Financial technical indicator based on chaotic bagging predictors for adaptive stock selection in Japanese and American markets

• Published in: Physica A Vol. 442; pp. 50 - 66
• Main Authors: Suzuki, Tomoya, Ohkura, Yuushi
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.01.2016
• Subjects: Econophysics; Ensemble learning; Nonlinear prediction; Technical analysis; Econophysics; Technical analysis; Ensemble learning; Nonlinear prediction
• ISSN: 0378-4371; 1873-2119
• DOI: 10.1016/j.physa.2015.08.042

In order to examine the predictability and profitability of financial markets, we introduce three ideas to improve the traditional technical analysis to detect investment timings more quickly. Firstly, a nonlinear prediction model is considered as an effective way to enhance this detection power by learning complex behavioral patterns hidden in financial markets. Secondly, the bagging algorithm can be applied to quantify the confidence in predictions and compose new technical indicators. Thirdly, we also introduce how to select more profitable stocks to improve investment performance by the two-step selection: the first step selects more predictable stocks during the learning period, and then the second step adaptively and dynamically selects the most confident stock showing the most significant technical signal in each investment. Finally, some investment simulations based on real financial data show that these ideas are successful in overcoming complex financial markets. •We propose new technical indicators based on chaotic bagging predictors.•Our technical indicators evaluate the consensus of the ensemble given by predicted price movements.•We adaptively select the most reliable and the least risky stock whose indicator shows the highest consensus.•We demonstrate the predictability and profitability of financial markets by our technical indicators.