Hinesh opened by saying that over the last forty years a generic quant strategy has seen a secular decay and alpha is harder to come by. This is despite an explosion in data and data sources over those years.
As part of our 2020 virtual conference, we held a panel session discussing "How to Implement a Low-Cost Data Strategy to Work With Alternative Data."
As part of our 2020 virtual conference, Pavan Arora, CTO/Chief AI Officer of Aramark delivered the day's keynote.
Two sessions from our 2020 virtual conference: Is Alternative Data Changing How Private Equity & Venture Capital Approach Deal Origination & Due Diligence and How Innovative Firms Leverage Data Lakes in Private Equity.
As part of this panel on our September virtual conference we held a deep data-driven discussion around the key developments of the COVID-19 vaccine race.
Buy-in from the top is essential but everyone in the investment process from engineers to data scientists, PMs and analysts all need to be on board, or risk failure.
Managers need to build out the capabilities, personnel and legal departments to address the ESG market. But funds should do it anyway as there is alpha potential in ESG investing. ESG outperforms so why not do it. Put the effort in it will pay off in performance and alpha.
Richard Excell, ex UBS trading and hedge fund manager, discussed the criteria and conditions for a bubble. Looking at ESG indices there is inconsistency in performance which means benchmark selection is a major factor.
In this paper, the authors use unique and proprietary data from a large Fintech lender to analyze whether alternative data captured from an individual’s mobile phone can substitute for traditional credit bureau scores. Their analysis suggests marginal benefit of using alternative data.
The following article is based on a roundtable hosted by Eagle Alpha in conjunction with FNA, a provider of analytics and software to Central Banks and Regulators. The roundtable took place on 15th July 2020.
When it comes to trading strategies, we develop models that are approximations of the reality of the market. We choose specific datasets or factors to incorporate into a model. Trying to incorporate every nuance of the market into a single model isn’t feasible.
