Innovations in Finance and harnessing of Technology have resulted in making the term Fintech a portmanteau word. In the evolution of the BFSI sector Fintech has assumed a pivotal role; but it has also disrupted traditional order. The rise of AI and Machine learning has impacted many aspects of investment models and technologies at the same time it has disrupted some other.
Applications of Alternative Data in Fintech are growing at a great pace; the question is how to discover new sources of alpha and create strategies and signals. Here the challenge is to discover hidden coupling of multiple data sources. Text analysis, Natural Language Processing and analysis of News, Micro blogs, investor sentiment are well established. Bringing all these advances together new applications in trading, fund management and risk control have continued to emerge.
Today Fintech has influenced all aspects of the finance industry – banking and capital markets, asset and wealth management, insurance, and funds transfer and payments.
There are three main themes for this Two-Day Online Event
Theme 1: Disruptive Progress in AI and ML
Progress in (i) News Analysis, (ii) Micro Blog Analysis (Filtered Twitter feed for finance), (iii) Online searches (iv) Data Analytics, Alternative Data and AI and Quant models
DISRUPTIONS: Predicting the Market directions and ‘Timing the Market’
Theme 2: Progress in Derivative Products
Theme 3: Impact of SRI and ESG in the Investment Industry
This event is mainly focused on participants from Europe and UK. The event time is set so that participants from UK or Europe may attend in the morning from 9:30 GMT or 10:30 CEST
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: email@example.com; Vandana.firstname.lastname@example.org]
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.
Gautam Mitra, CEO, OptiRisk / UNICOM
Blair Hull, Founder And Chairman, HULL TACTICAL
Pierce Crosby, General Manager, TradingView
Even a year ago, the idea of a crowd moving a $15 billion dollar company in a meaningful way was unheard of. Today, we see the bifurcation of platforms across the financial web that commence coordinated investing efforts in an open and transparent way. Staying on top of these conversations is essential to every asset manager.
DR. Ernie Chan, Founder, PredictNow.ai Inc
One major impediment to widespread adoption of machine learning (ML) in investment management is their black-box nature: how would you explain to an investor why the machine makes a certain prediction? If you don’t understand the underlying mechanisms of a predictive model, you may not trust its predictions. Feature importance ranking goes a long way towards providing better interpretability to ML models, and feature selection improves out-of-sample performance of ML models.
Mario Dell’Era, Quantiative Market Risk Sr. Group Manager, Citi
Andrea Nardon, Chief Quant Officer, Fund Manager, Black Alpha Capital
Machine learning tools have started to be widely accepted within the investor base. On one hand this allows quant researchers to explore newer input-output relationships that human eyes struggle to identify but on the other hand when these tools are not properly used, they can introduce new risks which can cause undesired outcomes.
David Jessop, Head Of Investment Risk, Columbia Threadneedle Investments Emea Apac
The path to a low carbon economy involves encouraging companies to lower their carbon intensity. It could be beneficial to find the companies where they are lowering their carbon output. But can we forecast this? The problem is a very short time series of carbon data – perhaps 10 years at best, which means
Gautam Mitra, CEO, OptiRisk / UNICOM
Dan Dibartolomeo, President and Founder, Northfield Information Services
Abnormal market behaviour, speculative bubbles and busts, are not new phenomena. Speculative trading has no doubt occurred since the dawn of time, often fuelled by over confidence, greed, easy access to credit, and the siren song of watching other people get rich. As risk model providers, how can we deal with market data that is disconnected from underlying economic reality? In this presentation we discuss the innovative approaches Northfield has pioneered to make risk models adapt to real-world problems with market data, harnessing alternative data and looking for regimes in volatility.
Gautam Mitra, CEO, OptiRisk / UNICOM
Miquel Noguer, Head Of Development, Global Ai
We consider an application of reinforcement learning to create a financial model-free asset allocation paradigm which uses deep neural networks. For an asset universe of top 24 US stocks we show that the deep reinforcement learning approach gives better results than traditional portfolio management approaches. Our method uses a time series of daily data of stock prices and a simple reward function.
Raul Glavan, Consultant Artificial Intelligence & Asset Management | Trader | Speaker | UBI Enthusiast
Thomas Oesch, Senior Portfolio Manager, UBS
We study the relationship between news sentiment and stock risks and returns by applying news sentiment scores from four different datasets to a comprehensive global single stock universe. We find that the sentiment scores from the different datasets differ in terms of sources and sentiment scores, making them complementary as opposed to competing in a holistic portfolio analysis as we have undertaken.
Simon Wicks, Managing Director, Multi-Asset Quantitative Solutions, Charles Schwab
Thematic Investing has seen significant growth in recent years, driven by interest in both innovation and sustainability related themes. Natural Language Processing – from BM25 to BERT – can combine with human insight to power thematic research, increasing both the depth and efficiency of the research process.
Dan Joldzic, Ceo And Quantitative Researcher, Alexandria Technology
Extracting ESG Insights from News Institutional Newswires continually publish ESG events for companies, from company disclosures to independent editorial work identifying sustainable trends. NLP can scan a larger corpus of information that can be used to capture ESG events faster, which can then be paired with traditional ESG research to have a bigger picture of a company or companies.