Blog Archives

Co-integration and Mean Reverting Portfolio

January 5, 2019
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In the previous post https://statcompute.wordpress.com/2018/07/29/co-integration-and-pairs-trading, it was shown how to identify two co-integrated stocks in the pair trade. In the example below, I will show how to form a mean reverting portfolio with three or more stocks, e.g. stocks with co-integration, and also how to find the linear combination that is stationary for these stocks.

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Statistical Assessments of AUC

December 25, 2018
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In the scorecard development, the area under ROC curve, also known as AUC, has been widely used to measure the performance of a risk scorecard. Given everything else equal, the scorecard with a higher AUC is considered more predictive than the one with a lower AUC. However, little attention has been paid to the statistical

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Phillips-Ouliaris Test For Cointegration

December 16, 2018
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In a project of developing PPNR balance projection models, I tried to use the Phillips-Ouliaris (PO) test to investigate the cointegration between the historical balance and a set of macro-economic variables and noticed that implementation routines of PO test in various R packages, e.g. urca and tseries, would give different results. After reading through the

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An Utility Function For Monotonic Binning

December 2, 2018
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In all monotonic algorithms that I posted before, I heavily relied on the smbinning::smbinning.custom() function contributed by Herman Jopia as the utility function generating the binning output and therefore feel deeply indebted to his excellent work. However, the availability of smbinning::smbinning.custom() function shouldn’t become my excuse for being lazy. Over the weekend, I drafted a

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Improving Binning by Bootstrap Bumping

November 25, 2018
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In the post (https://statcompute.wordpress.com/2018/11/23/more-robust-monotonic-binning-based-on-isotonic-regression), a more robust version of monotonic binning based on the isotonic regression was introduced. Nonetheless, due to the loss of granularity, the predictability has been somewhat compromised, which is a typical dilemma in the data science. On one hand, we don’t want to use a learning algorithm that is too greedy

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More Robust Monotonic Binning Based on Isotonic Regression

November 23, 2018
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More Robust Monotonic Binning Based on Isotonic Regression

Since publishing the monotonic binning function based upon the isotonic regression (https://statcompute.wordpress.com/2017/06/15/finer-monotonic-binning-based-on-isotonic-regression), I’ve received some feedback from peers. A potential concern is that, albeit improving the granularity and predictability, the binning is too fine and might not generalize well in the new data. In light of the concern, I revised the function by imposing two

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Creating List with Iterator

November 22, 2018
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In the post (https://statcompute.wordpress.com/2018/11/17/growing-list-vs-growing-queue), it is shown how to grow a list or a list-like queue based upon a dataframe. In the example, the code snippet was heavily relied on the FOR loop to do the assignment item by item, which I can’t help thinking of potential alternatives afterwards. For instance, is there an implementation

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Growing List vs Growing Queue

November 17, 2018
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### GROWING LIST ### base_lst1

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Convert Data Frame to Dictionary List in R

November 16, 2018
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In R, there are a couple ways to convert the column-oriented data frame to a row-oriented dictionary list or alike, e.g. a list of lists. In the code snippet below, I would show each approach and how to extract keys and values from the dictionary. As shown in the benchmark, it appears that the generic

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Monotonic Binning with Equal-Sized Bads for Scorecard Development

October 14, 2018
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In previous posts (https://statcompute.wordpress.com/2017/01/22/monotonic-binning-with-smbinning-package) and (https://statcompute.wordpress.com/2017/06/15/finer-monotonic-binning-based-on-isotonic-regression), I’ve developed 2 different algorithms for monotonic binning. While the first tends to generate bins with equal densities, the second would define finer bins based on the isotonic regression. In the code snippet below, a third approach would be illustrated for the purpose to generate bins with roughly equal-sized

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