پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 25 |
| تعداد شماره‌ها | 932 |
| تعداد مقالات | 7,652 |
| تعداد مشاهده مقاله | 12,494,336 |
| تعداد دریافت فایل اصل مقاله | 8,885,733 |
مقاله پژوهشی: پیش بینی فعالیت بازار سهام: نقش موتور جستجوی گوگل | ||
| راهبرد مدیریت مالی | ||
| مقاله 8، دوره 7، شماره 4 - شماره پیاپی 27، دی 1398، صفحه 175-200 اصل مقاله (1.14 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22051/jfm.2019.27357.2156 | ||
| نویسندگان | ||
| سیدعلی موسوی گوکی1؛ مهسا بهنام راد* 2 | ||
| 1دانشجوی دکتری حسابداری، دانشگاه فردوسی مشهد، مشهد، ایران | ||
| 2دانشجوی کارشناسی ارشد حسابداری مدیریت، دانشگاه فردوسی مشهد، مشهد، ایران | ||
| چکیده | ||
| این پژوهش به بررسی این موضوع می پردازد که آیا جستجوی نام و نماد شرکت در موتور جستجوی گوگل می تواند فعالیت سهام شرکت در بازار را پیش بینی کند؟ از این رو، داده های مربوط به جستجوی نام و نماد شرکت ها از گوگل ترند جمع آوری و نیز فعالیت بازار سهام با استفاده از چهار متغیر بازده غیرعادی، نوسان بازده سهام، حجم معاملات و تعداد معاملات سهام اندازه گیری شده است. در راستای پاسخ به سوال پژوهش، با استفاده از رگرسیون چندگانه و رگرسیون پانلی مدل پژوهش بر روی 13082 مشاهده شرکت – ماه طی سال های 1384 تا 1397 برآورد شده است. یافته ها حاکی از این بود که با افزایش جستجوی نام و نماد شرکت در گوگل، فعالیت آتی سهام شامل نوسان بازده سهام، حجم معاملات و تعداد معاملات شرکت افزایش می یابد؛ اما جستجوی نام شرکت با بازده غیرعادی آتی رابطه معنی داری نداشته است. نتایج پژوهش نشان می دهد که می توان فعالیت سهام شرکت را با استفاده از جستجوی گوگل پیش بینی نمود و علاوه بر این، جستجوی نماد شرکت ها نسبت به جستجوی نام شرکت ها، توانایی پیش بینی کنندگی بیشتری درباره فعالیت سهام دارد | ||
| کلیدواژه‌ها | ||
| فعالیت بازار؛ موتور جستجو؛ جستجوی گوگل؛ نوسان بازده؛ حجم معاملات | ||
| عنوان مقاله [English] | ||
| Predicting Stock Market Activity: Role of Google Search Engine | ||
| نویسندگان [English] | ||
| Sayyed Ali Mousavi Gowki1؛ Mahsa Behnamrad2 | ||
| 1Ph.D Student of Accounting, Ferdowsi University of Mashhad, Mashhad, Iran | ||
| 2MA Student of Management Accounting, Ferdowsi University of Mashhad, Mashhad, Iran | ||
| چکیده [English] | ||
| The main objective of this study was to investigate whether searching firm's ticker symbol and name in Google can predict its future stock market activities. In so doing, the data related to firms' ticker symbol and firms' name were collected using Google Trend and stock market activity was measured using four proxies, namely abnormal return, return volatility, stock trading volume and stock trading count. In order to meet the main objective of the study, multiple regression and panel regression were used over13082 firm- month observation during the years between 2005 and 2018. The results showed that the future market activity, including return volatility, stock trading volume and stock trading count, increased with searching the firms' ticker and name in Google. However, there was no significant relationship between the future abnormal return and Google searches. Findings also showed that future market activity can be predicted using Google searches. In addition, there was a more significant relationship between the searched firms' ticker symbol than the searched firms' name and future market activity. | ||
| کلیدواژه‌ها [English] | ||
| Market Activity, Search Engine, Google Search, Return Volatility, Trading Volume | ||
| مراجع | ||
|
- منابع - Aouadi, A., Arouri, M., & Teulon, F. (2013). Investor attention and stock market activity: Evidence from France. Economic Modelling, 35, 674-681.
- Ang, A., & Bekaert, G. (2006). Stock return predictability: Is it there?. The Review of Financial Studies, 20(3), 651-707.
- Askitas, N., & Zimmermann, K. F. (2009). Google econometrics and unemployment forecasting.
- Banerjee, S.; Gatchev, V. A. & Spindt, P. A. (2007). "Stock market liquidity and firm dividend policy". Journal of Financial and Quantitative Analysis. 42(02). Pp. 369-397.
- Banerjee, P. S., Doran, J. S., & Peterson, D. R. (2007). Implied volatility and future portfolio returns. Journal of Banking & Finance, 31(10), 3183-3199.
- Bangwayo-Skeete, P. F., &Skeete, R. W. (2015). Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach. Tourism Management, 46, 454-464.
- 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, 25(3), 239.
- Beer, F.; Herve, F.; Zouaoui M. (2013) Google Investor Sentiment and the Stock Market, Economic Bulletin, Vol.33 no.1 pp. 454-466.
- Bijl, L., Kringhaug, G., Molnár, 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 İstanbul Stock Market Index (BIST) (Doctoral dissertation, İstanbul BilgiÜniversitesi).
- Bollerslev, T., Tauchen, G., & Zhou, H. (2009). Expected stock returns and variance risk premia. The Review of Financial Studies, 22(11), 4463-4492.
- Campbell, J. Y., & Thompson, S. B. (2007). Predicting excess stock returns out of sample: Can anything beat the historical average?. The Review of Financial Studies, 21(4), 1509-1531.
