"In the social media cacophony, some of the noise rises to the level of stock market signal.

"That’s the finding of a working paper overseen by Massachusetts Institute of Technology’s Andrew Lo, which says a trading strategy based on views posted on Twitter prior to Federal Reserve policy meetings regularly turned a profit. A one-standard-deviation increase in tweet sentiment can be exploited to boost Fed-day equity returns by 0.62 percent, it found..."

"The investor of the future may already be incubating in a computer lab. Time to adapt to survive...

"A few years ago, Lo’s team was asked to explore the benefits of machine-learning tools within a commercial bank’s credit card business. The team’s 'recognition algorithms' processed 10 terabytes of data from half a million customers over five years. 'We were able to identify consumers who were likely to default or be delinquent on their credit card payments much more accurately than traditional tools,' Lo says. 'By addressing those particular hotspots in their credit card business, the bank was able to reduce the risk of the overall business but only by affecting 3% of its customers, as opposed to making broad credit reductions for the entire customer base.'"

"The MIT study looked at English-language tweets in 2007-14 about the Fed, measuring sentiment and adjusting for the reach of the tweeter.

"'We exploit a new dataset of tweets referencing the Federal Reserve and show that the content of tweets can be used to predict future returns, even after controlling for common asset pricing factors,' the authors, Andrew Lo, a professor at MIT, and Pablo Azar, a PhD student there, write."

Why do investors make stupid mistakes? Why do individuals consistently underperform the very funds they invest in? Are there strategies investors can follow to avoid self-destructive behavior? Those are some of the weighty questions Financial Thought Leader Andrew Lo is trying to answer from two vantage points, one as a professor of Finance at MIT and Director of its Laboratory for Financial Engineering, the other as strategist and fund manager at his firm AlphaSimplex Group. This conversation will start with his most recent research project at MIT, titled "Artificial Stupidity"!

In this blog post MIT’s Andrew Lo describes how technology can analyze language to suggest who might have authored unsigned U.S. Supreme Court opinions.

The electronic glitch that cost Knight Capital Group $440m points to a new threat to our digital economy: technological risk. As one of the largest and most technologically advanced broker-dealers in the US, much of Knight’s trading is handled electronically. Its growth is a reflection of Moore’s law, an observation on the trend towards ever cheaper, faster, and more powerful computer hardware. However, financial services differs from the semiconductor industry in at least one important respect: human behaviour. As the great physicist Richard Feynman once said: “Imagine how much harder physics would be if electrons had feelings.” While finance undoubtedly benefits from Moore’s law, it must also contend with Murphy’s law: “Whatever can go wrong will go wrong.”

...the same trading strategies led to terrible losses, as the firms all tried to sell the same stocks at the same time. As Andrew Lo, a professor at MIT's Sloan School of Management, observed in a September 2007 paper on the event, an "apparent demand for liquidity" that week "caused a fire sale liquidation."...

Andrew Lo evaluates the evidence of bizarre robot behavior in the markets and offers his interpretation.

There's high-frequency trading, and now there's high-frequency crashing. But identifying the causes of the 6th May 2010 'flash crash' has proved to be a relatively slow process. Bob Giffords goes in search of causes, outcomes, explanations and lessons to be learned. Andrew Lo gives his take on it.

"Technical advantage has far outstipped our capacity to manage it," says MIT Sloan Prof. Andrew Lo in this blog entry on the increasingly complex rule-bending practices of financial innovation.

MIT Sloan Prof. Andrew Lo co-authored a report on an experiment to find out if humans are able to distinguish between real and randomly generated financial data.