A stochastic programming approach to multicriteria portfolio optimization

Şakar, Ceren Tuncer; Köksalan, Murat

doi:10.1007/s10898-012-0005-2

Cited by 13 publications

(12 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To the best of our knowledge, previous studies do not formulate a multistage stochastic portfolio optimization model with the above-mentioned features. In addition, we extend the scenario generation method, proposed by Şakar and Köksalan [27], as unlike their study, we do not assume the returns of market index to be normally distributed. More precisely, our presented approach generates scenarios of stock returns based on empirical distributions of market index returns.…”

Section: Ardeshir Ahmadi and Hamed Davari-ardakanimentioning

confidence: 99%

“…Stochastic programming is a prominent approach that handles uncertainties in multi-period portfolio optimization problems. To provide investors with appropriate investment decisions, it needs a set of scenarios that capture the uncertain behavior of financial and economic factors in forward periods [27].…”

Section: Ardeshir Ahmadi and Hamed Davari-ardakanimentioning

confidence: 99%

“…Ji et al [21], assume that asset returns are normally distributed. Şakar and Köksalan [27] use scenarios of a market index as inputs of a single index model to generate scenarios of stock returns. In this regard, they assume that returns of market index are normally distributed, and use a random walk model to generate scenarios of index returns.…”

Section: Ardeshir Ahmadi and Hamed Davari-ardakanimentioning

confidence: 99%

“…As mentioned above, in this paper, a multistage stochastic programming model is proposed to deal with multi-period portfolio optimization under real world assumptions, namely presence of proportional transaction costs, minimum transaction lots, cardinality constraints, and restrictions on the proportion of all risky and risk-free assets. In addition, a random walk model, proposed based on Johnson transformation, is combined with a single index model to develop a new method that, unlike most previous studies as Şakar and Köksalan [27], generates scenarios of stock returns, independently from the distributions of returns. To the best of our knowledge, the proposed multistage stochastic portfolio optimization model and the scenario generation method have not been addressed in previous studies.…”

Section: Ardeshir Ahmadi and Hamed Davari-ardakanimentioning

confidence: 99%

“…The accurate prediction of financial asset prices is important for investors to make appropriate investment decisions. This issue is closely related to the efficient market hypothesis [27]. The efficiency of stock markets has been widely studied by many researchers.…”

Section: Notations Scenario Tree Notationmentioning

confidence: 99%

See 4 more Smart Citations

A multistage stochastic programming framework for cardinality constrained portfolio optimization

Ahmadi¹,

Davari-Ardakani²

2017

Numerical Algebra, Control &Amp; Optimization

View full text Add to dashboard Cite

This paper presents a multistage stochastic programming model to deal with multi-period, cardinality constrained portfolio optimization. The presented model aims to minimize investor's expected regret, while ensuring achievement of a minimum expected return. To generate scenarios of market index returns, a random walk model based on the empirical distribution of market-representative index returns is proposed. Then, a single index model is used to estimate stock returns based on market index returns. Afterward, historical returns of a number of stocks, selected from Frankfurt Stock Exchange (FSE), are used to implement the presented scenario generation method, and solve the stochastic programming model. In addition, the impact of cardinality constraints, transaction costs, minimum expected return and predetermined investor's target wealth are investigated. Results show that the inclusion of cardinality constraints and transaction costs significantly influences the investors risk-return tradeoffs. This is also the case for investors target wealth.

show abstract

Section: Ardeshir Ahmadi and Hamed Davari-ardakanimentioning

confidence: 99%

Section: Ardeshir Ahmadi and Hamed Davari-ardakanimentioning

confidence: 99%

Section: Ardeshir Ahmadi and Hamed Davari-ardakanimentioning

confidence: 99%

Section: Ardeshir Ahmadi and Hamed Davari-ardakanimentioning

confidence: 99%

Section: Notations Scenario Tree Notationmentioning

confidence: 99%

See 3 more Smart Citations

A multistage stochastic programming framework for cardinality constrained portfolio optimization

Ahmadi¹,

Davari-Ardakani²

2017

Numerical Algebra, Control &Amp; Optimization

View full text Add to dashboard Cite

show abstract

Reliability assessment of scenarios generated for stock index returns incorporating momentum

Guo

Ryan

2020

Int. J Fin Econ

View full text Add to dashboard Cite

Stochastic programming models for portfolio optimization rely on scenario paths for returns derived from stochastic process models. This paper investigates a variant of the geometric Brownian motion process for stock index returns that incorporates index momentum. Based on this model, three different processes for generating scenarios on a rolling basis are devised, which differ according to how frequently the momentum parameter is updated and whether it is estimated according to a simple moving average or an exponentially weighted moving average of returns. The reliability of scenario sets generated for multiple instances is assessed by applying a recently developed statistical tool. Backtesting is conducted in case studies of the Standard & Poor's 500 Index and the Financial Times Stock Exchange 100 Index for two different historical periods. The numerical results show that the frequency with which the expected return is updated does not significantly influence the performance of the scenario generation procedure, whereas how the expected return is calculated affects the autocorrelation and dispersion of generated scenarios drastically. All scenario generation schemes are highly sensitive to the estimated volatility of the index returns. Among the three processes tested, the algorithms that incorporate momentum estimates according to an exponentially weighted moving average can generate reliable scenarios when the volatility estimation error is small.

show abstract