A careful re-examination of seasonality in international stock markets: Comment on sentiment and stock returns

Kamstra, Mark J.; Kramer, Lisa A.; Levi, Maurice D.

doi:10.1016/j.jbankfin.2011.10.010

Cited by 44 publications

(18 citation statements)

References 92 publications

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“…They conjecture that the sleep disruption induced by a changing of the clock (known to sleep experts as a desynchronosticity in circadian rhythm) causes market participants to suffer greater anxiety, ultimately leading them to shun risk during the trading day following a time change. Kamstra, et al (2003) and Kamstra, Kramer and Levi (2012), amongst others, document a relation between seasonal affective disorder (SAD) and stock market cycles. Relying on the established psychological links between reduced daylight hours and depression, and between depression and risk aversion, these authors present evidence that reduced daylight hours help to explain lower returns in winter months.…”

Section: Seasonal Variations and Financial Market Participantsmentioning

confidence: 99%

Oh What a Beautiful Morning! Diurnal Influences on Executives and Analysts: Evidence from Conference Calls

2018

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This study provides novel evidence that expert economic agents' work-related activities are systematically influenced by the time of day. We use archival data derived from time-stamped quarterly earnings conference calls together with linguistic algorithms to measure and track the moods of executives and analysts at different times of the day. The evidence indicates that the tone of conference call discussions deteriorates markedly over the course of the trading day, with both analysts' and executives' moods becoming more negative as the day wears on. Capital market pricing tests reveal that the time-of-day-induced negative tone leads to temporary stock mispricings. Our findings are relevant because the diurnal variations in behavior documented in the context of quarterly earnings calls are likely to extend across other important corporate communication, decision making, and performance situations, leading to potentially significant economic consequences.JEL Codes: G02, G14, M41 levels of happy tweets 1 (Mislove, Lehmann, Ahn, Onnela and Rosenquist (2010); Golder and Macy (2011)). Diurnal variations also have important consequences in the context of more professional decision making and performance situations, such as the judiciary and medicine.Danziger, Levav and Avnaim-Pesso (2011), for example, find that judges' parole decisions vary in a predictable manner over the course of the day, while Dai, Milkman, Hofmann, and Staats (2015) and Linder, Doctor, Friedberg, Nieva, Birks, Meeker and Fox (2014) respectively document the disturbing finding that each of nurses' and doctors' professional performances deteriorate as the day wears on.The influence of the time of day documented in these and other studies is explained in part by two lines of research in the fields of psychology and physiology: "personal resources" theory and the study of circadian rhythms' influence on human biology and behavior. A core underlying assumption of the personal resources framework is that individuals have a limited "reservoir" of resources from which to draw in order to adequately perform the variety of demands and activities that they face throughout the day. As these resources are depleted over the course of the workday, individuals exhibit poorer task performance, greater hostility, and more aggressive interaction and communication (DeWall, Baumeister, Stillman and Gailliot (2007); Hagger, Wood, Stiff and Chatzisarantis (2010); Stucke and Baumeister (2006)). In addition to our limited resources, humans (like most other living beings, including plants, animals, fungi, and cyanobacteria) are also significantly affected by the roughly 24-hour circadian (circa, about; diem, a day) cycle.Circadian rhythms affect human biology, emotions, and cognitive function, and the diurnal variations that they induce are not trivial; by some estimates, depending on the task, the

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Section: Seasonal Variations and Financial Market Participantsmentioning

confidence: 99%

Oh What a Beautiful Morning! Diurnal Influences on Executives and Analysts: Evidence from Conference Calls

2018

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“…Second, the onset/recovery variable spans the full year, whereas Kamstra et al's (2003) length-of-night variable takes on nonzero values during the fall and winter months only and, therefore, does not account for the portion of individuals who experience seasonal depression earlier than fall or later than winter. (For a more complete discussion of the merits of the onset/recovery variable relative to Kamstra et al's (2003) original specification, see Kamstra, Kramer, and Levi (2012)). In light of these points, we conduct our analysis using the onset/recovery variable.…”

Section: Measuring Seasonal Variation In Risk Preferencementioning

confidence: 99%

Seasonal Asset Allocation: Evidence from Mutual Fund Flows

Kamstra¹,

Kramer²,

Levi³

et al. 2017

J. Financ. Quant. Anal.

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We analyze the flow of money between mutual fund categories, finding strong evidence of seasonality in investor risk aversion. Aggregate investor flow data reveal an investor preference for safe mutual funds in autumn and risky funds in spring. During September alone, outflows from equity funds average $13 billion, controlling for previously documented flow determinants (e.g., capital-gains overhang). This movement of large amounts of money between fund categories is correlated with seasonality in investor risk aversion, consistent with investors preferring safer (riskier) investments in autumn (spring). We find consistent evidence in Canada and also in Australia, where seasons are offset by 6 months.

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“…For example, the sale of ice cream could reach a peak during the summer and a valley in the winter. Such pattern can be widely found in finance [23], economy, medicine [31] and other fields. Understanding the cyclic pattern of a collection of time series is informative particularly in the context of co-movement patterns.…”

Section: Seasonal Modelmentioning

confidence: 85%

MaVis: Machine Learning Aided Multi-Model Framework for Time Series Visual Analytics

Zhao

Ward

Rundensteiner

et al. 2016

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The ultimate goal of any visual analytic task is to make sense of the data and gain insights. Unfortunately, the continuously growing scale of the data nowadays challenges the traditional data analytics in the "big-data" era. Particularly, the human cognitive capabilities are constant whereas the data scale is not. Furthermore, most existing work focus on how to extract interesting information and present that to the user while not emphasizing on how to provide options to the analysts if the extracted information is not interesting. In this paper, we propose a visual analytic tool called MaVis that integrates multiple machine learning models with a plug-andplay style to describe the input data. It allows the analysts to choose the way they prefer to summarize the data. The MaVis framework provides multiple linked analytic spaces for interpretation at different levels. The low level data space handles data binning strategy while the high level model space handles model summarizations (i.e. clusters or trends). MaVis also supports model analytics that visualize the summarized patterns and compare and contrast them. This framework is shown to provide several novel methods of investigating co-movement patterns of timeseries dataset which is a common interest of medical sciences, finance, business and engineering alike. Lastly we demonstrate the usefulness of our framework via case study and user study using a stock price dataset.

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A careful re-examination of seasonality in international stock markets: Comment on sentiment and stock returns

Cited by 44 publications

References 92 publications

Oh What a Beautiful Morning! Diurnal Influences on Executives and Analysts: Evidence from Conference Calls

Oh What a Beautiful Morning! Diurnal Influences on Executives and Analysts: Evidence from Conference Calls

Seasonal Asset Allocation: Evidence from Mutual Fund Flows

MaVis: Machine Learning Aided Multi-Model Framework for Time Series Visual Analytics

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