Araştırma Makalesi
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Yatırımcı İlgisinin Pay Piyasaları Üzerindeki Etkisi: Borsa İstanbul’da İşlem Gören Bankalar Üzerine Panel Veri Analizi

Yıl 2020, Cilt 28, Sayı 44, 191 - 214, 19.04.2020
https://doi.org/10.17233/sosyoekonomi.2020.02.09

Öz

Çalışmada GAT (Google Arama Trendleri) verileri ile ölçülen yatırımcı ilgisi ile pay senedi getirisi ve işlem hacmi arasındaki ilişkinin bankacılık sektöründe faaliyet gösteren ve Borsa İstanbul’a kote olan bankalarda 2010-2018 döneminde araştırılması amaçlanmıştır. Bu amaçla “banka adı hisse”, “banka adı borsa” ve “bankaların BİST kodu” anahtar kelimelerinin Google aranma sıklıkları ve aranma sıklıklarına ilişkin her bir endeksin toplamı olan “toplam GAT” bağımsız değişkenler olarak analize dâhil edilir iken bağımlı değişkenler olarak pay senedi getirisi ve işlem hacmi kullanılmıştır. Yapılan panel veri analizi sonucunda bağımlı değişken olan pay senedi getirisi ile “bankaların BİST kodu” arasında istatistiksel olarak anlamlı ve pozitif yönlü ilişki tespit edilirken diğer bağımsız değişkenler ile pay senedi getirisi arasında anlamlı ilişki tespit edilememiştir. Bunun yanı sıra, bir diğer bağımlı değişken olan işlem hacmi ile “banka adı hisse”, “bankaların BİST kodu” ve “toplam GAT” bağımsız değişkenleri ile istatistiksel olarak anlamlı ve pozitif yönlü bir ilişki tespit edilirken “banka adı borsa” değişkeni ile işlem hacmi arasında anlamlı ilişki tespit edilememiştir. Araştırma sonuçları genel olarak Merton (1987) Yatırımcı Tanınmışlık Hipotezi ve Barber ve Odean (2008) Fiyat Baskısı Hipotezi’ni destekler niteliktedir.

Kaynakça

  • Ahluwalia, S. (2018). Effect of online searches on stock returns. Accounting and Finance Research, 7(1), 70-81.
  • Akgün, B. E. (2016). Investor Attention And IPO Performance. Unpublished master thesis, The Graduate School Of Socıal Scıences Of Mıddle East Technıcal Unıversıty. Ankara.
  • Albayrak, A. S. (2005). Çoklu Doğrusal Bağlantı Halinde Enküçük Kareler Tekniğinin Alternatifi Yanlı Tahmin Teknikleri Ve Bir Uygulama. ZKÜ Sosyal Bilimler Dergisi, 1(1), 105-107.
  • Bai, J. & Ng, S. (2004). A PANIC attack On Unit Roots And Cointegration. Econometrica, 72(4), 1127–1177.
  • Baltagi, B. & Li, Q. (1991), A Joint Test For Serial Correlation And Random Individual Effects. Statistics and Probability Letters, 11, 277-280.
  • Bank, M., Larch, M., & 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. & Odean T. (2001). Boys Will Be Boys: Gender, Overconfidence, And Common Stock Investment. Quarterly Journal of Economics, 116, 261-292.
  • Beck, N. & Katz, J. (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. & Narendranathan, W. (1982). Serial Correlation And The Fixed Effects Model. The Review of Economic Studies, 49(4), 533–549.
  • Bijl, L., Kringhaug, G., Molnar, P. & Sandvik, E. (2016). Google Searches And Stock Returns. International Review of Financial Analysis, 45, 150-156.
  • Bilgiç, M.E. (2017). Google Trends Search Volume Index in Estimation of Istanbul Stock Market Index (BIST). Unpublished master thesis, Istanbul Bilgi University, İstanbul.
  • Born, B. & Breitung, J. (2016). Testing For Serial Correlation In Fixed-Effects Panel Data Models. Econometric Reviews, 35(7), 1290-1316.
  • Bozanta, A., Coşkun, M., Kutlu, B. & Özturan, M. (2017). Relationship Between Stock Market Indices And Google Trends. The Online Journal of Science and Technology, 7(4), 168-172.
  • Breusch, T. & Pagan, A. (1980). The Lagrange Multiplier Test And Its Applications To Model Specification In Econometrics. Review of Economic Studies, 47(1), 239-253.
  • Da, Z., Engelberg, J. & Gao, P. (2011). In Search Of Attention. The Journal of Finance, 66(5), 1461–1499.
  • Fink, C. & 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, 681-690
  • Joseph, K., Wintoki, M. B. & 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.
  • Korkmaz T., Çevik E. ve Çevik N. (2017). Yatırımcı İlgisi İle Pay Piyasası Arasındaki İlişki: BİST-100 Endeksi Üzerine Bir Uygulama. Business and Economics Research Journal, 8(2), 203-215.
  • Korkmaz, T. ve Ceylan, A. (2012). Sermaye Piyasası Ve Menkul Değer Analizi. Bursa: Ekin Kitabevi.
  • Latoeiro, P., Ramos, S.B. & Veiga, H.(2013). Predictability Of Stock Market Activity Using Google Search Queries. Universidad Carlos III de Madrid Working Paper. 13-06.
  • Liu, Y., Lv, B., Peng, G., & Zhang, C. (2012). Relationship Between Internet Search Data And Stock Return: Empirical Evidence From Chinese Stock Market. Recent Progress in Data Engineering and Internet Technology, 157, 25–30.
  • Loughlin, C. & Harnisch E. (2013). The Viability Of Stocktwits And Google Trends To Predict The Stock Market. ArXiv Working Paper, 1-19.
  • Mao, H., Counts, S. & 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, 483-510.
  • Pesaran, M.H., A. Ullah, & T. Yamagata (2008). A Bias Adjusted LM Test Of Error Cross Section Independence. Econometrics Journal, 11, 105–127.
  • Smith, V., Leybourne, S., Kim, T. H. & 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. & Wakao, T. (2014). Google Search Intensity And Its Relationship With Returns And Trading Volume Of Japanese Stocks. Pacific-Basin Finance Journal, 27, 1–18.
  • Tantaopas, P., Padungsaksawasdi, C. & Treepongkaruna, S. (2016). Attention Effect Via Internet Search Intensity In Asia-Pacific Stock Markets. Pacific-Basin Finance Journal, 38, 107–124.
  • Vlastakis, N. & 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, 17–35.
  • Wang, B., Long, W. & Wei, X. (2018). Investor Attention, Market Liquidity And Stock Return: A New Perspective. Asian Economic and Financial Review, 8(3), 341-352.
  • Zhang, W., Shen, D., Zhang, Y. & Xiong, X. (2013). Open Source Information, Investor Attention And Asset Pricing. Economic Modelling, 33, 613–619.

