• Corpus ID: 153825263

# A Multi-factor Adaptive Statistical Arbitrage Model

@article{Zhang2014AMA,
title={A Multi-factor Adaptive Statistical Arbitrage Model},
author={Wenbin Zhang and Zhen Dai and Bindu Pan and Milan Djabirov},
journal={arXiv: Portfolio Management},
year={2014}
}
• Published 9 May 2014
• Computer Science
• arXiv: Portfolio Management
This paper examines the implementation of a statistical arbitrage trading strategy based on co-integration relationships where we discover candidate portfolios using multiple factors rather than just price data. The portfolio selection methodologies include K-means clustering, graphical lasso and a combination of the two. Our results show that clustering appears to yield better candidate portfolios on average than naively using graphical lasso over the entire equity pool. A hybrid approach of…
1 Citations

### The $$\alpha$$-Tail Distance with an Application to Portfolio Optimization Under Different Market Conditions

• Computer Science
• 2020
A new distance measurement called the α-tail distance is introduced to measure the correlations of stock’s returns under the different market conditions and a mean variance model with variable cardinality constraints based on the hierarchical clustering is given as an application.

## References

SHOWING 1-5 OF 5 REFERENCES

### Statistical Arbitrage and Market Efficiency: Enhanced Theory, Robust Tests and Further Applications

• Economics
• 2005
Statistical arbitrage enables tests of market efficiency which circumvent the joint-hypotheses dilemma. This paper makes several contributions to the statistical arbitrage framework. First, we

### Loss protection in pairs trading through minimum profit bounds: A cointegration approach

• Economics
• 2006
This paper uses cointegration principles to develop a procedure that embeds a minimum profit condition within a pairs trading strategy and shows that, at reasonable minimum profit levels, the protocol does not greatly reduce trade numbers or absolute profits relative to an unprotected trading strategy.

### Principal Component Analysis and Effective K-Means Clustering

• Computer Science
SDM
• 2004
It is proved that the continuous solutions of the discrete K-means clustering membership indicators are the data projections on the principal directions (principal eigenvectors of the covariance matrix).

### Comparative study on normalization procedures for cluster analysis of gene expression datasets

• Computer Science
2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)
• 2008
A first large scale data driven comparative study of three normalization procedures applied to cancer gene expression data is presented in terms of the recovering of the true cluster structure as found by five different clustering algorithms.