At the height of the credit crunch in 2008, academics at the London School of Economics were infamously caught off guard when the Queen of England asked why no one saw the financial crisis coming. Now, 10 years after the collapse of Lehman Brothers Holdings Inc. on Sept. 15, 2008, economists, regulators, policymakers and finance industry insiders are asking themselves where the next financial crisis could come from, and what danger signals they should watch for, to avoid being blindsided again.
While there are several areas of potential concern, industry experts broadly do not believe a systemic collapse on the same scale of 2008 is on the horizon.
If your house is on fire, what is the plan? Head for the exit, naturally. But if a market crash is burning a big hole in your investment portfolio, selling out may not be the best course of action. The very same emotions and biases that can lead to good decisions in many areas of daily life can lead us astray in matters of money, experts say.
Statisticians use it. Non-statisticians have heard of it (likely in the context of the standard threshold of p = 0.05). While it’s not the only component of a clinical trial design, the p-value helps determine the maximum acceptable level of uncertainty associated with clinical evidence and, in a way, the fate of patient access to a new medical device.
But, is the traditional 0.05 threshold too restrictive for the patient populations willing to accept more uncertainty, depriving them of treatment options? Is it too permissive for other patient populations? Can we optimize clinical trial design by considering patients’ urgency for new therapeutic options, as well as their willingness to accept uncertainty?
Artificial intelligence will reshape the world of finance over the next decade or so by automating investing and other services—but it could also introduce troubling systematic weaknesses and risks, according to a new report from the World Economic Forum (WEF).
Compiled through interviews with dozens of leading financial experts and industry leaders, the report concludes that artificial intelligence will disrupt the industry by allowing early adopters to outmaneuver competitors. It also suggests that the technology will create more convenient products for consumers, such as sophisticated tools for managing personal finances and investments.
But most notably, the report points to the potential for big financial institutions to build machine-learning-based services that live in the cloud and are accessed by other institutions.
On June 17- 21, the Becker Friedman Institute at the University of Chicago (BFI) and the MIT Laboratory for Financial Engineering hosted the Macro Financial Modeling Summer Session for Young Scholars. For the third year, the camp brought together the next generation of economists and industry leaders to learn, discuss, collaborate, and find connections between macroeconomics and finance. Together, the “campers” explored the frontiers of this essential work, and provoked many stimulating discussions and new ideas.
Basic biomedical research may be in its most fruitful period in history: each year, with new technologies helping scientists advance our understandings of the underlying basis of human disease. At the same time, it’s increasingly difficult to undertake the process of translating promising biomedical discoveries into new drugs or diagnostics, especially those that will help relatively small numbers of people. “The problem rests not in the science,” says Whitehead Institute Founding Member Harvey Lodish, “but with the lack of funding for early-stage development.”
Alpha is the long-established measure of investment performance. But Andrew Lo has come up with a new twist on the metric.
He calls it dynamic alpha, and it tells you over what time horizon an individual investor or trading model does best.
Lo, who is an academic and investor, believes quant managers could use the measure to mould strategies to trade at the most effective frequency. Pension funds and insurers could use it to ensure diversification across investment styles.
Andrew Lo, a professor of finance at the Massachusetts Institute of Technology, dialled in from Boston to talk about regulation, markets and the future of machine learning.
Lo pioneered the adaptive markets hypothesis, which describes markets as a complex evolutionary ecosystem, populated by different stakeholders that adapt to changes on the basis of certain behavioural traits and biases.
There's big money in biotech in the Greater Boston area. Last year, venture capital firms invested more than $3 billion in the state's biotech and pharmaceutical companies, according to the Massachusetts Biotechnology Council. The amount has risen every year since 2012. Local companies have already produced revolutionary treatments for rare diseases, cancer and more. And there's promise and hope for cures and treatments still to come. Because of that, there's no shortage of capital.Biotech companies often go public even if they haven't yet put a product on the market, let alone turned a profit. MIT finance professor Andrew Lo says there's good reason for so much exuberance.
Life happens on social media first. When North Korea fires a ballistic missile, Korean news agencies tweet about it. When President Donald Trump has a beef with Amazon.com (ticker: AMZN) – or anyone, really – he tweets about it. Social media is becoming "a place where decision-makers go to share information," says Adela Quinones, news product manager at Bloomberg LP in New York. From public figures to company spokesmen, people are increasingly using social media to update the world about events that affect stock markets.