Abstract
This study used the amount of Internet search on Google Trend and analyzed the correlation between the search volume on Google Trend and Taiwan Weighted Stock Index. The keyword search volume provided by Google Trend was used in the correlation test and the unit root test. Then, the keywords obtained were analyzed in two experiments—first, machine learning, and second, search trend. After empirical analysis, it was found that neural network in experiment one performed better than support vector machine and decision trees. Therefore, neural network was selected to compare with the search trend in the second experiment. Through comparative analysis of calculation of return values, it was found that the return value in search trend is higher than that of the neural network. Therefore, this paper revealed that there was a correlation between using company names of Taiwan 50 Index as search keywords and the rise and fall of TAIEX index.
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References
Bijl L, Kringhaug G, Molnár P, Sandvik E (2016) Google searches and stock returns. Int Rev Financ Anal 45:150–156
Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and regression trees. CRC Press, Boca Raton. https://doi.org/10.1016/j.irfa.2016.03.015
Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27. https://doi.org/10.1145/1961189.1961199
Chatzis SP, Siakoulis V, Petropoulos A, Stavroulakis E, Vlachogiannakis N (2018) Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Syst Appl 112:353–371. https://doi.org/10.1016/j.eswa.2018.06.032
Chen SM, Chang YC (2011) Weighted fuzzy rule interpolation based on GA-based weight-learning techniques. IEEE Trans Fuzzy Syst 19(4):729–744. https://doi.org/10.1109/TFUZZ.2011.2142314
Chen MY, Chen BT (2015a) A hybrid fuzzy time series model based on granular computing for stock price forecasting. Inf Sci 294:227–241. https://doi.org/10.1016/j.ins.2014.09.038
Chen SM, Chen SW (2015b) Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships. IEEE Trans Cybern 45(3):405–417. https://doi.org/10.1109/TCYB.2014.2326888
Chen MY, Chen TH (2019) Modeling public mood and emotion: blog and news sentiment and socio-economic phenomena. Future Gener Comput Syst 96:692–699. https://doi.org/10.1016/j.future.2017.10.028
Chen SM, Huang CM (2003) Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms. IEEE Trans Fuzzy Syst 11(4):495–506. https://doi.org/10.1109/TFUZZ.2003.814837
Chen SM, Jian WS (2017) Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups, similarity measures and PSO techniques. Inf Sci 391–392:65–79. https://doi.org/10.1016/j.ins.2016.11.004
Chen SM, Wang JY (1995) Document retrieval using knowledge-based fuzzy information retrieval techniques. IEEE Trans Syst Man Cybern 25(5):793–803. https://doi.org/10.1109/21.376492
Chen SM, Chu HP, Sheu TW (2012) TAIEX forecasting using fuzzy time series and automatically generated weights of multiple factors. IEEE Trans Syst Man Cybern Part A Syst Humans 42(6):1485–1495. https://doi.org/10.1109/TSMCA.2012.2190399
Chen MY, Fan MH, Chen YL, Wei HM (2013a) Design of experiments on neural network’s parameters optimization for time series forecasting in stock markets. Neural Netw World 23(4):369–393. https://doi.org/10.14311/NNW.2013.23.023
Chen SM, Manalu GM, Pan JS, Liu HC (2013b) Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques. IEEE Trans Cybern 43(3):1102–1117. https://doi.org/10.1109/TSMCB.2012.2223815
Chen MY, Liao CH, Hsieh RP (2019) Modeling public mood and emotion: Stock market trend prediction with anticipatory computing approach. Human Behav, Comput. https://doi.org/10.1016/j.chb.2019.03.021
Cheng SH, Chen SM, Jian WS (2016) Fuzzy time series forecasting based on fuzzy logical relationships and similarity measures. Inf Sci 327:272–287. https://doi.org/10.1016/j.ins.2015.08.024
Chong E, Han C, Park FC (2017) Deep learning networks for stock market analysis and prediction: methodology, data representations, and case studies. Expert Syst Appl 83(15):187–205. https://doi.org/10.1016/j.eswa.2017.04.030
Chumnumpan P, Shi X (2019) Understanding new products’ market performance using Google Trends. Aust Market J 5:6. https://doi.org/10.1016/j.ausmj.2019.01.001
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297. https://doi.org/10.1023/A:1022627411411
