Deep Latent Variable Models
In our previous blog post, we introduced latent variable models, where the latent variable can be thought of as a feature vector that has been “encoded” efficiently. This encoding turns the feature...
View ArticleFeatures Selection in the Age of Generative AI
By QTS Capital Management LLC Prepared by Ernest Chan, Chairman, and Nahid Jetha, CEO Features are inputs to machine learning algorithms. Sometimes also called independent variables, covariates, or...
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Book and Workshop Introduction: Generative AI for Trading & Asset Management
By Hamlet Medina & Ernest Chan***A Weekend with Ernie Chan in London : Trading with GenAIImperial College London, United KingdomNov 22, 2025, 9:00 AM - Nov 23, 2025, 5:00 PMJoin Dr. Ernest P. Chan...
View ArticleDeep Reinforcement Learning for Portfolio Optimization
Is it really better than Predictnow.ai's Conditional Portfolio Optimization scheme?We wrote a lot about transformers in the last three blog posts. Their sole purpose was for feature transformation /...
View ArticleCross-Attention for Cross-Asset Applications: How to mash up features from...
In the previous blog post, we saw how we can apply self-attention transformers to a matrix of time series features of a single stock. The output of that transformer is a transformed feature vector r of...
View ArticleApplying Transformers to Financial Time Series
In the previous blog post, we gave a very simple example of how traders can use self-attention transformers as a feature selection method: in this case, to select which previous returns of a stock to...
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A Poor Person's Transformer: Transformer as a sample-specific feature...
For those of us who grew up before GenAI became a thing (e.g. Ernie), we often use tree-based algorithms for supervised learning. Trees work very well with heterogeneous and tabular feature sets, and...
View ArticleHave LLMs improved over the last year? Comparing their responses to our...
The answer to this question may seem obvious if you read the breathless proclamations of AI luminaries, but good quantitative investors should be hype-immune. We want to carefully compare the ChatGPT’s...
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Applying Corrective AI to Daily Seasonal Forex Trading
By Sergei Belov, Ernest Chan, Nahid Jetha, and Akshay Nautiyal ABSTRACTWe appliedCorrective AI (Chan, 2022) to a trading model that takes advantage of the intraday seasonality of forex returns....
View ArticleConditional Portfolio Optimization: Using machine learning to adapt capital...
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...
View ArticleThe demise of Zillow Offers: it is not AI's fault!
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...
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800+ New Crypto Features
By Quentin Viville, Sudarshan Sawal, and Ernest ChanPredictNow.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...
View ArticleWelcome to Our Feature Zoo with 600+ features!
By Akshay Nautiyal and Ernest ChanThis 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....
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Metalabeling and the duality between cross-sectional and time-series factors
By Ernest Chan and Akshay NautiyalFeatures are inputs to supervised machine learning (ML) models. In traditional finance, they are typically called “factors”, and they are used in linear regression...
View ArticleConditional Parameter Optimization: Adapting Parameters to Changing Market...
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,...
View ArticleThe Amazing Efficacy of Cluster-based Feature Selection
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?...
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What is the probability of profit of your next trade? (Introducing...
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...
View ArticleWhy does our Tail Reaper program work in times of market turmoil?
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...
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US nonfarm employment prediction using RIWI Corp. alternative data
IntroductionThe monthly US nonfarm payroll (NFP) announcement by the United States Bureau of Labor Statistics (BLS) is one of the most closely watched economic indicators, for economists and investors...
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Experiments with GANs for Simulating Returns (Guest post)
By Akshay Nautiyal, QuantinstiSimulating returns using either the traditional closed-form equations or probabilistic models like Monte Carlo has been the standard practice to match them against...
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