- Campbell, J. Y., & Yogo, M. (2006). Efficient tests of stock return predictability. Journal of financial economics, 81(1), 27-60.
- Challet, D., & Ayed, A. B. H. (2013). Predicting financial markets with Google Trends and not so random keywords. arXiv preprint arXiv:1307.4643.
- Cochrane, J. H. (2007). The dog that did not bark: A defense of return predictability. The Review of Financial Studies, 21(4), 1533-1575.
- Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2-9.
- Chowdhury, S.G.; Routh, S.; Chakrabarti, S. (2014), News Analytics and Sentiment Analysis to Predict Stock Price Trends, Int. J. Comput. Sci. Inform. Technol 5.3 (2014): 3595-3604.
- Da, Z., Engelberg, J., & Gao, P. (2014). The sum of all FEARS investor sentiment and asset prices. The Review of Financial Studies, 28(1), 1-32.
- Dimpfl, T., & Kleiman, V. (2019). Investor pessimism and the German stock market: Exploring Google search queries. German Economic Review, 20(1), 1-28.
- Engelberg, J. O. S. E. P. H., &Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461-1499.
- Fama, E. F. (1965). The behavior of stock-market prices. The Journal of Business, 38, 34-105.
- Foucault, T., D. Sraerand D. J. Thesmar (2011), ‘Individual Investors and Volatility’, Journalof Finance 66, 1369–1406.
- Fink, C., & Johann, T. (2014). May I have your attention, please: The market microstructure of investor attention. Please: The Market Microstructure of Investor Attention (September 17, 2014).
- Garman, M. B., & Klass, M. J. (1980). On the estimation of security price volatilities from historical data. Journal of business, 67-78.
- Goddard, J., Kita, A., & Wang, Q. (2015). Investor attention and FX market volatility. Journal of International Financial Markets, Institutions and Money, 38, 79-96.
- Gündüz, H.; Çataltepe, Z. (2015) “Borsa Istanbul (BIST) daily estimation using financial news and balanced feature selection.” Expert Syst. Appl. 42 (2015): 9001-9011.
- Harford, T. (2017). Just google it: The student project that changed the world. Accesible online http://www. bbc. com/news/business-39129619.
- Ishima, H.; Kazumi, T.; Maeda, A. (2014), Sentiment Analysis for the Japanese stock market, DOI: http://dx.doi.org/10.1504/GBER.2015.070303.
- Joshi, K.; Bharati, H. N.; Jyothi, R. (2015), Stock Trend Estimation Using News Sentiment Analysis, arXiv:1607.01958 [cs.CL].
- Kim, N., Lučivjanská, K., Molnár, P., & Villa, R. (2019). Google searches and stock market activity: Evidence from Norway. Finance Research Letters, 28, 208-220.
- Kringhaug, G., Bijl, L. R., & Sandvik, E. (2015). Predictive Power of Google Search Volume on StockReturns (Master's thesis, NTNU).
- Latoeiro, P., Ramos, S. B., & Veiga, H. (2013). Predictability of stock market activity using Google search queries.
- Li, X., Shang, W., Wang, S., & Ma, J. (2015). A MIDAS modelling framework for Chinese inflation index forecast incorporating Google search data. Electronic Commerce Research and Applications, 14(2), 112-125.
- Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 17(1), 59-82.
- Mao, H.; Counts, S.; Bollen, J. (2011), Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data, arXiv preprint p.10.
- Mondria, J., Wu, T., & Zhang, Y. (2010). The determinants of international investment and attention allocation: Using internet search query data. Journal of International Economics, 82(1), 85-95.
- Narayan, P. K., & Narayan, S. (2014). Psychological oil price barrier and firm returns. Journal of Behavioral Finance, 15(4), 318-333.
- Narayan, P. K., Phan, D. H. B., Narayan, S., &Bannigidadmath, D. (2017). Is there a financial news risk premium in Islamic stocks?.Pacific-Basin Finance Journal, 42, 158-170.
- Oliveira, N., Cortez, P., & Areal, N. (2017). The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Systems with Applications, 73, 125-144.
- Pierre, J. S., Klimkiewicz, M., Resom, A., &Kalampalikis, N. (2019). Trading the stock market using Google search volumes: a long short-term memory approach. International Journal of Financial Engineering and Risk Management, 3(1), 3-18.
- Porta, R. L., Lakonishok, J., Shleifer, A., & Vishny, R. (1997). Good news for value stocks: Further evidence on market efficiency. The Journal of Finance, 52(2), 859-874.
- Preis, T.; Maat, H. S.; Stanley, H. E. (2013), Quantifying Trading Behavior in Financial Markets Using Google Trends, Scientific Reports 3, Article number: 1684, doi:10.1038/srep01684.
- Preis, T., Reith, D., & Stanley, H. E. (2010). Complex dynamics of our economic life on different scales: insights from search engine query data. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 368(1933), 5707-5719.
- Rouse, M. (2014). Internet of Things (IOT),[ONLINE] Available: http://whatis. techtarget. com/definition. Internet-of-Things [Acedido em 23 Junho 2015].
- 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.
- Vlastakis, N., & Markellos, R. N. (2012). Information demand and stock market volatility. Journal of Banking & Finance, 36(6), 1808-1821.
- Vosen, S., & Schmidt, T. (2011). Forecasting private consumption: survey‐based indicators vs. Google trends. Journal of Forecasting, 30(6), 565-578.
- Welch, I., & Goyal, A. (2007). A comprehensive look at the empirical performance of equity premium prediction. The Review of Financial Studies, 21(4), 1455-1508.
- 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. | ||
|
آمار تعداد مشاهده مقاله: 1,547 تعداد دریافت فایل اصل مقاله: 983 |
||