Application of probability density forecast of demand in short term portfolio optimization

Bichpuriya, Yogesh; Soman, S. A.

doi:10.1109/powercon.2016.7753987

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…Markowitz's MV model, as well as others working in this area (Bichpuriya and Soman 2016;El Ghaoui, et. al.…”

Section: The Nonlinear Return Functionmentioning

confidence: 97%

“…Later models for asset pricing, such as the Capital Asset Pricing Model (CAPM) (Fama and French 2004), would continue to use the covariance among changes in asset values to quantify risk. Other models, for example in (Bichpuriya and Soman 2016;El Ghaoui, et. al.…”

Section: Introductionmentioning

confidence: 99%

“…This is done in (Yang and Liu 2016), where returns are treated as fuzzy numbers. Portfolio optimization based on MPT has also been used in electricity generation and distribution, where electricity demand is treated as a random variable (Bichpuriya and Soman 2016). Both of these examples formulate the portfolio return function in terms of expectation values and covariant risk, generating a linear (Bichpuriya and Soman 2016;El Ghaoui, et.…”

Section: Introductionmentioning

confidence: 99%

“…2003) or quadratic (Yang and Liu 2016) objective function problem that has the same form as the MV formulation. The linear return function in the MV model has been used by many authors in the automation literature (Bichpuriya and Soman 2016;El Ghaoui, et. al.…”

Section: Introductionmentioning

confidence: 99%

“…These changes can occur daily, weekly, etc. In some cases, particularly in(Bichpuriya and Soman 2016;Yang and Liu 2016) this is done by first determining a probability density function for changes in asset values between successive points in time and then calculating the value of 𝐸[𝑟 𝑖 ]…”

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confidence: 99%

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Kelly's Criterion in Portfolio Optimization: A Decoupled Problem

Peterson¹

2017

Preprint

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Kelly's Criterion is well known among gamblers and investors as a method for maximizing the returns one would expect to observe over long periods of betting or investing. These ideas are conspicuously absent from portfolio optimization problems in the financial and automation literature. This paper will show how Kelly's Criterion can be incorporated into standard portfolio optimization models. The model developed here combines risk and return into a single objective function by incorporating a risk parameter. This model is then solved for a portfolio of 10 stocks from a major stock exchange using a differential evolution algorithm. Monte Carlo calculations are used to verify the accuracy of the results obtained from differential evolution. The results show that evolutionary algorithms can be successfully applied to solve a portfolio optimization problem where returns are calculated by applying Kelly's Criterion to each of the assets in the portfolio.

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“…Markowitz's MV model, as well as others working in this area (Bichpuriya and Soman 2016;El Ghaoui, et. al.…”

Section: The Nonlinear Return Functionmentioning

confidence: 97%