Financial time series forecasting using independent component analysis and support vector regression

Lu, Chi-Jie; Lee, Tian–Shyug; Chiu, Chih‐Chou

doi:10.1016/j.dss.2009.02.001

Cited by 456 publications

(208 citation statements)

References 51 publications

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“…Last, we note that our work is very different from another great body of existing work in literature (Kimoto et al 1993;Tay and Cao 2001;Cao and Tay 2003;Tsang et al 2004;Lu et al 2009), which attempted to make financial time series forecasting and stock price predictions by applying machine learning techniques, such as neural networks (Kimoto et al 1993), decision trees (Tsang et al 2004), and support vector machines (SVM) (Tay and Cao 2001;Cao and Tay 2003;Lu et al 2009), etc. The key difference between these work and ours is that their learning goal is to make explicit predictions of future prices/trends while our learning goal is to directly optimize portfolio without predicting prices explicitly.…”

Section: Learning To Select Portfoliomentioning

confidence: 87%

PAMR: Passive aggressive mean reversion strategy for portfolio selection

et al. 2012

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This article proposes a novel online portfolio selection strategy named "Passive Aggressive Mean Reversion" (PAMR). Unlike traditional trend following approaches, the proposed approach relies upon the mean reversion relation of financial markets. Equipped with online passive aggressive learning technique from machine learning, the proposed portfolio selection strategy can effectively exploit the mean reversion property of markets. By analyzing PAMR's update scheme, we find that it nicely trades off between portfolio return and volatility risk and reflects the mean reversion trading principle. We also present several variants of PAMR algorithm, including a mixture algorithm which mixes PAMR and other strategies. We conduct extensive numerical experiments to evaluate the empirical performance of the proposed algorithms on various real datasets. The encouraging results show that in most cases the proposed PAMR strategy outperforms all benchmarks and almost all state-of-the-art portfolio selection strategies under various performance metrics. In addition to its superior performance, the proposed PAMR runs extremely fast and thus is very suitable for real-life online trading applications. The experimental testbed including source codes and data sets is available at http://www.cais.ntu.edu.sg/~chhoi/PAMR/.

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Section: Learning To Select Portfoliomentioning

confidence: 87%

PAMR: Passive aggressive mean reversion strategy for portfolio selection

et al. 2012

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“…The goal of ICA is to recover independent sources when given only sensor observations that are unknown linear mixtures of the unobserved independent source signals. It has been investigated extensively in image processing, financial time series data and statistical process control [1], [4]- [6].…”

Section: Introductionmentioning

confidence: 99%

An Improved Independent Component Analysis Algorithm Based on Artificial Immune System

Chen¹,

Lu²

2013

IJMLC

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Abstract-Traditional independent component analysis (ICA) method based on FastICA algorithm faced two main disadvantages. One is that the order of the independent components (ICs) is difficult to be determined and the other is that the FastICA algorithm often leads to local minimum solution, and the suitable source signals are not isolated. To alleviate these problems, an improved ICA algorithm based on artificial immune system (AIS) (called AIS-ICA) is presented. AIS is an attractive heuristic technique and has many advantages over other heuristic techniques such as it can be easily implemented and has great capability of escaping local optimal solutions The basic idea of the proposed AIS-ICA algorithm is to use AIS to determine the separating matrix of ICA. Simulation results from the artificial signal data illustrate the efficiency of the proposed AIS-ICA approach.Index Terms-Independent component analysis, artificial immune system, signal separation, heuristic algorithm.

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“…The RSI as a part of diverse calculations and formulas is commonly present in soft computing research (e.g., Chang & Liu, 2008;Chang et al, 2009;Chiam, Tan, & Al Mamun, 2009;Chiu & Chen, 2009;Kim, 2004;Lai, Fan, Huang, & Chang, 2009;Lu, Lee, & Chiu, 2009;Majhi et al, 2009;Tan, Quek, & Yow, 2008;Yao & Herbert, 2009). However, using soft computing methods in getting iRSI calculations is a research task with no pres ence in the literature.…”

Section: Introductionmentioning

confidence: 99%

CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator

Rodríguez-González

García-Crespo

Colomo‐Palacios

et al. 2011

Expert Systems with Applications

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a b s t r a c tStock price predictions have been a field of study from several points of view including, among others, artificial intelligence and expert systems. For short term predictions, the technical indicator relative strength indicator (RSI) has been published in many papers and used worldwide.CAST is presented in this paper. CAST can be seen as a set of solutions for calculating the RSI using arti ficial intelligence techniques. The improvement is based on the use of feedforward neural networks to calculate the RSI in a more accurate way, which we call the iRSI. This new tool will be used in two sce narios. In the first, it will predict a market in our case, the Spanish IBEX 35 stock market. In the second, it will predict single company values pertaining to the IBEX 35. The results are very encouraging and reveal that the CAST can predict the given market as a whole along with individual stock pertaining to the IBEX 35 index.

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Financial time series forecasting using independent component analysis and support vector regression

Cited by 456 publications

References 51 publications

PAMR: Passive aggressive mean reversion strategy for portfolio selection

PAMR: Passive aggressive mean reversion strategy for portfolio selection

An Improved Independent Component Analysis Algorithm Based on Artificial Immune System

CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator

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