follow: M(u) =, since M(u) x M(u)=M(u), then By matrix multiplication, we can obtain simultaneous equations as follow:,,, By solving these equations we get. Hence 3x3 rank 2 symmetric idempotent matrix as follow: M(u) =, where 0.
43 29 Let assume 4x4 rank 3 symmetry idempotent matrix M(u) as follow: M(u) =, since M(u) x M(u)=M(u), then, by matrix multiplication, we can obtain simultaneous equations as follow:,,, by solving these equations we get, hence 4x4 rank 3 symmetric idempotent matrix as follow: M(u) =, where 0 by repeating the same process we can obtain a 5x5 rank 4 symmetry idempotent matrix as follow:
44 30, where 0 In this research, we consider special idempotent Matrix such that it is symmetric which as shown below: where 0 for all real numbers. We consider real number for u as a parameter for the universal portfolio. matrix: The group of 3-stock data Set A, B and C is generated by symmetry idempotent Apply the formula in (5.2 ), we can generate a 3x3 matrix.
45 31 where all the elements in matrix shown as follow: After obtained the matrix, let vector = (0.3333, , ), by using the 3 stocks data set for 500 days to generate the, select a valid value by using equation so that all values in vector satisfying condition we can generate all the vector, where n=1, 2, 3,4 500 and m= 3. Also the final principal S 500 can be obtain via equation
46 32 S n = =. We will use this result to compare with other later. The group of 5-stock data Set D, E and F, G, H is generated by idempotent matrix: Apply the formula in (5.2), we can generate a 5x5 matrix.
47 33 After obtained the matrix, let vector = (0.2, 0.2, 0.2, 0.2, 0.2), use the 5 stocks data set D, E, F, G and H for 1500 days to generate the, select a valid value by using equation
48 34 so that all values in vector satisfying condition we can generate all the vector, where n=1, 2, 3, and m= 5. Also the final principal S 1500 can be obtain via equation S n = =. We will use this result to compare with other later. 5.3 The Universal Portfolio Generated by Five Special Distributions Order 1 Universal Portfolio Let s derive the formula for order 1 universal portfolio.
49 Order 2 Universal Portfolio The formula derivation for order 2 universal portfolio.
50 36
51 where 37
52 Order 3 Universal Portfolio The formula derivation for order 3 universal portfolio.
53 39
54 40
55 where 41
56 42
57 43
58 The Five Special Distributions In our research, we choose five probability distributions to generate the moments required in. The distributions are lognormal distribution, Gamma distribution, Beta distribution, Gaussian distribution and the inverse Gaussian distribution. Table contain the corresponding probability density functions which we selected. Table The 5 probability distributions and its corresponding probability density functions Distribution Probability Density Function f( x ) Gaussian, Lognormal, Inverse Gaussian, Beta, Gamma, 1 e 2 1 e x 2 2 x 3 e x x ln x x 2 2 2x x. is a gamma function 1 x 1 e x. is a gamma function
59 45 Table The First four moments of the selected probability distributions Distribution Gaussian , Lognormal, Inverse Gaussian, Beta, 1 E(X) E(X) E(X ) E(X) E(X ) E(X ) 3 Gamma,
60 46 CHAPTER 6 COMPUTATIONAL RESULTS ANALYSIS 6.1 Result of Universal Portfolio generated by idempotent matrix We prepare 8 sets of share price data to run the universal portfolio, that is data set A, B, C, D, E, F, G and H. Where the data set A, B and C consists of 3 different listed company stocks from Kuala Lumpur Stock Exchange (KLSE). Portfolio set A contain Maybank (1155), Genting(3182) and Amway (6351). Portfolio set B contain PBBank(1295), Sunrise(6165) and YTL(4677). Portfolio set C contain HLBank(5819), RHBCAP(1066) and YTL(4677). Recorded period(500 trade days) of these stocks is taken from 1 st January year 2003 until 30 th November year Portfolio set D contain IOIcorp (1961), Carlsbg(2836), BAT(4162), Nestle(4707) and DIGI(6947). Portfolio set E consist of PBBank(1295), Kulim(2003), KLCC(5235SS), AEON (6599) and KLK(2445). Portfolio set F contain AMBank(1015), BJTOTO(1562), AIRASIA(5099), GAMUDA(5398) and Genting(3182). Portfolio set G consist of listed company AEON(6599), BAT(4162), Kulim(2003), Nestle(4707) and DIGI(6947) while portfolio set H consist of listed company DIGI(6947), PBBank(1295), KLCC(5235SS), Carlsbg(2836) and KLK(2445). Each of these data sets consists of 5 different listed company from KLSE. The recorded period of these stocks is 1500 trading days. Assuming the starting principal is 1, that is and let the portfolio vector. We use the data set A, B, C and symmetric idempotent
61 47 matrix to generate the universal portfolio for 500 trading days. Use data set D, E, F, G, H and symmetric idempotent matrix to generate the universal portfolio for 1500 trading days. All the computation results are as shown below: Table 6.1: Values of the principal and the generated by idempotent matrix for Data Set A Set u Min Max Best A
62 48 Table 6.2: Values of the principal and the generated by idempotent matrix for Data Set B Set u Min Max Best B
63 49 Table 6.3: Values of the principal and the generated by idempotent matrix for Data Set C Set u Min Max Best C
64 50 Table 6.4: Values of the principal and the generated by idempotent matrix for Data Set D Set u Min Max Best D
65 51 Table 6.5: Values of the principal and the generated by idempotent matrix for Data Set E Set u Min Max Best E
66 52 Table 6.6: Values of the principal and the generated by idempotent matrix for Data Set F Set u Min Max Best F
