بررسی تطبیقی رابطه بین شاخص بورس و حجم جستجو به‌منظور شناسایی الگوی رفتاری معامله‌گران بازار بورس

نوع مقاله : مقاله پژوهشی

نویسندگان

1 کارشناسی ارشد مدیریت صنعتی (گرایش مدیریت عملکرد)، دانشگاه شاهد، تهران، ایران

2 نویسنده مسئول: استادیار، گروه مدیریت صنعتی و کارآفرینی، دانشگاه شاهد، تهران، ایران

چکیده

شاخص بورس در بسیاری از کشورها ازجمله ایران مبنای تصمیم­گیری معامله­گران به‌ویژه تازه­واردان در بازار سرمایه است و به‌همین‌دلیل مبنای جستجوهای اینترنتی افراد است. بررسی جستجوهای اینترنتی براین‌مبنا می­تواند الگوهای رفتاری معامله­گران در بازار را توصیف و امکان پیش­بینی آنها را فراهم آورد. گوگل‌ترندز داده­هایی را فراهم می­کند که ازطریق تجزیه‌وتحلیل آنها می­توان به الگوهای رفتاری معامله­گران دست یافت. در این پژوهش از دو شاخص «حجم جستجوی گوگل» و «شاخص بورس» کشورهای منتخب استفاده شد. پژوهش حاضر ترکیبی، از نوع تشریحی یا تبیینی است. در مرحله کمی، جامعه آماری داده­های گوگل‌ترندز استخراج شد و سپس با هدف تبیین یافته­های بخش کمی و ارائه راهکارهای بهبود شرایط بازار، از روش تحقیق کیفی استفاده و داده­ها به روش مصاحبه گردآوری شد. یافته‌های بخش کمی نشان داد، همبستگی بالا و معنی‌داری بین دو شاخص موردنظر در ایران و برخی دیگر از کشورهای موردنظر وجود دارد. درحالی‌که رابطه بین این دو شاخص در برخی از کشورهای دیگر ضعیف و حتی معکوس بود. با تحلیل داده‌های حاصل از بخش کیفی، روابط بین این دو شاخص در کشورها، تبیین و راهکارهایی جهت بهبود شرایط رفتاری معامله‌گران بازار ارائه شد.

کلیدواژه‌ها

عنوان مقاله [English]

A Comparative Study of the Relationship between Stock Index and Search Volume for Identifying the Behavioral Pattern Of Stock Market Traders

نویسندگان [English]

  • Ali Panahi 1
  • Amin Habibirad 2
1 Master of Industrial Management (Performance Management), Shahed University, Tehran, Iran
2 Corresponding Author: Assistant Professor, Department of Industrial Management and Entrepreneurship, Shahed University, Tehran, Iran
چکیده [English]

In many countries, including Iran, the stock market index is the basis for decision-making, especially for new importers in the capital market, and is therefore the basis for Internet searches. An investigation on Internet searches can therefore describe the behavioral patterns of market traders and enable them to predict. GoogleTrends (GT)  provides data that can be used to analyze the behavioral patterns of traders In this study, two indicators; "Google Search Volume Index (GSVI)" and "Stock Index" of the selected countries were used. The present study is a combination of descriptive or explanatory method. In the quantitative stage, the statistical population of GT data was extracted and then, a qualitative research method was followed and the data were collected through interviews with the aim of explaining the quantitative sector findings and providing solutions to improve market conditions. The quantitative findings demonstrated high and significant correlation between two indicators of "Google Search Volume Index (GSVI)" and "Stock Index" in Iran and some other countries. While the relationship between these two indicators in some other countries was weak and even inverse. By analyzing the data obtained from the qualitative section, the relationships between these two indicators in studied countries were explained and in order to improve the behavioral conditions of market traders, solutions were presented.

