Skip to main content

The Different View of Weather Anomalies on BIST100

  • Chapter
  • First Online:
New Approaches to CSR, Sustainability and Accountability, Volume V

Abstract

The main purpose of this paper is to determine the effects of different weather conditions on human behavior and decision-making processes in the financial markets. Weather and BIST100 data starting from January 1988 to December 2017 in Turkey are considered. The study covers the longest time period used so far for Turkey. Firstly, apparent temperature values are calculated by using the NWS heat index and wind chill, and the effect of apparent temperature values on returns and trading volume is also investigated. In addition, Kawamura's discomfort index was calculated and the differences in closing prices and trading volume at different comfort levels were also examined. The results show both positive and negative correlations among apparent temperature and trading volume and returns. In the last part of the study, time series analyses are carried out, and financial returns and trade volumes in BIST100 are comparatively analyzed by using seven different time series analysis methods. Our findings indicate that the most successful method is the ANN (Artificial Neural Network) method, which is an artificial intelligence method. In addition, analysis performed with VAR and VECM presented findings indicating the existence of relationships between weather and BIST100.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Agyeman J, Bullard R, Evans B (2003) Just sustainability: development in an unequal world. Earthscan/The MIT Press, London

    Google Scholar 

  • Allen AM, Fisher GJ (1978) Ambient temperature effects on paired associate learning. Ergonomics 21(2):95–101

    Article  Google Scholar 

  • Anderson CA (2001) Heat and violence. Curr Dir Psychol Sci 10(1):33–38

    Article  Google Scholar 

  • Andrei DM, Andrei LC (2015) Vector error correction model in explaining the association of some macroeconomic variables in Romania-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Selection and/or peer-review under responsibility of the Scientific Committee of ESPERA 2014. Procedia Econ Finance 22:13–14

  • Auliciems A (1978) Mood dependency on low intensity atmospheric variability. Int J Biometeorol 22:20–32

    Article  Google Scholar 

  • Barnston A (1988) The effect of weather on mood, productivity, and frequency of emotional crisis in a temperate continental climate. Int J Biometeorol 32:134–143

    Article  Google Scholar 

  • Bell PA, Baron RA (1976) Aggression and heat: the mediating role of negative affect. J Appl Soc Psychol 6:18–30

    Article  Google Scholar 

  • Bell PA (1981) Physiological comfort, performance and social effects Oh heat stress. J Soc Issues 37:71–94

    Article  Google Scholar 

  • Brockwell PJ (2010) Time series analysis. In: Peterson P, Baker E, McGaw BBT (eds) International encyclopedia of education, 3rd ed. Elsevier, Oxford, pp 474–481

    Google Scholar 

  • Cao M, Wei J (2005) Stock market returns: a note on temperature anomaly. J Bank Finance 29:1559–1573

    Article  Google Scholar 

  • Cesarini D, Johannesson M, Lichtenstein P, Wallace B (2009) Heritability of overconfidence. J Eur Econ Assoc 7(2–3):617–627

    Article  Google Scholar 

  • Cesarini D, Johannesson M, Magnusson PKE, Wallace B (2011) The behavioral genetics of behavioral anomalies. Manag Sci 58(1):21–34

    Article  Google Scholar 

  • Chan NH (2001) Time series: co-integration. In: Smelser NJ, Baltes PB (eds) International encyclopedia of the social and behavioral sciences. Pergamon, Oxford, pp 15709–15714

    Google Scholar 

  • Chatfield C, Xing H (2019) The analysis of time series: an introduction with R. CRC Press

    Book  Google Scholar 

  • Cheema A, Patrick VM (2012) Influence of warm versus cool temperatures on consumer choice: a resource depletion account. J Mark Res 49(6):984–995

    Article  Google Scholar 

  • Christoffersen PF (2012) A primer on financial time series analysis. In: Christoffersen PF (ed) Elements of financial risk management, 2nd ed. Academic Press, San Diego, pp 39–64

    Google Scholar 

  • Clayton S, Devine-Wright P, Stern PC, Whitmarsh L, Carrico A et al (2015) Psychological research and global climate change. Nat Clim Change 5:640–646

    Article  Google Scholar 

  • Coakley JR, Brown CE (2000) Artificial neural networks in accounting and finance: modeling issues. Int J Intell Syst Account Finance Manag (wiley) 9(2):119–144

    Article  Google Scholar 

  • Denissen J, Butalid L, Penke L, van Aken M (2008) The effects of weather on daily mood: a multilevel approach. Emotion 8:662–667

    Article  Google Scholar 

  • Dunis CL, Middleton PW, Karathanasopolous A, Theofilatos K (2016) Artificial intelligence in financial markets: cutting edge applications for risk management, portfolio optimization and economics, 1st ed. Palgrave Macmillan

    Google Scholar 

  • Fletcher R (1988) “Föhn illness” and human biometeorology in the Chinook area of Canada. Int J Biometeorol 32:168–175

    Article  Google Scholar 

  • Fritze JG, Blashki GA, Burke S, Wiseman J (2008) Hope, despair and transformation: climate change and the promotion of mental health and wellbeing. Int J Ment Heal Syst 2(13):1–10

