ABSTRACT
In the contemporary era, people have strong incentives to explore the underlying principles of stock markets and China and the US are the 2 largest economies across the world. So, it is the stock markets in these two countries that we need to explore and study in this paper. In order to test whether the trends of the US and Chinese stock market are predictable and identify the difference between these two markets, we employed various models to study the S&P 500 and CSI 300 indexes' trends. Specifically, in this paper, we included the Markov chain, hidden Markov model (HMM), logistical regression with lasso, autoregressive integrated moving average (ARIMA) and support vector machine (SVM) to achieve our target.Therefore, we obtained several interesting key findings in our paper. We found that the Chinese stock market is more likely to be affected by technical indicators instead of historical information, as logistical regression with lasso selected most of the technical indicators and the estimated order of the Markov chain is zero when modelling the CSI 300 trends, which is different from the US stock market. Also, the AUC value of SVM outperformed other models used in the US stock market, at 0.731, while ARIMA model resulted in high AUC values in both markets, at 0.606 and 0.622 for the US and Chinese stock markets respectively. So, we confirmed that the Chinese stock market is less efficient than the US stock market. What's more, to predict the future trends in the US stock market, SVM could be the best choice, while ARIMA model works effectively for both markets.
- Acero, A., Deng, L., Kristjansson, T., and Zhang, J. (2000). Hmm adaptation using vector taylor series for noisy speech recognition. In Sixth International Conference on Spoken Language Processing.Google ScholarCross Ref
- Akaikei, H. (1973). Information theory and an extension of maximum likelihood principle. In Proc. 2nd Int. Symp. on Information Theory, pages 267--281.Google Scholar
- Alam, I. M. S. and Sickles, R. C. (1998). The relationship between stock market returns and technical efficiency innovations: evidence from the us airline industry. Journal of Productivity Analysis, 9(1):35--51.Google ScholarCross Ref
- Asadi, S., Hadavandi, E., Mehmanpazir, F., and Nakhostin, M. M. (2012). Hybridization of evolutionary levenberg-marquardt neural networks and data pre-processing for stock market prediction. Knowledge-Based Systems, 35:245--258.Google ScholarDigital Library
- Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 31(3):307--327.Google ScholarCross Ref
- Di, X. (2014). Stock trend prediction with technical indicators using svm. Independent Work Report, Stanford Univ. Fama, E. F. (1995). Random walks in stock market prices. Financial analysts journal, 51(1):75--80.Google Scholar
- Forbes, K. J. and Rigobon, R. (2010). No contagion, only interdependence: Measuring stock market comovements. Journal of Finance, 57(5):2223--2261.Google ScholarCross Ref
- French, K. R., Schwert, G. W., and Stambaugh, R. F. (1987). Expected stock returns and volatility. Journal of financial Economics, 19(1):3--29.Google ScholarCross Ref
- Girdzijauskas, S. and Štreimikiene, D. (2009). Application of logistic models for stock market bubbles analysis. Journal of Business Economics and Management, 10(1):45--51.Google ScholarCross Ref
- Hassan, M. R. and Nath, B. (2005). Stock market forecasting using hidden markov model: a new approach. In 5th International Conference on Intelligent Systems Design and Applications (ISDA'05), pages 192--196. IEEE.Google ScholarDigital Library
- Hassan, M. R., Nath, B., and Kirley, M. (2007). A fusion model of hmm, ann and ga for stock market forecasting. Expert systems with Applications, 33(1):171--180.Google Scholar
- Hu, J., Brown, M. K., and Turin, W. (1996). Hmm based online handwriting recognition. IEEE Transactions on pattern analysis and machine intelligence, 18(10):1039--1045.Google ScholarDigital Library
- Huang, B.-N., Yang, C.-W., and Hu, J. W.-S. (2000). Causality and cointegration of stock markets among the united states, japan and the south china growth triangle. International Review of Financial Analysis, 9(3):281--297.Google ScholarCross Ref
- Hyndman, R. J., Khandakar, Y., et al. (2007). Automatic time series for forecasting: the forecast package for R. Number 6/07. Monash University, Department of Econometrics and Business Statistics.Google Scholar
- James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013). An introduction to statistical learning, volume 112. Springer.Google ScholarCross Ref
- Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of economic perspectives, 17(1):59--82.Google ScholarCross Ref
- Norris, J. R. (1998). Markov chains. Number 2. Cambridge university press.Google Scholar
- Oliver, N. M., Rosario, B., and Pentland, A. P. (2000). A bayesian computer vision system for modeling human interactions. IEEE transactions on pattern analysis and machine intelligence, 22(8):831--843.Google ScholarDigital Library
- Patel, J., Shah, S., Thakkar, P., and Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4):2162--2172.Google ScholarDigital Library
- Platt, J. et al. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in large margin classifiers, 10(3):61--74.Google Scholar
- Schwarz, G. et al. (1978). Estimating the dimension of a model. The annals of statistics, 6(2):461--464.Google Scholar
- Setiawan, B. (2018). Lasso technique application in stock market modelling: An empirical evidence in indonesia. Sriwijaya International Journal of Dynamic Economics and Business, 2(1):51--62.Google Scholar
- Stratonovich, R. L. (1965). Conditional markov processes. In Non-linear transformations of stochastic processes, pages 427--453. Elsevier.Google ScholarCross Ref
- Su, R., Zhao, Y., Yi, R., and Dutta, A. (2012). Persistence in mutual fund returns: Evidence from china. InternationalGoogle Scholar
- Journal of Business and Social Science, 3(13).Google Scholar
- Welch, L. R. (2003). Hidden markov models and the baum-welch algorithm. IEEE Information Theory Society Newsletter, 53(4):10--13.Google Scholar
- Zamowitz, V. and Boschan, C. (1975). Cyclical indicators: An evaluation and new leading indexes. Business Conditions Digest, 5:5--22.Google Scholar
- Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the royal statistical society: series B (statistical methodology), 67(2):301--320.Google ScholarCross Ref
Index Terms
- Inference of the Us and Chinese Stock Markets Using Statistical and Computational Methods
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