The Effect of Investor Attention on Equity Markets: Panel Data Analysis on Banks Traded on Borsa Istanbul

Yıl 2020, Cilt 28, Sayı 44, 191 - 214, 19.04.2020
https://doi.org/10.17233/sosyoekonomi.2020.02.09

Öz

In this study, it is aimed to investigate the relationship between investor’s attention measured by SVI (Google Search Volume Index) data and stock return and trading volume of the banks listed in Borsa İstanbul for the period 2010-2018. For this purpose, together with “bank name stock”, “bank name stock market”, “banks’ BIST code” keyword search volumes, “Total GAT” which is the sum of the each independent search volume index has been taken as independent variables while stock returns and trading volume are used as dependent variables. As a result of the panel data analysis, a statistically significant and positive relationship has been found between stock return, one of our dependent variables, and the “banks’ BIST code”, while no significant relationship has been found between other independent variables and stock return. In addition, while there exists a statistically significant and positive relationship between each of the independent variables namely “bank name stock market”, “banks’ BIST code”, “total GAT” and our other dependent variable trading volume, there is no statistically significant relationship between “bank name stock” variable and trading volume. The results of this research are generally supported by Merton (1987) Investor Recognition Hypothesis and Barber and Odean (2008) Price Pressure Hypothesis.