    Google Scholar 

  • Fuller WA (2009) Introduction to Statistical Time Series. Wiley

    Google Scholar 

  • Gifford R (2011) The dragons of inaction: psychological barriers that limit climate change mitigation and adaptation. Am Psychol 66:290–302

    Article  Google Scholar 

  • Güngör S, Tomris Küçün N (2019) BIST100 Endeksinde İşlem Hacmi ve İşlem Miktarinda Hava Durumu Anomalisi, Atatürk Üniversitesi Sosyal Bilimler Ensitüsü Dergisi, cilt 23, ss 1459–1469

    Google Scholar 

  • Güngör S (2017) Finansal Yatirim Kararlarinda Genetik Etkiler: Duygusal Ön Yargilar Analizi. Trakya Üniversitesi Sosyal Bilimler Enstitüsü, İşletme Anabilim Dali, Doktora Tezi, Edirne

    Google Scholar 

  • Heyes A, Saberian S (2017) Temperature and decisions: evidence from 207,000 court cases. https://anthonyheyes.files.wordpress.com/2017/04/judges-1.pdf

  • Hirshleifer D, Shumway T (2003) Good day sunshine: stock returns and the weather. J Finance 58:1009–1032

    Article  Google Scholar 

  • Houghton J, Ding Y, Griggs D, Noguer N, van der Linden X, Dai K, Johnson C (eds) (2001) The scientific basis. In: Contribution of working group I to the third assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

    Google Scholar 

  • Howarth E, Hoffman M (1984) A multidimensional approach to the relationship between mood and weather. Br J Psychol 75:15–23

    Article  Google Scholar 

  • Hribersek E, Van de Voorde H, Poppe H, Casselman J (1987) Influence of the day of the week and the weather on people using a telephone support system. Br J Psychol 150:189–192

    Article  Google Scholar 

  • Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice, 2nd ed. OTexts

    Google Scholar 

  • Jang K, Lam R, Livesley W, Vernon P (1997) The relationship between seasonal mood change and personality: more apparent then real? Acta Psychiatr Scand 95:539–543

    Article  Google Scholar 

  • Kahneman D, Knetsch JL, Thaler RH (1991) The endowment effect, loss aversion, and status Quo bias. J Econ Perspect 5(1), 193–206

    Google Scholar 

  • Kahneman D, Tversky A (1974) Judgment under uncertainty: heuristics and biases. Science, New Series 185(4157):1124–1131

    Google Scholar 

  • Kakitsuba N, Inoue Y (1999) Evaluation of optimal indoor climate duringcooling from psychological and physiological responses. In: Proceedings of annual meeting of AIJ, pp 845–846

    Google Scholar 

  • Kasperson RE, Dow K (1991) Developmental and geographical equity in global environmental change. Eval Rev 15:149–171

    Article  Google Scholar 

  • Keehn M, Wood L (2017) A comparison of the NWS heat index and the steadman apparent temperature formulas over southeast texas. In: Eighth conference on environment and health

    Google Scholar 

  • Kidner D (2007) Depression and the natural world: towards a critical ecology of psychological distress. Int J Crit Psychol 19:123–146

    Google Scholar 

  • Leiserowitz A (2007) Communicating the risks of global warming: American risk perceptions, affective images, and interpretive communities. In: Moser SC, Dilling L (eds) Creating a climate for change. Cambridge University Press, New York, pp 44–63

    Chapter  Google Scholar 

  • Levy BS, Patz JA (2015) Climate change and public health. Oxford Univ. Press, New York; Loewenstein G (2000) Emotions in economic theory and economic behavior. Am Econ Rev 65:426–432

    Google Scholar 

  • Li T, Li X, Zhang X (2017) The design and implementation of vector autoregressive model and structural vector autoregressive model based on spark. In: 2017 3rd international conference on big data computing and communications (BIGCOM), pp 386–394

    Google Scholar 

  • Loewenstein G (2000) Emotions in economic theory and economic behavior. Am Econ Rev 90(2):426–432

    Article  Google Scholar 

  • Lütkepohl H (2005) Introduction BT - new introduction to multiple time series analysis. In: Lütkepohl H (ed) Springer. Berlin Heidelberg, Berlin, Heidelberg, pp 1–7

    Google Scholar 

  • Macy J, Brown MY (1998) Coming back to life: practices to reconnect our lives, our world. New Society Publishers, Gabriola Island, British Columbia

    Google Scholar 

  • Min Yoon S, Kang SH (2009) Weather effects on returns: evidence from the Korean stock market. Physica A 388:682–690

    Article  Google Scholar 

  • Murray G, Hay D, Armstrong S (1995) Personality factors in seasonal affective disorder: is seasonality and aspect of neuroticism? Personal Individ Differ 19:613–617

    Article  Google Scholar 

  • Nicholsen SW (2002) The love of nature and the end of the world. MIT Press, Cambridge

    Google Scholar 

  • Palinkas L (2001) Mental and cognitive performance in the cold. Int J Circumpolar Health 60:430–439