67 53 Table 6.7: Values of the principal and the generated by idempotent matrix for Data Set G Set u Min Max Best G
68 54 Table 6.8: Values of the principal and the generated by idempotent matrix for Data Set H Set u Min Max Best H
69 55 Lets analyze the result of 3 stock portfolios for Set A,B and C. Based on data set A Table 6.1, the maximum principal for 500 trading days can be achieved when parameter u is 0 with. The correspondent best value is which is within the range ( , ). Base on the observation a decreasing trend in when the parameter u is increasing from 0 to 0.7. After the maximum is achieved, there is an increasing trend in for parameter u range from 0.70 to 1.0. The same nature goes to data set B table 6.2 and data set C table 6.3. For data Set B, the maximum principal for 500 trading days is achieved when the parameter u is 0 with. The correspondent best value is which is within the range ( , ). The decreasing trend occur in range u from 0 to 0.9, the increasing trend occur in range u from 0.9 to 1.0. For data Set C, the maximum principal for 500 trading days can be achieved when the parameter u is 0 with. The correspondent best value is which is within the range ( , ). The decreasing trend occur in range u from 0 to 0.55 and from 0.9 to 1.0, the increasing trend occur in range u from 0.55 to 0.9. Therefore, we conclude that Universal Portfolio generated by symmetric idempotent Matrix with highest wealth after 500 trading days achieved when the parameter u is 0 for the same 3 stocks portfolio data sets A,B and C. On the other hand, we analyse the performance of 5 stock portfolios data set D,E,F,G and H. Based on data set D Table 6.4, the maximum wealth after 1500 trading days = achieved when the parameter u is 1. The correspondent best value is which is within the range ( , ). There is an increasing trend in for parameter u range from 0.05 to 0.50 and parameter u range from 0.8 to 1.0. There is a decreasing trend for parameter u range from 0.00 to 0.05 and parameter u range from 0.50 to 0.8. Based on data Set E table 6.5, the maximum = is achieved when the parameter u is 0. The
70 56 correspondent best value is which is within the range ( , ). There is an increasing trend in for parameter u range from 0.90 to 1.0. The decreasing trend in in for parameter u range from 0.0 to 0.9. Based on data set F table 6.6, the maximum = is achieved when parameter u is The correspondent best value is which is within the range ( , ). There is an increasing trend in for parameter u range from 0.00 to The decreasing trend in for parameter u range from 0.85 to 1.0. Based on data set G table 6.7, the maximum = is achieved when the parameter u is The correspondent best value is which is within the range ( , ). There is an increasing trend in when the parameter u range from 0.00 to The decreasing trend in for parameter u range from 0.85 to Based on data set H table 6.8, the maximum = is achieved when the parameter u is 1.0. The correspondent best value is which is within the range ( , ). There is an increasing trend in when the parameter u range from 0.40 to The decreasing trend in for parameter u range from 0.00 to Performance of Finite Order Universal Portfolio There are 8 sets of data were used to generate the universal portfolio, that is data set A, B, C, D, E, F, G and H. Each set of data consists of 3 different stocks from Kuala Lumpur Stock Exchange(KLSE). Set A is the portfolio contain stocks Maybank, Genting and Amway (M) Holdings. Set B is the portfolio contain stocks Public Bank, Sunrise and YTL
71 57 Corporation. Set C is the portfolio contain stocks Hong Leong Bank, RHB Capital and YTL Corporation. The trading period recorded for these stocks is 500 days, which taken from 1 st January 2003 to 30 th November On the other hand, data set D is the portfolio contain Industrial Oxygen Industries (IOI), Carlsberg, British American Tobacco (BAT), Nestle and DIGI. Data set E is the portfolio contain Public Bank, Kulim, Kuala Lumpur City Centre (KLCC), AEON and Kuala Lumpur Kepong Berhad (KLK). Data set F is the portfolio contain AmBank, BJTOTO, AIRASIA, GAMUDA and Genting. Data set G is the portfolio contain AEON, BAT, Kulim, Nestle and DIGI. Data set H is the portfolio contain DIGI, Public Bank, KLCC, Carlsberg and KLK. Each set of data contain 5 different stocks from Kuala Lumpur Stock Exchange(KLSE). The trading period recorded for these sets is 1500 days. Assuming the initial capital is 1 unit, that is and the initial portfolio vector,. The universal portfolios generated by Gaussian, Inverse Gaussian, Lognormal, Beta and Gamma distribution for 500 days for data Set A, B and C. All the computation results are shown as tables below.
72 58 Table 6.9: The return after 500 days for the finite order universal portfolio generated by Gaussian distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when µ = 1 µ = 1 Set A Set B Set C Order 1 Order 2 Order 3 Order 1 Order 2 Order 3 Order 1 Order 2 Order Table 6.10: The return after 500 days for the finite order universal portfolio generated by Gaussian distribution with different parameter values for a portfolio of 3 stocks which is Set A, Set B and Set C when µ = 10 µ = 10 Set A Set B Set C Order 1 Order 2 Order 3 Order 1 Order 2 Order 3 Order 1 Order 2 Order
Learning Methods for Online Prediction Problems. Peter Bartlett Statistics and EECS UC Berkeley
Learning Methods for Online Prediction Problems Peter Bartlett Statistics and EECS UC Berkeley Course Synopsis A finite comparison class: A = {1,..., m}. Converting online to batch. Online convex optimization.