کلیدواژه‌ها [English]

  • Google Trends (GT)
  • Google Search Volume Index (GSVI)
  • Stock Index
  • Correlation
  • Financial- Behavioral

مراجع

بینشیان، زهرا، و دهدار، فرهاد (1397)، اراﺋﻪ ﻣﺪل راﺑﻄﻪ ﻫﻮش ﻣﺎﻟﯽ ﺑﺎ گرایش‌های رﻓﺘﺎری و اﺛﺮ آﻧﻬﺎ در ﺗﺼﻤﯿﻤﺎت ﺳﺮﻣﺎﯾﻪ‌ﮔﺬاری ﺑﺮاﺳﺎس ﻧﻈﺮﯾﻪ رﻓﺘﺎر ﺑﺮﻧﺎﻣﻪ‌رﯾﺰی‌ﺷﺪه، ﻓﺼﻠﻨﺎﻣـﻪ ﻋﻠﻤﯽ- ﭘﮋوﻫﺸﯽ داﻧﺶ ﺳﺮﻣﺎﯾﻪ‌ﮔـﺬاری، ﺳﺎل ﻫﻔﺘﻢ، ﺷﻤﺎره 25: 203-221.
تلنگی، احمد (1383)، ﺗﻘﺎﺑﻞ ﻧﻈﺮﯾﻪ ﻧﻮﯾﻦ ﻣﺎﻟﯽ و ﻣﺎﻟﯽ رﻓﺘﺎری، ﻓﺼﻠﻨﺎﻣﻪ ﭘﮋوﻫﺶﻫﺎی ﻣﺎﻟﯽ، ﺷﻤﺎره 17: 3-25.
دﯾﺎﻧﺘﯽ دﯾﻠﻤﯽ، زﻫﺮا؛ دﯾﻦ‌ﻣﺤﻤﺪی، ﻣﺼﻄفی و ﻣﻌﺘﻤﺪی، ﺳﻌﯿﺪ (1392)، ﺑﺮرﺳﯽ ﻋﮑﺲ‌اﻟﻌﻤﻞ ﺳﺮﻣﺎﯾﻪﮔﺬاران در ﻣﻘﺎﺑﻞ ﺧﺒﺮﻫﺎی اﻗﺘﺼﺎدی و ﺳﯿﺎﺳﯽ ﻏﯿﺮﻣﻨﺘﻈﺮه در ﺑﻮرس اوراق ﺑﻬﺎدار ﺗﻬﺮان، ﻓﺼﻠﻨﺎﻣـﻪ ﻋﻠﻤﯽ- ﭘﮋوﻫﺸﯽ داﻧﺶ ﺳﺮﻣﺎﯾﻪ‌ﮔـﺬاری ﺳﺎل دوم، ﺷﻤﺎره 7: 55-72.
سینایی، ﺣﺴﻨﻌﻠﯽ و ﻣﺤﻤﻮدی، ادرﯾﺲ (1384)، ﺑﺮرﺳﯽ ﺗأثیر ﺧﺒﺮ ﺗﺠﺰﯾﻪ ﺳﻬﺎم و ﺳﻬﺎم ﺟﺎﯾﺰه ﺑﺮ ﺑﺎزده ﺳﻬﺎم در ﺑﻮرس اوراق ﺑﻬﺎدار ﺗﻬﺮان، ﺑﺮرﺳﯽ‌ﻫﺎی ﺣﺴﺎﺑﺪاری و ﺣﺴﺎﺑﺮﺳﯽ، ﺳﺎل دوازدهم، ﺷﻤﺎره 1: 77-96.
ﮔﻞ ارﺿﻲ، ﻏﻼﻣﺤﺴﻴﻦ و داﻧﺎﻳﻲ، ﻛﻴﻤﻴﺎ (1398)، ﺑﺮرﺳﻲ ﺑﻴﺶ واﻛﻨﺸﻲ ﺳﻬﺎمداران و ﻣﻘﺎﻳﺴﻪ آن در ﺷﺮﻛﺖ‌ﻫـﺎی ﻛﻮﭼـﻚ و ﺑـﺰرگ در ﺑﻮرس اوراق ﺑﻬﺎدار ﺗﻬﺮان، ﭼﺸﻢ‌اﻧﺪاز ﻣﺪﻳﺮﻳﺖ ﻣﺎﻟﻲ، سال نهم، شماره 25: 59-76.
Adelhoefer, S., Henry, S.T., Blanksteind, R., Grahame, G., Blahaa, J.M., & Dzaye, O. (2021). Declining interest in clinical imaging during the COVID-19 pandemic: An analysis of Google Trends. data Clinical Imaging, 73, 20-22.
Baker, M., & Wurgler, J. )2006(. Investor sentiment and the cross-section of stock returns. The Journal of Finance, 61, 1645-1680.
Barua, S., & Chowdhury, A. (2009). General characteristics and investment determinants in bangladesh Capital markets: evidence from dhaka stock exchange. Finance and Tourism Management, 54, ISSN 2070-3724.
Biji, L., Kringhaug, G., Molnár, P., & Sandvik, E. (2016). Google searches and stock returns. International Review of Financial Analysis , 45, 150-156.
Bineshian, Z., & Dehdar, F. (2018). Present the Model of the relationship between financial intelligence behavioral trends and their impact on investors decisions based on the theory of planned behavior. Journal of Investment Knowledge, 7(25), 203-222. (in Persian)
Böhme, H.M., Gröger, A., & Stöhrde, T. (2020). Searching for a better life: Predicting international migration with online search keywords. Journal of Development Economics, 142, 102347.
Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2 (1), 1-8.
Carneiro, H., & Mylonakis, E. (2009). Google trends: A web-based tool for real-time surveillance of disease outbreaks. Clinical Infectious Diseases, 49, 1557-1564.
Chen, H., De, P., Yu, H., & Hwang, B. (2011). Sentiment evealed in Social Media and its Effect on the Stock Market. IEEE Statistical Signal Processing Workshop.
