trading the news
Inevitably one of the first ideas people have when they start thinking about how to write a trading algorithm turns out to be among the hardest: trading the news. The problems are many and in some cases not so obvious…but the natural appeal of the idea seems universally compelling.
Just after the dot.com craze, a brilliant friend of mine (who had just sold his web consulting startup) decided to write a book. The premise was glorious. A bunch of clever college-age kids formed a startup to predict the stock market. The method they used was to constantly comb the web with ultra-sophisticated algorithms which would run across giant server farms overnight and ultimately generate tomorrow’s headlines. Based on the headlines that their system generated, they would place trades that would take advantage of these predicted events.
Sadly, my friend never went on to complete his book, so I don’t know how it all turned out. (Instead, he went on to start another successful company, this time in the field of robotics.) While he was writing it, I loved getting new drafts as they were filled with clever ideas. But the core idea of predicting headlines and then using those headlines to trade always struck me as especially cute.
For those of us without access to news-predicting algos, writing strategies based on the news is rather less straight forward, though there are a growing variety of products and services aiming to fill the gaps. Today must have been trading-the-news-day as I found a few articles on the topic in my mailbox and even received a cold call from a vendor, Need to Know News, with just such an offering. Below I’ll look at some of these offerings and consider some of the issues involved in writing trading strategies based on the news.
The idea of trading the news resonates with me as it was one of the first things I tried to automate. In particular, I spent some time looking to trade crude and natural gas futures based on their respective weekly EIA reports. This experience led me to a couple conclusions.
The market knows before the news wires do. This is a big problem for two reasons. The first is just plain-Jane latency, and I know that vendors are now effectively reducing latency to the sub-second level, but the market still knows first. The second issue is deeper and is captured nicely by a quote I heard (sorry - I don’t remember where) which went something like:
“capital markets are the original social networks”
Which suggests that markets have their own internal languages and understandings. Thus, translating a market-impacting event, like an EIA report, into a human-readable form to reason about it (even if there’s no human doing the reasoning) and then action it back into the market struck me as a necessarily lossy transformation.
Another way of seeing this is just to consider: what to do based on the news? Sell a build of inventory? Maybe with your money. Compare the number against expectations? Whose? The semantic content that exists in the market seems to be intrinsically richer than that one might extract from a news wire. But this problem of actioning a news item brings up the next issue.
The problem of history. One of the wonderful and horrible things about market data is that there is a lot of it going back a long ways. This is an expensive pickle to manage, but it at least means that you have the ability to look back almost arbitrarily far into the past to see how markets responded to various conditions. This isn’t so true with most news wires.
To some degree, these issues are being addressed by vendors. And some of them will be addressed by how people utilize the news feeds. If, instead of trying to write a strategy based wholly on the news, I try to improve an existing strategy by annotating its model with data gleaned from a feed, I might wind up with much better results.
Indeed, one of the papers I came across today is a research report from Securities Industry News (incidentally, my first tip-off that Citi was going down the toilet was when my management told me I could no longer keep my subscription to that fine periodical). In it, market participants indicate that they expect to improve existing algos more than create brand-new ones. But they also failed to complain about the lack of back-testable feed histories, so…
The other paper is a much more detailed quantitative exposition on how to build a news reading algo and some statistical analysis on how well it annotated the market. This one is the work of Andrew Lo’s AlphaSimplex Group and as such is required reading for anyone who really wants to implement such systems.
Ever since my first experiences with trying to trade energy commodities based on the EIA report via news feed, I’ve been pretty skeptical of the utility of trying to algorithmically trade the news. That said, like everything else in this space, there’s an incredible amount of innovation going on and an incredible number of seriously smart and motivated folks working to ensure that this will be a productive path for those with the ability to tackle the formidable complexities presented.
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Updated: added link to Need to Know News’ site
back-testing, market data, startup, strategy development, technology
