Portfolio optimization using factor models
(2020) In Master's Thesis in Mathematical Sciences MASM01 20201Mathematical 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:
http://lup.lub.lu.se/student-papers/record/9074478
- author
- Ekelund, Ville LU
- supervisor
- organization
- alternative title
- Portföljoptimering med hjälp av faktormodeller
- course
- MASM01 20201
- year
- 2020
- 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}}, }