More informationEFFECT OF SOIL TYPE ON SEISMIC PERFROMANCE OF REINFORCED CONCRETE SCHOOL BUILDING
EFFECT OF SOIL TYPE ON SEISMIC PERFROMANCE OF REINFORCED CONCRETE SCHOOL BUILDING NUR AMIRAH BINTI MOHD NASAI B. ENG (HONS.) CIVIL ENGINEERING UNIVERSITI MALAYSIA PAHANG SUPERVISOR S DECLARATION I hereby
More informationEFFECT OF NOZZLE ANGLE ON JET IMPINGEMENT COOLING SYSTEM KHAIDER BIN ABU BAKAR
EFFECT OF NOZZLE ANGLE ON JET IMPINGEMENT COOLING SYSTEM KHAIDER BIN ABU BAKAR Report submitted in fulfilment of the requirements for the award of the degree of Bachelor of Mechanical Engineering Faculty
More informationAN APPROPRIATE LOT SIZING TECHNIQUE FOR INVENTORY POLICY PROBLEM WITH DECREASING DEMAND
AN APPROPRIATE LOT SIZING TECHNIQUE FOR INVENTORY POLICY PROBLEM WITH DECREASING DEMAND A THESIS Submitted in Partial Fulfillment of the Requirement for the Bachelor Degree of Engineering in Industrial
More informationMATHEMATICS (MATH) Calendar
MATHEMATICS (MATH) This is a list of the Mathematics (MATH) courses available at KPU. For information about transfer of credit amongst institutions in B.C. and to see how individual courses transfer, go
More informationRESEARCH STUDY ON ADOPTION OF SOCIAL MEDIA MARKETING IN THE ENTERPRISE (MALAYSIA CONTEXT) KEE YONG HONG LEOW XIN YI TANG XIN YI WONG SIONG MUNG
RESEARCH STUDY ON ADOPTION OF SOCIAL MEDIA MARKETING IN THE ENTERPRISE (MALAYSIA CONTEXT) KEE YONG HONG LEOW XIN YI TANG XIN YI WONG SIONG MUNG BACHELOR OF MARKETING (HONS) UNIVERSITI TUNKU ABDUL RAHMAN
More informationInstitute of Actuaries of India
Institute of Actuaries of India Subject CT3 Probability and Mathematical Statistics For 2018 Examinations Subject CT3 Probability and Mathematical Statistics Core Technical Syllabus 1 June 2017 Aim The
More informationThus necessary and sufficient conditions for A to be positive definite are:
14 Problem: 4. Define E = E 3 E 2 E 1 where E 3 is defined by (62) and E 1 and E 2 are defined in (61). Show that EAE T = D where D is defined by (60). The matrix E and the diagonal matrix D which occurs
More informationUNIVERSITI PUTRA MALAYSIA
UNIVERSITI PUTRA MALAYSIA SOLUTIONS OF DIOPHANTINE EQUATION FOR PRIMES p, 2 p 13 SHAHRINA BT ISMAIL IPM 2011 10 SOLUTIONS OF DIOPHANTINE EQUATION FOR PRIMES, By SHAHRINA BT ISMAIL Thesis Submitted to the
More informationSTRUCTURE Of ECONOMICS A MATHEMATICAL ANALYSIS
THIRD EDITION STRUCTURE Of ECONOMICS A MATHEMATICAL ANALYSIS Eugene Silberberg University of Washington Wing Suen University of Hong Kong I Us Irwin McGraw-Hill Boston Burr Ridge, IL Dubuque, IA Madison,
More informationRABI OSCILLATIONS IN THE JAYNES-CUMMINGS MODEL TING HOCK KING. A project report submitted in partial fulfilment of the
RABI OSCILLATIONS IN THE JAYNES-CUMMINGS MODEL TING HOCK KING A project report submitted in partial fulfilment of the requirements for the award of Bachelor of Science (Hons.) Physics Faculty of Engineering
More informationDecision Models Lecture 5 1. Lecture 5. Foreign-Currency Trading Integer Programming Plant-location example Summary and Preparation for next class
Decision Models Lecture 5 1 Lecture 5 Foreign-Currency Trading Integer Programming Plant-location example Summary and Preparation for next class Foreign Exchange (FX) Markets Decision Models Lecture 5
More informationUNIVERSITI TEKNOLOGI MARA
UNIVERSITI TEKNOLOGI MARA GEO IMS (GEOMATICS INVENTORY MANAGEMENT SYSTEM) NIK NUR IZZATIE BINTI ABDUL HADI Thesis submitted in fulfillment of the requirements for the degree of Bachelor Science of Geomatics
More informationPROBABILITY DISTRIBUTIONS. J. Elder CSE 6390/PSYC 6225 Computational Modeling of Visual Perception
PROBABILITY DISTRIBUTIONS Credits 2 These slides were sourced and/or modified from: Christopher Bishop, Microsoft UK Parametric Distributions 3 Basic building blocks: Need to determine given Representation:
More informationUNCERTAINTY ANALYSIS OF TWO-SHAFT GAS TURBINE PARAMETER OF ARTIFICIAL NEURAL NETWORK (ANN) APPROXIMATED FUNCTION USING SEQUENTIAL PERTURBATION METHOD
UNCERTAINTY ANALYSIS OF TWO-SHAFT GAS TURBINE PARAMETER OF ARTIFICIAL NEURAL NETWORK (ANN) APPROXIMATED FUNCTION USING SEQUENTIAL PERTURBATION METHOD HILMI ASYRAF BIN RAZALI Report submitted in partial
More informationA Guide to Modern Econometric:
A Guide to Modern Econometric: 4th edition Marno Verbeek Rotterdam School of Management, Erasmus University, Rotterdam B 379887 )WILEY A John Wiley & Sons, Ltd., Publication Contents Preface xiii 1 Introduction
More informationSequential Investment, Universal Portfolio Algos and Log-loss
1/37 Sequential Investment, Universal Portfolio Algos and Log-loss Chaitanya Ryali, ECE UCSD March 3, 2014 Table of contents 2/37 1 2 3 4 Definitions and Notations 3/37 A market vector x = {x 1,x 2,...,x
More informationInferences about Parameters of Trivariate Normal Distribution with Missing Data
Florida International University FIU Digital Commons FIU Electronic Theses and Dissertations University Graduate School 7-5-3 Inferences about Parameters of Trivariate Normal Distribution with Missing
More informationIntroduction to Game Theory: Simple Decisions Models
