9amLanguage AI: The Age of Disruption

What Is Disruptive Technology? An innovation that significantly alters the way that consumers, industries, or businesses operate. Recent developments in Language AI are poised to become the next disruptive innovation, resembling in their own times, those of the telegraph, the telephone, and the computer. Companies that fail to account for the effects of Language AI may find themselves losing market share to competitors that have discovered ways to integrate this new disruptive technology.

9.30amHow Novelty and Narratives Drive the Stock Market: Black Swans, Animal Spirits, and Scapegoats

Animal spirits" is a term that describes the instincts and emotions driving human behavior in economic settings. In recent years, this concept has been discussed in relation to the emerging field of narrative economics. When unscheduled events hit the stock market, from corporate scandals and technological breakthroughs to recessions and pandemics, relationships driving returns change in unforeseeable ways. To deal with uncertainty, investors engage in narratives which simplify the complexity of real-time, non-routine change. Based on his recent book, Pr Mangee will describe the Novelty- Narrative Hypothesis for the U.S. stock market by conducting a comprehensive investigation of unscheduled events using big data textual analysis of financial news.

10.15amThematic Boosting of Investment Strategies Using Language AI

Language AI has a wide range of applications beyond simply gauging sentiment. The most powerful trading indicators account for context and help investors better understand the half-life of a sentiment signal. In his talk, Peter will show how to utilize the RavenPack Event Taxonomy to create three thematic signals including Earnings Intelligence, ESG Controversy, and Economic Activity. He will demonstrate how these signals can help drive alpha by providing additional context and reducing noise in investment strategies across multiple holding periods and asset classes.

11.15amExpert Panel: Applications of Machine Learning

Are we still in a heuristic world, or are self-taught models dominating now? Have transformers truly changed the landscape? are ML-driven engines better suited for specific context?

1.15pm Deep Dive into Job Data

Breakout Session 1 (Hub One)

Job postings contain valuable information to measure business growth, innovation, financial health and strategic direction. During this session, Marko Kangrga leads us in an exploration of some of the insights that can be found when job data is analyzed in a systematic way.

2:30pm NLP in ESG: Two Examples

Supervised Learning: Identifying Greenwashing in Annual Reports and Conference calls Unsupervised Learning: Clarifying and sharpening the UN SDGs

3pm Emerging Biotech: The Wild West of Public Equity

Emerging biotech is the Wild West of the public equity universe. It fits better in the venture capital world—a world in which one in ten companies is a hundred bagger and the other nine fail. While biotech investing is not for the faint of heart, the payoff appears dramatic. Current frameworks used by both fundamental and quantitative managers may be missing the point. A different mindset needs to take hold to seek to unleash the wealth of alpha that appears to be available in this fascinating corner of the equity world.

3.45pm A Case Study in Designing Thematic Equity Strategies Using Big Data and NLP

The AUM tracking thematic equity investments has grown considerably over the last decade. Equities selected according to a theme can provide alternative sources of return compared with traditional country, sector or factor investments. However, it remains challenging to screen thematic companies given the lack of standardized or classical financial metrics. Leveraging Big Data and NLP from RavenPack’s vast database of news articles, this presentation introduces QUEST - a practical method developed by J.P. Morgan of selecting thematic stocks.

4.15pm ESG: How to Enhance our Understanding of Investment Impact?

The road from ESG intent to ESG achievements remains murky. What data should investors look for to analyze, monitor, and qualify companies; sustainability targets?