Gurraj Singh Sangha, quant trader and data scientist, prepared the below presentation in order to explain recent trends in quantamental investing.
Quants have been using various statistical tools for years, but the next-generation algorithms will allow asset managers to combine and analyze different datasets at the same time (e.g. traditional financial, web-crawled and satellite data).
Paul Tudor Jones, the founder of Tudor Investment Corp., is betting on data and machine learning to drive the profits of his $10.6 billion hedge fund, Tudor Investment Corp.
OpenFDA was launched in June 2014 with the goal to make it easier for researchers and analysts to access and use data from FDA. The data is available for direct downloads and can be queried via APIs (Application Programming Interfaces).
Asset managers are challenged to grow and analyze the ever-increasing number of datasets.
The Consumer Financial Protection Bureau (CFPB) announced it would further improve its Complaint Database by giving consumers an option to “rate the company’s handling of his or her complaint on a one-to-five scale and provide a narrative description in support of the rating.”
The preliminary results look promising: the system chose a number of stocks six months ago and it yielded more than 30% since. The startup is now waiting for certain patents that involve AI and bots while also closing a private funding round.
Foursquare introduced Attribution, a dashboard that companies can use in order to analyze marketing campaigns and gain real-time insights.
Eagle Alpha has released a new white paper created by our advisory board member Gene Ekster, CFA on the topic of web crawling and the associated compliance risks. The paper is split into 5 sections:
The topic of personal data is discussed in this article, particularly new ways of collection and usage.
While traditionally considered a relatively safe investment, recent municipal bankruptcies and defaults have resulted in restructurings of bond obligations.
Users generating data with sensors are not likely to represent the population. Social media users are usually young and tech savvy representing rather a specific demographic group. Search data, on the other hand, can be used in econometric modelling more straightforwardly.