Dewan V, Sur H (2018) Using Google trends to assess for seasonal variation in knee injuries. J Arthrosc Joint Surg 5(3):175–178. https://doi.org/10.1016/j.jajs.2018.02.002
Dickey DA, Fuller WA (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74(366a): 427–431. https://www.jstor.org/stable/2286348
Elliott RN (1938) The Wave Principle. Republished (1980, 1994). In: Prechter RR (ed), R.N. Elliott's Masterworks. New Classics Library, Gainesville, GA, p 144
Fan MH, Liao EC, Chen MY (2014) A TAIEX forecasting model based on changes of keyword search volume on Google Trends. In: 2014 IEEE International Symposium on Independent Computing (IEEE ISIC 2014), Orlando, FL, USA, December 9-12, 96-99
Granger CW, Newbold P (1974) Spurious regressions in econometrics. J Econ 2(2):111–120. https://doi.org/10.1016/0304-4076(74)90034-7
Grodinsky J (1953) Investments. Ronald Press Company, New York
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. https://doi.org/10.1016/j.neucom.2018.01.038
Joseph K, Babajide Wintoki M, Zhang Z (2011) Forecasting abnormal stock returns and trading volume using investor sentiment: evidence from online search. Int J Forecast 27(4):1116–1127. https://doi.org/10.1016/j.ijforecast.2010.11.001
Lee LW, Chen SM (2008) Fuzzy risk analysis based on fuzzy numbers with different shapes and different deviations. Expert Syst Appl 34(4):2763–2771. https://doi.org/10.1016/j.eswa.2007.05.009
Long W, Lu Z, Cui L (2019) Deep learning-based feature engineering for stock price movement prediction. Knowl Based Syst 164:163–173. https://doi.org/10.1016/j.knosys.2018.10.034
Nelson CR, Plosser CR (1982) Trends and random walks in macroeconomic time series: some evidence and implications. J Monet Econ 10(2):139–162. https://doi.org/10.1016/0304-3932(82)90012-5
Phillips PC, Perron P (1988) Testing for a unit root in time series regression. Biometrika 75(2): 335-346. https://www.jstor.org/stable/2336182
Preis T, Moat HS, Stanley HE (2013) Quantifying trading behavior in financial markets using Google Trends Scientific reports 3
Quinlan JR (1993) C4. 5: programs for machine learning (Vol. 1). Morgan kaufmann
Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J (1994, October). GroupLens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM conference on Computer supported cooperative work (pp. 175-186). ACM
Robert R (1932) The Dow theory: an explanation of its development and an attempt to define its usefulness as an aid in speculation. Barron's, New York
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning Internal Representations by Error Propagation, Parallel Distributed Processing, Explorations in the Microstructure of Cognition, ed. DE Rumelhart and J. McClelland. Vol. 1
Said SE, Dickey DA (1984) Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika 71(3): 599-607. https://www.jstor.org/stable/2336570
Smith GP (2012) Google internet search activity and volatility prediction in the market for foreign currency. Finance Res Lett 9(2):103–110. https://doi.org/10.1016/j.frl.2012.03.003
Song Q, Chissom BS (1993) Forecasting enrollments with fuzzy time series—part I. Fuzzy Sets Syst 54(1):1–9. https://doi.org/10.1016/0165-0114(93)90355-L
Song Q, Chissom BS (1994) Forecasting enrollments with fuzzy time series–Part II. Fuzzy Sets Syst 62(1):1–8. https://doi.org/10.1016/0165-0114(94)90067-1
Takeda F, Wakao T (2014) Google search intensity and its relationship with returns and trading volume of Japanese stocks. Pacific-Basin Finance J 27:1–18. https://doi.org/10.1016/j.pacfin.2014.01.003
Yu L, Zhao Y, Tang L, Yang Z (2019) Online big data-driven oil consumption forecasting with Google trends. Int J Forecast 35(1):213–223. https://doi.org/10.1016/j.ijforecast.2017.11.005
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X
Zeng S, Chen SM, Teng MO (2019) Fuzzy forecasting based on linear combinations of independent variables, subtractive clustering algorithm and artificial bee colony algorithm. Inf Sci 484:350–366. https://doi.org/10.1016/j.ins.2019.01.071
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Fan, MH., Chen, MY. & Liao, EC. A deep learning approach for financial market prediction: utilization of Google trends and keywords. Granul. Comput. 6, 207–216 (2021). https://doi.org/10.1007/s41066-019-00181-7
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DOI: https://doi.org/10.1007/s41066-019-00181-7