Kaynakça

  • Ahluwalia, S. (2018). Effect of online searches on stock returns. Accounting and Finance Research, 7(1), 70-81.
  • Akgün, B. E. (2016). Investor Attention And IPO Performance. Unpublished master thesis, The Graduate School Of Socıal Scıences Of Mıddle East Technıcal Unıversıty. Ankara.
  • Albayrak, A. S. (2005). Çoklu Doğrusal Bağlantı Halinde Enküçük Kareler Tekniğinin Alternatifi Yanlı Tahmin Teknikleri Ve Bir Uygulama. ZKÜ Sosyal Bilimler Dergisi, 1(1), 105-107.
  • Bai, J. & Ng, S. (2004). A PANIC attack On Unit Roots And Cointegration. Econometrica, 72(4), 1127–1177.
  • Baltagi, B. & Li, Q. (1991), A Joint Test For Serial Correlation And Random Individual Effects. Statistics and Probability Letters, 11, 277-280.
  • Bank, M., Larch, M., & 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. & Odean T. (2001). Boys Will Be Boys: Gender, Overconfidence, And Common Stock Investment. Quarterly Journal of Economics, 116, 261-292.
  • Beck, N. & Katz, J. (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. & Narendranathan, W. (1982). Serial Correlation And The Fixed Effects Model. The Review of Economic Studies, 49(4), 533–549.
  • Bijl, L., Kringhaug, G., Molnar, P. & Sandvik, E. (2016). Google Searches And Stock Returns. International Review of Financial Analysis, 45, 150-156.
  • Bilgiç, M.E. (2017). Google Trends Search Volume Index in Estimation of Istanbul Stock Market Index (BIST). Unpublished master thesis, Istanbul Bilgi University, İstanbul.
  • Born, B. & Breitung, J. (2016). Testing For Serial Correlation In Fixed-Effects Panel Data Models. Econometric Reviews, 35(7), 1290-1316.
  • Bozanta, A., Coşkun, M., Kutlu, B. & Özturan, M. (2017). Relationship Between Stock Market Indices And Google Trends. The Online Journal of Science and Technology, 7(4), 168-172.
  • Breusch, T. & Pagan, A. (1980). The Lagrange Multiplier Test And Its Applications To Model Specification In Econometrics. Review of Economic Studies, 47(1), 239-253.
  • Da, Z., Engelberg, J. & Gao, P. (2011). In Search Of Attention. The Journal of Finance, 66(5), 1461–1499.
  • Fink, C. & 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, 681-690
  • Joseph, K., Wintoki, M. B. & 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.
  • Korkmaz T., Çevik E. ve Çevik N. (2017). Yatırımcı İlgisi İle Pay Piyasası Arasındaki İlişki: BİST-100 Endeksi Üzerine Bir Uygulama. Business and Economics Research Journal, 8(2), 203-215.
  • Korkmaz, T. ve Ceylan, A. (2012). Sermaye Piyasası Ve Menkul Değer Analizi. Bursa: Ekin Kitabevi.
  • Latoeiro, P., Ramos, S.B. & Veiga, H.(2013). Predictability Of Stock Market Activity Using Google Search Queries. Universidad Carlos III de Madrid Working Paper. 13-06.
  • Liu, Y., Lv, B., Peng, G., & Zhang, C. (2012). Relationship Between Internet Search Data And Stock Return: Empirical Evidence From Chinese Stock Market. Recent Progress in Data Engineering and Internet Technology, 157, 25–30.
  • Loughlin, C. & Harnisch E. (2013). The Viability Of Stocktwits And Google Trends To Predict The Stock Market. ArXiv Working Paper, 1-19.
  • Mao, H., Counts, S. & 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, 483-510.
  • Pesaran, M.H., A. Ullah, & T. Yamagata (2008). A Bias Adjusted LM Test Of Error Cross Section Independence. Econometrics Journal, 11, 105–127.
  • Smith, V., Leybourne, S., Kim, T. H. & 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. & Wakao, T. (2014). Google Search Intensity And Its Relationship With Returns And Trading Volume Of Japanese Stocks. Pacific-Basin Finance Journal, 27, 1–18.
  • Tantaopas, P., Padungsaksawasdi, C. & Treepongkaruna, S. (2016). Attention Effect Via Internet Search Intensity In Asia-Pacific Stock Markets. Pacific-Basin Finance Journal, 38, 107–124.
  • Vlastakis, N. & 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, 17–35.
  • Wang, B., Long, W. & Wei, X. (2018). Investor Attention, Market Liquidity And Stock Return: A New Perspective. Asian Economic and Financial Review, 8(3), 341-352.
  • Zhang, W., Shen, D., Zhang, Y. & Xiong, X. (2013). Open Source Information, Investor Attention And Asset Pricing. Economic Modelling, 33, 613–619.

