Ali Alp

Prediction of financial information manipulation by using support vector machine and probabilistic neural network

Different methods have been used to predict financial information manipulation that can be defined as the distortion of the information in the financial statements. The purpose of this paper is to predict financial information manipulation by using support vector machine (SVM) and probabilistic neural network (PNN). A number of financial ratios are used as explanatory variables. Test performance of classification accuracy, sensitivity and specificity statistics for PNN and SVM are compared with the results of discriminant analysis, logistics regression (logit), and probit classifiers, which have been used in other studies. We have found that the performance of SVM and PNN are higher than that of the other classifiers analyzed before. Thus, both classifiers can be used as automated decision support system for the detection of financial information manipulation.

Housing price dynamics and bubble risk: the case of Turkey

Abstract Housing prices have increased substantially in some emerging markets in recent years. Turkish housing market has also experienced a boom over the last decade with rapid house price appreciations. This study is the first to employ two different house price indexes to analyze housing bubble in Turkey in two different time periods, 2010:M1–2014:M12 and 2007:M6–2014:M12. We first capture the determinants of housing price by employing Bounds test and then examine whether rising house prices have been justified by fundamentals by employing OLS/FMOLS/DOLS, Kalman filter and ARIMA models. The Bounds test results suggest that there is a long-term cointegration among house price indexes and housing rent, construction cost and real mortgage interest rate. The results imply that the Turkish housing market has experienced some cases of overvaluation, but not bubble formation. This evidence has several implications for house price dynamics and risks in the Turkish housing market. Based on Turkish experience, the study also draws policy implications for emerging housing markets.

Testing for Bubbles in Turkish Housing Markets: A Comparison of Alternative Methods

Real estate economy has showed unusual performance in last decade in Turkey. Growing house supply/sales, credit volume, and rising house prices have mostly marked as boom. The perception of housing boom has also raised concerns among local/inernational investors, consumers, and policy-makers. The objective of the paper is to present the findings of the first comprehensive bubble analysis of Turkish housing markets utilizing/comparing several alternative methods. In order to address research questions, the study established two research objectives, 1) To identify characteristic variables of a housing bubble analysis in the context of Turkish housing market, and 2) To emprically measure whether rising house prices have been justified by fundamental factors. The outcomes of the study provides critical knowledges to domestic/international stakeholders of Turkish housing (and real estate) market.