This thesis is concerned with designing a novel model for cash flow prediction. Cash flow and earnings are both important measures of a firm’s profit. The extant literature has discussed different models that have been applied to cash flow prediction. However, previous studies have not made attempts to address the dynamics in the cash flow model parameters, which are potentially nonlinear processes. This thesis proposes a grey-box model to capture the nonlinearity and dynamics of the cash flow model parameters. The parameters are modelled as a black box, which adopts a Padé approximant as the functional form and two exogenous variables as input variables that are considered to have explanatory power for the parameter process. Besides, this thesis also employs a Bayesian forecasting model in an attempt to capture the parameter dynamics of the cash flow modelling process. The Bayesian model has the advantage of applicability in the case of a limited number of observations. Compared with the grey-box model, the Bayesian model places linear restriction on the parameter dynamics. The prior is required for the implementation of the Bayesian model and this thesis uses the results of a random parameter model as the prior. In addition, panel data estimation methods are also applied to see whether they could outperform the pooled regression that is widely applied in the extant literature. There are four datasets employed in this thesis for the examination of various models’ performance in predicting cash flow. All datasets are in panel form. This work studies the pattern of net operating cash flow (or cash flow to asset ratio) along with time for different datasets. Out-of-sample comparison is conducted among the applied models and two measures of performance are selected to compare the practical predictive power of the models. The designed grey-box model has promising and encouraging performance in all the datasets, especially for U.S. listed firms. However, the Bayesian model does not appear to be superior compared to the simple benchmark models in making practical prediction. Similarly, the panel data models also cannot beat pooled regression. In this thesis, the traditional discounted cash flow model for equity valuation is employed to take account of the cash flow prediction models that have been developed to obtain the theoretical value of equities based on the cash flows predicted by the various models developed in this thesis. The reported results show that simpler models such as the random walk model is closer to market expectation of future cash flows because it leads to a better fitness for the market share prices using the new discounting model. The results reported in this thesis show that the new valuation models developed in this thesis could have investment value. This thesis has made contributions in both theoretical and practical aspects. Through the derivation of various models, it is found that there exists potential nonlinearity and dynamic feature in cash flow prediction models. Therefore, it is crucial to capture the nonlinearity using particular tools. In addition, this thesis builds up a framework, which can be used to analyse problems of similar kinds, such as panel data prediction. The models are derived from theoretical level and then applied to analyse empirical data. The promising results suggest that in practice, the models developed in this work could provide useful guidance for people who make decisions.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:668120 |
| Date | January 2015 |
| Creators | Pang, Yang |
| Publisher | University of Glasgow |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://theses.gla.ac.uk/6775/ |
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