Asset Allocation with Combined Models Based on Game-Theory Approach and Markov Chain Models

Salih Çam

Makale detayları

Asset Allocation with Combined Models Based on Game-Theory Approach and Markov Chain Models

Yazarlar:

Salih Çam

Yayıncı:

Istanbul University

Ana Başlıklar:

Ekonometri (Diğer)

Dergi:

EKOIST Journal of Econometrics and Statistics Issue 39, pp.26-36

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001311536

pISSN:

2651-396X

Anahtar kelimeler:

Özet:

 The measurement of expected returns has a major impact on portfolio performance. While there are several methods used for estimating expected returns in existing studies, the mean-variance model most commonly used in portfolio theory utilizes the method of expected returns calculated from historical data. However, the problem with estimating expected returns is that estimating parameters based on historical data, such as the arithmetic mean, may not reflect the distributional characteristics of the return series and may not be an appropriate statistic for the population parameters. Therefore, using robust statistics or combined portfolio models can lead to better portfolios that minimize estimation error while maximizing expected returns. In this paper, we use game theory and Markov chain models to estimate expected asset returns and compare portfolios constructed based on these methods. The analysis results show that the portfolio constructed based on game theory yielded higher returns than the target index and mean-variance model, while the model based on Markov chains yielded portfolios with the lowest portfolio risk. In all out-of-sample investment periods, the game theory based portfolio produced better returns than the portfolios estimated in the study, except for the period from January 2022 to December 2022.

 The measurement of expected returns has a major impact on portfolio performance. While there are several methods used for estimating expected returns in existing studies, the mean-variance model most commonly used in portfolio theory utilizes the method of expected returns calculated from historical data. However, the problem with estimating expected returns is that estimating parameters based on historical data, such as the arithmetic mean, may not reflect the distributional characteristics of the return series and may not be an appropriate statistic for the population parameters. Therefore, using robust statistics or combined portfolio models can lead to better portfolios that minimize estimation error while maximizing expected returns. In this paper, we use game theory and Markov chain models to estimate expected asset returns and compare portfolios constructed based on these methods. The analysis results show that the portfolio constructed based on game theory yielded higher returns than the target index and mean-variance model, while the model based on Markov chains yielded portfolios with the lowest portfolio risk. In all out-of-sample investment periods, the game theory based portfolio produced better returns than the portfolios estimated in the study, except for the period from January 2022 to December 2022.

 The measurement of expected returns has a major impact on portfolio performance. While there are several methods used for estimating expected returns in existing studies, the mean-variance model most commonly used in portfolio theory utilizes the method of expected returns calculated from historical data. However, the problem with estimating expected returns is that estimating parameters based on historical data, such as the arithmetic mean, may not reflect the distributional characteristics of the return series and may not be an appropriate statistic for the population parameters. Therefore, using robust statistics or combined portfolio models can lead to better portfolios that minimize estimation error while maximizing expected returns. In this paper, we use game theory and Markov chain models to estimate expected asset returns and compare portfolios constructed based on these methods. The analysis results show that the portfolio constructed based on game theory yielded higher returns than the target index and mean-variance model, while the model based on Markov chains yielded portfolios with the lowest portfolio risk. In all out-of-sample investment periods, the game theory based portfolio produced better returns than the portfolios estimated in the study, except for the period from January 2022 to December 2022.

 The measurement of expected returns has a major impact on portfolio performance. While there are several methods used for estimating expected returns in existing studies, the mean-variance model most commonly used in portfolio theory utilizes the method of expected returns calculated from historical data. However, the problem with estimating expected returns is that estimating parameters based on historical data, such as the arithmetic mean, may not reflect the distributional characteristics of the return series and may not be an appropriate statistic for the population parameters. Therefore, using robust statistics or combined portfolio models can lead to better portfolios that minimize estimation error while maximizing expected returns. In this paper, we use game theory and Markov chain models to estimate expected asset returns and compare portfolios constructed based on these methods. The analysis results show that the portfolio constructed based on game theory yielded higher returns than the target index and mean-variance model, while the model based on Markov chains yielded portfolios with the lowest portfolio risk. In all out-of-sample investment periods, the game theory based portfolio produced better returns than the portfolios estimated in the study, except for the period from January 2022 to December 2022.

Makale detayları

Asset Allocation with Combined Models Based on Game-Theory Approach and Markov Chain Models

Yazarlar:

Salih Çam

Yayıncı:

Istanbul University

Ana Başlıklar:

Ekonometri (Diğer)

Dergi:

EKOIST Journal of Econometrics and Statistics Issue 39, pp.26-36

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001311536

pISSN:

2651-396X

Portfolio Theory

Game Theory

Markov Chains Model

BIST30

Sayı

Makale detayları

Asset Allocation with Combined Models Based on Game-Theory Approach and Markov Chain Models

Yazarlar: Salih Çam

Yayıncı: Istanbul University

Ana Başlıklar: Ekonometri (Diğer)

Dergi: EKOIST Journal of Econometrics and Statistics Issue 39, pp.26-36

Belge Tipi: Makale

Yayın Tarihi: 12/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001311536

pISSN: 2651-396X

Portfolio Theory

Game Theory

Markov Chains Model

BIST30

Sayı

Yazarlar:

Salih Çam

Yayıncı:

Istanbul University

Ana Başlıklar:

Ekonometri (Diğer)

Dergi:

EKOIST Journal of Econometrics and Statistics Issue 39, pp.26-36

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001311536

pISSN:

2651-396X