Introduction to Game Theory: Simple Decisions Models John C.S. Lui Department of Computer Science & Engineering The Chinese University of Hong Kong John C.S. Lui (CUHK) Advanced Topics in Network Analysis
More informationCITY UNIVERSITY OF HONG KONG
CITY UNIVERSITY OF HONG KONG Topics in Optimization: Solving Second-Order Conic Systems with Finite Precision; Calculus of Generalized Subdifferentials for Nonsmooth Functions Submitted to Department of
More informationMATHEMATICS FOR ECONOMISTS. An Introductory Textbook. Third Edition. Malcolm Pemberton and Nicholas Rau. UNIVERSITY OF TORONTO PRESS Toronto Buffalo
MATHEMATICS FOR ECONOMISTS An Introductory Textbook Third Edition Malcolm Pemberton and Nicholas Rau UNIVERSITY OF TORONTO PRESS Toronto Buffalo Contents Preface Dependence of Chapters Answers and Solutions
More informationCurve Fitting Re-visited, Bishop1.2.5
Curve Fitting Re-visited, Bishop1.2.5 Maximum Likelihood Bishop 1.2.5 Model Likelihood differentiation p(t x, w, β) = Maximum Likelihood N N ( t n y(x n, w), β 1). (1.61) n=1 As we did in the case of the
More informationModelling Ireland s exchange rates: from EMS to EMU
From the SelectedWorks of Derek Bond November, 2007 Modelling Ireland s exchange rates: from EMS to EMU Derek Bond, University of Ulster Available at: https://works.bepress.com/derek_bond/15/ Background
More informationGRADIENT-TYPE METHODS FOR UNCONSTRAINED OPTIMIZATION
GRADIENT-TYPE METHODS FOR UNCONSTRAINED OPTIMIZATION By GUAN HUI SHAN A project report submitted in partial fulfilment of the requirements for the award of Bachelor of Science (Hons.) Applied Mathematics
More informationxvi xxiii xxvi Construction of the Real Line 2 Is Every Real Number Rational? 3 Problems Algebra of the Real Numbers 7
About the Author v Preface to the Instructor xvi WileyPLUS xxii Acknowledgments xxiii Preface to the Student xxvi 1 The Real Numbers 1 1.1 The Real Line 2 Construction of the Real Line 2 Is Every Real
More informationLecture 16: Small Sample Size Problems (Covariance Estimation) Many thanks to Carlos Thomaz who authored the original version of these slides
Lecture 16: Small Sample Size Problems (Covariance Estimation) Many thanks to Carlos Thomaz who authored the original version of these slides Intelligent Data Analysis and Probabilistic Inference Lecture
More informationFormat of Holding of Specified securities
Format of Holding of Specified 1. Name of Listed Entity: MANPASAND BEVERAGES LIMITED 2. Scrip Code/Name of Scrip/Class of Security:539207,MANPASAND,EQUITY SHARES 3. Share Holding Pattern Filed under: Reg.
More informationFormat of Holding of Specified securities. YES BANK LIMITED ,YESBANK, 2. Scrip Code/Name of Scrip/Class of Security:
Format of Holding of Specified 1. Name of Listed Entity: YES BANK LIMITED 532648,YESBANK, 2. Scrip Code/Name of Scrip/Class of Security: EQUITY SHARES 3. Share Holding Pattern Filed under: Reg. 31(1)(a)/Reg.31(1)(b)/Reg.31(1)(c)
More informationECON4515 Finance theory 1 Diderik Lund, 5 May Perold: The CAPM
Perold: The CAPM Perold starts with a historical background, the development of portfolio theory and the CAPM. Points out that until 1950 there was no theory to describe the equilibrium determination of
More informationEfficient Algorithms for Universal Portfolios
Efficient Algorithms for Universal Portfolios Adam Kalai CMU Department of Computer Science akalai@cs.cmu.edu Santosh Vempala y MIT Department of Mathematics and Laboratory for Computer Science vempala@math.mit.edu
More informationProblem Set 7 Due March, 22
EE16: Probability and Random Processes SP 07 Problem Set 7 Due March, Lecturer: Jean C. Walrand GSI: Daniel Preda, Assane Gueye Problem 7.1. Let u and v be independent, standard normal random variables
More informationPURE MATHEMATICS Unit 1
PURE MATHEMATICS Unit 1 FOR CAPE EXAMINATIONS DIPCHAND BAHALL CAPE is a registered trade mark of the Caribbean Examinations Council (CXC). Pure Mathematics for CAPE Examinations Unit 1 is an independent
More informationEstimation for state space models: quasi-likelihood and asymptotic quasi-likelihood approaches
University of Wollongong Research Online University of Wollongong Thesis Collection 1954-2016 University of Wollongong Thesis Collections 2008 Estimation for state space models: quasi-likelihood and asymptotic
More informationApplication of a Helmholtz resonator excited by grazing flow for manipulation of a turbulent boundary layer
Application of a Helmholtz resonator excited by grazing flow for manipulation of a turbulent boundary layer Farzin Ghanadi School of Mechanical Engineering The University of Adelaide South Australia, 5005
More information:RELIANCE NIPPON LIFE ASSET MANAGEMENT LIMITED
Format of Holding of Specified (Post Issue), Date: 02nd November, 2017 1. Name of Listed Entity:RELIANCE NIPPON LIFE ASSET MANAGEMENT LIMITED 2. Scrip Code/Name of Scrip/Class of Security : BSE - NSE -
More informationCurvature measures for generalized linear models
University of Wollongong Research Online University of Wollongong Thesis Collection 1954-2016 University of Wollongong Thesis Collections 1999 Curvature measures for generalized linear models Bernard A.
