… Maybe the experts can beat the monkeys after all. That is, if the experts are software engineers writing sophisticated algorithms for computer-generated trading. …
Algorithms are aimed at optimizing everything. They can save lives, make things easier and conquer chaos. Still, experts worry they can also put too much control in the hands of corporations and governments, perpetuate bias, create filter bubbles, cut choices, creativity and serendipity, and could result in greater unemployment.
Bloomberg recently wrote that “It’s no secret that hedge fund managers are always looking for new sources of data that will help them in their never-ending quest to beat the market.” (1) One of the most interesting new sources of data is social sentiment.
Believe the hype. Quants have never been more popular. After doubling over the past decade, assets run by so-called systematic funds have hit a record $500 billion this year, according to estimates from Barclays Plc.
“Everyone is looking into alternative data sets, sometimes without really understanding their value,” says Dr. Arun Verma, Ph.D.,
Alternative data providers see huge potential in providing their data to discretionary asset managers who are losing assets to quantitative and systematic funds.
Call them cyborgs. Morgan Stanley is about to augment its 16,000 financial advisers with machine-learning algorithms that suggest trades, take over routine tasks and send reminders when your birthday is near.
CloudQuant’s CEO Morgan Slade will be speaking on Crowdfunding Algo Developers and Data Scientists. The primary question on his panel will be “How does the mechanism work and which business model is showing early signs of success?”
If you aren’t being recruited because you didn’t work at Bridgewater, maybe it is time to create your own hedge fund operation by creating an algo on CloudQuant.
Although alternative data sets are helping funds with systematic investment strategies, those funds that employ discretionary strategies are finding it harder to separate the new trading signals from the noise.