Multiscale optimal portfolios using CAPM fractal regression: estimation for emerging stock markets

Tilfani, Oussama; Ferreira, Paulo; Boukfaoui, My Youssef El

doi:10.1080/14631377.2019.1640983

Cited by 13 publications

(7 citation statements)

References 48 publications

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“…Multifractal analysis extends the CAPM and its quantitative underpinnings in directions beyond mainstream economics. For example, exploring multifractality across different timescales has the intended effect of revealing investor heterogeneity [175,176]. This method is especially useful in describing heterogeneity across national boundaries, since it reveals differences in investment horizons [176,177].…”

Section: Applications and Implications Of Multifractal Analysismentioning

confidence: 99%

“…For example, exploring multifractality across different timescales has the intended effect of revealing investor heterogeneity [175,176]. This method is especially useful in describing heterogeneity across national boundaries, since it reveals differences in investment horizons [176,177]. A comparison of four formerly Communist countries, for instance, aligned Chinse and Czech markets more closely with Germany as a mature market benchmark, while discovering a preference for risk-free assets in Russia and Hungary [176] (p. 89).…”

Section: Applications and Implications Of Multifractal Analysismentioning

confidence: 99%

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The Capital Asset Pricing Model

Chen

2021

Encyclopedia

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Section: Applications and Implications Of Multifractal Analysismentioning

confidence: 99%

Section: Applications and Implications Of Multifractal Analysismentioning

confidence: 99%

The Capital Asset Pricing Model

Chen

2021

Encyclopedia

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“…This model extends the market portfolio model introduced initially by Markowitz [ 35 ] who argue that investors are risk averse investors and will choose a portfolio by trading off between risk and return for one investment period, [ 36 ]. The CAPM a static model of portfolio allocation in conditions of uncertainty and risk aversion, which relates the return r i of the i- th investment with the risk-free return r f and the market return r m [ 37 – 39 ] according to the formula:…”

Section: The Investment Risk In the Building Construction Projectsmentioning

confidence: 99%

Estimation of Risk Levels for Building Construction Projects

Maselli

Nestıcò

Mare

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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This paper aims to define an innovative method to estimate the investment risk thresholds, in order to provide the analyst with essential terms for the economic evaluation of the projects. The idea is to borrow from the As Low As Reasonably Practicable (ALARP) principle the concepts of the acceptability threshold and tolerability threshold of risk. Following this principle, generally used to assess the risk of human life loss, a risk is ALARP: when the costs for its mitigation appear to be disproportionate to the achievable benefits; that is when it lies between the unacceptable and the broadly acceptable region. To estimate the thresholds of acceptability and tolerability, the theoretical reference is the Capital Asset Pricing Model (CAPM), which enables to compare the investment risk not only to the return of the production sector in which the project under consideration falls, but also to the return of the market as a whole. The combined use of CAPM and statistical survey methods makes it possible to attribute the two risk thresholds to investments in a specific sector. In this paper, the focus is on risk assessment for building construction projects. For this production sector, the proposed analysis model is implemented with reference to official data concerning the Italian economy.

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“…FMH examines the market's daily randomness and the turbulence experienced during crashes and crises, and provides a compelling explanation for investor behavior over the course of a market cycle, including booms and busts (Moradi et al 2021). Interestingly, it also considers the non-linear relationships in time series problems, hence making it a suitable theory for stock prediction and other time series (Aslam et al 2021;Kakinaka and Umeno 2021;Naeem et al 2021;Tilfani et al 2020), or financial market related problems (Anderson and Noss 2013;Kumar et al 2017;Singh et al 2013). Nowadays, the value and benefits of forecasting in decision-and policy-making are unquestionably recognized across multiple dimensions.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

Chopra

Sharma

2021

JRFM

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The stock market is characterized by extreme fluctuations, non-linearity, and shifts in internal and external environmental variables. Artificial intelligence (AI) techniques can detect such non-linearity, resulting in much-improved forecast results. This paper reviews 148 studies utilizing neural and hybrid-neuro techniques to predict stock markets, categorized based on 43 auto-coded themes obtained using NVivo 12 software. We group the surveyed articles based on two major categories, namely, study characteristics and model characteristics, where ‘study characteristics’ are further categorized as the stock market covered, input data, and nature of the study; and ‘model characteristics’ are classified as data pre-processing, artificial intelligence technique, training algorithm, and performance measure. Our findings highlight that AI techniques can be used successfully to study and analyze stock market activity. We conclude by establishing a research agenda for potential financial market analysts, artificial intelligence, and soft computing scholarship.

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Multiscale optimal portfolios using CAPM fractal regression: estimation for emerging stock markets

Cited by 13 publications

References 48 publications

The Capital Asset Pricing Model

The Capital Asset Pricing Model

Estimation of Risk Levels for Building Construction Projects

Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda

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