Return Forecasting by Quantile Regression

Pohlman, Lawrence; Ma, Lingjie

doi:10.3905/joi.2010.19.4.116

Cited by 11 publications

(4 citation statements)

References 9 publications

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“…QR has been first suggested by Chan & Lakonishok (1992) as a robust measurement of beta risk in asset pricing test given non-normal return-generating process distribution due to fat-tailed, outliers and extreme returns observations. Following this article, some scholars have employed and proved the estimation efficiency advantages of QR compared to Ordinary Least Squares (OLS) in capital asset pricing model and multifactor asset pricing tests (Allen, Powell, & Singh, 2011; Bassett & Chen, 2001; Lee & Li, 2012; Naifar, 2015; Pohlman & Ma, 2010). To recap, the issue of non-normality and its severe implications in asset pricing test has been raised since in Affleck-Graves and McDonald (1989).…”

Section: Empirical Verification Using Malaysia Stock Market Datamentioning

confidence: 99%

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Psychoanalysis of Investor Irrationality and Dynamism in Stock Market

Tuyon

Ahmad

2017

Journal of Interdisciplinary Economics

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This article provides an alternative theoretical framework to explain investors’ irrational behaviours in finance theories (mainly asset pricing) based on psychoanalysis approach. This is an approach used by psychoanalysts and psychiatrists to investigate human minds. The investigation is facilitated by interdisciplinary theories, namely (a) bounded rationality theory which differentiates intuition and reasoning, (b) prospect theory which explains framing and valuation and (c) theory of mind which divides behavioural risks into cognitive heuristics and affective biases. These theories collectively explain the origin of irrational behaviours. Additionally, (d) the ABC (Activating–Beliefs–Consequences) model is also used to interpret the causes and effects of irrational behaviours on investors and market behaviour. Last theory, (e) the dual system model of preference is used to conceptualize the bounded human mind that contains both rational and irrational elements. The proposed theoretical framework provides the theoretical foundation of investors’ irrational origin, forces, causes as well as their systematic effects on investors, asset prices and stock market behaviours dynamism. The validity of the theoretical framework is supported by empirical test using a representative of emerging stock market data and behavioural risk proxies.

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Section: Empirical Verification Using Malaysia Stock Market Datamentioning

confidence: 99%

“…The lower quantiles (below 0.5) represent negative or lower returns and the upper quantiles (above 0.5) represent positive or higher returns. As such, employing QR can assist to discover the full spectrum of data distributions (Pohlman & Ma, 2010).…”

Section: Empirical Verification Using Malaysia Stock Market Datamentioning

confidence: 99%

Psychoanalysis of Investor Irrationality and Dynamism in Stock Market

Tuyon

Ahmad

2017

Journal of Interdisciplinary Economics

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“…Following this, the quantile regression has been first suggested by Chan & Lakonishok (1992) as a robust measurement for beta risk in asset pricing test given non‐normal return‐generating process distribution due to fat tailed, outliers and extreme returns observations. Following this article, some scholars have employed and proved the estimation efficiency advantages of quantile regression in CAPM and multi‐factor asset pricing tests (see Bassett Jr & Chen, 2001; Lee & Li, 2012; Mensi, Hammoudeh, Reboredo, & Nguyen, 2014; Naifar, 2016; Pohlman & Ma, 2010). The application of quantile regression has been extended for panel data by Lamarche (2010).…”

Section: Methodsmentioning

confidence: 99%

“…The lower quantiles (0.1, 0.2, 0.3, 0.4) represents negative or lower returns and the upper quantiles (0.6, 0.7, 0.8, 0.9) represents positive or higher returns. As such, employing quantile regression can assist to discover the full spectrum of data distributions (Pohlman & Ma, 2010).…”

Section: Methodsmentioning

confidence: 99%

Dynamic risk attributes in Malaysia stock markets: Behavioural finance insights

Tuyon

Ahmad

2020

Int. J Fin Econ

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This paper proposes a quasi‐rational multi‐factor stock‐pricing determinants model with fundamental (rational) and behavioural (irrational) risk factors that highlight academic and practical merits. Academically, this study provides proofs to the notion of bounded rational and dynamic risk–return relationships. Practically, ways to uncover dynamics risk attributes in equity markets are highlighted. The empirical tests are performed based on a sample of 238 Malaysian firm stock returns with monthly data spanning from 1996:01 to 2014:12. The panel regression and quantile panel regression methods are employed in estimations to uncover the dynamic risk–return relationships. Collectively, the analyses provide evidence to support the dynamic of risk attributes due to quasi‐rational risk factors and given different prospects namely; firm characteristics, market states, as well as losses and gains. In theory, the results lend support to the validity of the suggested stock‐pricing determinants model. In practice, the study provides valuable insights to capture multi‐attributes risk dynamics in stock market that will benefits practitioners for trading strategies and policymakers for market efficiency governance policymaking.

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Quantitative Investing with Tail Behavior—A Distributional Approach

2020

Quantitative Investing

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Return Forecasting by Quantile Regression

Cited by 11 publications

References 9 publications

Psychoanalysis of Investor Irrationality and Dynamism in Stock Market

Psychoanalysis of Investor Irrationality and Dynamism in Stock Market

Dynamic risk attributes in Malaysia stock markets: Behavioural finance insights

Quantitative Investing with Tail Behavior—A Distributional Approach

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