Browsing: deep learning

Posts Tagged ‘ deep learning ’

Diving Deeper into the Deep Learning Pool

May 17th, 2020 | Filed under: Newly Added, Technology, Artificial Intelligence, Machine Learning, Other Topics in A.I.

Dmitry Borisenko, an independent scholar and a former quantitative analyst at Krauspartner Investment Solutions, posted a complicated article last year with the deceptively simple title, Dissecting Momentum: We Need to Go Deeper. As the title suggests, Borisenko begins with the momentum strategy as a means of reaching alpha. Variables basedRead More

Artificial Intelligence and the Cambrian Explosion

Mar 17th, 2020 | Filed under: Algorithmic and high-frequency trading, Newly Added, Business News, The A.I. Industry, Machine Learning, Other Topics in A.I.

Three scholars affiliated with University College London have posted a paper on the dangers that increasingly sophisticated algorithms pose for markets, employing a fascinating analogy from paleontology. But we begin with their big picture. The authors are concerned that in the near future an “ecology of trading algorithms across differentRead More

Are Data Scientists the ‘New’ Rockstars?

Mar 21st, 2019 | Filed under: Newly Added, Alternative data, Technology, Operations, The A.I. Industry, Risk Management & Operations

Daniel Hill, a research analyst for the global equity team at William Blair, has written an insightful piece about the hot competition for data scientists underway in the alpha-seeking world today.  Hill begins with the observation that there are lots of different buzzwords, hashtag-worthy words and phrases, at use inRead More

Bayesian Probability Theory and a Hierarchical Learning Portfolio

Mar 19th, 2019 | Filed under: Algorithmic and high-frequency trading, Financial Economics Theory, Newly Added, The Global Economy & Currencies, Business News, The A.I. Industry, Finance & Economics

Two scholars working with Bayesian probability theory recently published a fascinating discussion of market timing and portfolio efficiency. They have proposed what they call a “hierarchical ensemble learning portfolio.” Yes, that sounds rather heavy on the jargon. We’ll break it down a bit in what follows. The authors of theRead More

The Quants, the Algorithms, and the Performance

Mar 18th, 2018 | Filed under: Alpha & Beta, Newly Added, Benchmarking & Performance Attribution, Hedge Funds, Allocating to A.I.

A new paper by J.B. Heaton, forthcoming in the Journal of Financial Transformation, offers a skeptical view of the algorithmic trading of securities, its actuality and its potential. Heaton is a lawyer (admitted to the bars of both Illinois and New York) and has a Ph.D. in finance, University ofRead More