Introduction to Deep Learning Trading in Hedge Funds

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Originally authored by Neven Pičuljan, Freelance Software Engineer at Toptal, this article also features an illustrative case study on how hedge funds can use such systems, presented through experiments. We will also consider how deep learning systems can be improved and how hedge funds can go about hiring talent to build those systems, i.e., what sort of background does deep learning talent need to have.

A Few Tricks in the Bag of a Credit Risk Modeler

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I revealed solutions you can’t find in any popular textbook that I have applied for problems faced while developing credit risk models. Credit risk models predict whether a borrower will pay their loan back, and people like me in effect decide if you will be approved for a credit card or a mortgage loan. Statistical classification algorithms work best for such problems with a binary outcome. In particular, the logistic regression is preferred in practice due to its quantification of a probability of default between 0 and 1.

Algorithmic Trading with a Twist

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In his presentation, Peter revealed how important are news and their interpretation for making money from trading on the stock exchange. Traders try to buy low after any market overreaction to bad news and sell high when the price moves back to the level before the news. The amount of information released every day about the companies, the economy and the markets themselves is enormous and difficult to process and interpret by a trader.