By Sergei Belov, Ernest Chan, Nahid Jetha, and Akshay Nautiyal     ABSTRACT We appliedCorrective 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 significan ..read more

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. Traditio ..read more

The story is now familiar: Zillow Group built a home price prediction system based on AI in order  to become a market-maker in the housing industry. As a market maker, the goal is simply to buy low and sell high, quickly, and with minimal transaction cost. Backtests showed that its AI model's predictive accuracy was over 96% (Hat tip: Peter U., for that article). In reality, though, it lost half a billion dollars. This is a cautionary tale for anyone using AI to predict prices or returns, including those of us in more liquid markets than housing. Despite Zillow’s failure, the root cause ..read more

 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 cr ..read more

 By Akshay Nautiyal and Ernest Chan This has been a summer of feature engineering for PredictNow.ai. First, we launched the US stock cross-sectional features and the time-series market-wide features. Now we have launched the features based on options activities, ETFs, futures, and macroeconomic indicators. In total, we are now offering 616 ready-made features to our subscribers.  There is a lot to read here. If you would rather join our October 1, 12pm EST webinar where Ernie and I will discuss these factors / features and answer your questions, please sign up here. NOPE - Net op ..read more

By Ernest Chan and Akshay Nautiyal Features are inputs to supervised machine learning (ML) models. In traditional finance, they are typically called “factors”, and they are used in linear regression models to either explain or predict returns. In the former usage, the factors are contemporaneous with the target returns, while in the latter the factors must be from a prior period. There are generally two types of factors: cross-sectional vs time-series. If you are modeling stock returns, cross-sectional factors are variables that are specific to an individual stock, such as its earnings yi ..read more

Every trader knows that there are market regimes that are favorable to their strategies, and other regimes that are not. Some regimes are obvious, like bull vs bear markets, calm vs choppy markets, etc. These regimes affect many strategies and portfolios (unless they are market-neutral or volatility-neutral portfolios) and are readily observable and identifiable (but perhaps not predictable). Other regimes are more subtle, and may only affect your specific strategy. Regimes may change every day, and they may not be observable. It is often not as simple as saying the market has two regimes, and ..read more

One major impediment to widespread adoption of machine learning (ML) in investment management is their black-box nature: how would you explain to an investor why the machine makes a certain prediction? What's the intuition behind a certain ML trading strategy? How would you explain a major drawdown? This lack of "interpretability" is not just a problem for financial ML, it is a prevalent issue in applying ML to any domain. If you don’t understand the underlying mechanisms of a predictive model, you may not trust its predictions. Feature importance ranking goes a long way towards providing bett ..read more

What is the probability of profit of your next trade? You would think every trader can answer this simple question. Say you look at your historical trades (live or backtest) and count the winners and losers, and come up with a percentage of winning trades, say 60%. Is the probability of profit of your next trade 0.6? This might be a good initial estimate, but it is also a completely useless number. Let me explain.   This 0.6 is what may be called an unconditional probability of profit. It is the same for every trade that you will ever make (unless your winning ratio changes sign ..read more

I generally don't like to write about our investment programs here, since the good folks at the National Futures Association would then have to review my blog posts during their regular audits/examinations of our CPO/CTA. But given the extraordinary market condition we are experiencing, our kind cap intro broker urged me to do so. Hopefully there is enough financial insights here to benefit those who do not wish to invest with us. As the name of our Tail Reaper program implies, it is designed to benefit from tail events. It did so (+20.07%) during August-December, 2015’s Chinese stock market ..read more