Overconfidence and bubbles in experimental asset markets

Şahin, Serkan
The aim of this study is to investigate uncertainty levels of industries and explore those financial ratios that have the highest information content in determining the set of industry characteristics and use the most informative ratios selected in developing industry specific financial distress models. First, we employ factor analysis to determine the set of ratios that are most informative in specified industries. Second, we use entropy method as a Multiple Attribute Decision Making Model, to measure the level of uncertainty for these industries providing the framework of information theory and further specify those ratios that best reflect the industry specific uncertainty levels. Finally, we conduct logistic analysis and derive industry specific financial distress models to examine the predictive ability of financial ratios selected for each industry. Data for this study are obtained from Datastream for the period 1990-2011. The companies in the sample cover S&P 1500 firms that operate in 9 different industries. We reclassify the sample of firms in 4 industry groups according to their similarity in terms of accounting applications and derive industry specific financial distress models for these industry groups. The results show that financial ratios illustrate industry characteristics and that informativeness of ratios varies among sectors. We further observe that industry specific models predict financial distress better than the benchmark model and most of the ratios selected for each industry significantly contribute to the prediction of financial distress.
Citation Formats
S. Şahin, “Overconfidence and bubbles in experimental asset markets,” Ph.D. - Doctoral Program, Middle East Technical University, 2013.