Academic journal article Academy of Accounting and Financial Studies Journal

CAPM Works in Singapore

Academic journal article Academy of Accounting and Financial Studies Journal

CAPM Works in Singapore

Article excerpt


This paper presents a study that has successfully predicted excess returns on the Stock Exchange of Singapore (SES) during the period 1986-1993. Results are positive in six out of the seven years tested. Only the year that uses estimated beta from time-series data in 1987 failed the prediction model; and, it is likely to be an exceptional case because of the Wall Street Market Crash phenomenon in 1987. To support the usefulness of this reconstructed capital asset pricing model (CAPM), I ran another set of test results with a naive model which assumes the excess return for stocks are the same every year. Both sets of test results are compared. The naive model results are negative. They show that the excess return for stocks vary from year to year. Therefore, the predictive capability of my model is useful.

The above success is achieved with three distinct approaches: a) a reconstructed model of the CAPM, b) an updated data set covering the post reform period of the SES and c) a different methodology.


In a parsimonious way, the capital asset pricing model CAPM of Sharpe (1964), Linter (1965) and Mossin (1966) presents a structure of capital market equilibrium by relating the equilibrium asset returns to a single risk factor--the market beta. This model is reconstructed for regressions with time series to make tests on the Singapore Stock Exchange Main Board (SESMB). I study risk and equilibrium models for the Singapore stock market covering the period immediately following the reformation of the SES. All other studies on the SES covered the post reform period only. The importance of my study over the post reform period will be raised and discussed in the relevant sections. In addition to the above, I have used a methodology different from all previous studies on the SES to do my time-series tests.

By using literature and theory and by choosing the appropriate model or statistical tool, I have made my case to justify introducing a different methodology. Together the test results, and my approaches used to make the tests, create a positive way towards financial research on the SES. It may be useful for research on other small stock markets, in part or in its entirety. I have controlled any problem in estimation from escalating by limiting it from happening beyond the first pass timeseries regression.

My methodology builds upon the capital asset pricing model (CAPM) in a forecast as well as ex post model. I use time-series models. Past research studies have shown and discussed various methodologies. There are two areas that all the studies (eg Ta and Wan, 1986) on the SES have demonstrated in common: a) every study accepted the use of weekly or monthly data and b) they have all obtained good 'r squared' readings for the first pass time-series regressions on individual stocks, indicating strong 'goodness to fit' for this type of regression. I have developed upon these two empirical points in this paper.


There are seven sections in this paper. A brief introduction to the study and the organization of the paper is given in section I. I make a brief review of the literature in section II. Section III provides the statement of the problem and hypotheses, while section IV defines the scope of the research and describes the methodology. In section V test results are presented and discussed. Conclusions are made in section VI and some opportunities for research are suggested.


Because of the parsimonious approach to the asset pricing relationship, the CAPM became one of the dominant paradigms in modern finance. If it is empirically true, the relationship given by equation (III-1) has wide ranging implications for problems in portfolio selection and for other economic problems requiring knowledge of the relation between risk and return (Jensen, 1972).

However, empirical tests on the CAPM have produced varying results. …

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