More informationMathematics 1104B. Systems of Equations and Inequalities, and Matrices. Study Guide. Text: Mathematics 11. Alexander and Kelly; Addison-Wesley, 1998.
Adult Basic Education Mathematics Systems of Equations and Inequalities, and Matrices Prerequisites: Mathematics 1104A, Mathematics 1104B Credit Value: 1 Text: Mathematics 11. Alexander and Kelly; Addison-Wesley,
More informationSect 2.4 Multiplying and Dividing Integers
55 Sect 2.4 Multiplying and Dividing Integers Objective a: Understanding how to multiply two integers. To see how multiplying and dividing a negative and a positive number works, let s look at some examples.
More informationStudy Skills in Mathematics. Edited by D. Burkhardt and D. Rutherford. Nottingham: Shell Centre for Mathematical Education (Revised edn 1981).
Study Skills in Mathematics. Edited by D. Burkhardt and D. Rutherford. Nottingham: Shell Centre for Mathematical Education (Revised edn 1981). (Copies are available from the Shell Centre for Mathematical
More informationPractice Final Exam Solutions for Calculus II, Math 1502, December 5, 2013
Practice Final Exam Solutions for Calculus II, Math 5, December 5, 3 Name: Section: Name of TA: This test is to be taken without calculators and notes of any sorts. The allowed time is hours and 5 minutes.
More informationFormat of Holding of Specified securities
Format of Holding of Specified 1. Name of Listed Entity:YES BANK LIMITED 2. Scrip Code/Name of Scrip/Class of Security:532648,YESBANK,EQUITY SHARES 3. Share Holding Pattern Filed under: Reg. 31(1)(a)/Reg.31(1)(b)/Reg.31(1)(c)
More informationOn The Behavior of Malaysian Equities: Fractal Analysis Approach
On The Quarterly Behavior Journal of Malaysian of Quantitative Equities: Economics, Spring 2015, 12(1): 1-18 1 On The Behavior of Malaysian Equities: Fractal Analysis Approach Alireza Bahiraie (PhD) and
More informationUNDRAINED SHEAR STRENGTH OF SOFT CLAY REINFORCE WITH SINGLE 16MM DIAMETER ENCAPSULATED BOTTOM ASH COLUMN NABILAH BINTI MD MASHOD FAKEH
1 UNDRAINED SHEAR STRENGTH OF SOFT CLAY REINFORCE WITH SINGLE 16MM DIAMETER ENCAPSULATED BOTTOM ASH COLUMN NABILAH BINTI MD MASHOD FAKEH B. ENG (HONS.) CIVIL ENGINEERING UNIVERSITI MALAYSIA PAHANG 1 UNIVERSITI
More informationC. Finding roots of trinomials: 1st Example: x 2 5x = 14 x 2 5x 14 = 0 (x 7)(x + 2) = 0 Answer: x = 7 or x = -2
AP Calculus Students: Welcome to AP Calculus. Class begins in approimately - months. In this packet, you will find numerous topics that were covered in your Algebra and Pre-Calculus courses. These are
More informationStat 5101 Lecture Notes
Stat 5101 Lecture Notes Charles J. Geyer Copyright 1998, 1999, 2000, 2001 by Charles J. Geyer May 7, 2001 ii Stat 5101 (Geyer) Course Notes Contents 1 Random Variables and Change of Variables 1 1.1 Random
More informationChapter 6. Estimation of Confidence Intervals for Nodal Maximum Power Consumption per Customer
Chapter 6 Estimation of Confidence Intervals for Nodal Maximum Power Consumption per Customer The aim of this chapter is to calculate confidence intervals for the maximum power consumption per customer
More informationFACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures
FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING Lectures MODULE 8 MATRICES III Rank of a matrix 2 General systems of linear equations 3 Eigenvalues and eigenvectors Rank of a matrix
More informationAP Calculus AB Summer Assignment
Name: AP Calculus AB Summer Assignment Due Date: The beginning of class on the last class day of the first week of school. The purpose of this assignment is to have you practice the mathematical skills
More informationRequisite Approval must be attached
Requisite Approval must be attached CITRUS COMMUNITY COLLEGE DISTRICT DEPARTMENT Mathematics COURSE NUMBER MATH 150 TITLE Intermediate Algebra THIS COURSE IS CLASSIFIED AS: DEGREE APPLICABLE UNIT VALUE
More informationShareholding Pattern under Regulation 31 of SEBI (Listing Obligations and Disclosure Requirements) Regulations, 2015
Shareholding Pattern under Regulation 31 of SEBI (Listing Obligations and Disclosure Requirements) Regulations, 2015 1. Name of Listed Entity: Future Market Networks Limited 2. Scrip Code/Name of Scrip/Class
More informationDoctor of Philosophy
MAINTAINING A COMMON ARBITRARY UNIT IN SOCIAL MEASUREMENT STEPHEN HUMPHRY 2005 Submitted in fulfillment of the requirements of the degree of Doctor of Philosophy School of Education, Murdoch University,
More informationSummer Review Packet. for students entering. AP Calculus BC
