چكيده لاتين
This research seeks to develop threshold asset pricing models with the aim of improving the performance of conventional multifactor models. In the last three decades, the trend in the development of asset pricing models within financial literature has been based on pricing anomalies. These anomalies were not justifiable by previous models and have been incorporated into models as new factors. This trend has led to the emergence of hundreds of anomaly-based factors in existing models. Given the abundance of potential variables influencing asset returns, the importance of adhering to the principle of “parsimony” in the development of pricing models has been emphasized. This principle advocates for models with the minimum number of factors while maximizing explanatory power. In line with the aforementioned principle, this research aims to develop threshold pricing models where the proposed factor is applied to some companies but not all. By conducting cross-sectional tests and threshold cross-sectional regressions, the threshold effect of variables such as credit risk on expected returns, considering the debt ratio as a threshold variable, is investigated. It is anticipated that the impact of credit risk or financial distress variables on expected returns will differ for various levels of debt. If the threshold effect of the debt ratio is significant, then in time-series tests, credit risk-related factors can be used only for a subset of assets, and for another subset, a model without these factors can be employed. The ultimate goal is to develop a model that, instead of a factor being present as a binary (zero or one), considers the factor’s presence for only certain assets. To evaluate the performance of threshold asset pricing models, data from companies listed on the Iranian capital market is used, spanning the period from 2001 to 2023 (Gregorian calendar equivalent of 1380-1402 in the Persian calendar), after applying filters commonly used in the development of pricing models. Threshold regression techniques are used to investigate the threshold effect, and to test the research hypotheses, the GRS test, A|a_i | , (A|a_i |/A|(r_i ) ̅ | ), and (AR^2) tests are employed. The results indicate that the distance-to-default variable, either alone or in the presence of other characteristics, is affected by the debt ratio threshold. Specifically, a significant and negative relationship (low distance-to-default implying high returns) exists for stocks with high debt ratios. It appears that utilizing the threshold effect of variables influencing asset returns in cross-sectional regressions allows for a more precise examination of the impact of variables on individual stock returns. Furthermore, adding a default risk factor to the pricing models under study enhances the explanatory power and predictive ability of the models for test assets with high debt thresholds. The findings suggest that to adhere to the principle of parsimony in pricing models, threshold pricing models can be used for the development of asset pricing models and the pricing of a segment of test assets with specific characteristics, yielding favorable outcomes in explanatory power and out-of-sample performance.
