Document Type

Thesis

Publication details

Chotkunakitti, P 2005, 'Cash flows and accrual accounting in predicting future cash flows of Thai listed companies', DBA thesis, Southern Cross University, Lismore, NSW.

Copyright P Chotkunakitti 2005

Abstract

Cash flow prediction is involved in a number of economic decisions, particularly in investment. Previous research conducted in the United States has provided inconsistency in the results of investigating accounting data, cash flow and accrual accounting data in predicting future cash flows. No published research has studied cash flow prediction in Thailand. The current study investigates the ability of accrual and cash flows accounting data to predict future cash flows of Thai listed companies. Three regression models are constructed namely earnings, cash flows, accrual components and cash flows models. In addition, cash flow ratios are investigated to predict future cash flows by using a stepwise regression. Data used in this study is collected from the financial statements of non-financial companies listed on the Stock Exchange of Thailand from 1994 to 2002. Cash flow data are selected directly from the cash flow statements. The empirical results show that past earnings, cash flows, cash flow and accrual component of earnings can be used to predict future cash flows of Thai listed companies and cash flows have better predictive power than past earnings. Additionally, the cash flow model and the cash flow and accrual components of earnings model have better predictive power than the earnings model. The findings of testing the models in an out-of-sample period suggest that the cash flow model is a better predictor of future cash flows than the other models. Furthermore, additional year lags of accounting data can improve the predictive power of the model. However, the results indicate that cash flow ratios are not a good predictor of future cash flows. In addition, this study finds that the Asian economic crisis had an impact on the predictive power of accounting data.

Share

COinS