How can Google trends be used as a technical indicator for investor interest?

Yıl 2024, Cilt: 14 Sayı: 1, 99 - 116, 08.03.2024

Gözde Bozkurt

https://doi.org/10.48146/odusobiad.1233817

Öz

Since investor interest is not a directly measurable concept, search engine and social media data can be used to measure active investor interest. Google search volume data has the potential to help customers, investors, and policymakers make better decisions. When looking for information to make investment decisions, investors consider Google trends as they provide news about changes in prices. Studies examining investor interest in the literature have often been carried out with the applications of linear models. This suggests that possible structural changes are not taken into account in the time series. When we look at the literature, it has been shown that the prediction performance of nonlinear models is better than linear models. In addition, it is seen that the studies conducted to investigate the relationship between the return of the stock markets and the trading volume and the investor interest are frequently included in the international literature. In contrast, the studies are limited in the national literature. In this direction of the study, the relationship between the trade volume and the investor's discovery of information on the stock market by Google is examined through linear and nonlinear econometric techniques in the investor reputation hypothesis. According to the investor reputation hypothesis, investors only invest in stocks they are aware of without adequate research and knowledge. In this context, the study results were realized in a way that supports the investor recognition hypothesis within the scope of 2020. In the context of 2021, it is seen that it does not support the investor reputation hypothesis. In future studies to be carried out in this area, it seems possible to determine the degree of effect of nonlinear regression estimations and the relationship between variables.

Anahtar Kelimeler

Investor Reputation Hypothesis, Investor Interest, Google Trend Searches, Nonlinear Causality Analysis

Google Trendler Yatırımcı İlgisi için Teknik Gösterge Olarak Nasıl Kullanılabilir?

Öz

Yatırımcı ilgisi doğrudan ölçülebilen bir kavram olmadığı için aktif yatırımcı ilgisinin ölçümünde arama motoru ve sosyal medya verilerinden yararlanılabilmektedir. Google arama hacmi verileri, müşterilere, yatırımcılara ve politika yapıcılara daha iyi kararlar verme konusunda yardımcı olma potansiyeline sahiptir. Yatırım kararları almak için bilgi ararken, yatırımcılar fiyatlardaki değişikliklerle ilgili haberler sağladıkları için Google trendlerini dikkate almaktadırlar. Literatürde yatırımcı ilgisinin incelendiği çalışmalar sıklıkla lineer modellerin uygulamalarıyla gerçekleştirilmiştir. Bu durum zaman serilerinde olası yapı değişikliklerinin dikkate alınmadığı ihtimalini düşündürmektedir. Nitekim literatüre bakıldığında doğrusal olmayan modellerin tahmin performansının doğrusal modellerden daha iyi olduğu gösterilmiştir. Bu doğrultuda, çalışmada yatırımcıların pay piyasasına ilişkin Google üzerinden araştırma yaparak bilgiyi ortaya çıkarmalarının işlem hacmi ile ilişkisi yatırımcı tanınmışlık hipotezi bağlamında doğrusal ve doğrusal olmayan ekonometrik teknikler aracılığıyla incelenmiştir. Yatırımcı tanınmışlık hipotezine göre yatırımcılar yeterince araştırma yapmadan ve bilgi sahibi olmadan sadece farkında oldukları pay senetlerine yatırım yapmaktadırlar. Bu kapsamda çalışmanın sonuçları 2020 yılı kapsamında yatırımcı tanınmışlık hipotezini destekler nitelikte gerçekleşmiştir. 2021 yılı kapsamında ise yatırımcı tanınmışlık hipotezini desteklemediği görülmektedir. Bu alanda gerçekleştirilecek olan sonraki çalışmalarda doğrusal olmayan regresyon tahminleri ile değişkenler arası ilişkilerin etki derecesinin belirlenmesi de mümkün gözükmektedir.

Anahtar Kelimeler

Yatırımcı Tanınmışlık Hipotezi, Yatırımcı İlgisi, Google Trend Aramaları, Doğrusal Olmayan Nedensellik Analizi, Investor Reputation Hypothesis, Investor Interest, Google Trend Searches, Nonlinear Causality Analysis

