Abstract
Various methods to predict stock prices have been studied. A typical method is based on time-series analysis; other methods are based on machine-learning techniques using cross-sectional data as feature values. In the field of empirical finance, feature values for prediction include “momentum”. The momentum strategy is simply based on past prices. Following the nearest trend, we buy current performers. From the different viewpoint from momentum, We’d like to challenge EMH. Our proposed method is following the similar trend. In other word, we look for past pattern similar to the current and predict from that. When predicting stock prices, investors sometimes refer to past markets that are similar to the current market. In this research, we propose a method to predict future stock prices with the past fluctuations similar to the current. As the levels of stock prices differ depending on the measured period, we develop a scaling method to compensate for the difference of price levels and the proposed new method; specifically, we propose indexing dynamic time war** (IDTW) to evaluate the similarities between time-series data. We apply the \(k^*\)-nearest neighbor algorithm with IDTW to predict stock prices for major stock indices and to assist users in making informed investment decisions. To demonstrate the advantages of the proposed method, we analyze its performance using major world indices. Experimental results show that the proposed method is more effective for predicting monthly stock price changes than other methods proposed by previous studies (Based on the comments received in Ai-Biz 2017, we clarified the differences from previous studies. And we added economic discussions about our proposed method such as differences from “momentum”, a challenge to Efficient Market Hypothesis and meanings as investment behavior).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Volatility clustering is a phenomenon by which a period of high (low) volatility continues for some time after the volatility rises (decreases) [19].
- 2.
The ticker codes are TPX Index, SPX Index, UKX Index, DAX Index, and CAC Index, respectively.
- 3.
The parameter k ranged from 1 to 10 and parameter \(L/C\in \{0.001,0.005,0.01,0.05,0.1,0.5,1,5,10 \}\). These are the same setting as Anava and Levy [1].
References
Anava, O., Levy, K.: \(k^*\)-nearest neighbors: from global to local. In: Advances in Neural Information Processing Systems, pp. 4916–4924 (2016)
Andrade, S.C., Chhaochharia, V., Fuerst, M.E.: “Sell in may and go away” just won’t go away. Finan. Anal. J. 69(4), 94 (2013)
Asness, C.S., Moskowitz, T.J., Pedersen, L.H.: Value and momentum everywhere. J. Finan. 68(3), 929–985 (2013)
Bergmeir, C., Benítez, J.M.: On the use of cross-validation for time series predictor evaluation. Inf. Sci. 191, 192–213 (2012)
Bollerslev, T.: Generalized autoregressive conditional heteroskedasticity. J. Econom. 31(3), 307–327 (1986)
Bouman, S., Jacobsen, B.: The halloween indicator, “sell in may and go away”: another puzzle. Am. Econ. Rev. 92(5), 1618–1635 (2002)
Box, G.E., Jenkins, G.M., Reinsel, G.C., Ljung, G.M.: Time Series Analysis: Forecasting and Control. Wiley, Hoboken (2015)
Cavalcante, R.C., Brasileiro, R.C., Souza, V.L., Nobrega, J.P., Oliveira, A.L.: Computational intelligence and financial markets: a survey and future directions. Expert Syst. Appl. 55, 194–211 (2016)
Coelho, M.S.: Patterns in financial markets: dynamic time war**. Ph.D. thesis, NSBE-UNL (2012)
Fama, E.F.: Efficient capital markets: a review of theory and empirical work. J. Finan. 25(2), 383–417 (1970)
Fama, E.F., French, K.R.: The cross-section of expected stock returns. J. Finan. 47(2), 427–465 (1992)
Fama, E.F., French, K.R.: Common risk factors in the returns on stocks and bonds. J. Financ. Econ. 33(1), 3–56 (1993)
Hamilton, J.D.: Time Series Analysis, vol. 2. Princeton University Press, Princeton (1994)
Imamura, M., Nakagawa, K., Yoshida, K.: Evaluation of financial market forecasting with using similarity of asset price fluctuation patterns (in Japanese). In: The 31st Annual Conference of the Japanese Society for Artificial Intelligence, p. 2D1-2 (2017)
Itakura, F.: Minimum prediction residual principle applied to speech recognition. IEEE Trans. Acoust. Speech Sig. Process. 23(1), 67–72 (1975)
Jegadeesh, N., Titman, S.: Returns to buying winners and selling losers: implications for stock market efficiency. J. Finan. 48(1), 65–91 (1993)
Keogh, E.J., Pazzani, M.J.: Scaling up dynamic time war** for datamining applications. In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 285–289. ACM (2000)
Kuramoto, T., Izumi, K., Yoshimura, S., Ishida, T., Nakashima, A., Matsui, T., Yoshida, M., Nakagawa, H.: Analysis of long-term market trend by text-mining of news articles (in Janapnese). Trans. Jpn. Soc. Artif. Intell. 28(3), 291–296 (2013)
Mandelbrot, B.: The variation of certain speculative prices. J. Bus. 36(4), 394–419 (1963)
Nakagawa, K., Imamura, M., Yoshida, K.: Stock price prediction using similarity of stock price fluctuation patterns (in Japanese). In: The 31st Annual Conference of the Japanese Society for Artificial Intelligence, p. 2D1-1 (2017)
Stone, M.: Cross-validatory choice and assessment of statistical predictions. J. R. Stat. Soc. Ser. B (Methodol.) 36, 111–147 (1974)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Nakagawa, K., Imamura, M., Yoshida, K. (2018). Stock Price Prediction with Fluctuation Patterns Using Indexing Dynamic Time War** and \(k^*\)-Nearest Neighbors. In: Arai, S., Kojima, K., Mineshima, K., Bekki, D., Satoh, K., Ohta, Y. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2017. Lecture Notes in Computer Science(), vol 10838. Springer, Cham. https://doi.org/10.1007/978-3-319-93794-6_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-93794-6_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93793-9
Online ISBN: 978-3-319-93794-6
eBook Packages: Computer ScienceComputer Science (R0)