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Portfolio optimization using factor models

Ekelund, Ville LU (2020) In Master's Thesis in Mathematical Sciences MASM01 20201
Mathematical Statistics
Abstract
In this thesis model predictive control (MPC) is used to dynamically optimize
a portfolio where data is sampled at the closing price. Previous research has
shown that MPC optimization applied on financial data can yield a portfolio
that exceeds the value of traditional portfolio strategies. MPC has also been
observed having computational advantages when return forecasts are updated

when a new observation are sampled. Factor models such as the Capital As-
set Pricing Model (CAPM) and Fama and French factor models are used to

forecast the financial return of stocks taken from the Standard & Poor’s 500
index Global. Portfolio optimization are performed using single-period forecast
where the portfolio contains one stock and a zero... (More)
In this thesis model predictive control (MPC) is used to dynamically optimize
a portfolio where data is sampled at the closing price. Previous research has
shown that MPC optimization applied on financial data can yield a portfolio
that exceeds the value of traditional portfolio strategies. MPC has also been
observed having computational advantages when return forecasts are updated

when a new observation are sampled. Factor models such as the Capital As-
set Pricing Model (CAPM) and Fama and French factor models are used to

forecast the financial return of stocks taken from the Standard & Poor’s 500
index Global. Portfolio optimization are performed using single-period forecast
where the portfolio contains one stock and a zero interest rate cash account
and also a large portfolio with 10 stocks and a risk-free asset. Transactions
cost are included to better reflect the real world and address prediction-error.
The MPC portfolio are outperforming a buy and hold strategy in both risk and
return. Between the factor models then difference is negligible in case of the
small portfolio but both Fama and French models outperforms CAPM in the
larger portfolio. (Less)
Please use this url to cite or link to this publication:
author
Ekelund, Ville LU
supervisor
organization
alternative title
Portföljoptimering med hjälp av faktormodeller
course
MASM01 20201
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master's Thesis in Mathematical Sciences
report number
LUNFMS-3104-2021
ISSN
1404-6342
other publication id
2021:E59
language
English
id
9074478
date added to LUP
2022-02-14 14:41:05
date last changed
2022-02-14 14:41:05
@misc{9074478,
  abstract     = {{In this thesis model predictive control (MPC) is used to dynamically optimize
a portfolio where data is sampled at the closing price. Previous research has
shown that MPC optimization applied on financial data can yield a portfolio
that exceeds the value of traditional portfolio strategies. MPC has also been
observed having computational advantages when return forecasts are updated

when a new observation are sampled. Factor models such as the Capital As-
set Pricing Model (CAPM) and Fama and French factor models are used to

forecast the financial return of stocks taken from the Standard & Poor’s 500
index Global. Portfolio optimization are performed using single-period forecast
where the portfolio contains one stock and a zero interest rate cash account
and also a large portfolio with 10 stocks and a risk-free asset. Transactions
cost are included to better reflect the real world and address prediction-error.
The MPC portfolio are outperforming a buy and hold strategy in both risk and
return. Between the factor models then difference is negligible in case of the
small portfolio but both Fama and French models outperforms CAPM in the
larger portfolio.}},
  author       = {{Ekelund, Ville}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Thesis in Mathematical Sciences}},
  title        = {{Portfolio optimization using factor models}},
  year         = {{2020}},
}