This is a sophisticated conference that not only interrogates and explores the implications of AI & ML in the financial services industry but also goes on to identify the investment opportunities of sharing knowledge and exploiting IP in the finance domain.
The event is mainly focused on participant from Europe and UK. So the Event time is set so that participants from UK or Europe may attend in the AM 08:30 GMT or 09:30 CET.
Attend this event and earn GARP/CPD credit hours.
UNICOM has registered this program with GARP for Continuing Professional Development (CPD) credits. Attending this program qualifies for 14 GARP CPD credit hours. If you are a Certified Financial Risk Manager (FRM®), or Energy Risk Professional (ERP®), please record this activity in your Credit Tracker.
Discounted attendance offer:
If you are a GARP Alumni, that is, an ERP or FRM certificate holder then avail yourself a 20% discount offer for the event registration fee. To know more about the offer contact us [email: info@unicom.uk; ankita.talit@unicom.uk]
We are inviting speakers – thought leaders, subject experts and start up entrepreneurs – to share their knowledge and enthusiasm about their work and their vision in the field of AI, Machine Learning, Sentiment Analysis.
Please complete the speaker’s response form and submit a proposal to present at this event.
UNICOM’s Code of Conduct & Views on Diversity
Our approach is that our events are dedicated to providing a harassment-free experience for everyone, regardless of gender, gender identity and expression, age, sexual orientation, disability, physical appearance, body size, race, ethnicity or religion. We do not tolerate intimidation, stalking, harassing photography or recording, sustained disruption of sessions or events, and unwelcome physical contact or sexual attention. We do not tolerate harassment of conference participants in any form. Sexual language and imagery is not appropriate for any conference venue, including talks, workshops, Twitter and other online media. Event participants violating these rules may be sanctioned or expelled from the event without a refund at the discretion of the conference organisers. Please bring your concerns to the immediate attention of the event staff.
Diversity: In our endeavour to be the provider of knowledge to the business community, we understand that this depends on hearing from and listening to a variety of perspectives that come from people of all races, ethnicities, genders, ages, abilities, religions, sexual orientation, and military service. We welcome diverse speakers for all our events, we do not always fully achieve this goal, but it is an ongoing process.
Read more…
Raul Glavan, Consultant Artificial Intelligence & Asset Management | Trader | Speaker | UBI Enthusiast
I will talk about AIs earning money, while we sleep; data driven trading; the future of digital asset management; inequality in the hyper capitalistic society and unconditional basic income.
Dr. Ernie Chan, Founder, Predictnow.ai Inc
Most machine learners new to finance think that they should use the machine to predict how the market moves. This is actually a very hard problem due to ‘reflexivity’, We will describe a real success story of applying machine learning to trading.
Ivailo Dimov, Quant Research Solutions, CTO Office, Bloomberg L.P. and Michael Ang, Quantitative Researcher, Bloomberg L.P.
Stories on the Bloomberg newsfeed are tagged with “topic codes” containing information about their origin, subject matter, or other characteristics. One might expect that sentiment analysis of news stories may be enhanced by taking into account these topic codes, but the sheer number of topic codes is an obstacle to doing so systematically.
In this talk, we present evidence that some groups of topic codes are indeed associated with stronger sentiment impact on stock prices than others, and discuss a method to condense the mass of topic codes by identifying and retrieving latent factors which may be interpreted as broad themes shared by groups of topic codes.
Dr. John Elder, Founder, Elder Research, Inc.
The most widely used metric of investment performance is the Sharpe ratio, which measures risk-adjusted excess return. It allows one to identify an efficient frontier of alternatives along all risk levels. Yet, the Sharpe ratio really only reveals the quality of your returns and not the quality of your strategy. For instance, just buying and holding bonds — a brainless system — has had a Sharpe above 1.0 for some time. (What genius!)
I will describe how to measure the quality of market timing systems. This new metric evaluates a system’s information quality, taking into account not only return and volatility, but also the trend of the market being traded and the system’s exposure to the market. Most importantly, the new metric is a better predictor of which timing systems will succeed in the only place that matters: tomorrow.
Prof Gautam Mitra, CEO, OptiRisk
SUMMARY: We describe 3 strategies involving Market Indices
Dr. Dror Y. Kenett, Economist, Financial Industry Regulatory Authority (FINRA)
Is AI in the financial services industry risk or opportunity? Is the perception of risk, as outlined and defined by the regulatory and supervisory community, shared by private financial institutions? In this talk I will present some recent efforts to identify risk themes present in the public disclosures of key financial organizations and discuss similarities and differences. We find that for established (traditional) risk themes there is relatively greater consensus between regulators and industry participants. However, for emerging risk themes, such as AI and ML, we find a divergence in perspectives between industry and regulators and, at times, even within each group. I will discuss this spectrum of risk perspectives, as well as approaches to mitigate the resulting “risk-gap” between the regulators and the industry. Additionally, I will discuss some recent FINRA initiatives and outreach efforts to scope the adoption of AI in the financial services industry, and some of the related regulatory aspects.
Saeed Amen, Founder, Cuemacro and Alexander Denev, Head of AI – Financial Services Advisory, Deloitte
Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject.
Dr. Katharina Schwaiger, Investment Researcher, Blackrock
ESG signals are an important part of factor-based investing as they can stem from the same economic rationales as general factor premiums. We explore how portfolios combining factors exposures with sustainability outcomes can be built and we show how sustainable signal can be integrated in definitions of factor themselves.
Dan diBartolomeo, President and Founder, Northfield Information Services and Christopher Kantos, Sales and Marketing Director, EMEA at Northfield Information Services
Dan Joldzic, CEO and Quantitative Researcher, Alexandria Technology
TBC
Dr. Giuliano De Rossi, Executive Director, Goldman Sachs and Dr. Ryoko Ito, Quantitative Execution Services, Equities Execution Research Strat, Associate, Goldman Sachs
Dr. Matteo Campellone, Executive Chairman, Brain
In this talk we present some applications of Brain Natural Language Processing platform. Inspired by relatively recent literature we measure different metrics on the public regulatory filings of US companies. Literature works claim inefficiencies in the way the market processes the information available on these reports on quite long time scales. We illustrate how Brain dataset is used to implement these investment cases. We also show a possible application of the systematic analysis of the regulatory filings to create thematic baskets of stocks.
Richard Peterson, CEO, MarketPsych
Our recent research examined the impact of news and social media sentiment on long-term Eurozone and global stock prices. There are systematic underreaction effects due to general sentiment and specific themes that generate overreaction in Eurozone stocks. We also describe the results of uncorrelated sentiment-based predictive models built on European media sentiment data.
Saeed Amen, Founder, Cuemacro and Alexander Denev, Head of Ai – Financial Services Advisory, Deloitte
Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject.
Marta Lopata, Chief Growth Officer, Thinknum
TBC
