An application of independent component analysis in the arbitrage pricing theory

Yip, Fung Ping; Xu, Lei

doi:10.1109/ijcnn.2000.861471

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…Back & Weigend (1997) apply ICA and PCA on the Tokyo Stock Exchange, showing that while the reconstruction of the observed stock prices derived from the independent components extracted is outstanding, the reproduction resulting from the principal components is not. Yip & Xu (2000) carry ICA and PCA over to stocks from the S&P 500, finding that ICA gives a better indication of the underlying structure of the US stock market, in terms of the linear relationship between the components extracted through both techniques and some predefined macroeconomic factors.…”

Section: Review Of the Literaturementioning

confidence: 99%

Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange

Cortés

Porras

Monte

2021

Finanz. polit. econ

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This paper compares the dimension reduction or feature extraction techniques, e.g., Principal Component Analysis, Factor Analysis, Independent Component Analysis and Neural Networks Principal Component Analysis, which are used as techniques for extracting the underlying systematic risk factors driving the returns on equities of the Mexican Stock Exchange, under a statistical approach to the Arbitrage Pricing Theory. We carry out our research according to two different perspectives. First, we evaluate them from a theoretical and matrix scope, making a parallelism among their particular mixing and demixing processes, as well as the attributes of the factors extracted by each method. Secondly, we accomplish an empirical study in order to measure the level of accuracy in the reconstruction of the original variables.

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Section: Review Of the Literaturementioning

confidence: 99%

Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange

Cortés

Porras

Monte

2021

Finanz. polit. econ

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Statistical arbitrage in the stock markets by the means of multiple time horizons clustering

Gatta

Iorio

Chiaro

et al. 2023

Neural Comput & Applic

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Nowadays, statistical arbitrage is one of the most attractive fields of study for researchers, and its applications are widely used also in the financial industry. In this work, we propose a new approach for statistical arbitrage based on clustering stocks according to their exposition on common risk factors. A linear multifactor model is exploited as theoretical background. The risk factors of such a model are extracted via Principal Component Analysis by looking at different time granularity. Furthermore, they are standardized to be handled by a feature selection technique, namely the Adaptive Lasso, whose aim is to find the factors that strongly drive each stock’s return. The assets are then clustered by using the information provided by the feature selection, and their exposition on each factor is deleted to obtain the statistical arbitrage. Finally, the Sequential Least SQuares Programming is used to determine the optimal weights to construct the portfolio. The proposed methodology is tested on the Italian, German, American, Japanese, Brazilian, and Indian Stock Markets. Its performances, evaluated through a Cross-Validation approach, are compared with three benchmarks to assess the robustness of our strategy.

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Modeling macroeconomic indexes from S&P 500 stocks via arbitrage pricing theory and temporary factor analysis

Tan

IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222)

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An application of independent component analysis in the arbitrage pricing theory

Cited by 4 publications

References 3 publications

Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange

Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange

Statistical arbitrage in the stock markets by the means of multiple time horizons clustering

Modeling macroeconomic indexes from S&P 500 stocks via arbitrage pricing theory and temporary factor analysis

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