Portfolio Selection Problem Using CVaR Risk Measures Equipped with DEA, PSO, and ICA Algorithms

Hamdi, Abdelouahed; Karimi, Arezou; Mehrdoust, Farshid; Belhaouari, Samir Brahim

doi:10.3390/math10152808

Cited by 9 publications

(6 citation statements)

References 44 publications

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“…By solving this task, we will determine the optimal weights of assets in the portfolio at the specified minimum value of the expected return while minimizing the CVaR risk function. The interested reader can find more about CVaR in Hamdi, Karimi, Mehrdoust, & Belhaouari, (2022), Bodnar, Lindholm, Niklasson, & Thorsen, (2022), Arici, Campi, Care, Dalai, & Ramponi, (2021), Wang & Zhu, (2021) and Kang, Li, & Li, (2020).…”

Section: Cvar-based Portfolio Selection Modelmentioning

confidence: 99%

Determining the investors' strategy during the COVID-19 crisis based on the S&P 500 stock index

Pekár¹,

Brezina²,

Reiff³

2024

Strategic Management

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Background: The most significant changes caused by the COVID-19 crisis were the sharp increase in working from home and the growing importance of e-commerce, which affected the development of some industries. This change also affects the investors' investment operations, which are based on analysis to ensure an unquestionable certainty of the invested financial amount and a satisfactory return. It is, therefore, interesting to analyze the possible return of the chosen investment strategy based on the optimization model of portfolio selection based on the CVaR risk measure. Purpose: The paper aims to present the possible use of the analysis of returns of effective portfolios constructed based on the optimization model of portfolio selection based on the CVaR risk measure during the crisis (COVID-19) and the pre-crisis period. Study design/methodology/approach: Paper presents the impact of the COVID-19 crisis on investor decision-making through the CVaR risk measure, which was implemented on the historical data of the components of the Standard and Poor's 500 stock index (S&P 500) in the crisis period as well as in the pre-crisis period. Findings/conclusions: The presented approach based on the CVaR risk rate measure and the relevant portfolio selection model provides the investor with an effective tool for allocating funds to the financial market in particular segments in both monitored periods. Limitations/future research: Time series data are divided into two periods based on visible factors such as the number of COVID-19 cases. In future research, we aim to divide monitored periods based on unobservable factors influencing investors' decisions, such as bull or bear mood on the market.

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Section: Cvar-based Portfolio Selection Modelmentioning

confidence: 99%

Determining the investors' strategy during the COVID-19 crisis based on the S&P 500 stock index

Pekár¹,

Brezina²,

Reiff³

2024

Strategic Management

View full text Add to dashboard Cite

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“…An RSI value of 100 (0) indicates that the price is rising (falling) without any decrease (increase). Overbought (oversold) signals are indicated by RSI values of 70 (30) or higher (lower) on the 14-day RSI [40,41]. Furthermore, Shik and Chong [42] note that RSI trading rules can boost currency market risk-adjusted returns; Chong and Ng [43] show that investors can earn higher returns utilizing the RSI trading rule compared to a buy-and-hold strategy.…”

Section: Relative Strength Index (Rsi)mentioning

confidence: 99%

Mastery of “Monthly Effects”: Big Data Insights into Contrarian Strategies for DJI 30 and NDX 100 Stocks over a Two-Decade Period

Chiu,

Huang,

Day

et al. 2024

Mathematics

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In contrast to finding better monthly performance shown in a specific month, such as the January effect (i.e., better stock price performance in January as opposed to other months), which has been extensively studied, the goal of this study is to determine whether investors would obtain better subsequent performance as technical trading signals emitted in a specific month because, from the investment perspective, investors purchasing stocks now would not know their performance until later. We contend that our analysis emphasizes its critical role in steering investment decisions and enhancing profitability; nonetheless, this issue appears to be overlooked in the relevant literature. As such, utilizing big data to analyze the constituent stocks of the DJI 30 and NDX 100 indices from 2003 to 2022 (i.e., two-decade data), this study investigates whether trading these stocks as trading signals emitted via contrarian regulation of stochastic oscillator indicators (SOIs) and the relative strength index (RSI) in specific months would result in superior subsequent performance (hereafter referred to as “monthly effects”). This study discovers that the oversold signals generated by these two contrarian regulations in March were associated with higher subsequent performance for holding 100 to 250 trading days (roughly one year) than other months. These findings highlight the importance of the trading time and the superiority of the RSI over SOIs in generating profits. This study sheds light on the significance of oversold trading signals and suggests that the “monthly effect” is crucial for achieving higher returns.

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“…An Evolutionary Algorithm (EA) is a computational method that solves problems by mimicking the behaviour of living organisms using nature-inspired mechanisms 21 . The use of EAs for feature selection has received significant attention in academia, with various algorithms being proposed, including Particle Swarm Optimization (PSO) [22][23][24] , Genetic Algorithm (GA) 25,26 , Artificial Bee Colony (ABC) 27 , Genetic Programming (GP) 28 , Gravitational Search Algorithm (GSA) 29 and Ant Colony Optimization (ACO) 30,31 . One advantage of EAs is their population-based search approach, which involves a team of entities exploring the fitness landscape to find the globally optimum solution.…”

Section: B Evolutionary Algorithmsmentioning

confidence: 99%

Bird's Eye View Feature Selection for High-Dimensional Data

Belhaouari

Shakeel

Erbad

et al. 2023

Preprint

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In machine learning, an informative dataset is crucial for accurate predictions. However, high dimensional data often contains irrelevant features, outliers, and noise, which can negatively impact model performance and consume computational resources. To tackle this challenge, the Bird's Eye View (BEV) feature selection technique is introduced. This approach is inspired by the natural world, where a bird searches for important features in a sparse dataset, similar to how a bird search for sustenance in a sprawling jungle. BEV incorporates elements of Evolutionary Algorithms with a Genetic Algorithm to maintain a population of top-performing agents, Dynamic Markov Chain to steer the movement of agents in the search space, and Reinforcement Learning to reward and penalize agents based on their progress. The proposed strategy in this paper leads to improved classification performance and a reduced number of features compared to conventional methods, as demonstrated by outperforming state-of-the-art feature selection techniques across multiple benchmark datasets.

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Portfolio Selection Problem Using CVaR Risk Measures Equipped with DEA, PSO, and ICA Algorithms

Cited by 9 publications

References 44 publications

Determining the investors' strategy during the COVID-19 crisis based on the S&P 500 stock index

Determining the investors' strategy during the COVID-19 crisis based on the S&P 500 stock index

Mastery of “Monthly Effects”: Big Data Insights into Contrarian Strategies for DJI 30 and NDX 100 Stocks over a Two-Decade Period

Bird's Eye View Feature Selection for High-Dimensional Data

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