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THE EFFECT OF INVESTOR ATTENTION ON STOCK RETURN VOLATILITY: AN ECONOMETRIC APPLICATION ON BANKS

Yıl 2021, Sayı: 3, 223 - 246, 30.07.2021
https://doi.org/10.51551/verimlilik.701270

Öz

Purpose: In the study, it is aimed to investigate the relationship between investor attention and stock volatility measured by using GST (Google Search Trends) data between 2010-2018 of the banks of the BIST Bank index.

Methodology: In the study, the relationship between investor attention measured on GST data and volatility measured using conditional heteroscedasticity models was analyzed using panel regression analysis. Based on volatility, which is the dependent variable of the study, two models were established on the basis of variable and total GST.

Findings: As a result of the analysis, while the effects of “bank name stock” and “banks’ BIST code” on volatility could not be determined, a positive relationship has been detected between the search for “bank name stock market” and “Total GAT” variables and volatility. The findings of this study are important for investors who can interpret the relationship between investor interest and volatility and use them in investment decisions.

Originality: When the literature on the subject is examined, it is observed that the number of studies on companies is limited. To reveal whether the findings to be obtained as a result of the research support the efficient market hypothesis or behavioral finance theories, it is considered as the main contribution and originality of the study to the literature.

Kaynakça

  • ADACHI, Y., MASUDA, M. ve TAKEDA, F. (2017), Google Search Intensity and Its Relationship to the Returns and Liquidity of Japanese Startup Stocks, Pacific-Basin Finance Journal, 46 (B), 243-257.
  • AHLUWALIA, S. (2018), Effect of Online Searches on Stock Returns, Accounting and Finance Research, 7 (1), 70-81.
  • ALBAYRAK, A. S. (2005), Çoklu Doğrusal Bağlantı Halinde En küçük Kareler Tekniğinin Alternatifi Yanlı Tahmin Teknikleri ve Bir Uygulama, ZKÜ Sosyal Bilimler Dergisi, 1 (1), 105-107.
  • AOUADİ, A., AROURİ, M. ve TEULON, F. (2013), Investor Attention and Stock Market Activity: Evidence from France, Economic Modelling, 35, 674-681.
  • BALTAGI, B.H. (2005). Econometric Analysis of Panel Data, John Wiley&Sons, Ltd., England.
  • BALTAGI, B.H. ve LI, Q. (1991), A Joint Test for Serial Correlation and Random Individual Effects, Statistics and Probability Letters, 11 (3), 277-280.
  • BANK, M., LARCH, M., ve PETER, G. (2011), Google Search Volume and Its Influence on Liquidity and Returns of German Stocks, Financial Markets and Portfolio Management, 253, 239-264.
  • BARBER, B. M. ve ODEAN, T. (2008), All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors, Review of Financial Studies, 2 (2), 785-818.
  • BECK, N. ve KATZ, J. N. (1995), What to Do (and not to Do) with Time-Series Cross-Section Data, American Political Science Review, 89 (3), 634-647.
  • BHARGAVA, A., FRANZINI, L. ve NARENDRANATHAN, W. (1982), Serial Correlation and The Fixed Effects Model, The Review of Economic Studies, 49 (4), 533-549.
  • BİJL, L., KRİNGHAUG, G., MOLNAR, P. ve SANDVİK, E. (2016), Google Searches and Stock Returns, International Review of Financial Analysis, 45, 150-156.
  • BODIE, Z., KANE, A. ve MARCUS, A. (2003), Essentials of Investments, 5th Edition, The McGraw Hill, USA.
  • BORN, B. ve BREITUNG, J. (2016), Testing for Serial Correlation in Fixed-Effects Panel Data Models, Econometric Reviews, 35 (7), 1290-1316.
  • BREUSCH, T.S. ve PAGAN, A. (1980), The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics, Review of Economic Studies, 47 (1), 239-253.
  • BROCK, W., DECHERT, W. D. ve SCHEINKMAN, J. (1987), A Test for Indepence Based on the Correlation Dimension, University of Wisconsin at Madison, Working Paper.
