scholarly journals Optimal Portfolio for Parabolic Type Infinite-Dimensional Factor Model with Power Utility

2004 ◽  
Vol 2004 (0) ◽  
pp. 65-70
Author(s):  
ShinIchi AIHARA ◽  
Arunabha BAGCHI
Keyword(s):  
Factor Model ◽  
Parabolic Type ◽  
Optimal Portfolio ◽  
Power Utility ◽  
Infinite Dimensional

scholarly journals Optimal portfolio with power utility of absolute and relative wealth

2021 ◽  
pp. 109225
Author(s):  
Andrey Sarantsev
Keyword(s):  
Optimal Portfolio ◽  
Power Utility ◽  
Relative Wealth

scholarly journals Dynamic factor model with infinite‐dimensional factor space: Forecasting

2018 ◽  
Vol 33 (5) ◽  
pp. 625-642 ◽  
Author(s):  
Mario Forni ◽  
Alessandro Giovannelli ◽  
Marco Lippi ◽  
Stefano Soccorsi
Keyword(s):  
Factor Model ◽  
Dynamic Factor ◽  
Dynamic Factor Model ◽  
Factor Space ◽  
Infinite Dimensional

MARKOWITZ PORTFOLIO AND THE BLUR OF HISTORY

2020 ◽  
Vol 23 (05) ◽  
pp. 2050030
Author(s):  
CHI TIM NG ◽  
YUE SHI ◽  
NGAI HANG CHAN
Keyword(s):  
Factor Model ◽  
Asset Returns ◽  
Optimal Portfolio ◽  
Arbitrage Pricing Theory ◽  
Arbitrage Pricing ◽  
Sample Mean ◽  
Arbitrage Opportunities ◽  
Markowitz Portfolio ◽  
Pricing Theory ◽  
Mean Vector

It is shown in this paper that when the true mean return vector is replaced by the inferred mean vector obtained indirectly from factor model and arbitrage pricing theory, its impact on the resulting optimal portfolio is insignificant. To achieve this goal, several assumptions are imposed: (i) the asset returns are generated from a factor model, (ii) the number of assets goes to infinity, and (iii) there is no asymptotic arbitrage opportunities. Issues related to the efficiency of the estimated optimal portfolio for high-frequency data are discussed. The portfolio constructed using the sample mean vector and using the inferred mean vector from arbitrage pricing theory are compared.

scholarly journals Portfolio selection and optimal portfolio management with methods of artificial intelligence and computational intelligence

2016 ◽  
Author(s):  
Νικόλαος Λουκέρης
Keyword(s):  
Artificial Intelligence ◽  
Logistic Regression ◽  
Portfolio Selection ◽  
Asset Allocation ◽  
Computational Intelligence ◽  
Factor Model ◽  
Past Research ◽  
Higher Moments ◽  
Recurrent Networks ◽  
Power Utility

The scope of this Doctorate of Philosophy is triple: A) at first to investigate the Utility Function of investors in terms of overall characteristics and investor attributes to incorporate more detailed information that will offer an advanced Portfolio Theory based on Markowitz’s (1952) ideas,and B) secondly to examine thoroughly models of Artificial Intelligence in the domain of Neural Networks and Hybrid models, of Computational Intelligence and further Heuristics that will be implemented on more efficient Portfolio Selection by professionals or the academiaC) Examining further alternative models in optimal Asset Allocation, and Risk measurementThe fulfillment of the previous scope is achieved in case A) elaborating the Isoelastic Utility Fuction, and the Power Utility Functions (continuing past research on this one), and examining further higher moments: the fourth, the fifth and the sixth moments to incorporate more detailed information on the investor preferences towards asset allocation, B) investigating the Support Vector Machines, Time Lag Recurrent Networks, Recurrent Networks, Jordan Elmans, MultiLayer Perceptrons, Voted Perceptron, Radial Basis Functions, models of Neural Networks, their Neuro – Genetic Hybrids and the Regressions: Multinomial Logistic Regression-Logistc, Linear Logistic Regression-Simple Logistic, Logistic Model Trees, Additive Logistic Regression-Logitboost, Bayesian Logistic Regression, AdaBoost M1, whilst in Computational Intelligence Genetic Algorithms are examined, and Heuristics: Differential Evolution, Particle Swarm Otimisation, to define the best models in terms of performance and efficiency into Portfolio Selection and Financial Management. C) determine the ability of Three Factor Model to track the anomalies of average returns, introducing a more detailed portfolio creation, and examining alternatives on VaR and CVaR models(continuing past research).The innovations are A) the implementation of special forms of the Power Utility and Isoelastic Utility Functions, the incorporation of the fourth, the fifth and the sixth moments of the investors wealth on the utility function, B) the thorough evaluation of the SVM, TLRN, RNN, MLP, Voted Perceptron, RBFN Neural Nets and their Neuro-Genetic Hybrids architectures in various topologies from 0 to 10 hidden layers, the evaluation of MLR, LLR, LMT, ALR, BLR, AdaBoost M1 Regressions, of GA, DE, PSO Computational Intelligencee and Heuristic models to define the best model for Portfolio Selection and Corporate Evaluation.C) the extended portfolio creation following the Morningstar 3x3 bonds matrix and introducing a new 5x5 matrix further to the well known 3x2 FF’s matrix, and two different sorting methods, Proportional, and Value categorisation in three different cases of 6, 9 and 25 portfolios, whilst on Risk further backtesting and VaR and CVaR alternatives are examined. In Chanpter 1 the theoretical substratu on the Portflio Selection probem beyond higher moments. Chapter 2 describes th metyhodology I followed, the models I created and the data of the neural computation. Chapter 3 has the combine discussion of the Artificial Intelligence in Portfolio Selection. Chapter 4 discusses my past research during my MSc at Essex University, on the Heuristics and Utlity Functions. Similarly Chapter 5 describes my past research rom Essex Uni. on the Three Factor Model that has a close contribution.

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