- Time Series Properties of Stock Returns, Comparative Analy...
Time Series Properties of Stock Returns, Comparative Analysis of High- and Low-Frequency Data
Fama (1970) suggests that stock returns exhibit random walks
through time. This means that one cannot observe any trend in the
movement of returns on assets and that returns cannot be forecasted
based on information about past returns. Following Fama (1965) and
Mandelbrot (1963), the unconditional distribution of returns has
been found to be Lepturokurtic, skewed and exhibits volatility
clustering. This means that returns are not normally distributed as
one may expect them to be and using them in models that assume
normality can lead to misleading findings. Returns have been widely
used to determine the risks of a company as well as determine the
cost of capital to be used in making investment decisions. The
question then arises. How frequent should the data used in
estimating risk and determining the cost of capital be observed?
Should it be monthly, daily, weekly or annually?
Based on the above analysis, one can conclude that the
statistical properties of daily returns are different from those of
monthly returns. Moreover, returns observed over a high frequency
are approximately normally distributed and thus have better
statistical properties. However, to make better decisions, it is
important to combine both primary and secondary data.