Jae Hong Kil, CEO and Portfolio Manager, Sentiment Alpha Capital Management
Automated Trader: Why did you choose a sentiment analysis-focused fund?
Jae Hong Kil : In the past, I was actually in the US military in intelligence. That's all about information and how it's connected. Then I got into computer science, and then finance. Right now all those different fields really go together.
I went to university at Stony Brook, I was a computer science graduate student from 2004 to 2006, and we developed a system to aggregate news and social media.
It was a big project and team. 11 years ago, that was pretty new. We tried to build a search engine a bit like Google, a named entity based engine, which means if you type a named entity, like a person's name, it builds results on the named entity - like how many times people mentioned the name over that day. We collected raw text and applied natural language processing (NLP) technology into the raw text and pull out the information and analysis. That's what's going on in the background.
One of the analytics is sentiment analysis. We looked at the polarity of the sentiment - positive or negative - and it shows the score of how it changed. That was a really big project, and now lots of the team works at Google. It was pretty successful.
AT: And then you worked as a quant for a bit?
JK: I joined Natixis and worked for about six years as a quant trader. But I was watching how the university project kept going. The technology became a company, and the company was spun out. They called me and were looking for a person who can run it. I knew the technology behind it and have a trading background, and that is how I ended up joining.
At the time there was no trading strategies, but I had a pretty good view, and was following the trend and growth of social media and saw that as a huge potential. I believe in the technology.
AT: Can you tell me a bit more about the size and scale of the fund?
JK: AuM is still single digit millions and our strategy capacity is a billion dollars or more. Right now, the last couple of years we are really focused on building a track record.
Asset class is US equity and strategy type is long/short. We plan to launch long-only too. Average holding period is one month, so this is not short term strategies. We have a proprietary sentiment data engine, everything is built in-house and we get a market data feed from Bloomberg.
AT: What technology underpins the NLP engine?
JK: We use many algorithms including machine learning and there are many different phases. The first one is marking up the raw text. Then we apply rule-based algorithms, and also we can apply a lexicon, for example, once we identify all the named entities, we classify those named entities into types - person or country, etc.
The big difference between us and data providers, like Thomson Reuters and Bloomberg, is we are not just getting sentiment analysis for a company, we are getting everything. A stock price is impacted by a lot of things. People think that we just monitor what people say about the company, but we also look at what the company's performance is directly and indirectly based on, for example, products and executives. For example, it matters what people think about iPhone 6 or iPads when it comes to Apple's reputation. But then we also differentiate from iPhone 4, the product is outdated.
The bottom line is we are capable of getting sentiment data for every single name. Then we can build any sort of analysis that can be sliced and diced on top of that.
AT: When you say you use social media, what does that mean exactly?
JK: Facebook, blogs, any other major names. But Twitter is the major one. We've archived and backtested since 2009. Twitter volume significantly increased only since 2011 though. Also the period was not natural because from 2009, the market was recovering. It would be much better if you could have 10 years.