Kaynakça

• Adachi, Y., Masuda, M. & 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.
• Akgün, B.E. (2016). Investor attention and ipo performance. [Unpublished master thesis]. Middle East Technical University.
• Baek, E. & Brock, W. (1992). A general test for nonlinear granger causality: Bivariate model. Working Paper, Iowa State University and University of Wisconsin, Madison, WI.
• Beer, F., Hervé, F., & Zouaoui, M. (2012). Is big brother watching us? Google, investor sentiment and the stock market. http://papers.ssrn.com/abstract=2185979 (accessed on 23 Şubat 2022).
• 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.
• Bollen, J., Mao, H. & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2, 1-8.
• Borup, D., Christian, E., & Schütte, M. (2020). In search of a job: Forecasting employment growth using Google trends. Journal of Business & Economic Statistics, 1–38. doi:10.1080/07350015.2020.1791133
• 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.
• Bozkurt, G. (2022). Karma frekanslı zaman serilerinin modellenmesi: Büyük veri örneği. [Doktora Tezi, Marmara Üniversitesi]. İstanbul.
• Broock W.A., Hsieh D.A. & LeBaron, B. (1991). Nonlinear dynamics, chaos, and instability: Statistical theory and economic evidence. MIT Press, London.
• Broock, W. A., Dechert, W. & Scheinkman, J. (1987). A test for independence based on the correlation dimension. Working Paper, University Of Winconsin At Madison, University of Houston, and University of Chicago.
• Da, Z., Engelberg, J. & Pengjie, G. (2011). In Search of Attention. The Journal of Finance, 66, 1461–99.
• Dickey, D. A. & Fuller, W.A. (1979). Distributions of the estimators for autoregressive time series with a unit root. Journal Of The American Statistical Association, 74, 427-31.
• Diks, C. & Panchenko, V. (2006). A new statistic and practical guidelines for nonparametric granger causality testing. Journal of Economic Dynamics and Control, 30, 1647-1669.
• Drake, M.S., Darren T. R. & Jacob, R. T. (2012). Investor information demand: Evidence from Google searches around earnings announcements. Journal of Accounting Research, 50, 1001–40.
• Dzielinski, M. (2012). Measuring economic uncertainty and its impact on the stock market. Finance Research Letters, 93, 167–175.
• Fink, C. & Johann, T. (2014). May i have your attention, please: The market microstructure of investor attention. University of Mannheim Working Paper, 1-59.
• Granger, C. (1988). Causality, cointegration and control. Journal of Economic Dynamics and Control, 12, 551-559.
• Hicks, J.R. (1950). A contribution to the theory of trade cycle. Clarendon Press, Oxford.
• Hiemstra, C. & Jones, J.D. (1994). Testing for linear and nonlinear granger causality in the stock price-volume relation. J. Finance, 49(5), 1639-1664.
• Jain, A. & Biswal, P.C. (2019). Does ınternet search ınterest for gold move the gold spot, stock and exchange rate markets? a study from ındia. Resources Policy, 61, 501-507.
• 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.
• Kaldor, N. (1940). A Model of the Trade Cycle. Economic Journal. 50, 78- 92.
• Kapetanios, G., Shin, Y. & Snell, A. (2003). Testing for a unit root in the nonlinear STAR framework. Journal of Econometrics, 112(2), 359-379.
• Keynes, J.M. (1936). The general theory of employment, interest and money. MacMillan, London.
• Kim, N., Lučivjanská, K., Molnár, P., Villa, R. (2019). Google searches and stock market activity: Evidence from Norway. Finance Research Letters, 28, 208-220.
• Korkmaz, T., Çevik, E. & Çevik, N. (2017). Yatırımcı ilgisi ile pay piyasası arasındaki ilişki: Bist-100 endeksi üzerine bir uygulama. Business and Economics Research Journal, 8(2), 203-215.
• Liu, R., An E. & Zhou, W. (2021). The effect of online search volume on financial performance: Marketing insight from Google trends data of the top five us technology firms. Journal of Marketing Theory and Practice, 29(4), 423-434.
• Merton, R.C. (1987). A simple model of capital market equilibrium with incomplete information. Journal of Finance, 42(3), 483-510.
• Mondria, J., Wu, T., & Zhang, Y. (2010). The determinants of international investment and attention allocation: Using internet search query data. Journal of International Economics, 821, 85–95.
• Nur, D.P. (2021). The impact of social media on firm value: A case study of oil and gas firms in Indonesia. Journal of Asian Finance, Economics and Business, 8(3), 987-996.
• Nurazi, R., Kanunlua, P. S. & Usman, B. (2015). The effect of Google trend as determinant of return and liquidity in Indonesia stock exchange. Jurnal Pengurusan, 45, 131-142.
• Padungsaksawasdi, C., Treepongkaruna, S. & Brooks, R. (2019). Investor attention and stock market activities: New evidence from panel data. International Journal of Financial Studies, 7, 30.
• Papadamou, S., Athanasios P. F., Dimitris, K. & Dimitrios, D. (2021). Flight-to-Quality between global stock and bond markets in the COVID era. Finance Research Letters, 38, 101852.
• Phillips, P.C.B. & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335 346.
• Poutachidou, N. & Papadatantaomou, S. (2021). The effect of quantitative easing through google metrics on us stock indices. International Journal of Financial Studies, 9, 56.
• Preis, T., Reith, D. & Stanley, H. (2010). Complex dynamics of our economic life on different scales: Insights from search engine query data. Philosophical Transactions: Mathematical, Physical and Engineering Sciences, 368, 5707-5719.
• Qian, B. & Rasheed, K. (2007). Stock market prediction with multiple classifiers. Applied Intelligence 26, 25–33.
• Salisu, A.A., Ogbonna, A.E. & Adewuyi, A. (2020). Google trends and the predictability of precious metals. Resources Policy, 65, 101542.
• Shiller, R. J. (1999). Human behavior and the efficiency of the financial system. . B. Taylor & M. Woodford (Ed.), Handbook of Macroeconomics içinde (1nd ed., pp. 1305-1340). Elsevier. https://doi.org/10.1016/S1574-0048(99)10033-8
• 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.
• Tank, A., Covert, I., Foti, N., Shojaie, A. & Fox, E.B. (2021). Neural granger causality. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(8), 4267-4279.
• 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.
• Topaloğlu, T.N. & Ege, İ. (2020). Yatırımcı ilgisinin pay piyasaları üzerindeki etkisi: Borsa İstanbul’da işlem gören bankalar üzerine panel veri analizi. Sosyoekonomi, 28(44), 191-214.
• Van, D. & Franses, P.H. (1999). Modelling multiple regimes in the business cycles. Macroeconomic Dynamics, 3(3), 311-340.
• Vlastakis, N. & Raphael, N. M. (2012). Information demand and stock market volatility. Journal of Banking & Finance, 36, 1808–21.
• 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.