In an earlier post
we examined the out-of-sample performance of a DJIA intraday strategy
developed, and actively traded, here at Giller Investments.
That post provides some detail as to how the strategy was built and how it is
operated. In addition to the DJIA system we also operate a similar system for
the NASDAQ-100 futures.
In the chart above, as before, there are four panels. The two on the left hand
side are regressions of the intraday price change onto the forecast. The upper
panel is for all data, and the lower resticted for dates on which trades were
done. Regressions are computed both for simple linear regression and when
weighted with the forecast variance. The R² is ≅ 1.5%,
which corresponds to a correlation coefficient of order 12%. According the the
rule of thumb from
Grinold & Kahn's Active Portfolio Management
,
if we could trade every day with negligible costs, this would give a
Sharpe Ratio of approximately 1.9. Note how the linear relationship,
although statistically well established, is difficult to observe by eye due to
the low R². The t-Statistic for the fitted gradients is
referenced relative to the null hypothesis value of 1 (not 0). This is because
the null for this out-of-sample regression is that the system works as
modelled (β = 1).
On the right hand side, there is a chart showing each day's forecast at trade
time and how that compares to the trade entry barriers. This illustrates the
variability of the scale of the alpha with the local volatility conditions.
Finally, for a contextual reference, we present a chart showing the time series
of the index level and the daily point volatility.