  • DA, Z., ENGELBERG, J. ve GAO, P. (2011), In Search of Attention, The Journal of Finance, 66 (5), 1461-1499.
  • DRAKE, M. S., ROULSTONE, D. T. ve THORNOCK, J. R. (2012), Investor Information Demand: Evidence from Google Searches Around Earnings Announcements, Journal of Accounting Research, 50 (4), 1001-1040.
  • ERTEN, E. ve KORKMAZ, T. (2018), Google Trends Arama Hacim Endeksi ve Borsa İstanbul İlişkisi, 22. Finans Sempozyumu, Mersin.
  • FAMA, E. F. (1970), Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance, 25 (2), 383-417.
  • FINK, C.ve JOHANN, T. (2014), May I Have Your Attention, Please: The Market Microstructure of Investor Attention, University of Mannheim Working Paper. 1-59.
  • HONDA, Y. (1985), Testing the Error Components Model with non-Normal Disturbances, Review of Economic Studies, 52 (4), 681-690.
  • JOSEPH, K., WINTOKI, M. B. ve ZHANG, Z, (2011), Forecasting Abnormal Stock Returns and Trading Volume Using Investor Sentiment: Evidence from Online Search, International Journal of Forecasting, 27 (4), 1116-1127.
  • KAHNEMAN, D. (1973), Attention and Effort, Prentice-Hall, Englewood Cliffs, NJ.
  • KAHNEMAN, D. ve TVERSKY, A. (1974), Judgement under Uncertainly: Heuristics and Biases, Sciene, 185 (4157), 1124-1131.
  • KAHNEMAN, D. ve TVERSKY, A. (1979), Prospect Theory: An Analysis of Decision under Risk, Econometrica, 47 (2), 263-291.
  • KORKMAZ, T., ÇEVİK, E. İ. ve ÇEVİK, N. (2017), Yatırımcı İlgisi ile Pay Piyasası Arasındaki İlişki: BIST-100 Endeksi Üzerine Bir Uygulama, Business and Economics Research Journal, 8 (2), 203-215.
  • LATOEIRO, P., RAMOS, S. B. ve VEIGA, H. (2013), Predictability of Stock Market Activity Using Google Search Queries, Universidad Carlos III de Madrid Working Paper. 13-06.
  • LOUGHLIN, C. ve HARNISCH, E. (2013), The Viability of Stocktwits and Google Trends to Predict the Stock Market, ArXiv Working Paper, 1-19.
  • MAO, H., COUNTS, S. ve BOLLEN, J. (2011), Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data, ArXiv Preprint, 1-10.
  • MERTON, R. C. (1987), A Simple Model of Capital Market Equilibrium with Incomplete Information, The Journal of Finance, 42 (3), 483-510.
  • NGUYEN, C.P., SCHINCKUS, C. ve NGUYEN, T. V. (2019), Google Search and Stock Returns in Emerging Markets, Borsa Istanbul Review, 19 (4), 288-296.
  • NURAZİ, R., KANUNLUA, P. S. ve USMAN, B. (2015), The Effect of Google Trend as Determinant of Return and Liquidity in Indonesia Stock Exchange, Jurnal Pengurusan, 45, 131-142.
  • PESARAN, H. (2007), A Simple Panel Unit Root Test in The Presence of Cross Section Dependence. Journal of Applied Econometrics, 22 (2), 265-312.
  • PESARAN, M. H. (2004), General Diagnostic Tests for Cross Section Dependence in Panels, Cambridge Working Papers in Economics, 435.
  • PESARAN, M. H., ULLAH, A. ve YAMAGATA, T. (2008), A Bias Adjusted LM Test of Error Cross Section Independence, Econometrics Journal, 11 (1), 105-127.
  • SMITH, G. P. (2012), Google Internet Search Activity and Volatility Prediction in the Market for Foreign Currency, Finance Research Letters, 9 (2), 103-110.
  • SMITH, V., LEYBOURNE, S., KIM, T. H. ve NEWBOLD, P. (2004), More Powerful Panel Data Unit Root Tests with an Application to Mean Reversion in Real Exchange Rates. Journal of Applied Econometrics, 19, 147–170.
  • TAKEDA, F. ve WAKAO, T. (2014), Google Search Intensity and Its Relationship with Returns and Trading Volume of Japanese Stocks, Pacific-Basin Finance Journal, 27 (C), 1-18.
  • TANTAOPAS, P., PADUNGSAKSAWASDI, C. ve TREEPONGKARUNA, S. (2016), Attention Effect via Internet Search Intensity in Asia-Pacific Stock Markets, Pacific-Basin Finance Journal, 38 (C), 107-124.
  • VLASTAKIS, N. ve MARKELLOS, R. N. (2012), Information Demand and Stock Market Volatility, Journal of Banking & Finance, 36 (6), 1808-1821.
  • VOZLYUBLENNAIA, N. (2014), Investor Attention, Index Performance, and Return Predictability, Journal of Banking & Finance, 41 (C), 17-35.

