Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/146985
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dc.titleTHE EFFECT OF DIFFERENT LENGTH TREND VARIABLES ON THE PREDICTION OF FIRM DEFAULT PROBABILITIES.
dc.contributor.authorLIM TIONG WEI BENNY
dc.date.accessioned2018-09-05T06:01:27Z
dc.date.available2018-09-05T06:01:27Z
dc.date.issued2018-04-09
dc.identifier.citationLIM TIONG WEI BENNY (2018-04-09). THE EFFECT OF DIFFERENT LENGTH TREND VARIABLES ON THE PREDICTION OF FIRM DEFAULT PROBABILITIES.. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/146985
dc.description.abstractThis paper expands on the forward intensity model in Duan et al.(2012) by including a second trend component of firm-specific variables. We performed a modified grid search of moving averages for both trend variables. Altogether, 67 combinations were tried and their performance across prediction horizons from 1 to 36 months were compared. For the in-sample, out-of-sample (Cross-sectional), and out-of-sample (Time) tests, the best models showed average improvements in Accuracy Ratios across all 36 prediction horizons of 0.76%, 0.59%, and 1.34% respectively. However, these models underperformed the original model for the out-of-sample (Cross-Sectional) prediction horizons of 30 months and above. An equal-weighted rank system was employed for model selection. The best models had significantly different durations of trend moving averages. Separately, net-income-to-total-asset and distance-to-default were found to have significantly different coefficients for its 2 trend components. This supports our hypothesis of a bi-directional relationship between firm-specific variables and firm’s financial health.
dc.typeThesis
dc.contributor.departmentECONOMICS
dc.contributor.supervisorDUAN JIN-CHUAN
dc.description.degreeBachelor's
dc.description.degreeconferredJoint Bachelor of Social Sciences (Honours) in Actuarial Studies and Economics
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