The digital age gave professional investors near-instantaneous access to a vast trove of data, but organizational silos have sometimes prevented subsequent insights from spreading widely. That’s changing, however, as new digital platforms make it easier for money managers to share real-time information with colleagues across asset classes and strategies.
What do airline ticket prices, car sales and thousands of corporate filings have in common? In each of these areas, we applied advanced big data techniques to tackle an equity investing conundrum that couldn’t be solved by human researchers alone.
When a group of graduate students asked Warren Buffett about the best way to prepare for an investing career, he held up a stack of Securities and Exchange Commission filings. “Read 500 pages like this every day,” he told them. “That’s how knowledge works. It builds up like compound interest.”
Data science, artificial intelligence (AI) and machine learning are hot topics in investment management, as firms look for new ways to model investing problems and generate differentiated insights. But data science is also relatively new to the industry—and that means growing pains. What does it take to get it right?
China’s rise as a preeminent economic power makes it impossible for globally minded investors to ignore. With the integration of China’s domestic-listed equities and bonds into major global indices, the potential of investing in the broad economy is increasingly opening to the world. It’s time, then, for investors to familiarize themselves with misconceptions that can distort the view of China’s economy and corporate landscape.