Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

A Dynamic Analysis of Fixed- and Adjustable-Rate Mortgage Terminations

This paper provides a side-by-side comparison of loan-level statistical models for fixed- and adjustable-rate mortgages. Multinomial logit models for quarterly conditional probabilities of default and prepayment are estimated. We find that the estimated impacts of embedded option values for prepayment and default are generally quite similar across both FRM and ARM loans, providing additional empirical support for the basic predictions of the options theory. We also find that differences in estimates of conditional probabilities of prepayment and default associated with mortgage age, origination period, original LTV, and relative loan size, indicate the continued significance of these other economic and demographic factors for empirical models of mortgage terminations.     

跨局部属排空车走行系数测算方法探讨

在阐述跨局部属排空车走行系数定义的基础上,分析了合理测算跨局部属排空车走行公里对铁路局经济效益的影响,提出了跨局部属排空车走行系数测算方法和经济效果评价。     

The Multilevel Approach to Repeated Measures for Complete and Incomplete Data

Repeated measurements often are analyzed by multivariate analysis of variance (MANOVA). An alternative approach is provided by multilevel analysis, also called the hierarchical linear model (HLM), which makes use of random coefficient models. This paper is a tutorial which indicates that the HLM can be specified in many different ways, corresponding to different sets of assumptions about the covariance matrix of the repeated measurements. The possible assumptions range from the very restrictive compound symmetry model to the unrestricted multivariate model. Thus, the HLM can be used to steer a useful middle road between the two traditional methods for analyzing repeated measurements. Another important advantage of the multilevel approach to analyzing repeated measures is the fact that it can be easily used also if the data are incomplete. Thus it provides a way to achieve a fully multivariate analysis of repeated measures with incomplete data. This revised version was published online in June 2006 with corrections to the Cover Date.     

Environmental Valuation with Imperfect Information The Case of the Random Utility Model

This paper considers welfare analysis with therandom utility model (RUM) when perceptions ofenvironmental quality differ from objectivemeasures of environmental quality. Environmental quality is assumed to be anexperience good, so that while perceptions ofquality determine choices, ex postutility is determined by objective quality. Given this assumption, I derive a measure ofthe welfare impact of changes in environmentalquality, and I show how this new welfaremeasure differs from the traditional welfaremeasure developed by Hanemann (1982). This newwelfare measure provides an approach tomeasuring the value of information aboutenvironmental quality within the framework ofthe random utility model.     

The Random Walk Hypothesis in the Emerging Indian Stock Market

This paper examines the random walk hypothesis in the emerging Indian stock market using daily data on individual stocks. The statistical evidence in this paper rejects the random walk hypothesis. The results suggest that daily returns earned by individual stocks and by an equally weighted portfolio show significant non–linear dependence and persistent volatility effects. The non–linear dependence takes the form of ARCH–type conditional heteroskedasticity and does not appear to be caused by nonstationarity of underlying economic variables. Though conditional volatility is time varying, it does not explain expected returns.     

Turbo码解码过程中的加权算法

本文通过调整迭代解码过程中系统位接收值的加权系数，提出了一种Turbo码加权迭代解码算法。该算法改变了迭代运算后Turbo码解码器输出软值中系统位接收值信息和它的外部估计信息的比重，使Turbo码无论在低信噪比或是在高信噪比时均具有优良的纠错性能。仿真结果显示，采用Turbo码加权迭代解码算法，不仅能提高Turbo码的收敛速度，而且能进一步降低Turbo码解码时的地板值，使Turbo码的比特误码率在高、低信噪比时都能够得到进一步改善。     

EMBEDDED MATRICES FOR FINITE MARKOV CHAINS

For an arbitrary subset A of the finite state space 5 of a Markov chain the so–called embedded matrix P _A is introduced. By use of these matrices formulas expressing all kinds of probabilities can be written down almost automatically, and calculated very easily on a computer. Also derivations can be given very systematically.     

On efficiency of the ratio estimator

Summary The exact mean square error for the ratio estimator of a finite population total based on simple random sampling without replacement is shown to have an expected value less than that of the variance of the ratio estimator based on Midzuno’s scheme, under a usual super-population model.