Avoiding the Downside: A Practical Review of the Critical Line Algorithm for Mean–Semivariance Portfolio Optimization

Markowitz, Harry M.; Starer, David; Fram, Harvey; Gerber, Sander

doi:10.1142/9789811222634_0017

Cited by 8 publications

(3 citation statements)

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“…The sequential least square quadratic programming (SLS) [99,100,101] is considered to be one of the most efficient computational method to solve general nonlinear constrained optimization problems. [1], and its computational implementation has become increasingly popular [105,106]. CLA also solves constrained problems with conditions in inequalities, but unlike SLS, it divides a constrained problem into series of unconstrained sub-problems.…”

Section: Portfolio Optimization Methodsmentioning

confidence: 99%

“…The Critical Line Algorithm (CLA) is an efficient alternative to the quadratic optimizer for mean-variance model, as it is specifically designed for inequality portfolio optimization. It was already originally introduced in the Markowitz Portfolio Selection paper [1], and its computational implementation has become increasingly popular [105,106]. CLA also solves constrained problems with conditions in inequalities, but unlike SLS, it divides a constrained problem into series of unconstrained sub-problems by invoking the concept of turning point.…”

Section: Appendix a Off Sample Log-likelihood And Performances For St...mentioning

confidence: 99%

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Dynamic Portfolio Optimization with Inverse Covariance Clustering

Wang¹,

Aste²

2021

Preprint

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Market conditions change continuously. However, in portfolio's investment strategies, it is hard to account for this intrinsic non-stationarity. In this paper, we propose to address this issue by using the Inverse Covariance Clustering (ICC) method to identify inherent market states and then integrate such states into a dynamic portfolio optimization process. Extensive experiments across three different markets, NASDAQ, FTSE and HS300, over a period of ten years, demonstrate the advantages of our proposed algorithm, termed Inverse Covariance Clustering-Portfolio Optimization (ICC-PO). The core of the ICC-PO methodology concerns the identification and clustering of market states from the analytics of past data and the forecasting of the future market state. It is therefore agnostic to the specific portfolio optimization method of choice. By applying the same portfolio optimization technique on a ICC temporal cluster, instead of the whole train period, we show that one can generate portfolios with substantially higher Sharpe Ratios, which are statistically more robust and resilient with great reductions in maximum loss in extreme situations. This is shown to be consistent across markets, periods, optimization methods and selection of portfolio assets.

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Section: Portfolio Optimization Methodsmentioning

confidence: 99%

Section: Appendix a Off Sample Log-likelihood And Performances For St...mentioning

confidence: 99%

Dynamic Portfolio Optimization with Inverse Covariance Clustering

Wang¹,

Aste²

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The Critical Line Algorithm (CLA) is an efficient alternative to the quadratic optimizer for mean-variance model, as it iss specifically designed for inequality portfolio optimization. It was already originally introduced in the Markowitz Portfolio Selection paper (Markowitz, 1952), and its computational implementation has become increasingly popular (Singh et al, 2016;Markowitz et al, 2020). CLA also solves constrained problems with conditions in inequalities, but unlike SLS, it divides a constrained problem into series of unconstrained sub-problems by invoking the concept of turning point.…”

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%