Choi, H., & Varian, H. (2009). Predicting initial claims for unemployment benefits. Google Inc. working paper. https://static.googleusercontent.com/media/research. 
        https://www.google.com/en/archive/papers/initialclaimsUS.pdf.
Da, Z., Engelberg, J., & Gao, Pengjie. (2011). In search of attention. The Journal of Finance, 66 (5), 1461-1499.
Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and security market under- and over- reactions. The Journal of Finance, 53 (6), 1839-1885.
DeBondt, F.M.W., & Thaler, H.R. (1987). Further evidence on investor overreaction and stockmarket seasonality. The Journal of Finance, 42 (3), 557-581.
Dianati deylami, Z., Dinmohamadi, M., & Motamedi, S. (2012). Examination of investor reaction to unexpected economic and political events in Tehran stock market. Journal of Investment Knowledge, 2 (7), 55-72. (in Persian)
Duclos, R., WenWan, E., & Jiang, Y. (2013). Show me the honey! Effects of social exclusion on financial risk-taking. Journal of Consumer Research, 40, 122-135.
Fantazzini, D., & Toktamysova, Z. (2015).Forecasting German car sales using Google data and multivariate models. International Journal of Production Economics, 170, Part A, 97-135.
Frank, Z.M., & Sanati, A. (2018). How does the stock market absorb shocks? Journal of
Financial Economics, 129 (1), 136-153.
Frino, A., Jones, S., Lepone, A., & Boon, J. (2014), Market Behavior of Institutional Investors around Bankruptcy Announcements. Journal of Business Finance & Accounting, 41 (1-2), 270-295.
Golarzi, G., & Danayi, K. (2019). Evaluation of Shareholders’ Overreaction and its Comparison in Small and Large Companies (Case Study: Accepted Companies in Tehran Stock Exchange). Journal of Financial Management Perspective, 9(25), 59–76. (in Persian)
Gomes, P., & Taamouti, A. (2016). In search of the determinants of European asset market comovements. International Review of Economics & Finance, 44, 103-117.
Hamid, A. & Heiden, M. (2015). Forecasting volatility with empirical similarity and Google Trends. Journal of Economic Behavior & Organization, 117, 62-81.
Hu, H., Tang, L., Zhang, S., & Wang, H. (2018). Predicting the direction of stock markets using optimized neural networks with Google Trends. Neurocomputing, 285, 188-195.
Kurov, A. (2010). Investor sentiment and the stock market’s reaction to monetory policy. Journal of banking and finance, 34 (1), 139-149.
Mishra, K.C., & Metilda, M.J. (2015). A study on the impact of investment experience, gender, and level of education on overconfidence and self-attribution bias. IIMB Management Review, 27 (4), 228-239.