    Article  Google Scholar 

  • Peña D, Tiao GC, Tsay RS (2011) A course in time series analysis. Wiley

    Google Scholar 

  • Persinger M (1975) Lag responses in mood reports to changes in the weather matrix. Int J Biometeorol 19:108–114

    Article  Google Scholar 

  • Ramasubramanian K, Singh A (2018) Machine learning using R: with time series and industry-based use cases in R, 2nd ed. Apress

    Google Scholar 

  • Reuveny R (2008) Ecomigration and violent conflict: case studies and public policy implications. Hum Ecol 36:1–13

    Article  Google Scholar 

  • Rothfusz LP (1990) The heat index “equation” (or, more than you ever wanted to know about heat index). In: SR 90–23. Fort Worth, TX: National Oceanic and Atmospheric Administration, National Weather Service, Office of Meteorology

    Google Scholar 

  • Saunders EM (1993) Stock prices and wall street weather. Am Econ Rev 83:1337–1345

    Google Scholar 

  • Schneider M (2014) Under pressure: stock returns and the weather, April 28. Available at SSRN: https://ssrn.com/abstract=2218805 or https://doi.org/10.2139/ssrn.2218805

  • Stock JH (2001) Time series: economic forecasting. In: International encyclopedia of the social and behavioral sciences. Elsevier, pp 15721–15724

    Google Scholar 

  • Sulman F (1980) Keine Angst mehr vor dem Föhn: die Wetterfühligkeit und ihre Behandlung, Umsch. Frankf. a/M, 80, 291–292, 294, 295

    Google Scholar 

  • Swim JK, Stern PC, Doherty T, Clayton S, Reser JP et al (2011) Psychology’s contributions to understanding and addressing global climate change. Am Psychol 66:241–250

    Article  Google Scholar 

  • Thomson W (1979) A change of air. Charles Scribner, New York

    Google Scholar 

  • Tiao GC (2001) Time series: ARIMA methods. In: Smelser NJ, Baltes PB (eds) International encyclopedia of the social & behavioral sciences. Pergamon, Oxford, pp 15704–15709

    Google Scholar 

  • Trippi RR, Turban E (eds) (1992) Neural networks in finance and investing: using artificial intelligence to improve real world performance. McGraw-Hill Inc., New York, NY, USA

    Google Scholar 

  • Tromp S (1979) Studies on the origin and biological effects of the Chinook in western Canada. In: Tromp S, Bouma J (eds) Biometeorological survey, vol. 1, part A: human biometeorology. Heyden, London, pp 191–194

    Google Scholar 

  • Tsutsumi H et al (2002) Effects of low humidity on sensation of eye drynesscaused by using different type of contact lenses in summer season. In: Proceedings of indoor air, pp 394–399

    Google Scholar 

  • Tsutsumi H, Tanabe S et al (2004) Human comfort and productivity underhumidity conditions with different indoor air quality levels in summerand winter. In: Proceedings of Roomvent

    Google Scholar 

  • Vital LAB, Moreira EBM, Nobrega RS (2012) Estimativa De Índice De Desconforto Humano Em Um Transecto No Município De Olinda/PE. Revista Geonorte, Edição Especial 2:761–772

    Google Scholar 

  • Wang S, Liu W (2018) Weather impacts on trading volume-evidence from Hang Seng index

    Google Scholar 

  • Watson D (2000) Situational and environmental influence on mood. In: Mood and temperament. Guilford Press, New York

    Google Scholar 

  • Watts N, Adger WN, Agnolucci P, Blackstock J, Byass P et al (2015) Health and climate change: policy responses to protect public health. Lancet 386:1861–1914

    Article  Google Scholar 

  • Wyndham HC (1969a) Adaptation to heat and cold. Environ Res 2:442–469

    Article  Google Scholar 

  • Wei W (2016) Vertical specialization and strengthening indigenous innovation: comparing impacts of conventional trade and processing trade patterns on innovation in China. In: Wei W (ed) Achieving inclusive growth in China through vertical specialization. Chandos Publishing, pp 245–270

    Google Scholar 

  • Wyndham CH (1969b) Adaptation to heat and cold. Environ Res 2(5–6):442–469

    Article  Google Scholar 

  • Xiong J, Lian Z, Zhou X, You J, Lin Y (2015) Investigation of gender difference in human response to temperature step changes. Physiol Behav 151(1):426–440

    Article  Google Scholar 

  • Yu W, Vaneckova P, Mengersen K, Pan X, Tong S (2010) Is the association between temperature and mortality modified by age, gender and socioeconomic status? Sci Total Environ 408(17):3513–3518

    Article  Google Scholar 

  • www.borsaistanbul.com

  • www.mgm.gov.tr

  • www.eviews.com

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sezen Güngör .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Güngör, S., Vardari, L. (2024). The Different View of Weather Anomalies on BIST100. In: Çalıyurt, K.T. (eds) New Approaches to CSR, Sustainability and Accountability, Volume V. Accounting, Finance, Sustainability, Governance & Fraud: Theory and Application. Springer, Singapore. https://doi.org/10.1007/978-981-99-9145-7_2

Download citation

Publish with us

Policies and ethics