A novel regularization-based optimization approach to sparse mean-reverting portfolios selection

Sadik, Somaya; Et-tolba, Mohamed; Nsiri, Benayad

doi:10.1007/s11081-022-09784-7

Cited by 4 publications

(2 citation statements)

References 30 publications

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“…The partial testing results indicate that Return on Investment (RoI) does not have a significant influence on stock underpricing. This is consistent with the findings ofCapizzo & Harrison (2023),,Hubáček & Šír (2023),Peters et al (2023), andSadik et al (2023). Return on Investment (RoI) may not fully reflect the intrinsic value of the company or its actual operational performance, thus diminishing its significance in relation to stock underpricing.…”

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confidence: 85%

The Influence of Financial Indicators and Underwriter Reputation on Depreciated Stock Prices

Gunanto

2023

JAF

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This research aims to analyze the influence of financial indicators and underwriter reputation on the level of stock underpricing during Initial Public Offerings (IPOs) on the Indonesia Stock Exchange (IDX). The phenomenon of underpricing has gained attention in securities market research in various countries. This study adopts a conceptual analysis approach based on a review of existing literature. The theory of asymmetric information also serves as the foundation for explaining the underpricing phenomenon in IPOs. Factors include Return on Equity (RoE), Financial Leverage, Current Ratio, Earnings per Share (EPS), Size, Proceeds, Company Age, Auditor Reputation, Underwriter, and Industry Type. Therefore, this research aims to test the influence of financial indicators and underwriter reputation on the level of stock underpricing in IPOs on the IDX. Consequently, the findings from this research can be used as a basis for comparison with similar studies in other countries. This can help in understanding the differences and similarities in the phenomenon of stock valuation in various global capital markets. Keywords: IPO Underpricing, Financial indicators and Underwriter reputation

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confidence: 85%

The Influence of Financial Indicators and Underwriter Reputation on Depreciated Stock Prices

Gunanto

2023

JAF

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“…Despite the extensive literature on solving similar, yet simpler, problems [20], [21], [22], [23], [24], [25], [26], [27], [28], [11], [29], [30], the only approach for handling (P ) suggests to apply the following semidefinite (SDP) relaxation [13]:…”

Section: Introductionmentioning

confidence: 99%

A penalty decomposition algorithm with greedy improvement for mean‐reverting portfolios with sparsity and volatility constraints

Mousavi

Shen

2022

Int Trans Operational Res

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Mean-reverting portfolios with few assets, but sufficient variance, are of great interest for investors in financial markets. Such portfolios are straightforwardly profitable because they include a small number of assets whose prices not only oscillate predictably around a long-term mean but also possess enough volatility. Roughly speaking, sparsity minimizes trading costs, volatility provides arbitrage opportunities, and mean-reversion property equips investors with ideal investment strategies. Finding such appealing portfolios can be formulated as a nonconvex quadratic optimization problem with an additional sparsity constraint. To the best of our knowledge, there is no method in the literature that directly solves this problem with its solution achieving favorable theoretical properties. In this paper, we develop an efficient two-stage algorithm for solving this problem which, in the worst case, provably obtains a stationary point and further, demonstrates numerical efficiency. In the first stage of our algorithm, we apply a tailored penalty decomposition method for finding a stationary point of this nonconvex problem. For a fixed penalty parameter, the block coordinate descent method is utilized to find a stationary point of the associated penalty subproblem. In the second stage, we improve the result from the first stage via a greedy scheme that solves restricted nonconvex quadratically constrained quadratic programs (QCQPs). We show that the optimal value of such a QCQP can be obtained by solving their semidefinite relaxations. Numerical experiments on S&P 500 are conducted to demonstrate the efficiency of the proposed algorithm.

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Dictionary Learning-Based Denoising for Portfolio Selection

Sadik

Et-tolba

Nsiri

2023

2023 24th International Conference on Digital Signal Processing (DSP)

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A novel regularization-based optimization approach to sparse mean-reverting portfolios selection

Cited by 4 publications

References 30 publications

The Influence of Financial Indicators and Underwriter Reputation on Depreciated Stock Prices

The Influence of Financial Indicators and Underwriter Reputation on Depreciated Stock Prices

A penalty decomposition algorithm with greedy improvement for mean‐reverting portfolios with sparsity and volatility constraints

Dictionary Learning-Based Denoising for Portfolio Selection

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