YATIRIMCI İLGİSİNİN PAY SENEDİ GETİRİ VOLATİLİTESİNE ETKİSİ: BANKALAR ÜZERİNE EKONOMETRİK BİR UYGULAMA

Yıl 2021, Sayı: 3, 223 - 246, 30.07.2021
https://doi.org/10.51551/verimlilik.701270

Öz

Amaç: Çalışmada BIST Banka endeksinde faaliyet gösteren bankaların 2010-2018 yılları arasında GAT (Google Arama Trendleri) verileri kullanılarak ölçülen yatırımcı ilgisi ile pay senedi volatilitesi arasındaki ilişkinin araştırılması amaçlanmıştır.

Yöntem: Çalışmada GAT verileri üzerinden ölçülen yatırımcı ilgisi ile koşullu varyans modelleri kullanılarak hesaplanan volatilite arasındaki ilişki, panel regresyon analizi ile incelenmiştir. Çalışmanın bağımlı değişkeni olan volatilite esas alınarak değişken bazında ve toplam GAT bazında olmak üzere iki model kurulmuştur.

Bulgular: Yapılan analiz sonucunda, “banka adı hisse” ve “bankaların BIST Kodu” aramalarının volatiliteye etkisi yokken “banka adı borsa” araması ve değişkenlerin toplamından oluşan “Toplam GAT” değişkenleri ile volatilite arasında pozitif yönlü ilişki tespit edilmiştir. Çalışmada elde edilen bulgular, yatırımcı ilgisinin pay senedi volatilitesi ile olan ilişkisini yorumlayabilen ve yatırım kararlarında kullanabilen yatırımcılar açısından önem arz etmektedir.

Özgünlük: Konuya ilişkin literatür incelendiğinde firmalar üzerine yapılmış çalışma sayısının sınırlı olduğu gözlemlenmektedir. Araştırma sonucunda elde edilecek bulguların etkin piyasa hipotezi ya da davranışsal finans teorilerinden hangisini destekler nitelikte olduğunun ortaya konması, çalışmanın literatüre temel katkısı ve özgünlüğü olarak değerlendirilmektedir.

