Browsing by Author "Singh, Kiran Ryan"

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    Predicting equity movements using structural models of debt pricing and statistical learning
    (Stellenbosch : Stellenbosch University, 2019-04) Singh, Kiran Ryan; Van der Merwe, Carel Johannes; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.
    ENGLISH SUMMARY : Valuation is not an interesting problem in corporate finance, it is the only problem. Price and value are assumed to be the same number in economic theories of equilibrium and perfect capital markets. The economic theories of equilibrium asset pricing offer very weak practical suggestions for stock price behaviour at the firm level. The fundamental approach to stock price investing operates on the basis that price and value are two separate quantities and the stock price is fully determined by its intrinsic value. In this research the option-theoretic approach to default modelling is amended to provide an alternate view of value. Structural models apply an option-theoretic approach inspired by Merton (1974) that uses equity market and financial statement data in order to determine default probabilities. Default probabilities obtainable from the reduced form class of models provides the basis for extending the Merton model to estimate the firms value from market observable credit spreads. The probability of default is then a known constant provided from the reduced form model. The Merton model is reformulated with equity or firm value being used as the subject of the formula. The re-appropriated Merton model then provides a unique estimate of the firm's value based on current market information. The expected return on equity is then estimated from market credit spreads using individual capital structure and traded equity information. In this research it was found that historic estimates of return are poor predictors of future return at the firm level. The structural models provide good forecasts of return in some instances although have many challenges in implementation. The use of statistical learning methods was found to greatly improve predictions of future equity return movements using both debt and equity predictor variables, including unique predictor variables constructed from the structural models of the firm.