A causal feature selection algorithm for stock prediction modeling

Zhang, Xiangzhou; Hu, Yong; Xie, Kang; Wang, Shouyang; Ngai, Eric W.T.; Liu, Mei

doi:10.1016/j.neucom.2014.01.057

Cited by 75 publications

(23 citation statements)

References 32 publications

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“…As related research [44, 55], we discretize all the continuous factors into two levels, namely high and low levels (represented by 1 and 0), through equal-frequency method.…”

Section: Resultsmentioning

confidence: 99%

Identifying Key Drivers of Return Reversal with Dynamical Bayesian Factor Graph

et al. 2016

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In the stock market, return reversal occurs when investors sell overbought stocks and buy oversold stocks, reversing the stocks’ price trends. In this paper, we develop a new method to identify key drivers of return reversal by incorporating a comprehensive set of factors derived from different economic theories into one unified dynamical Bayesian factor graph. We then use the model to depict factor relationships and their dynamics, from which we make some interesting discoveries about the mechanism behind return reversals. Through extensive experiments on the US stock market, we conclude that among the various factors, the liquidity factors consistently emerge as key drivers of return reversal, which is in support of the theory of liquidity effect. Specifically, we find that stocks with high turnover rates or high Amihud illiquidity measures have a greater probability of experiencing return reversals. Apart from the consistent drivers, we find other drivers of return reversal that generally change from year to year, and they serve as important characteristics for evaluating the trends of stock returns. Besides, we also identify some seldom discussed yet enlightening inter-factor relationships, one of which shows that stocks in Finance and Insurance industry are more likely to have high Amihud illiquidity measures in comparison with those in other industries. These conclusions are robust for return reversals under different thresholds.

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“…As related research [44, 55], we discretize all the continuous factors into two levels, namely high and low levels (represented by 1 and 0), through equal-frequency method.…”

Section: Resultsmentioning

confidence: 99%

Identifying Key Drivers of Return Reversal with Dynamical Bayesian Factor Graph

et al. 2016

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“…Detection System by KDD'99 data set [178], question answering [138], air transport safty (Netherland) [144], Detecting Network Neutrality violations [143], gene regulatory network [157][158][159][160], gene-gene and gene-environment interaction [166],cancer diagnosis in breast cancer study [180,181], cancer subgroup mining with heterogeneous treatment causal effects [148], identify semantic relations in text [165,176],fMRI data [168],genome-wide causal variants study [167], triggering relation discovery on cyber security [170], clinical diagnose and treatment [161,169], stock market in Shanghai [140],Spanish mining accident [146], industrial occupational safety [171], the Titanic data set, the adult data set census income and 5 groups of synthetic data set [147], SemEval-2010-Task8 dataset [177] fast algorithm to discover causal signals in large-scale data set especially when the target or outcome variable is fixed, mining and selecting optimal parameters for further causal analysis modelling, identify the causes of failures in large Internet sites, demonstrate human volitionally regulate hemodynamic signals from circumscribed regions of the brain leading to area-specific behavioral consequences, identify genetic variants associated with disease, determine classification model to help on obtaining efficient decision for treating cancer patients.…”

Section: Associationmentioning

confidence: 99%

“…Kargupta et al [139] proposed an experimental mobile data mining system named MobiMine that adopted decision tree mining technique to facilitate the monitoring process by identifying the interesting behaving stocks and detecting their causal relationship with different features characterizing the stocks. Moreover, by studying 13 years data from the Shanghai Stock Exchanges, observational data-based causal analysis was applied on stock predictions in [140]. In particular, the authors applied the CART algorithm (decision tree mining) and proposed the causal feature selection algorithm to select more representative features for better stock prediction modeling.…”

Section: Decision Treesmentioning

confidence: 99%

Big Data and Causality

2017

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Causality analysis continues to remain one of the fundamental research questions and the ultimate objective for a tremendous amount of scientific studies. In line with the rapid progress of science and technology, the age of Big Data has significantly influenced the causality analysis on various disciplines especially for the last decade due to the fact that the complexity and difficulty on identifying causality among Big Data has dramatically increased. Data Mining, the process of uncovering hidden information from Big Data is now an important tool for causality analysis, and has been extensively exploited by scholars around the world. The primary aim of this paper is to provide a concise review of the causality analysis in Big Data. To this end the paper reviews recent significant applications of Data Mining techniques in causality analysis covering a substantial quantity of research to date, presented in chronological order with an overview table of Data Mining applications in causality analysis domain as a reference directory.

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“…With the development of data acquisition and storage technology, high-dimensional data widely exist in nature [1], finance [2], industry [3][4][5], biomedicine [6][7][8] and many other fields, which contain complicated nonlinear relationship among multiple features. Finding potential useful information and building prediction model from high-dimensional data have become one of the most important aspects of data mining and knowledge discovery [9].…”

Section: Introductionmentioning

confidence: 99%