Kaynakça

  • ADACHI, Y., MASUDA, M. ve TAKEDA, F. (2017), Google Search Intensity and Its Relationship to the Returns and Liquidity of Japanese Startup Stocks, Pacific-Basin Finance Journal, 46 (B), 243-257.
  • AHLUWALIA, S. (2018), Effect of Online Searches on Stock Returns, Accounting and Finance Research, 7 (1), 70-81.
  • ALBAYRAK, A. S. (2005), Çoklu Doğrusal Bağlantı Halinde En küçük Kareler Tekniğinin Alternatifi Yanlı Tahmin Teknikleri ve Bir Uygulama, ZKÜ Sosyal Bilimler Dergisi, 1 (1), 105-107.
  • AOUADİ, A., AROURİ, M. ve TEULON, F. (2013), Investor Attention and Stock Market Activity: Evidence from France, Economic Modelling, 35, 674-681.
  • BALTAGI, B.H. (2005). Econometric Analysis of Panel Data, John Wiley&Sons, Ltd., England.
  • BALTAGI, B.H. ve LI, Q. (1991), A Joint Test for Serial Correlation and Random Individual Effects, Statistics and Probability Letters, 11 (3), 277-280.
  • BANK, M., LARCH, M., ve PETER, G. (2011), Google Search Volume and Its Influence on Liquidity and Returns of German Stocks, Financial Markets and Portfolio Management, 253, 239-264.
  • BARBER, B. M. ve ODEAN, T. (2008), All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors, Review of Financial Studies, 2 (2), 785-818.
  • BECK, N. ve KATZ, J. N. (1995), What to Do (and not to Do) with Time-Series Cross-Section Data, American Political Science Review, 89 (3), 634-647.
  • BHARGAVA, A., FRANZINI, L. ve NARENDRANATHAN, W. (1982), Serial Correlation and The Fixed Effects Model, The Review of Economic Studies, 49 (4), 533-549.
  • BİJL, L., KRİNGHAUG, G., MOLNAR, P. ve SANDVİK, E. (2016), Google Searches and Stock Returns, International Review of Financial Analysis, 45, 150-156.
  • BODIE, Z., KANE, A. ve MARCUS, A. (2003), Essentials of Investments, 5th Edition, The McGraw Hill, USA.
  • BORN, B. ve BREITUNG, J. (2016), Testing for Serial Correlation in Fixed-Effects Panel Data Models, Econometric Reviews, 35 (7), 1290-1316.
  • BREUSCH, T.S. ve PAGAN, A. (1980), The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics, Review of Economic Studies, 47 (1), 239-253.
  • BROCK, W., DECHERT, W. D. ve SCHEINKMAN, J. (1987), A Test for Indepence Based on the Correlation Dimension, University of Wisconsin at Madison, Working Paper.
  • DA, Z., ENGELBERG, J. ve GAO, P. (2011), In Search of Attention, The Journal of Finance, 66 (5), 1461-1499.
  • DRAKE, M. S., ROULSTONE, D. T. ve THORNOCK, J. R. (2012), Investor Information Demand: Evidence from Google Searches Around Earnings Announcements, Journal of Accounting Research, 50 (4), 1001-1040.
  • ERTEN, E. ve KORKMAZ, T. (2018), Google Trends Arama Hacim Endeksi ve Borsa İstanbul İlişkisi, 22. Finans Sempozyumu, Mersin.
  • FAMA, E. F. (1970), Efficient Capital Markets: A Review of Theory and Empirical Work, The Journal of Finance, 25 (2), 383-417.
  • FINK, C.ve JOHANN, T. (2014), May I Have Your Attention, Please: The Market Microstructure of Investor Attention, University of Mannheim Working Paper. 1-59.
  • HONDA, Y. (1985), Testing the Error Components Model with non-Normal Disturbances, Review of Economic Studies, 52 (4), 681-690.
  • JOSEPH, K., WINTOKI, M. B. ve ZHANG, Z, (2011), Forecasting Abnormal Stock Returns and Trading Volume Using Investor Sentiment: Evidence from Online Search, International Journal of Forecasting, 27 (4), 1116-1127.
