Payment Rules through Discriminant-Based Classifiers

pdf: publications/dfjo_svmmd.pdf ps: publications/dfjo_svmmd.ps.gz tr: http://arxiv.org/abs/1208.1184 slides: publications/slides_svmmd.pdf http: http://dx.doi.org/10.1145/2559049 keywords: web,journal,selected,recent webnote: Earlier version appeared in the proc13thecold sort: 1401a cvnote: \contrib16%\selectedpdf: publications/dfjo_svmmd.pdf ps: publications/dfjo_svmmd.ps.gz tr: http://arxiv.org/abs/1208.1184 slides: publications/slides_svmmd.pdf http: http://dx.doi.org/10.1145/2559049 keywords: web,journal,selected,recent webnote: Earlier version appeared in the proc13thecold sort: 1401a cvnote: \contrib16%\selecte

Payment Rules through Discriminant-Based Classifiers

In mechanism design it is typical to impose incentive compatibility and then derive an optimal mechanism subject to this constraint. By replacing the incentive compatibility requirement with the goal of minimizing expected ex post regret, we are able to adapt statistical machine learning techniques to the design of payment rules. This computational approach to mechanism design is applicable to domains with multi-dimensional types and situations where computational efficiency is a concern. Specifically, given an outcome rule and access to a type distribution, we train a support vector machine with a special discriminant function structure such that it implicitly establishes a payment rule with desirable incentive properties. We discuss applications to a multi-minded combinatorial auction with a greedy winner-determination algorithm and to an assignment problem with egalitarian outcome rule. Experimental results demonstrate both that the construction produces payment rules with low ex post regret, and that penalizing classification errors is effective in preventing failures of ex post individual rationality

New trends in data mining.

Trends; Data; Data mining;

An academic review: applications of data mining techniques in finance industry

With the development of Internet techniques, data volumes are doubling every two years, faster than predicted by Moore’s Law. Big Data Analytics becomes particularly important for enterprise business. Modern computational technologies will provide effective tools to help understand hugely accumulated data and leverage this information to get insights into the finance industry. In order to get actionable insights into the business, data has become most valuable asset of financial organisations, as there are no physical products in finance industry to manufacture. This is where data mining techniques come to their rescue by allowing access to the right information at the right time. These techniques are used by the finance industry in various areas such as fraud detection, intelligent forecasting, credit rating, loan management, customer profiling, money laundering, marketing and prediction of price movements to name a few. This work aims to survey the research on data mining techniques applied to the finance industry from 2010 to 2015.The review finds that Stock prediction and Credit rating have received most attention of researchers, compared to Loan prediction, Money Laundering and Time Series prediction. Due to the dynamics, uncertainty and variety of data, nonlinear mapping techniques have been deeply studied than linear techniques. Also it has been proved that hybrid methods are more accurate in prediction, closely followed by Neural Network technique. This survey could provide a clue of applications of data mining techniques for finance industry, and a summary of methodologies for researchers in this area. Especially, it could provide a good vision of Data Mining Techniques in computational finance for beginners who want to work in the field of computational finance

Predicting Auditor Changes With Financial Distress Variables: Discriminant Analysis And Problems With Data Mining Approaches

Our study extends previous research that uses financial distress factors in predicting auditor changes by evaluating the effectiveness of the traditional discriminant analysis model, not used in previous auditor change studies, and by highlighting the importance of evaluating the likelihood that data mining approach classification results occurred by chance. Significance of individual predictor variables, as well as of the full set of 13 financial variables, can be tested using discriminant analysis. Kwak et al. (2011) document overall classification accuracy rates ranging from 61 to 63.5 percent for the four data mining models they compared but did not address whether these rates occurred by chance. Using Kwak et al.’s (2011) data set of firms changing auditors in 2007 or 2008 and matching non-auditor change firms, our discriminant analysis test results show overall accuracy rates of less than 56 percent and true positive rates over 85 percent, but these rates are influenced by a disproportionate number of non-auditor change firms being classified as auditor change firms. Individual predictor variables that are important in the discriminant equation based on standardized canonical coefficients include losses (LOSS) and no payment of dividends (DIV) in the year prior to the auditor change, retained earnings as a percent of total assets (RE/TA), and earnings before interest and taxes as a percent of total assets (EBIT/TA). The Kappa statistic and AUC metrics for all 13 data mining algorithms we used indicate that classifications using these algorithms are no better than random classifications

Corporate Bankruptcy Prediction

Bankruptcy prediction is one of the most important research areas in corporate finance. Bankruptcies are an indispensable element of the functioning of the market economy, and at the same time generate significant losses for stakeholders. Hence, this book was established to collect the results of research on the latest trends in predicting the bankruptcy of enterprises. It suggests models developed for different countries using both traditional and more advanced methods. Problems connected with predicting bankruptcy during periods of prosperity and recession, the selection of appropriate explanatory variables, as well as the dynamization of models are presented. The reliability of financial data and the validity of the audit are also referenced. Thus, I hope that this book will inspire you to undertake new research in the field of forecasting the risk of bankruptcy