Quantitative investment decisions based on machine learning and investor attention analysis (2024)

Anadu, K., Kruttli, M., McCabe, P., & Osambela, E. (2020). The shift from active to passive investing: Risks to financial stability? Financial Analysts Journal, 76(4), 23–39. https://doi.org/10.1080/0015198X.2020.1779498

Andrei, D., & Hasler, M. (2015). Investor attention and stock market volatility. Review of Financial Studies, 28(1), 33–72. https://doi.org/10.1093/rfs/hhu059

Audrino, F., Sigrist, F., & Ballinari, D. (2020). The impact of sentiment and attention measures on stock market volatility. International Journal of Forecasting, 36(2), 334–357. https://doi.org/10.1016/j.ijforecast.2019.05.010

Barber, B. M., & Odean, T. (2008). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. The Review of Financial Studies, 21(2), 785–818. https://doi.org/10.1093/rfs/hhm079

Brooks, C., Rew, A. G., & Ritson, S. (2001). A trading strategy based on the lead–lag relationship between the spot index and futures contract for the FTSE 100. International Journal of Forecasting, 17(1), 31–44. https://doi.org/10.1016/S0169-2070(00)00062-5

Caldeira, J. F., & Moura, G. V. (2013). Selection of a portfolio of pairs based on cointegration: A statistical arbitrage strategy. Brazilian Review of Finance, 11(1), 49–80. https://doi.org/10.12660/rbfin.v11n1.2013.4785

Camerer, C. F., & Loewenstein, G. (2004). Behavioral economics: Past, present, future. In Advances in behavioral economics (pp. 3–51). Princeton University Press. https://doi.org/10.1515/9781400829118-004

Cao, J., Li, Z., & Li, J. (2019). Financial time series forecasting model based on CEEMDAN and LSTM. Physica A: Statistical Mechanics and its Applications, 519, 127–139. https://doi.org/10.1016/j.physa.2018.11.061

Chan, E. P. (2021). Quantitative trading: How to build your own algorithmic trading business. John Wiley & Sons.

Chen, C., Zhang, P., Liu, Y., & Liu, J. (2020). Financial quantitative investment using convolutional neural network and deep learning technology. Neurocomputing, 390, 384–390. https://doi.org/10.1016/j.neucom.2019.09.092

Covel, M. (2006). Trend following: How great traders make millions in up or down markets. FT Press.

Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The Journal of Finance, 66(5), 1461–1499. https://doi.org/10.1111/j.1540-6261.2011.01679.x

Daniel, K., Hirshleifer, D., & Teoh, S. H. (2002). Investor psychology in capital markets: Evidence and policy implications. Journal of Monetary Economics, 49(1), 139–209. https://doi.org/10.1016/S0304-3932(01)00091-5

Deng, C., Zhou, X., Peng, C., & Zhu, H. (2022). Going green: Insight from asymmetric risk spillover between investor attention and pro-environmental investment. Finance Research Letters, 47, 102565. https://doi.org/10.1016/j.frl.2021.102565

De Prado, M. L. (2018). Advances in financial machine learning. John Wiley & Sons.

Fang, J., Gozgor, G., Lau, C. K. M., & Lu, Z. (2020). The impact of Baidu Index sentiment on the volatility of China’s stock markets. Finance Research Letters, 32, 101099. https://doi.org/10.1016/j.frl.2019.01.011

García, F., Guijarro, F., Oliver, J., & Tamošiūnienė, R. (2018). Hybrid fuzzy neural network to predict price direction in the German DAX-30 index. Technological and Economic Development of Economy, 24(6), 2161–2178. https://doi.org/10.3846/tede.2018.6394

Guilbaud, F., & Pham, H. (2013). Optimal high-frequency trading with limit and market orders. Quantitative Finance, 13(1), 79–94. https://doi.org/10.1080/14697688.2012.708779

Han, L., Zhang, R., Wang, X., Bao, A., & Jing, H. (2019). Multi-step wind power forecast based on VMD-LSTM. IET Renewable Power Generation, 13(10), 1690–1700. https://doi.org/10.1049/iet-rpg.2018.5781

Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226–251. https://doi.org/10.1016/j.eswa.2019.01.012

Hirshleifer, D., & Teoh, S. H. (2003). Limited attention, information disclosure, and financial reporting. Journal of Accounting and Economics, 36(1–3), 337–386. https://doi.org/10.1016/j.jacceco.2003.10.002

Huang, J., Li, Y., & Yao, H. (2022). Nonparametric mean-lower partial moment model and enhanced index investment. Computers & Operations Research, 144, 105814. https://doi.org/10.1016/j.cor.2022.105814

Huang, N. E., Wu, Z., Long, S. R., Arnold, K. C., Chen, X., & Blank, K. (2009). On instantaneous frequency. Advances in Adaptive Data Analysis, 01(02), 177–229. https://doi.org/10.1142/S1793536909000096