  • KAHNEMAN, D. (1973), Attention and Effort, Prentice-Hall, Englewood Cliffs, NJ.
  • KAHNEMAN, D. ve TVERSKY, A. (1974), Judgement under Uncertainly: Heuristics and Biases, Sciene, 185 (4157), 1124-1131.
  • KAHNEMAN, D. ve TVERSKY, A. (1979), Prospect Theory: An Analysis of Decision under Risk, Econometrica, 47 (2), 263-291.
  • KORKMAZ, T., ÇEVİK, E. İ. ve ÇEVİK, N. (2017), Yatırımcı İlgisi ile Pay Piyasası Arasındaki İlişki: BIST-100 Endeksi Üzerine Bir Uygulama, Business and Economics Research Journal, 8 (2), 203-215.
  • LATOEIRO, P., RAMOS, S. B. ve VEIGA, H. (2013), Predictability of Stock Market Activity Using Google Search Queries, Universidad Carlos III de Madrid Working Paper. 13-06.
  • LOUGHLIN, C. ve HARNISCH, E. (2013), The Viability of Stocktwits and Google Trends to Predict the Stock Market, ArXiv Working Paper, 1-19.
  • MAO, H., COUNTS, S. ve BOLLEN, J. (2011), Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data, ArXiv Preprint, 1-10.
  • MERTON, R. C. (1987), A Simple Model of Capital Market Equilibrium with Incomplete Information, The Journal of Finance, 42 (3), 483-510.
  • NGUYEN, C.P., SCHINCKUS, C. ve NGUYEN, T. V. (2019), Google Search and Stock Returns in Emerging Markets, Borsa Istanbul Review, 19 (4), 288-296.
  • NURAZİ, R., KANUNLUA, P. S. ve USMAN, B. (2015), The Effect of Google Trend as Determinant of Return and Liquidity in Indonesia Stock Exchange, Jurnal Pengurusan, 45, 131-142.
  • PESARAN, H. (2007), A Simple Panel Unit Root Test in The Presence of Cross Section Dependence. Journal of Applied Econometrics, 22 (2), 265-312.
  • PESARAN, M. H. (2004), General Diagnostic Tests for Cross Section Dependence in Panels, Cambridge Working Papers in Economics, 435.
  • PESARAN, M. H., ULLAH, A. ve YAMAGATA, T. (2008), A Bias Adjusted LM Test of Error Cross Section Independence, Econometrics Journal, 11 (1), 105-127.
  • SMITH, G. P. (2012), Google Internet Search Activity and Volatility Prediction in the Market for Foreign Currency, Finance Research Letters, 9 (2), 103-110.
  • SMITH, V., LEYBOURNE, S., KIM, T. H. ve NEWBOLD, P. (2004), More Powerful Panel Data Unit Root Tests with an Application to Mean Reversion in Real Exchange Rates. Journal of Applied Econometrics, 19, 147–170.
  • TAKEDA, F. ve WAKAO, T. (2014), Google Search Intensity and Its Relationship with Returns and Trading Volume of Japanese Stocks, Pacific-Basin Finance Journal, 27 (C), 1-18.
  • TANTAOPAS, P., PADUNGSAKSAWASDI, C. ve TREEPONGKARUNA, S. (2016), Attention Effect via Internet Search Intensity in Asia-Pacific Stock Markets, Pacific-Basin Finance Journal, 38 (C), 107-124.
  • VLASTAKIS, N. ve MARKELLOS, R. N. (2012), Information Demand and Stock Market Volatility, Journal of Banking & Finance, 36 (6), 1808-1821.
  • VOZLYUBLENNAIA, N. (2014), Investor Attention, Index Performance, and Return Predictability, Journal of Banking & Finance, 41 (C), 17-35.
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Tuğba Nur Topaloğlu 0000-0002-0974-4896

İlhan Ege 0000-0002-5765-1926

Yayımlanma Tarihi 30 Temmuz 2021
Gönderilme Tarihi 9 Mart 2020
Yayımlandığı Sayı Yıl 2021 Sayı: 3

Kaynak Göster

APA Nur Topaloğlu, T., & Ege, İ. (2021). YATIRIMCI İLGİSİNİN PAY SENEDİ GETİRİ VOLATİLİTESİNE ETKİSİ: BANKALAR ÜZERİNE EKONOMETRİK BİR UYGULAMA. Verimlilik Dergisi(3), 223-246. https://doi.org/10.51551/verimlilik.701270

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