Summer Review Packet for students entering AP Calculus BC The problems in this packet are designed to help you review topics that are important to your success in AP Calculus. Please attempt the problems
More informationProbabilities & Statistics Revision
Probabilities & Statistics Revision Christopher Ting Christopher Ting http://www.mysmu.edu/faculty/christophert/ : christopherting@smu.edu.sg : 6828 0364 : LKCSB 5036 January 6, 2017 Christopher Ting QF
More informationExamining the accuracy of the normal approximation to the poisson random variable
Eastern Michigan University DigitalCommons@EMU Master's Theses and Doctoral Dissertations Master's Theses, and Doctoral Dissertations, and Graduate Capstone Projects 2009 Examining the accuracy of the
More informationLinear programming on Cell/BE
Norwegian University of Science and Technology Faculty of Information Technology, Mathematics and Electrical Engineering Department of Computer and Information Science Master Thesis Linear programming
More informationFormat of Holding of Specified securities
Format of Holding of Specified 1. Name of Listed Entity: YES BANK LIMITED 2. Scrip Code/Name of Scrip/Class of Security: 532648,YESBANK,EQUITY SHARES 3. Share Holding Pattern Filed under: Reg.31(1)(b)
More informationModern Urban and Regional Economics
Modern Urban and Regional Economics SECOND EDITION Philip McCann OXFORD UNIVERSITY PRESS Contents List of figures List of tables Introduction xii xiv xvii Part I Urban and Regional Economic Models and
More informationIndependent Component Analysis. Contents
Contents Preface xvii 1 Introduction 1 1.1 Linear representation of multivariate data 1 1.1.1 The general statistical setting 1 1.1.2 Dimension reduction methods 2 1.1.3 Independence as a guiding principle
More informationPARTICIPATING ORGANISATIONS CIRCULAR
PARTICIPATING ORGANISATIONS CIRCULAR Date : 24 November 2011 R/R No. : 10 of 2011 DIRECTIVES ON SUBMISSION BY PARTICIPATING ORGANISATIONS OF PERIODIC REPORTS BY ELECTRONIC TRANSMISSION TO BURSA MALAYSIA
More informationReview questions for Math 111 final. Please SHOW your WORK to receive full credit Final Test is based on 150 points
Please SHOW your WORK to receive full credit Final Test is based on 150 points 1. True or False questions (17 pts) a. Common Logarithmic functions cross the y axis at (0,1) b. A square matrix has as many
More informationANALYTICAL MATHEMATICS FOR APPLICATIONS 2018 LECTURE NOTES 3
ANALYTICAL MATHEMATICS FOR APPLICATIONS 2018 LECTURE NOTES 3 ISSUED 24 FEBRUARY 2018 1 Gaussian elimination Let A be an (m n)-matrix Consider the following row operations on A (1) Swap the positions any
More informationLAKELAND COMMUNITY COLLEGE COURSE OUTLINE FORM
LAKELAND COMMUNITY COLLEGE COURSE OUTLINE FORM ORIGINATION DATE: 8/2/99 APPROVAL DATE: 3/22/12 LAST MODIFICATION DATE: 3/28/12 EFFECTIVE TERM/YEAR: FALL/ 12 COURSE ID: COURSE TITLE: MATH2800 Linear Algebra
More informationFormat of holding of specific securities. Particulars Yes* No*
Format of holding of specific Annexure - I Name of the Listed Entity : RASOI LIMITED Scrip Code : 507649 Name of the scrip : RASOI Class of Security : EQUITY SHARES Share Holding Pattern Filed under Reg.31(1)(b)
More informationSimplifying Radical Expressions
Simplifying Radical Expressions Product Property of Radicals For any real numbers a and b, and any integer n, n>1, 1. If n is even, then When a and b are both nonnegative. n ab n a n b 2. If n is odd,
More informationME 597: AUTONOMOUS MOBILE ROBOTICS SECTION 2 PROBABILITY. Prof. Steven Waslander
ME 597: AUTONOMOUS MOBILE ROBOTICS SECTION 2 Prof. Steven Waslander p(a): Probability that A is true 0 pa ( ) 1 p( True) 1, p( False) 0 p( A B) p( A) p( B) p( A B) A A B B 2 Discrete Random Variable X
More informationSTATE COUNCIL OF EDUCATIONAL RESEARCH AND TRAINING TNCF DRAFT SYLLABUS
STATE COUNCIL OF EDUCATIONAL RESEARCH AND TRAINING TNCF 2017 - DRAFT SYLLABUS Subject :Business Maths Class : XI Unit 1 : TOPIC Matrices and Determinants CONTENT Determinants - Minors; Cofactors; Evaluation
More informationCalculus Summer TUTORIAL
Calculus Summer TUTORIAL The purpose of this tutorial is to have you practice the mathematical skills necessary to be successful in Calculus. All of the skills covered in this tutorial are from Pre-Calculus,
More informationPRODUCTION OF ETHYL ACETATE USING CATALYTIC REACTION METHOD
PRODUCTION OF ETHYL ACETATE USING CATALYTIC REACTION METHOD Bamunusingha Arachchige Nadeeka Niroshinie Bamunusingha (108001 T) Thesis submitted in partial fulfillment of the requirements for the degree
More informationAdaptive Filtering. Squares. Alexander D. Poularikas. Fundamentals of. Least Mean. with MATLABR. University of Alabama, Huntsville, AL.