Hurst, B., Ooi, Y. H., & Pedersen, L. H. (2017). A century of evidence on trend-following investing. The Journal of Portfolio Management, 44(1), 15–29. https://doi.org/10.3905/jpm.2017.44.1.015

Jiang, Y. (2022). Prediction model of the impact of innovation and entrepreneurship on China’s digital economy based on neural network integration systems. Neural Computing and Applications, 34(4), 2661–2675. https://doi.org/10.1007/s00521-021-05899-7

Ji, C., Zhang, C., Hua, L., Ma, H., Nazir, M. S., & Peng, T. (2022). A multi-scale evolutionary deep learning model based on CEEMDAN, improved whale optimization algorithm, regularized extreme learning machine and LSTM for AQI prediction. Environmental Research, 215, 114228. https://doi.org/10.1016/j.envres.2022.114228

Kahneman, D. (1973). Attention and effort (Vol. 1063, pp. 218–226). Prentice-Hall.

Kohzadi, N., Boyd, M. S., Kermanshahi, B., & Kaastra, I. (1996). A comparison of artificial neural network and time series models for forecasting commodity prices. Neurocomputing, 10(2), 169–181. https://doi.org/10.1016/0925-2312(95)00020-8

Kou, G., Chao, X., Peng, Y., Alsaadi, F. E., & Herrera-Viedma, E. (2019). Machine learning methods for systemic risk analysis in financial sectors. Technological and Economic Development of Economy, 25(5), 716–742. https://doi.org/10.3846/tede.2019.8740

Krollner, B., Vanstone, B., & Finnie, G. (2010, April). Financial time series forecasting with machine learning techniques: A survey. In 18th European Symposium on Artificial Neural Networks (ESANN 2010): Computational Intelligence and Machine Learning (pp. 25–30). Bruges, Belgium.

Kuang, Y., Singh, R., Singh, S., & Singh, S. P. (2017). A novel macroeconomic forecasting model based on revised multimedia assisted BP neural network model and ant Colony algorithm. Multimedia Tools and Applications, 76(18), 18749–18770. https://doi.org/10.1007/s11042-016-4319-9

Li, X. (2006). Temporal structure of neuronal population oscillations with empirical model decomposition. Physics Letters A, 356(3), 237–241. https://doi.org/10.1016/j.physleta.2006.03.045

Li, Y., Shen, D., Wang, P., & Zhang, W. (2020). Does intraday time-series momentum exist in Chinese stock index futures market? Finance Research Letters, 35, 101292. https://doi.org/10.1016/j.frl.2019.09.007

Liu, F., Kang, Y., Guo, K., & Sun, X. (2021). The relationship between air pollution, investor attention and stock prices: Evidence from new energy and polluting sectors. Energy Policy, 156, 112430. https://doi.org/10.1016/j.enpol.2021.112430

Lou, D. (2014). Attracting investor attention through advertising. The Review of Financial Studies, 27(6), 1797–1829. https://doi.org/10.1093/rfs/hhu019

Makridakis, S., & Hibon, M. (1997). ARMA models and the Box-Jenkins methodology. Journal of Forecasting, 16(3), 147–163. 3.0.CO;2-X> https://doi.org/10.1002/(SICI)1099-131X(199705)16:3<147::AID-FOR652>3.0.CO;2-X

Mangram, M. E. (2013). A simplified perspective of the Markowitz Portfolio Theory. Global Journal of Business Research, 7(1), 59–70. https://ssrn.com/abstract=2147880

Markowitz, H. M. (1968). Portfolio selection: Efficient diversification of investments. Yale University Press.

Masini, R. P., Medeiros, M. C., & Mendes, E. F. (2021). Machine learning advances for time series forecasting. Journal of Economic Surveys, 1–36. https://doi.org/10.1111/joes.12429

Mbanga, C., Darrat, A. F., & Park, J. C. (2019). Investor sentiment and aggregate stock returns: The role of investor attention. Review of Quantitative Finance and Accounting, 53(2), 397–428. https://doi.org/10.1007/s11156-018-0753-2

Montoya-Cruz, E., Ramos-Requena, J. P., Trinidad-Segovia, J. E., & Sánchez-Granero, M. Á. (2020). Exploring arbitrage strategies in corporate social responsibility companies. Sustainability, 12(16), 1–17. https://doi.org/10.3390/su12166293

Mullainathan, S., & Thaler, R. H. (2000). Behavioral economics (NBER Working Paper No. 7948). https://doi.org/10.3386/w7948

Olgun, O., & Yetkiner, I. H. (2011). Determination of optimal hedging strategy for index futures: Evidence from Turkey. Emerging Markets Finance and Trade, 47(6), 68–79. https://doi.org/10.2753/REE1540-496X470604

Peng, L., & Xiong, W. (2006). Investor attention, overconfidence and category learning. Journal of Financial Economics, 80(3), 563–602. https://doi.org/10.1016/j.jfineco.2005.05.003

