A statistical analysis of investor preferences for portfolio selection

Nisani, Doron; Shelef, Amit

doi:10.1007/s00181-020-01947-8

Cited by 7 publications

(6 citation statements)

References 29 publications

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“…Yet, the results of this study suggest that investors are risk-averse, as well as ambiguity-seeking, i.e., investors are willing to take chances for the possibility of increased returns. The theoretical meaning of this finding is that investors are risk-averse with decreasing risk aversion, which was already empirically documented [23]. This empirical result is based on the Lorenz curve ranking method [24], in conjunction with the Schechtman-Shelef-Yitzhaki-Zitikis test [25].…”

Section: Empirical Analysismentioning

confidence: 59%

Risk and Uncertainty at the Outbreak of the COVID-19 Pandemic

2022

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The classic paradigm in finance maintains that asset returns are paid as a compensation for bearing risk. This study extends the literature and explores whether asset prices are also affected by uncertainty. This research invokes the Expected Utility with Uncertainty Probabilities Model and utilizes the natural experiment conditions of the COVID-19 pandemic outbreak, in order to determine whether investors’ behavior during the sharp economic decline was driven by risk, or uncertainty. We limit this research only to the outbreak of the pandemic, since the recovery of the markets suggests investors have adjusted to the unexpected nature of the crisis. Using high-frequency data of the S&P 500 Index, we estimate the investors’ risk and ambiguity aversions, finding that in the pre-pandemic period investors exhibited risk aversion as well as an ambiguity-seeking attitude, while during the pandemic they demonstrated risk- and ambiguity-neutral behavior. The implications of these findings could suggest that in regular times, the financial markets are operated by risk-averse investors with decreasing risk-averse behavior.

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Section: Empirical Analysismentioning

confidence: 59%

Risk and Uncertainty at the Outbreak of the COVID-19 Pandemic

2022

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“…In another example, the effect of an increase on the economy's interest rate which took place three years ago is not relevant information to determining the asset's current risk. This argument was adopted by other similar studies (Shalit, 2014;Nisani, 2019b;Nisani and Shelef, 2021).…”

Section: Resultsmentioning

confidence: 89%

“…The Stochastic Dominance Rules have an equivalent set of alternative conditions that are based on the general definition of the Lorenz Curve (Lorenz, 1905;Gastwirth, 1971; Aumann-Serrano Riskiness Index Shorrocks, 1983;Nisani, 2019a). Joining these alternative conditions and the Schechtman, Shelef, Yitzhaki and Zitikis test for examining the relation between curves (Schechtman et al, 2008), it was possible to show that index portfolios might be considered as just another asset to the investors (Nisani and Shelef, 2021) or to show the investors' behavior under severe risk and uncertainty conditions (Nisani et al, 2022). The general risk attitude describes an economic agent's attitude under risk conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Putting the Aumann–Serrano Riskiness Index to work

Nisani

Shelef

David

2023

RAF

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Purpose The purpose of this study is to estimate the convergence order of the Aumann–Serrano Riskiness Index. Design/methodology/approach This study uses the equivalent relation between the Aumann–Serrano Riskiness Index and the moment generating function and aggregately compares between each two statistical moments for statistical significance. Thus, this study enables to find the convergence order of the index to its stable value. Findings This study finds that the first-best estimation of the Aumann–Serrano Riskiness Index is reached in no less than its seventh statistical moment. However, this study also finds that its second-best approximation could be achieved with its second statistical moment. Research limitations/implications The implications of this research support the standard deviation as a statistically sufficient approximation of Aumann–Serrano Riskiness Index, thus strengthening the CAPM methodology for asset pricing in the financial markets. Originality/value This research sheds a new light, both in theory and in practice, on understanding of the risk’s structure, as it may improve accuracy of asset pricing.

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“…Thus, it also highlights the asymmetry of the random variable, which is ignored by the standard deviation. Second, the Gini coefficient is monotonic with the investor's preference under risk conditions for much wider distributions (Rothschild & Stiglitz, 1970), which could explain documented decision‐making results (Nisani, 2019; Nisani & Shelef, 2021; Shalit, 2014).…”

Section: Definitions and Resultsmentioning

confidence: 99%

On the General Deviation Measure and the Gini coefficient

Nisani

2023

Int J of Economic Theory

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The General Deviation Measure introduces a progressive definition for financial risk measurement, which presents an alternative to the Coherent Risk Measure. This definition replaces the Translation Invariance and Monotonicity axioms with the Shift Invariance and Nonnegativity axioms, and it includes the Mean Absolute Deviation measure and other variations of the Value‐at‐Risk measurements. This research shows that Coherent Risk Measure holds an intrinsic contradiction regarding riskless assets, and it proves that the Gini coefficient is also a General Deviation Measure. These contributions improve the efficiency of risk measurement and asset pricing in the financial markets.

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A statistical analysis of investor preferences for portfolio selection

Cited by 7 publications

References 29 publications

Risk and Uncertainty at the Outbreak of the COVID-19 Pandemic

Risk and Uncertainty at the Outbreak of the COVID-19 Pandemic

Putting the Aumann–Serrano Riskiness Index to work

On the General Deviation Measure and the Gini coefficient

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