Ayrıntılar

Birincil Dil Türkçe
Konular Sosyal
Bölüm Makaleler
Yazarlar

Tuğba NUR TOPALOĞLU> (Sorumlu Yazar)
ŞIRNAK ÜNİVERSİTESİ
0000-0002-0974-4896
Türkiye


İlhan EGE>
MERSİN ÜNİVERSİTESİ
0000-0002-5765-1926
Türkiye

Yayımlanma Tarihi 19 Nisan 2020
Yayınlandığı Sayı Yıl 2020, Cilt 28, Sayı 44

Kaynak Göster

Bibtex @araştırma makalesi { sosyoekonomi576167, journal = {Sosyoekonomi}, issn = {1305-5577}, address = {}, publisher = {Sosyoekonomi Derneği}, year = {2020}, volume = {28}, number = {44}, pages = {191 - 214}, doi = {10.17233/sosyoekonomi.2020.02.09}, title = {Yatırımcı İlgisinin Pay Piyasaları Üzerindeki Etkisi: Borsa İstanbul’da İşlem Gören Bankalar Üzerine Panel Veri Analizi}, key = {cite}, author = {Nur Topaloğlu, Tuğba and Ege, İlhan} }
APA Nur Topaloğlu, T. & Ege, İ. (2020). Yatırımcı İlgisinin Pay Piyasaları Üzerindeki Etkisi: Borsa İstanbul’da İşlem Gören Bankalar Üzerine Panel Veri Analizi . Sosyoekonomi , 28 (44) , 191-214 . DOI: 10.17233/sosyoekonomi.2020.02.09
MLA Nur Topaloğlu, T. , Ege, İ. "Yatırımcı İlgisinin Pay Piyasaları Üzerindeki Etkisi: Borsa İstanbul’da İşlem Gören Bankalar Üzerine Panel Veri Analizi" . Sosyoekonomi 28 (2020 ): 191-214 <https://dergipark.org.tr/tr/pub/sosyoekonomi/issue/53814/576167>
Chicago Nur Topaloğlu, T. , Ege, İ. "Yatırımcı İlgisinin Pay Piyasaları Üzerindeki Etkisi: Borsa İstanbul’da İşlem Gören Bankalar Üzerine Panel Veri Analizi". Sosyoekonomi 28 (2020 ): 191-214
RIS TY - JOUR T1 - Yatırımcı İlgisinin Pay Piyasaları Üzerindeki Etkisi: Borsa İstanbul’da İşlem Gören Bankalar Üzerine Panel Veri Analizi AU - Tuğba Nur Topaloğlu , İlhan Ege Y1 - 2020 PY - 2020 N1 - doi: 10.17233/sosyoekonomi.2020.02.09 DO - 10.17233/sosyoekonomi.2020.02.09 T2 - Sosyoekonomi JF - Journal JO - JOR SP - 191 EP - 214 VL - 28 IS - 44 SN - 1305-5577- M3 - doi: 10.17233/sosyoekonomi.2020.02.09 UR - https://doi.org/10.17233/sosyoekonomi.2020.02.09 Y2 - 2020 ER -
EndNote %0 Sosyoekonomi Yatırımcı İlgisinin Pay Piyasaları Üzerindeki Etkisi: Borsa İstanbul’da İşlem Gören Bankalar Üzerine Panel Veri Analizi %A Tuğba Nur Topaloğlu , İlhan Ege %T Yatırımcı İlgisinin Pay Piyasaları Üzerindeki Etkisi: Borsa İstanbul’da İşlem Gören Bankalar Üzerine Panel Veri Analizi %D 2020 %J Sosyoekonomi %P 1305-5577- %V 28 %N 44 %R doi: 10.17233/sosyoekonomi.2020.02.09 %U 10.17233/sosyoekonomi.2020.02.09
ISNAD Nur Topaloğlu, Tuğba , Ege, İlhan . "Yatırımcı İlgisinin Pay Piyasaları Üzerindeki Etkisi: Borsa İstanbul’da İşlem Gören Bankalar Üzerine Panel Veri Analizi". Sosyoekonomi 28 / 44 (Nisan 2020): 191-214 . https://doi.org/10.17233/sosyoekonomi.2020.02.09
AMA Nur Topaloğlu T. , Ege İ. Yatırımcı İlgisinin Pay Piyasaları Üzerindeki Etkisi: Borsa İstanbul’da İşlem Gören Bankalar Üzerine Panel Veri Analizi. Sosyoekonomi. 2020; 28(44): 191-214.
Vancouver Nur Topaloğlu T. , Ege İ. Yatırımcı İlgisinin Pay Piyasaları Üzerindeki Etkisi: Borsa İstanbul’da İşlem Gören Bankalar Üzerine Panel Veri Analizi. Sosyoekonomi. 2020; 28(44): 191-214.
IEEE T. Nur Topaloğlu ve İ. Ege , "Yatırımcı İlgisinin Pay Piyasaları Üzerindeki Etkisi: Borsa İstanbul’da İşlem Gören Bankalar Üzerine Panel Veri Analizi", Sosyoekonomi, c. 28, sayı. 44, ss. 191-214, Nis. 2020, doi:10.17233/sosyoekonomi.2020.02.09