LONDON | 11 OCTOBER, 2022
LONDON NATIONAL GALLERY
RavenPack Research Symposiums have consistently provided data-driven finance professionals with riveting forward-looking content, new research and insights, and practical use cases from industry leaders and top scholars.
Today, the field of language AI is at an exhilarating turning point, on the cusp of transforming the financial industry and spawning new multi-billion-dollar markets.
This year's RavenPack symposium brings together top experts in natural language processing, quantitative investing, and machine learning to explore how firms can leverage new language models to not only generate alpha and better manage risk, but respond to calls for more socially responsible investment practices.
The agenda of the symposium will cover the latest developments in Language AI including:
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.
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.
"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.
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.
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.
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.
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.
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.
The road from ESG intent to ESG achievements remains murky. What data should investors look for to analyze, monitor, and qualify companies; sustainability targets?
When 9am to 7pm on 11 October, 2022
Where London National Gallery, Trafalgar Square, London, WC2N 5DN
For more than a decade, RavenPack Research Symposiums have consistently provided data-driven finance professionals with riveting forward-looking content, new research and insights, and practical use cases from industry leaders and top scholars.
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