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.

9.30amBuilding a Lab and Factory for Scientific Investing

Investing can be characterized as a data science problem. While investment firms have attracted scientific talent, they have done a poor job at developing it. Firms hire specialists, but entice them to become generalists (e.g., portfolio managers). Under the ubiquitous silo/platform structure, quants succumb to the Sisyphean trap, and do not achieve their full potential. A research lab structure offers a unique environment for developing scientists, by means of: (a) co-specialization, working in a highly cooperative lab environment; (b) tackling well-defined open investment problems; and (c) applying the scientific method.

10.15amHow 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.

11.05amThematic 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.

1.15pm Deep Dive Into Job Data

Breakout Session 1

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.

1.15pm Exploring ESG Controversies in Power BI

Breakout Session 2

Discover how visual dashboards can help you uncover insights. Our team of PowerBI experts has created a visualization suite to enable you to track your portfolio exposure to sustainability-linked controversies.

2pmThe Evolution Towards Cloud Native Risk Computing

The session will introduce AWS HTC initiative – a configurable cloud native blueprint platform enabling high throughput / high scale out market risk calculation. The session will explain the design tenets, the implementation and the relevance w.r.t the next generation of algorithmic techniques.

2.30pm 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.

3pm Four-fold News Sentiment and the Cross-Section of Equity Returns and Risks

We study the relationship between news sentiment and the cross-section of stock returns and risks by applying news sentiment scores from four different dataset providers. We find that the sentiment scores from the different datasets differ in terms of the value and the source, making them complementary as opposed to competing. We find consistent results that firms with more positive sentiment scores exhibit higher returns in the following month. Such predictive power is more significant when the sentiment is generated from traditional news sources than social media. In the risk dimension, we find that firms with higher news sentiment exhibit lower volatility in the next month. These findings highlight how different news sentiments can predict market movements.

3.45pm Enhancing Macroeconomic Forecasting Using Sentiment Analytics

Tracking down the economic variables in real-time has always been a challenge. We show how statistical models used to predict both economic activity and inflation can be enhanced by the addition of RavenPack Edge analytics . We developed two different applications. The first is about nowcasting the economic activity for multiple countries in order to identify changes in the business cycle in advance. The second application has the aim to produce real-time US inflation predictions (core and headline) for investing during period of high risk of inflation which can deteriorate the value of the portfolios. We use our inflation prediction to build a basic strategy consisting of inflationary regimes detection and hedging components.

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?