Moat, S.H., Curme, C., Avakian, A., Kennett, Y.D., Stanley, H.E., & Preis, T. (2013). Quantifying Wikipedia usage patterns before stock market moves. Scientific Reports, 3, 1-5.
Nguyen, C., Schinckus, C., & Nguyen, T. (2019). Google search and stock returns in emerging markets, Borsa İstanbul Review, 1-8.
Nicholas, B. & Thaler, R. (2004). A survey of behavioral finance in Constantinides. Journal of Psychology and Financial Markets, 2, 182-189.
Nuti, V.S., Wayda, B., Ranasinghe, I., Wang, S., Dreyer, P.R., Chen, I.S., & Murugiah, K. (2014). The use of Google Trends in health care research: a systematic review. PLoS One, 9 (10), Article e109583.
Ortiz-Martínez, Y., Garcia-Robledo, J.E., Vásquez-Castañeda, L.D., Bonilla-Aldana, D. K., & J.Rodriguez-Morales, J.A. )2020(. Can Google trends predict COVID-19 incidence and help preparedness? The situation in Colombia. Travel Medicine and Infectious Disease, 37, Article 101703.
Philippas, D., Rjiba, H., Guesmi, K., & Goutte, S. )2019(. Media attention and Bitcoin prices. Finance Research Letters, 30, 37-43.
Shen, D., Urquhart, A., & Wang, P. (2019). Does Twitter predict Bitcoin? Economics Letters, 174, 118-122.
Sinayi, H., & Mahmoudi, E. (2005). Investigating the effect of stock breakdown news and bonus shares on stock returns in Tehran Stock Exchange. Accounting and Auditing Review, 12(1), 77-96. (in Persian)
Swamy, V., Dharanib, M., & Takeda, F. (2019). Investor attention and Google Search Volume Index: Evidence from an emerging market using quantile regression analysis. Research in International Business and Finance, 50, 1-17.
Talangi, A. (2004). Confrontation of new financial theory and behavioral finance. 17, 3-25.  (in Persian)
van der Wielen, W., & Barrios, S. (2020). Economic sentiment during the COVID pandemic: Evidence from search behaviour in the EU. Journal of Economics and Business, 105970.
Wu, B., Wang, L., Lv, S-X., & Zeng, Y-R. )2021(. Effective crude oil price forecasting using new text-based and big-data-driven model. Measurement, 168, 108468.
Xu, Q., Bo, Z., Jiang, C., & Liu, Y. (2019). Does Google search index really help predicting stock market volatility? Evidence from a modified mixed data sampling model on volatility. Knowledge-Based Systems, 166, 170-185.
پژوهش‌های راهبردی بودجه و مالیه
دوره 2، شماره 1 - شماره پیاپی 5
فصلنامه بهار
خرداد 1400
صفحه 141-169

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سابقه مقاله

  • تاریخ دریافت: 30 خرداد 1400
  • تاریخ بازنگری: 07 تیر 1400
  • تاریخ پذیرش: 10 تیر 1400

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