Robust global mood influences in equity pricing

Dowling, Michael; Lucey, Brian M.

doi:10.1016/j.mulfin.2007.06.002

Cited by 91 publications

(49 citation statements)

References 31 publications

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“…Both of these proxies are uncommon in the literature and their accuracy is unknown. Dowling and Lucey (2008) Kliger and Levy (2003). These authors find using S&P 500 index options data that bad mood, as proxied by total cloud cover and precipitation, make investors place higher-than-usual probabilities on adverse events.…”

Section: Introductionmentioning

confidence: 99%

Does the weather affect stock market volatility?

Symeonidis

Daskalakis

Markellos

2010

Finance Research Letters

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Abstract. This paper investigates the empirical association between stock market volatility and investor mood-proxies related to the weather (cloudiness, temperature and precipitation) and the environment (nighttime length). Overall, our results suggest that cloudiness and length of nighttime are inversely related to historical, implied and realized measures of volatility. The strength of association seems to vary with the location of an exchange on Earth with respect to the equator. Weather deviations from seasonal norms and dummies representing extreme weather conditions do not offer additional explanatory power in our datasets. JEL Classification: G14, G32Keywords: Stock market anomalies, Volatility, Sunshine effect, SAD effect, Behavioral Acknowledgements: We would like to thank Mark Kamstra, David Hirshleifer and an anonymous referee for providing us with valuable comments and suggestions which significantly improved the final version of the paper. We assume responsibility for any remaining errors.

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Section: Introductionmentioning

confidence: 99%

Does the weather affect stock market volatility?

Symeonidis

Daskalakis

Markellos

2010

Finance Research Letters

View full text Add to dashboard Cite

show abstract

“…Forgas (1995) proposed that investors with limited knowledge tended to allow mood to interfere with decision-making. In Thailand, these investors are small, local, individual investors (Dowling & Lucey, 2008). Comparing the results of the SET 50 index returns, in which large investors are dominant, against the MAI index returns, in which small individuals are dominant, sheds light on Forgas' (1995) proposal.…”

Section: Time-varying Weather Effects and Market Efficiencymentioning

confidence: 95%

“…For example, in national studies, Brahmana, Hooy and Ahmad (2015) found cloud-cover effects for Indonesia, and Nirojsil (2009) found temperature effects for Thailand. In international studies, Hirshleifer and Shumway (2003) and Dowling and Lucey (2008) found the effects for Brazil, Indonesia, Malaysia, Mexico and Singapore.…”

Section: The Datamentioning

confidence: 99%

“…These variables are a collection of weather variables that have also been considered in previous studies (e.g., Dowling & Lucey, 2008); they are the most comprehensive set of variables among all weather studies for Thailand (e.g., Hirshleifer & Shumway, 2003;Nirojsil, 2009). …”

Section: The Datamentioning

confidence: 99%

“…Despite these many variables, some variables that were included in previous studies are omitted. For example, the geomagnetic storms in Dowling and Lucey (2008) are omitted because the storm data are not available. The wind direction in Worthington (2009) is omitted because the direction cannot be averaged to represent the daily direction data and because it is not a significant variable in that study.…”

Section: Introductionmentioning

confidence: 99%

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