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### Applying Corrective AI to Daily Seasonal Forex Trading

By Sergei Belov, Ernest Chan, Nahid Jetha, and Akshay Nautiyal     ABSTRACT We applied Corrective AI (Chan, 2022) to a trading model that takes advantage of the intraday seasonality of forex returns. Breedon and Ranaldo (2012)   observed that foreign currencies depreciate vs. the US dollar during their local working hours and appreciate during the local working hours of the US dollar. We first backtested the results of Breedon and Ranaldo on recent EURUSD data from September 2021 to January 2023 and then applied Corrective AI to this trading strategy to achieve a significant increase in performance. Breedon and Ranaldo (2012) described a trading strategy that shorted EURUSD during European working hours (3 AM ET to 9 AM ET, where ET denotes the local time in New York, accounting for daylight savings) and bought EURUSD during US working hours (11 AM ET to 3 PM ET). The rationale is that large-scale institutional buying of the US dollar takes place during European working hours to pa

### Mean reversion, momentum, and volatility term structure

Everybody know that volatility depends
on the measurement frequency: the standard deviation of 5-minute returns is
different from that of daily returns. To be precise, if z is the log price,
then volatility, sampled at intervals of τ, is

volatility(τ)=√(Var(z(t)-z(t-τ)))

where Var means taking the variance over many sample times. If the prices really
follow a geometric random walk, then Var(τ)≡Var((z(t)-z(t-τ)) ∝ τ, and the volatility simply scales with the square root of
the sampling interval. This is why if we measure daily returns, we need to
multiply the daily volatility by √252 to obtain the annualized volatility.

Traders also know that prices do not really follow a geometric random walk. If
prices are mean reverting, we will find that they do not wander away from their
initial value as fast as a random walk. If prices are trending, they wander
away faster. In general,
we can write

Var(τ)  ∝ τ^(2H)

where H is called the "Hurst exponent", and it is equal to 0.5 for a
true geometric random walk, but will be less than 0.5 for mean reverting
prices, and greater than 0.5 for trending prices.

If we annualize the volatility of a mean-reverting price series, it will end up
having a lower annualized volatility than that of a geometric random walk, even
if both have exactly the same volatility measured at, say, 5-min bars. The
opposite is true for a trending price series.  For example, if we try this
on AUDCAD, an obviously mean-reverting time series, we will get H=0.43.

All of the above are well-known to many traders, and are in fact discussed in
my book. But what is more interesting is
that the Hurst exponent itself can change at some time scale, and this change sometimes signals a shift from a mean reversion to a momentum regime, or vice
versa. To see this, let's plot volatility (or more conveniently, variance) as a
function of τ. This is often called the term structure of (realized)
volatility.

midprices from 1 minutes to 2^10 minutes (~17 hrs), and plot the
log(Var(τ)) against log(τ). The fit, shown below,  is excellent. (Click
figure to enlarge). The slope, divided by 2, is the Hurst exponent, which turns
out to be 0.494±0.003, which is very slightly mean-reverting.

But if we do the same for daily returns of
SPY, for intervals of 1 day up to 2^8 (=256) days, we find that H is
now 0.469±0.007, which is significantly mean reverting.

Conclusion: mean reversion
strategies on SPY should work better interday than intraday.

We can do the same analysis for USO (the WTI crude oil futures ETF). The
intraday H is 0.515±0.001, indicating significant trending behavior. The
daily H is 0.56±0.02, even more significantly trending. So momentum
strategies should work for crude oil futures at any reasonable time scales.

Let's
turn now to GLD, the gold ETF. Intraday H=0.505±0.002, which is slightly
trending. But daily H=0.469±0.007: significantly mean reverting! Momentum
strategies on gold may work intraday, but mean reversion strategies certainly
work better over multiple days. Where does the transition occur? We can examine
the term structure closely:

We can see that at around 16-32 days, the volatilities depart from
straight line extrapolated from intraday frequencies. That's where we should switch from momentum to mean reversion strategies.

One side note of interest: when we compute
the variance of returns over periods that straddle two trading days and plot them as function of log(
τ), should τ
include the hours when the market was closed? It turns out that the answer is
yes, but not completely.  In order to produce the chart above where the
daily variances initially fall on the same straight line as the intraday
variances, we have to count 1 trading day as equivalent to 10 trading hours.
Not 6.5 (for the US equities/ETF markets), and not 24. The precise number of equivalent trading hours, of course, varies across different instruments.

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