81–90 of 134 posts
Honest question. Let's say that I want to experiment with some language processing and bet some dollars on parsing news like these. I have decent general programming skills, and have experimented with parsing stuff, but never touched trading. Where do I even start?
Generally, there are two types of financial news events. First is scheduled, for example unemployment data - here, there are APIs to get the number and place trades, and as a human it is impossible to compete. Similarly, for FOMC statements, there is an API feed which provides objective answers to certain questions about the statement, e.g. "Did any Fed members vote for a rate increase?" Again, computers dominate. The other type of news events are surprises - unscheduled events that people are unprepared for. I'm certainly no NLP expert, but I do watch financial news feeds every day, and I can't imagine it being remotely easy to write a program to filter out the false positives. I've certainly watched a lot of market reactions to headlines, and I can tell that a lot of headlines that people would assume would be easy to write algorithms to trade on, produce market reactions that look far more "human" than the instantaneous reactions to unemployment data or embargoed Fed statements.
A lot of people seem to assume that humans are too slow to react to news, but this is definitely not the case. As an example, when the SNB dropped the swiss franc cap earlier this year (one of the biggest financial news stories for years), I know people who had time to read the headline on Bloomberg, look to see where the market was trading, and sell EURCHF within 0.1cents of where it was previously trading. As I wat…
Earlier quoted context omitted.
It would be easy to write software to parse that particular phrasing of that particular relationship ("Company X in Talks to Buy Company Y"), but to catch any arbitrary relationship between two entities in a given domain? That's no longer an easy problem. Not to mention that you'll need to represent that world knowledge coherently in ontologies / knowledge bases and write complex logic around it to make the data acti…
Sentiment analysis has plenty of research around it, and once you've got a big enough training and validation set, it can give very good results. My old boss works for a company that runs sentiment analysis on comments made about companies to automatically highlight positive/negative messages - it's not a massive leap from there to repurpose that to analyse positive or negative news reports about a company.
Earlier quoted context omitted.
The hedge fund only had one person's money in it. When he made money on a trade, that money had to come from somewhere.
One can make money in the stock market without any trading at all; Merton Miller often emphasized that prices could adjust to new information without any trades taking place, if asset holders and potential buyers agreed on the impact of the new information on the asset value. Trades only take place when asset buyers and sellers have differing valuations for the same asset, whether it be for reasons of uncertainty, as…
The owner of the hedge fund had a model that was built on decades of CBOT commodities futures prices. His model would buy and sell a basket of about 1000 many times per day. There was some error in his model - at times he would lose money - but over the long run he was able to earn far, far more money than any other fund I've ever heard of.
Earlier quoted context omitted.
Agreed. I don't really understand the surprise in article and wince at the "My friend reckons....." style. This is indeed occurring on a daily basis. A number of funds trade automatically based on fully automated analysis of the news wire and other sources. I believe most use in house analytics but there are 3rd party solutions: http://www.ravenpack.com/ I think people outside the industry don't appreciate the depth…
Yeah you remember a few weeks ago the Tesla stock moved $2 when Tesla announced its april fools new product? That was completely automated. It may not even have had to discern a new product announcement or launch. It may have just looked at twitter volume mentioning Tesla which doubled that day, and did sentiment analysis (much easier, which showed the communication that day was very positive), and purchased a bit of…
Interestingly, they don't discuss the possibility that someone just has a radically better UI for acting on market gossip. "“It would be impossible for me to do. By the time you could read the news, process it, and press the ‘buy everything’ button, it would take too long." I'm picturing the context switch between reading a tweet or news article and some rocket ship of a program which gives you enormous power but tak…
What's probably happening is they have a "bot" that does the sentiment analysis, readies the trade, then pops a big message up in front of the trader: New WSJ Headline: Intel in Talks to Buy Altera ALTR Stock: Trading within 30-day range {ALTR 30-day stock graph} March 2015 Call Options available ..and a live-updating, clickable GUI displaying, in a linear manner (i.e. low risk, small bet at one end; high risk, large…
Earlier quoted context omitted.
It is insider trading, if you use the insider information to write a very simple tweet parsing rule instead of having to write a complicated AI algorithm to parse this information before any prior knowledge. Also, if I understood the article correctly, this trade involved to transactions: first, purchasing the options, and then, purchasing actual stock using those options. And (once again, correct me if I'm wrong), t…
> And (once again, correct me if I'm wrong), the options themselves were purchased before the information was made public No, according to the article, the options were purchased one second after the information first became public in some news wire headline. The fact that it was also 19 second before a journalist's tweet was apparently irrelevant, since there was that headline before the tweet.