On April 21, we held our Second Annual Natural Language Processing (NLP) and Machine Learning (ML) Investment Conference. This year’s NLP and ML conference was presented in virtual format. Our virtual conference was attended by over 400 buy-side investment managers and researchers from around the world.
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The rapid development of the China’s coronavirus outbreak, reminiscent of the SARS fallout in Asia in 2003, is causing significant volatilities across the global financial markets. In this month’s JQES, we list a few academic research papers that examine the implications of the SARS case, which could guide investors to understand the current outbreak.
On March 22, we held our first NLP (Natural Language Processing) and Machine Learning Investment Conference in New York City. Almost 150 buy-side investment managers and researchers from around the world attended the event.
For this month’s JQES, we decide to conduct a comprehensive review of all academic papers published in either working paper repositories or peer-reviewed journals in 2018 and 2019 year-to-date. After going through thousands of articles, we pick over 150 papers for this issue.
On December 29th and 30th, 2018, we held our 2nd annual global quantitative and macro investing conference in New York City. Over 400 buy-side investment professionals, academics, and researchers from Europe, US, Canada, China, Japan, and Australia attended the event.
Given the strength of the US economy, surprisingly, the yield curve has been flattening this year and is almost inverted in the last few months. Finally, last week, the 10-year US treasury yield spiked to over 3.2%, hitting the highest level since May 2011, which widened the yield spread. Whether this is a temporary movement or marks the turning point of the bond market, remains to be seen. Regardless, the slope of the yield curve will be watched closely by investors, as it has an outstanding record of predicting recessions. In this month’s JQES, we provide a list of relevant academic papers on how the yield curve (yield spread) is applied in statistical models to forecast economic activities and asset pricing.
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