Adaptive Filtering Fundamentals of Least Mean Squares with MATLABR Alexander D. Poularikas University of Alabama, Huntsville, AL CRC Press Taylor & Francis Croup Boca Raton London New York CRC Press is
More informationName of the Listed Entity : NORBEN TEA & EXPORTS LIMITED
Format of holding of specific securities Annexure - I Name of the Listed Entity : NORBEN TEA & EXPORTS LIMITED CIN : L01132WB1990PLC048991 Share Holding Pattern Filed under Reg.31(1)(b) for Quarter ending
More informationMI&CCMIllILIL&JN CHEMISTRY
MI&CCMIllILIL&JN IQ)llCClrll(Q)JN&IRl)1 @IF CHEMISTRY MACCMIIJ1J1A~ JTI)nCClln@~Aill)! (Q)]F CHEMISTRY D.B. HIBBERT & A.M. JAMES M MACMILLAN REFERENCE BOOKS The Macmillan Press Ltd, 1987 All rights reserved.
More informationALGEBRA AND GEOMETRY. Cambridge University Press Algebra and Geometry Alan F. Beardon Frontmatter More information
ALGEBRA AND GEOMETRY This text gives a basic introduction and a unified approach to algebra and geometry. It covers the ideas of complex numbers, scalar and vector products, determinants, linear algebra,
More informationAppendix A. Math Reviews 03Jan2007. A.1 From Simple to Complex. Objectives. 1. Review tools that are needed for studying models for CLDVs.
Appendix A Math Reviews 03Jan007 Objectives. Review tools that are needed for studying models for CLDVs.. Get you used to the notation that will be used. Readings. Read this appendix before class.. Pay
More informationBoundary Problems for One and Two Dimensional Random Walks
Western Kentucky University TopSCHOLAR Masters Theses & Specialist Projects Graduate School 5-2015 Boundary Problems for One and Two Dimensional Random Walks Miky Wright Western Kentucky University, miky.wright768@topper.wku.edu
More informationa Whether the Listed Entity has issued any partly paid up shares NO b Whether the Listed Entity has issued any Convertible Securities or Warrants?
Format of Holding of Specified securities 1. Name of Listed Entity: YES BANK LIMITED 2. Scrip Code/Name of Scrip/Class of Security: 532648,YESBANK,EQUITY SHARES 3. Share Holding Pattern Filed under: Reg.31(1)(b)
More informationFormat of holding of specific securities. Particulars Yes* No*
Format of holding of specific securities Annexure - I Name of the Listed Entity : NORBEN TEA & EXPORTS LIMITED CIN : L01132WB1990PLC048991 Share Holding Pattern Filed under Reg.31(1)(b) for Quarter ending
More informationFormat of Holding of Specified securities
Format of Holding of Specified securities 1. Name of Listed Entity:RELIGARE ENTERPRISES LIMITED 2. Scrip Code/Name of Scrip/Class of Security:532915,RELIGARE,EQUITY SHARES 3. Share Holding Pattern Filed
More informationName of the Listed Entity : NORBEN TEA & EXPORTS LIMITED
Format of holding of specific securities Annexure - I Name of the Listed Entity : NORBEN TEA & EXPORTS LIMITED CIN : L01132WB1990PLC048991 Share Holding Pattern Filed under Reg.31(1)(b) for Quarter ending
More informationDEVELOPMENT OF LAND SUITABILITY EVALUATION SYSTEM FOR COASTAL AQUACULTURE USING ARTIFICIAL NEURAL NETWORK AND GEOGRAPHICAL INFORMATION SYSTEMS
DEVELOPMENT OF LAND SUITABILITY EVALUATION SYSTEM FOR COASTAL AQUACULTURE USING ARTIFICIAL NEURAL NETWORK AND GEOGRAPHICAL INFORMATION SYSTEMS Case Study: Mahakam Delta, East Kalimantan I KETUT SUTARGA
More informationMathematics for Economics and Finance
Mathematics for Economics and Finance Michael Harrison and Patrick Waldron B 375482 Routledge Taylor & Francis Croup LONDON AND NEW YORK Contents List of figures ix List of tables xi Foreword xiii Preface
More informationReformulation of chance constrained problems using penalty functions
Reformulation of chance constrained problems using penalty functions Martin Branda Charles University in Prague Faculty of Mathematics and Physics EURO XXIV July 11-14, 2010, Lisbon Martin Branda (MFF
More informationSeventeen generic formulas that may generate prime-producing quadratic polynomials
Seventeen generic formulas that may generate prime-producing quadratic polynomials Marius Coman Bucuresti, Romania email: mariuscoman13@gmail.com Abstract. In one of my previous papers I listed forty-two
More informationMR. YATES. Vocabulary. Quadratic Cubic Monomial Binomial Trinomial Term Leading Term Leading Coefficient
ALGEBRA II WITH TRIGONOMETRY COURSE OUTLINE SPRING 2009. MR. YATES Vocabulary Unit 1: Polynomials Scientific Notation Exponent Base Polynomial Degree (of a polynomial) Constant Linear Quadratic Cubic Monomial
More informationMeta Algorithms for Portfolio Selection. Technical Report
Meta Algorithms for Portfolio Selection Technical Report Department of Computer Science and Engineering University of Minnesota 4-192 EECS Building 200 Union Street SE Minneapolis, MN 55455-0159 USA TR
More informationIntegrated reliable and robust design
