Drawdown beta and portfolio optimization

Ding, Rui; Uryasev, Stan

doi:10.1080/14697688.2022.2037698

Cited by 7 publications

(3 citation statements)

References 19 publications

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“…Greotzner and Werner [ 28 ] provided a robust definition of regret by broadening the concept’s scope from a single objective to a multi-objective set of phenomena. Ding and Uryasevy [ 29 ] explained a new risk measure for portfolio performance called the expected regret of drawdown, which is based on the expected regret of a drawdown above the threshold. Stoltz and Lugosi [ 30 ] designed sequential investment strategies to minimize internal regrets.…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic portfolio optimization: A regret-based approach on volatility risk measures: An empirical evidence from The New York stock market

Larni-Fooeik,

Sadjadi,

Mohammadi

2024

PLoS ONE

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Portfolio optimization involves finding the ideal combination of securities and shares to reduce risk and increase profit in an investment. To assess the impact of risk in portfolio optimization, we utilize a significant volatility risk measure series. Behavioral finance biases play a critical role in portfolio optimization and the efficient allocation of stocks. Regret, within the realm of behavioral finance, is the feeling of remorse that causes hesitation in making significant decisions and avoiding actions that could lead to poor investment choices. This behavior often leads investors to hold onto losing investments for extended periods, refusing to acknowledge mistakes and accept losses. Ironically, by evading regret, investors may miss out on potential opportunities. in this paper, our purpose is to compare investment scenarios in the decision-making process and calculate the amount of regret obtained in each scenario. To accomplish this, we consider volatility risk metrics and utilize stochastic optimization to identify the most suitable scenario that not only maximizes yield in the investment portfolio and minimizes risk, but also minimizes resulting regret. To convert each multi-objective model into a single objective, we employ the augmented epsilon constraint (AEC) method to establish the Pareto efficiency frontier. As a means of validating the solution of this method, we analyze data spanning 20, 50, and 100 weeks from 150 selected stocks in the New York market based on fundamental analysis. The results show that the selection of the mad risk measure in the time horizon of 100 weeks with a regret rate of 0.104 is the most appropriate research scenario. this article recommended that investors diversify their portfolios by investing in a variety of assets. This can help reduce risk and increase overall returns and improve financial literacy among investors.

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Section: Literature Reviewmentioning

confidence: 99%

Stochastic portfolio optimization: A regret-based approach on volatility risk measures: An empirical evidence from The New York stock market

Larni-Fooeik,

Sadjadi,

Mohammadi

2024

PLoS ONE

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“…Within this category, the conditional drawdown (CDD) measure and the conditional expected drawdown (CED) have been distinguished [18]. The CDD measure proposed by [19] includes the maximum drawdown and the average drawdown, which are often used in practice and suitable for portfolio allocation, optimization, and as an input for the β CDD measure described in [20,21]. The CED measure developed by [22] allows for a study of the distribution of possible future drawdowns.…”

Section: Background and Related Studiesmentioning

confidence: 99%

A Rational Risk Policy? Why Path Dependence Matters

Geboers

Depaire

2023

Entropy

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The Kelly criterion determines optimal bet sizes that maximize long-term growth. While growth is definitely an important consideration, the focus on growth alone can lead to significant drawdowns, leading to psychological discomfort for a risk-taker. Path-dependent risk measures, such as drawdown risk, provide a means to assess the risk of significant portfolio retracements. In this paper, we provide a flexible framework for assessing path dependent risk for a trading or investment operation. Given a certain set of profitable trading characteristics, a risk-taker who maximizes expected growth can still be faced with significant drawdowns to the point where a strategy becomes unsustainable. We demonstrate, through a series of experiments, the importance of path dependent risks in the case of outcomes subject to various return distributions. Based on Monte Carlo simulation, we analyze the medium-term behavior of different cumulative return paths and study the impact of different return outcome distributions. We show that in the case of heavier tailed outcomes, extra care is needed, and optimal might not be so optimal in the end.

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“…In this study, the presented OALOFS-MLC model designs a novel OALOFS technique to choose an optimal subset of features which helps in attaining improved classification results. Reference [ 17 ] proposed an Ant Lion optimizer (ALO) that is a nature-inspired metaheuristic approach that simulates the hunting system of antlion in catching their prey. Constructing traps, random walking (RW) of ants, catching ants, reconstructing, and traps entrapment of ants in traps are different measures of the ALO.…”

Section: The Proposed Modelmentioning

confidence: 99%

An Efficient Outlier Detection with Deep Learning-Based Financial Crisis Prediction Model in Big Data Environment

Venkateswarlu¹,

Baskar²,

Anupong

et al. 2022

Computational Intelligence and Neuroscience

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As Big Data, Internet of Things (IoT), cloud computing (CC), and other ideas and technologies are combined for social interactions. Big data technologies improve the treatment of financial data for businesses. At present, an effective tool can be used to forecast the financial failures and crises of small and medium-sized enterprises. Financial crisis prediction (FCP) plays a major role in the country’s economic phenomenon. Accurate forecasting of the number and probability of failure is an indication of the development and strength of national economies. Normally, distinct approaches are planned for an effective FCP. Conversely, classifier efficiency and predictive accuracy and data legality could not be optimal for practical application. In this view, this study develops an oppositional ant lion optimizer-based feature selection with a machine learning-enabled classification (OALOFS-MLC) model for FCP in a big data environment. For big data management in the financial sector, the Hadoop MapReduce tool is used. In addition, the presented OALOFS-MLC model designs a new OALOFS algorithm to choose an optimal subset of features which helps to achieve improved classification results. In addition, the deep random vector functional links network (DRVFLN) model is used to perform the grading process. Experimental validation of the OALOFS-MLC approach was conducted using a baseline dataset and the results demonstrated the supremacy of the OALOFS-MLC algorithm over recent approaches.

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Drawdown beta and portfolio optimization

Cited by 7 publications

References 19 publications

Stochastic portfolio optimization: A regret-based approach on volatility risk measures: An empirical evidence from The New York stock market

Stochastic portfolio optimization: A regret-based approach on volatility risk measures: An empirical evidence from The New York stock market

A Rational Risk Policy? Why Path Dependence Matters

An Efficient Outlier Detection with Deep Learning-Based Financial Crisis Prediction Model in Big Data Environment

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