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 also had an impact on traditional order. The rise of AI and Machine learning has enhanced 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.
Theme: To be announced.
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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.
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Gautam Mitra, CEO, OptiRisk / UNICOM
Blair Hull, Founder And Chairman, HULL TACTICAL
Applying what has been called a disruptive innovation, Mr. Hull beat the casinos counting cards in blackjack in the 1970s. He parlayed this success into the securities business, building a flagship global options market firm acquired by Goldman Sachs in 1999. Today Mr. Hull applies AI, ML, and the latest academic findings to guide investment decisions and alpha creation in futures and options trading at 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
In computational finance, numerical methods are commonly used for the valuation of financial derivatives. These price models are often multi-dimensional and heavy from the time consuming view point.
Artificial Neural Networks (ANNs) with multiple hidden layer became successful machine learning methods to extract features and detect patterns from a large data set.
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.
Moderator: Gautam Mitra
Panellist: Blair Hull, Pierce Crosby, Dr. Ernie Chan, Thomas Oesch, Andrea Nardon
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.
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
Moderator: Gautam Mitra
Panellist: David Jessop, Kevin Spellman.
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.
Prof Gautam Mitra, Ceo, Optirisk Systems/Visiting Professor, Ucl
MIQUEL NOGUER I ALONSO, 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.
Dr. G. Kevin Spellman, Senior Advisor to Institutional Shareholder Services | Senior Lecturer and David O. Nicholas Director of Investment Management at University of Wisconsin-Milwaukee | Adjunct Professor at IE Business School
ESG investing can take on several forms. One can invest in companies to make an impact, avoid certain industries, and in recent research, it has been shown that a focus on firms with good ESG may enhance returns. That is, being good to the world may be consistent with being good to shareholders. This presentation will show how:
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.
Moderator: Gautam Mitra
Panellists: Dr. G Kevin Spellman, Dan Joldzic, David Jessop, Dan Dibartolomeo.
Hilton Zurich Airport