Quantitative Trading Fri, 26 Apr 2019 09:55:00 GMT language
By Ernest Chan and Roger Hunter
Nowadays 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. Our team at QTS has made a vigorous effort in the past trying to extract value from such data, with indifferent results. But the central quandary of testing pre-processed alternative data is this: is the null result due to the lack of alpha in such data, or is the data pre-processing by the vendor faulty? We, like many quants, do not have the time to build a natural language processing engine ourselves to turn raw news stories into sentiment and relevance scores (though NLP was the specialty of one of us back in the day), and we rely on the data vendor to do the job for us. The fact that we couldn't extract much alpha from one such vendor does not mean news sentiment is in general useless.
So it was with some excitement that we heard Two Sigma, the $42B+ hedge fund, was sponsoring a news sentiment competition at Kaggle, providing free sentiment data from Thomson-Reuters for testing. That data started from 2007 and covers about 2,000 US stocks (those with daily trading dollar volume of roughly $1M or more), and complemented with price and volume of those stocks provided by Intrinio. Finally, we get to look for alpha from an industry-leading source of news sentiment data!
The evaluation criterion of the competition is effectively the Sharpe ratio of a user-constructed market-neutral portfolio of stock positions held over 10 days. (By market-neutral, we mean zero beta. Though that isn't the way Two Sigma put it, it can be shown statistically and mathematically that their criterion is equivalent to our statement.) This is conveniently the Sharpe ratio of the "alpha", or excess returns, of a trading strategy using news sentiment.
It may seem straightforward to devise a simple trading strategy to test for alpha with pre-processed news sentiment scores, but Kaggle and Two Sigma together made it unusually cumbersome and time-consuming to conduct this research. Here are some common complaints from Kagglers, and we experienced the pain of all of them:
- As no one is allowed to download the precious news data to their own computers for analysis, research can only be conducted via Jupyter Notebook run on Kaggle's servers. As anyone who has tried Jupyter Notebook knows, it is a great real-time collaborative and presentation platform, but a very unwieldy debugging platform
- Not only is Jupyter Notebook a sub-optimal tool for efficient research and software development, we are only allowed to use 4 CPU's and a very limited amount of memory for the research. GPU access is blocked, so good luck running your deep learning models. Even simple data pre-processing killed our kernels (due to memory problems) so many times that our hair was thinning by the time we were done.
- Kaggle kills a kernel if left idle for a few hours. Good luck training a machine learning model overnight and not getting up at 3 a.m. to save the results just in time.
- You cannot upload any supplementary data to the kernel. Forget about using your favorite market index as input, or hedging your portfolio with your favorite ETP.
- There is no "securities master database" for specifying a unique identifier for each company and linking the news data with the price data.
Compared to the price features, these categorical news features are much less important, and we find that adding them to the simple news strategy above does not improve performance.
So let's return to the question of why it is that our simple news strategy suffered such deterioration of performance going from validation to test set. (We should note that it isn’t just us that were unable to extract much value from the news data. Most other kernels published by other Kagglers have not shown any benefits in incorporating news features in generating alpha either. Complicated price features with complicated machine learning algorithms are used by many leading contestants that have published their kernels.) We have already ruled out overfitting, since there is no additional information extracted from the validation set. The other possibilities are bad luck, regime change, or alpha decay. Comparing the two equity curves, bad luck seems an unlikely explanation. Given that the strategy uses news features only, and not macroeconomic, price or market structure features, regime change also seems unlikely. Alpha decay seems a likely culprit - by that we mean the decay of alpha due to competition from other traders who use the same features to generate signals. A recently published academic paper (Beckers, 2018) lends support to this conjecture. Based on a meta-study of most published strategies using news sentiment data, the author found that such strategies generated an information ratio of 0.76 from 2003 to 2007, but only 0.25 from 2008-2017, a drop of 66%!
Does that mean we should abandon news sentiment as a feature? Not necessarily. Our predictive horizon is constrained to be 10 days. Certainly one should test other horizons if such data is available. When we gave a summary of our findings at a conference, a member of the audience suggested that news sentiment can still be useful if we are careful in choosing which country (India?), or which sector (defence-related stocks?), or which market cap (penny stocks?) we apply it to. We have only applied the research to US stocks in the top 2,000 of market cap, due to the restrictions imposed by Two Sigma, but there is no reason you have to abide by those restrictions in your own news sentiment research.
We have launched a new online course "Lifecycle of Trading Strategy Development with Machine Learning." This is a 12-hour, in-depth, online workshop focusing on the challenges and nuances of working with financial data and applying machine learning to generate trading strategies. We will walk you through the complete lifecycle of trading strategies creation and improvement using machine learning, including automated execution, with unique insights and commentaries from our own research and practice. We will make extensive use of Python packages such as Pandas, Scikit-learn, LightGBM, and execution platforms like QuantConnect. It will be co-taught by Dr. Ernest Chan and Dr. Roger Hunter, principals of QTS Capital Management, LLC. See www.epchan.com/workshops for registration details.