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Sentimental smarts

Published in Automated Trader Magazine Issue 38 Autumn 2015

The era of unstructured information flow has made quant shops keenly interested in news sentiment analysis. Automated Trader finds out what the latest developments are and what's getting practitioners excited about the future.

Jae Hong Kil

Jae Hong Kil, CEO and portfolio manager at Sentiment Alpha Capital Management

The sentiment analysis space, says Jae Hong Kil, CEO and portfolio manager at Sentiment Alpha Capital Management, keeps growing and evolving. There's more data, more connections to data, and a greater trust in the results of backtesting as that data builds up over the years.

"All the quant shops are interested in it," said Kil. "The social media space is really gaining ground. People will find a way to use sentiment data for trading and there are many funds and day trading shops using it now."

Getting an estimate for what portion of the wider buy side community is using it is a tricky business, however. There's no specific research available, though an informal survey among experts pegs the figure at some 10%. And for the buy side that is using it, vendors providing data feeds report that there is little to no communication on how the information is implemented.

Founded in 2011, Sentiment Alpha aims to incorporate theories relating to the wisdom of crowds, behavioural finance and the predictive power of social media. The fund launched in 2013 and has 'single-digit millions' in AuM, with family and friends as the main investors. Once it establishes a track record, Kil plans to approach institutional investors.

Annual return is a 'low return, low risk' model, which translates to between 5% and 10% annualised return with a Sharpe Ratio of about 1.5 to 2.0. There's a new strategy in the works targeting a return between 20% and 30%, with a Sharpe Ratio of 2.0.

The company was born out of a research project at Stony Brook University started in 2004, located in Long Island, New York, where Kil was a research assistant. The project aggregated news and social media, generating analytics, including sentiment data, for named entities.

After stints as a quant trader for Natixis, Kil was recruited back to Sentiment Alpha, which was ultimately spun out of the research project.

With a proprietary natural language processing (NLP) engine, the firm collects hundreds of millions of online sources and has an archive of some 100 terabytes of raw data that it processes using many algorithms, machine learning being one.

"Each named entity has a sentiment score given a day. It can be iPhone 6, or it can be Apple, or Microsoft, or a person's name. We keep track of every single named entity mentioned - that's like 2 billion named entities," said Kil.

Meanwhile, social media usage is exploding. When Stony Brook began the project, Twitter didn't even exist. Now, it's the darling of financial circles.

As usage grows and years tick by, Kil expects confidence among investors to grow.

"It's still hard to raise funds, but much better compared to three or four years ago. We have to fight to convince investors why this works, why we have competitive advantages. This is new, this is not traditional," said Kil.

Indexing sentiment

Richard Peterson, Managing Director, MarketPsych

Richard Peterson, Managing Director, MarketPsych

MarketPsych's managing director, Richard Peterson, is all too familiar with capital raising troubles. Between September 2008 and the end of 2010, he put his experience - which includes a degree in electrical engineering, a doctorate in medicine, a residency in psychiatry, and postdoctoral research in neuroeconomics - to the test with a market neutral US equities fund. Net returns (minus fees) over the S&P 500 were 24% during that period.

But the financial crisis made it difficult to raise money "Few trusted fund-raisers after Madoff," he said.

At the same time, financial institutions were calling him up for data, and he has pursued that through a partnership with Thomson Reuters to produce and distribute a sentiment index.

Peterson estimates that some 90% of clients are buy side, albeit a wide variety - high frequency traders, long only mutual funds, CTAs. There's also economic research departments.

But he does note a lack of adoption across the financial community.

"One of the barriers to adoption is the use of inappropriate use of linear statistical techniques during testing," Peterson said.

Linear regression models, for example, are not particularly well suited for non-linear sentiment events, like fear associated with market panic.

"Linear models can't account for VIX index spikes. And ignorance of how to model such spikes accounts for why 'black swan' funds do well over time," he said, referring to Universa, a hedge fund for which Nassim Taleb is a consultant and had reportedly made a billion dollars during the August market plunge.

Preferred techniques, Peterson added, include decision trees, cross-sectional arbitrage of extreme values, regime specific models, or moving average crossovers of information.

"Many traders aren't mathematically literate in the way they need to be to model markets. Some apply linear techniques assuming a market with normal price change distribution," he said.

In general, Peterson identifies a swath of weak research and unreliable results. For example, many academics create their own sentiment analysis engines, which are necessarily 'primitive', he noted.

In the past, some academics used the Harvard General Inquirer, an open source dictionary based on 19 th century British literature, with words identified as connoting negative sentiment including 'investor' and 'financier'.

"Many articles about investing default to negative sentiment based on such a dictionary, which is ridiculous," Peterson said.

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