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.     

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.     

Environmental protection versus economic growth

This paper presents and analyses the differences in the eco-models implemented worldwide （such as whether and how carbon taxes being ＂recycled＂）, or in their efficiency parameters （inconsistent parameter values that account for different results）. This is the assumption that a real tradeoff exists between the production of environmental goods. The present article empirically proves that something must be given up in order to gain something else, and once equations are specified to trace out the path of the economy over time, the natural economic formulation of such equations will embody the notion of economic and bio-tradeoffs.     

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.     

When to accept a sequence of gambles

When is a sequence of gambles, which is initially rejected eventually accepted? The eventual acceptance is defined as a pair property between the utility function and the sequences of gambles. A sequence of gambles is accepted when the gambles follow a large deviation principle and the utility function is non-satiated and bounded from below in a certain way. The number of gambles required for acceptance is computed.     

Statistical Decision Problems and Bayesian Nonparametric Methods

This paper considers parametric statistical decision problems conducted within a Bayesian nonparametric context. Our work was motivated by the realisation that typical parametric model selection procedures are essentially incoherent. We argue that one solution to this problem is to use a flexible enough model in the first place, a model that will not be checked no matter what data arrive. Ideally, one would use a nonparametric model to describe all the uncertainty about the density function generating the data. However, parametric models are the preferred choice for many statisticians, despite the incoherence involved in model checking, incoherence that is quite often ignored for pragmatic reasons. In this paper we show how coherent parametric inference can be carried out via decision theory and Bayesian nonparametrics. None of the ingredients discussed here are new, but our main point only becomes evident when one sees all priors—even parametric ones—as measures on sets of densities as opposed to measures on finite-dimensional parameter spaces.     

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.