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More Data or Fewer Predictors: Which is a Better Cure for Overfitting?
One of the perennial problems in building trading models is the spareness of data and the attendant danger of overfitting. Fortunately, there are systematic methods of dealing with both ends of the...
View ArticleParadox Resolved: Why Risk Decreases Expected Log Return But Not Expected Wealth
I have been troubled by the following paradox in the past few years. If a stock's log returns (i.e. change in log price per unit time) follow a Gaussian distribution, and if its net returns (i.e....
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Building an Insider Trading Database and Predicting Future Equity Returns
By John Ryle, CFA===I’ve long been interested in the behavior of corporate insiders and how their actions may impact their company’s stock. I had done some research on this in the past, albeit in a...
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StockTwits Sentiment Analysis
By Colton Smith===Exploring alternative datasets to augment financial trading models is currently the hot trend among the quantitative community. With so much social media data out there, its place in...
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Optimizing trading strategies without overfitting
By Ernest Chan and Ray Ng===Optimizing the parameters of a trading strategy via backtesting has one major problem: there are typically not enough historical trades to achieve statistical significance....
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A novel capital booster: Sports Arbitrage
By Stephen HopeAs traders, we of course need money to make money, but not everyone has 10-50k of capital lying around to start one's trading journey. Perhaps the starting capital is only 1k or less....
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FX Order Flow as a Predictor
Order flow is signed trade size, and it has long been known to be predictive of future price changes. (See Lyons, 2001, or Chan, 2017.) The problem, however, is that it is often quite difficult or...
View ArticleLoss aversion is not a behavioral bias
In his famous book "Thinking, Fast and Slow", the Nobel laureate Daniel Kahneman described one common example of a behavioral finance bias:"You are offered a gamble on the toss of a [fair] coin.If the...
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The most overlooked aspect of algorithmic trading
Many algorithmic traders justifiably worship the legends of our industry, people like Jim Simons, David Shaw, or Peter Muller, but there is one aspect of their greatness most traders have overlooked....
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Is News Sentiment Still Adding Alpha?
By Ernest Chan and Roger HunterNowadays it is nearly impossible to step into a quant trading conference without being bombarded with flyers from data vendors and panel discussions on news sentiment....
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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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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...
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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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 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?...
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,...
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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 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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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 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...
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...
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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 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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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 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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