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本研究利用離散時間涉險模型，分析台灣上市公司之多角化程度、公司治理與財務危機之關係。本研究分為三階段逐步加入多角化程度變數、公司治理與多角化程度之交叉相乘項及控制變數。首先探討相關或非相關多角化程度是否與公司發生財務危機之可能性具有關聯性。接著納入公司治理之考量，探究公司治理、相關或非相關多角化程度與公司發生財務危機可能性間之關係。 實證結果顯示，無論是相關多角化或是非相關多角化均可顯著降低公司發生財務危機之可能性。當納入公司治理之考量後，實證結果顯示，當公司的董監質押比率、控制股東持股比率、關係人進貨比率、關係人融資比率、席次控制比率、董事席次等6項公司治理指標愈差時，公司的相關多角化程度愈高，發生財務危機的可能性也會提高；而當公司的控制股東持股比率、關係人進貨比率、關係人融資比率、席次控制比率、董事席次等5項公司治理指標愈差時，公司的非相關多角化程度愈高，發生財務危機的可能性也會愈高。 / This study employs discrete-time hazard model to investigate how the distress-diversification sensitivity is moderated depending on the level of corporate governance in nested models which sequentially incorporate diversification and then corporate governance as a moderator. The findings show that diversification reduces the possibility of financial distress while corporate governance moderates the diversification effect on financial distress.

MSc (Statistics) / Department of Statistics / Selection of variables is vital in high dimensional statistical modelling as it aims to identify the right subset model. However, variable selection for discrete survival analysis poses many challenges due to a complicated data structure. Survival data might have unobserved heterogeneity leading to biased estimates when not taken into account. Conventional variable selection methods have stability problems. A simulation approach was used to assess and compare the performance of Least Absolute Shrinkage and Selection Operator (Lasso) and gradient boosting on discrete survival data. Parameter related mean squared errors (MSEs) and false positive rates suggest Lasso performs better than gradient boosting. Frailty models outperform discrete survival models that do not account for unobserved heterogeneity. The two methods were also applied on Zimbabwe Demographic Health Survey (ZDHS) 2016 data on age at first marriage and did not select exactly the same variables. Gradient boosting retained more variables into the model. Place of residence, highest educational level attained and age cohort are the major influential factors of age at first marriage in Zimbabwe based on Lasso. / NRF

本研究延續Shumway (2001) 從存活分析(Survival Analysis)觀點切入，利用離散型風險模型(Discrete-time Hazard Model)──亦即Shumway 所稱之多期邏輯斯迴歸模型(Multi-period Logistic Regression Model)，建立企業財務危機預警模型。研究選取1986 年至2008 年間718 家上市公司，其中110 家發生財務危機事件，共計6,782 公司/年資料 (firm-year)。有別於Shumway 提出的Log 基期風險型式，本文根據事件發生率圖提出Quadratic 基期風險型式，接著利用4組（或基於會計測量，或基於市場測量）時間相依共變量 (Time-dependent Covariate)建立2 組離散型風險模型(Log 與Quadratic)，並與傳統僅考量單期資料的邏輯斯迴歸模型比較。實證結果顯示，離散型風險模型的解釋變數與破產機率皆符合預期關係，而傳統邏輯斯迴歸模型則有時會出現不符合預期關係的情況；研究亦顯示離散型風險模型預測能力絕大多數情況下優於傳統邏輯斯迴歸模型，在所有模型組合中，以Quadratic 基期風險型式搭配財務變數、市場變數的解釋變數組合而成的離散型風險模型，擁有最佳預測能力。 / Based on the viewpoint of survival analysis from Shumway (2001), the presentthesis utilizes discrete-time hazard model, also called multi-period logistic regression model, to forecast corporate financial distress. From 1986 to 2008, this research chooses 718 listed companies within, which includes 110 failures, as the subjects, summing to 6,782 firm-year data. Being different from Shumway’s log baseline hazard form,we proposed to use quadratic baseline hazard form according to empirical evidence. Then, four groups of time-dependent covariates, which are accounting-based measure or market-based measure, are applied to build two sets of discrete-time hazard model, which is compared with the single-period logistic regression model. The results show that there exists the expected relationship between covariates and predict probability in discrete-time hazard model, while there sometimes lacks it in single-period logistic regression model. The results also show that discrete-time hazard model has better predictive capability than single-period logistic regression model. The model, which combines quadratic baseline hazard form with market and accounting variables, has the best predictive capability among all models.