Scholars' Mine Masters Theses Student Research & Creative Works Spring 011 Integrated reliable and robust design Gowrishankar Ravichandran Follow this and additional works at: http://scholarsmine.mst.edu/masters_theses
More informationCourse Goals and Course Objectives, as of Fall Math 102: Intermediate Algebra
Course Goals and Course Objectives, as of Fall 2015 Math 102: Intermediate Algebra Interpret mathematical models such as formulas, graphs, tables, and schematics, and draw inferences from them. Represent
More informationFATIGUE BEHAVIOUR OF OFFSHORE STEEL JACKET PLATFORMS
FATIGUE BEHAVIOUR OF OFFSHORE STEEL JACKET PLATFORMS by ASHOK GUPTA THESIS SUBMITTED TO THE INDIAN INSTITUTE OF TECHNOLOGY, DELHI FOR THE AWARD OF THE DEGREE OF DOCTOR OF PHILOSOPHY Department of Civil
More informationE-Community Check Request Checklist
E-Community Check Request Checklist The E-Community must complete the following information on each business approved for a loan or grant in order for the Kansas Center of Entrepreneurship (KCFE) to disburse
More informationUNIVERSITI TEKNOLOGI MARA PREPARATION AND CHARACTERIZATION OF POLYMER ELECTROLYTES METHYL CELLULOSE BASED POTASSIUM HYDROXIDE FOR ELECTROCHEMICAL CELL
UNIVERSITI TEKNOLOGI MARA PREPARATION AND CHARACTERIZATION OF POLYMER ELECTROLYTES METHYL CELLULOSE BASED POTASSIUM HYDROXIDE FOR ELECTROCHEMICAL CELL MAS FIZA BINTI MUSTAFA Thesis submitted in fulfillment
More informationAnnexure - I. Declaration: The Listed entity is required to submit the following declaration to the extent of submission of information:-
Annexure - I Annexure - I Holding of specified ( Statement Showing Pattern ) 1. 2. 3. 4. Name of Listed Entity : MANGALORE CHEMICALS & FERTILIZERS LTD Scrip Code / Name of Scrip / of Security 530011 /
More informationGaussian Slug Simple Nonlinearity Enhancement to the 1-Factor and Gaussian Copula Models in Finance, with Parametric Estimation and Goodness-of-Fit
Gaussian Slug Simple Nonlinearity Enhancement to the 1-Factor and Gaussian Copula Models in Finance, with Parametric Estimation and Goodness-of-Fit Tests on US and Thai Equity Data 22 nd Australasian Finance
More informationCollege Algebra. Third Edition. Concepts Through Functions. Michael Sullivan. Michael Sullivan, III. Chicago State University. Joliet Junior College
College Algebra Concepts Through Functions Third Edition Michael Sullivan Chicago State University Michael Sullivan, III Joliet Junior College PEARSON Boston Columbus Indianapolis New York San Francisco
More informationComputing Maximum Entropy Densities: A Hybrid Approach
Computing Maximum Entropy Densities: A Hybrid Approach Badong Chen Department of Precision Instruments and Mechanology Tsinghua University Beijing, 84, P. R. China Jinchun Hu Department of Precision Instruments
More informationExperimental designs for multiple responses with different models
Graduate Theses and Dissertations Graduate College 2015 Experimental designs for multiple responses with different models Wilmina Mary Marget Iowa State University Follow this and additional works at:
More informationTABLE OF CONTENTS CHAPTER 1 COMBINATORIAL PROBABILITY 1
TABLE OF CONTENTS CHAPTER 1 COMBINATORIAL PROBABILITY 1 1.1 The Probability Model...1 1.2 Finite Discrete Models with Equally Likely Outcomes...5 1.2.1 Tree Diagrams...6 1.2.2 The Multiplication Principle...8
More informationIndustrial Rotating Kiln Simulation
Industrial Rotating Kiln Simulation This thesis is presented for the degree of Doctor of Philosophy Faculty of Science University of Technology, Sydney 1999 Submitted by Dennis Van Puyvelde, B. Chem. Eng.
More informationMODELLING AND ANALYSIS OF SOCCER HEADING AND PROTECTIVE HEADGEAR TO UNDERSTAND AND PREVENT MILD TRAUMATIC BRAIN INJURY
MODELLING AND ANALYSIS OF SOCCER HEADING AND PROTECTIVE HEADGEAR TO UNDERSTAND AND PREVENT MILD TRAUMATIC BRAIN INJURY MOHD HASNUN ARIF HASSAN DOCTOR OF PHILOSOPHY UNIVERSITI MALAYSIA PAHANG UNIVERSITI
More informationINFORMATION APPROACH FOR CHANGE POINT DETECTION OF WEIBULL MODELS WITH APPLICATIONS. Tao Jiang. A Thesis
INFORMATION APPROACH FOR CHANGE POINT DETECTION OF WEIBULL MODELS WITH APPLICATIONS Tao Jiang A Thesis Submitted to the Graduate College of Bowling Green State University in partial fulfillment of the
More informationWeek 1 Quantitative Analysis of Financial Markets Distributions A
Week 1 Quantitative Analysis of Financial Markets Distributions A Christopher Ting http://www.mysmu.edu/faculty/christophert/ Christopher Ting : christopherting@smu.edu.sg : 6828 0364 : LKCSB 5036 October
More informationIS-LM Analysis. Math 202. Brian D. Fitzpatrick. Duke University. February 14, 2018 MATH
IS-LM Analysis Math 202 Brian D. Fitzpatrick Duke University February 14, 2018 MATH Overview Background History Variables The GDP Equation Definition of GDP Assumptions The GDP Equation with Assumptions
More information