Langsung ke konten utama

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

What are we hedging here?

I wrote a blog article last year on why hedging isn't always better. The more I try to practice what I preached, the more I am convinced that most of the time, we are hedging the wrong risks.

Hedging should not be about reducing volatility in our portfolio. If reducing overall volatility is our goal, we should simply reduce leverage, as I have argued in my previous article. If volatility in a particular industry group is too much for us, (banks? brokerages? energy stocks?), just reduce the capital allocation in that group.

Sure, if hedging does increase your overall Sharpe ratio, go ahead and hedge to your heart's content. Kelly's formula tells us that the higher the Sharpe ratio, the higher the compounded growth rate of your wealth. The problem is, many of us hedge even when doing so do not clearly increase Sharpe ratio. A further problem is that we can achieve this maximum growth rate only if we use the high leverage recommended by Kelly's formula, but this leverage often exceeds what our brokerage would allow us. It is not clear that it is beneficial to waste our buying power on the hedge if we can only operate at sub-optimal leverage.

To me, hedging should be about eliminating the risk of ruin (equity reduced to zero) due to unexpected, catastrophic events. (Many sophisticated hedge fund managers cannot even meet this simple survival criterion, giving lie to the whole notion of "hedge" funds.)

For instance, let's assume that the worst one-day drop in the market index can be 20%. Furthermore, let's assume that you are able to endure a 30% reduction in equity during one trading period. Then you should not be afraid to have a net long exposure of 150% of your equity. In other words, not only should you not hedge, but you should go ahead and leverage your long-only portfolio 1.5 times.

I believe this notion of hedging, or buying insurance, extends to all spheres of our lives. We should avoid ruin, not mere losses. Otherwise, you will be paying too much on the insurance policy over the long term. In other words, max out the deductible on your insurance policy!

Komentar

Postingan populer dari blog ini

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

Conditional Portfolio Optimization: Using machine learning to adapt capital allocations to market regimes

By Ernest Chan, Ph.D., Haoyu Fan, Ph.D., Sudarshan Sawal, and Quentin Viville, Ph.D. Previously on this blog, we wrote about a machine-learning-based parameter optimization technique we invented, called Conditional Parameter Optimization (CPO). It appeared to work well on optimizing the operating parameters of trading strategies, but increasingly, we found that its greatest power lies in its potential to optimize portfolio allocations . We call this Conditional Portfolio Optimization (which fortuitously shares the same acronym). Let’s recap what Conditional Parameter Optimization is. Traditionally, optimizing the parameters of any business process (such as a trading strategy) is a matter of finding out what parameters give an optimal outcome over past data. For example, setting a stop loss of 1% gave the best Sharpe ratio for a trading strategy backtested over the last 10 years. Or running the conveyor belt at 1m per minute led to the lowest defect rate in a manufacturing process. O

800+ New Crypto Features

 By Quentin Viville, Sudarshan Sawal, and Ernest Chan PredictNow.ai is excited to announce that we’re expanding our feature zoo to cover crypto features! This follows our work on US stock features, and features based on options activities, ETFs, futures, and macroeconomic indicators. To read more on our previous work, click here . These new crypto features can be used as input to our machine-learning API to help improve your trading strategy. In this blog we have outlined the new crypto features as well as demonstrated  how we have used them for short term alpha generation and crypto portfolio optimization. Our new crypto features are designed to capture market activity  from subtle movements to large overarching trends. These features will quantify the variations of the price, the return, the order flow, the volatility and the correlations that appear among them. To create these features, we first constructed the Base Features  using raw market data that includes microstructure inform