Pruitt, G., & Hill, J. R. (2012). Building winning trading systems with Tradestation (2nd ed.). (Wiley Trading Book 542). John Wiley & Sons. https://doi.org/10.1002/9781119204954

Rodriguez, D. (2020). Backtrader. https://www.backtrader.com/

Sampath, V. S., O’Connor, A. J., & Legister, C. (2022). Moral leadership and investor attention: An empirical assessment of the potus’s tweets on firms’ market returns. Review of Quantitative Finance and Accounting, 58(3), 881–910. https://doi.org/10.1007/s11156-021-01012-0

Sansa, N. A. (2020). The impact of the COVID-19 on the financial markets: Evidence from China and USA. Electronic Research Journal of Social Sciences and Humanities, 2(2), 29–39. https://doi.org/10.2139/ssrn.3567901

Schumaker, R. P., & Chen, H. (2009). A quantitative stock prediction system based on financial news. Information Processing & Management, 45(5), 571–583. https://doi.org/10.1016/j.ipm.2009.05.001

Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied Soft Computing, 90, 106181. https://doi.org/10.1016/j.asoc.2020.106181

Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425–442. https://doi.org/10.1111/j.1540-6261.1964.tb02865.x

Shen, D., Zhang, Y., Xiong, X., & Zhang, W. (2017). Baidu index and predictability of Chinese stock returns. Financial Innovation, 3(1), 4. https://doi.org/10.1186/s40854-017-0053-1

Siami-Namini, S., & Namin, A. S. (2018). Forecasting economics and financial time series: ARIMA vs. LSTM. arXiv. https://doi.org/10.48550/arXiv.1803.06386

Smales, L. A. (2021). Investor attention and global market returns during the COVID-19 crisis. International Review of Financial Analysis, 73, 101616. https://doi.org/10.1016/j.irfa.2020.101616

Statcounter GlobalStats. (2020). Search Engine Market Share China. https://gs.statcounter.com/search-engine-market-share/all/china/#yearly-2020-2020-bar

Su, F., & Wang, X. (2021). Investor co-attention and stock return co-movement: Evidence from China’s A-share stock market. The North American Journal of Economics and Finance, 58, 101548. https://doi.org/10.1016/j.najef.2021.101548

Sushko, V., & Turner, G. (2018). The implications of passive investing for securities markets. BIS Quarterly Review, 3, 113–131.

Szakmary, A. C., Shen, Q., & Sharma, S. C. (2010). Trend-following trading strategies in commodity futures: A re-examination. Journal of Banking & Finance, 34(2), 409–426. https://doi.org/10.1016/j.jbankfin.2009.08.004

Vozlyublennaia, N. (2014). Investor attention, index performance, and return predictability. Journal of Banking & Finance, 41, 17–35. https://doi.org/10.1016/j.jbankfin.2013.12.010

Yeh, J. R., Shieh, J. S., & Huang, N. E. (2010). Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. Advances in Adaptive Data Analysis, 2(02), 135–156. https://doi.org/10.1142/S1793536910000422

Yu, L., Wang, S., & Lai, K. K. (2008) Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics, 30(5), 2623–2635. https://doi.org/10.1016/j.eneco.2008.05.003

Zhang, D., Hu, M., & Ji, Q. (2020). Financial markets under the global pandemic of COVID-19. Finance Research Letters, 36, 101528. https://doi.org/10.1016/j.frl.2020.101528

Zhang, D., & Lou, S. (2021). The application research of neural network and BP algorithm in stock price pattern classification and prediction. Future Generation Computer Systems, 115, 872–879. https://doi.org/10.1016/j.future.2020.10.009

Zhang, Y., Chu, G., & Shen, D. (2021). The role of investor attention in predicting stock prices: The long short-term memory networks perspective. Finance Research Letters, 38, 101484. https://doi.org/10.1016/j.frl.2020.101484

Zhu, B., Wang, P., Chevallier, J., & Wei, Y. (2015). Carbon price analysis using empirical mode decomposition. Computational Economics, 45(2), 195–206. https://doi.org/10.1007/s10614-013-9417-4

Quantitative investment decisions based on machine learning and investor attention analysis (2024)

References

Top Articles
Latest Posts
Article information

Author: Greg Kuvalis

Last Updated:

Views: 6289

Rating: 4.4 / 5 (75 voted)

Reviews: 82% of readers found this page helpful

Author information

Name: Greg Kuvalis

Birthday: 1996-12-20

Address: 53157 Trantow Inlet, Townemouth, FL 92564-0267

Phone: +68218650356656

Job: IT Representative

Hobby: Knitting, Amateur radio, Skiing, Running, Mountain biking, Slacklining, Electronics

Introduction: My name is Greg Kuvalis, I am a witty, spotless, beautiful, charming, delightful, thankful, beautiful person who loves writing and wants to share my knowledge and understanding with you.