Expert Judgments: Financial Analysts Versus Weather Forecasters

Tyszka, Tadeusz; Zielonka, Piotr

doi:10.1207/s15327760jpfm0303_3

Cited by 30 publications

(18 citation statements)

References 6 publications

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“…Research on financial forecasting seems to be more consistent in revealing that financial analysts do not make more accurate probabilistic forecasts of stock prices than people without knowledge about stock market (Stael von Holstein, 1972;Tyszka & Zielonka, 2002;Yates, McDaniel, & Brown, 1991;Önkal & Muradoglu, 1994). In a relatively recent study, Törngren and Montgomery (2004) compared the accuracy of the stock performance predictions in two groups: financial professionals and laypeople.…”

Section: Introductionmentioning

confidence: 99%

Financial forecasts during the crisis: Were experts more accurate than laypeople?

Zaleśkiewicz¹

2011

Journal of Economic Psychology

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Section: Introductionmentioning

confidence: 99%

Financial forecasts during the crisis: Were experts more accurate than laypeople?

Zaleśkiewicz¹

2011

Journal of Economic Psychology

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“…Additional research is undoubtedly needed on how to better gain knowledge from experts in human population forecasting, including, in particular, efforts to limit their overconfidence. This issue has been tackled in other areas of forecasting, in fields as diverse as politics, economics and finance epidemiology, and population ecology (e.g., Speirs-Bridge et al 2010;Tetlock 2005;Tyszka and Zielonka 2002).…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Stochastic Population Forecasting Based on Combinations of Expert Evaluations Within the Bayesian Paradigm

2014

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This article suggests a procedure to derive stochastic population forecasts adopting an expert-based approach. As in previous work by Billari et al. (2012), experts are required to provide evaluations, in the form of conditional and unconditional scenarios, on summary indicators of the demographic components determining the population evolution: that is, fertility, mortality, and migration. Here, two main purposes are pursued. First, the demographic components are allowed to have some kind of dependence. Second, as a result of the existence of a body of shared information, possible correlations among experts are taken into account. In both cases, the dependence structure is not imposed by the researcher but rather is indirectly derived through the scenarios elicited from the experts. To address these issues, the method is based on a mixture model, within the so-called SupraBayesian approach, according to which expert evaluations are treated as data. The derived posterior distribution for the demographic indicators of interest is used as forecasting distribution, and a Markov chain Monte Carlo algorithm is designed to approximate this posterior. This article provides the questionnaire designed by the authors to collect expert opinions. Finally, an application to the forecast of the Italian population from 2010 to 2065 is proposed.

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“…When investors give as much attention to downside standard deviations as to upside standard deviations as a measures of risk, they run the risk that their return will be lower than expected. The phenomenon is known as "excessive optimism" (TYSZKA, ZIELONKA 2002;UTKUS 2006;FELLNER 2009;DE LA ROSA 2011;GAJDKA 2013 p. 37). According to some studies, using the downside measures of risk may lead to the underestimation of investment risk when asset prices, e.g.…”

Section: Measures Of Investment Riskmentioning

confidence: 99%

Risk and Return in the Real Estate, Bond and Stock Markets

Wolski

2017

Real Estate Management and Valuation

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Studies investigating the relation between risk and return occupy an important place in the discussion about the effectiveness of investing in real estate. A review of the available studies shows that real estate investments are less profitable than stocks, but in terms of risk and return, are usually the best option. This worldwide regularity may not necessarily be presented in Poland, as the Polish market is not fully fledged yet. The analysis presented in this article was performed with a view to reducing a research gap resulting from the lack of comprehensive Polish studies in this field.In the article, data spanning the years from 2006 to 2016 are examined by means of descriptive statistics, measures of risk, and the analysis of variance (ANOVA) to determine which of the following investment vehicles -bonds, real estate or stocks -offer the best risk-return ratio.The article has two parts. The analytical part is a review of studies on risk measurement methods and of earlier studies investigating risk and return by a class of assets (particularly real estate). In the empirical part, assets are compared with the use of statistical methods.The results of the risk-return analysis point to the money market as the best option for investors. Stocks and real estate ranked second and third, respectively.

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Expert Judgments: Financial Analysts Versus Weather Forecasters

Cited by 30 publications

References 6 publications

Financial forecasts during the crisis: Were experts more accurate than laypeople?

Financial forecasts during the crisis: Were experts more accurate than laypeople?

Stochastic Population Forecasting Based on Combinations of Expert Evaluations Within the Bayesian Paradigm

Risk and Return in the Real Estate, Bond and Stock Markets

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