QUANTMINDS CONFERENCE - LISBON…Finance is perhaps the last remaining sector of the economy that is still virtually unaffected by these technologies. Here we are, in the 21st century, when cult-like activities such as technical analysis still have greater following or assets under management than ML-based funds. Part of the problem is that finance is a particularly difficult field for ML. Standard ML techniques tend to fail when applied to investments problem. Finance accounts for about 10%-20% of the GDP in the United States, depending on various definitions. That gives you an idea of the magnitude of the disruption that we are about to experience…
Most discoveries in empirical finance are false, as a consequence of selection bias under multiple testing. This may explain why so many hedge funds fail to perform as advertised or as expected. These false discoveries may have been prevented if academic journals and investors demanded that any reported investment performance incorporates the false positive probability, adjusted for selection bias under multiple testing. In this paper, we present a real example of how this adjusted false positive probability can be computed and reported for public consumption.
Financial firms today are the pharmaceutical companies of a century ago. As a result of promoting false strategies, every year financial firms defraud investors for tens of billions of dollars. The Madoff scandal is negligible in comparison. It is, perhaps, the greatest scam in financial history, and it will only worsen as more powerful computers allow for an ever-larger number of trials.
Half a century ago, the pioneers of chaos theory discovered that the “butterfly effect” makes long-term prediction impossible. Even the smallest perturbation to a complex system (like the weather, the economy or just about anything else) can touch off a concatenation of events that leads to a dramatically divergent future. Unable to pin down the state of these systems precisely enough to predict how they’ll play out, we live under a veil of uncertainty.
But now the robots are here to help...
The Sixers trust the numbers.
They won 17 consecutive games before Monday’s Game 2 loss, the last nine of those wins without all-star center Joel Embiid. They also won their 15th straight without Dario Saric and their 16th straight without JJ Redick. Their 17th straight win was the first playoff victory since 2012.
It took talent, chemistry and fine coaching.
It also took 10 math whizzes and computer programmers, most of them with PhDs...
(ETF.com) -- We trust our life savings to financial quants who impress us because we don’t understand them. Quantitative research provides a voluptuous cushion of reassurance, but what if it’s all based on bad science?
Factor-beta investors are full of passionate intensity, but we’ve read so many deceptive reports that we get misled into thinking we know something, when in fact we have no more insight into smart beta than a parrot into its profanities...
(Globe Newswire) -- Guggenheim Partners today agreed to transfer its Quantitative Investment Strategies (“QIS”) unit to Dr. Marcos Lopez de Prado, who built that business and its technology as a Senior Managing Director of Guggenheim.
(Financial Times) -- Marcos Lopez de Prado, a quant researcher and fellow at the Berkeley Lab, says: “You need to decode markets and find the invisible patterns. The people that do that best have the best models and the most powerful computers. It gives you an edge. It's amazing what we could do with quantum computers."
(Institutional Investor) -- "The presence of financial academia is fading, something that was unthinkable 10 years ago," writes López de Prado. "The edge is not yet another reincarnation of the capital asset pricing model [...] FinTech, big data, machine learning, and even quantum computing will render formal finance education even more irrelevant, he believes."
(Bloomberg Markets) -- Marcos López de Prado, a senior managing director at Guggenheim Partners LLC who’s also a scientific adviser at 1QBit and a research fellow at the U.S. Department of Energy’s Lawrence Berkeley National Laboratory, says it’s all about context. “The reason quantum computing is so exciting is its perfect marriage with machine learning,” he says. “I would go as far as to say that currently this is the main application for quantum computing.”