Artificial Intelligence and Machine Learning (AI & ML) and Sentiment Analysis are said to “predict the future through analysing the past” – the Holy Grail of the finance sector. They can replicate cognitive decisions made by humans yet avoid the behavioural biases inherent in humans.
Processing news data and social media data and classifying (market) sentiment and how it impacts Financial Markets is a growing area of research. The field has recently progressed further with many new “alternative” data sources, such as email receipts, credit/debit card transactions, weather, geo-location, satellite data, Twitter, Micro-blogs and search engine results. AI & ML are gaining adoption in the financial services industry especially in the context of compliance, investment decisions and risk management.
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.
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 7 credit hours. If you are a Certified ERP® or FRM®, please record this activity in your Credit Tracker.
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.
The author identifies and explains asymmetric reactions in the implied volatility of S&P 500 Index options across the term structure based on news sentiment. The asymmetry of the reaction is more pronounced for fear (proxied by put options) than for greed (proxied by call options). This asymmetry is termed factor volatility aversion, which is more pronounced the shorter the time to maturity of the option.
In this talk we are addressing some issues associated with FX volatility surface construction. Regular economic data releases, as well as one-off events like elections, can drastically change the implied volatility profile. On the Bloomberg terminal users may manipulate volatilities by adjusting weights for those events in the function VCAL. We describe here a methodology on how to obtain those economic weights. In addition, we use sentiment data to help us better gauge the relative importance between these events.
The mortgage market in the US is the perfect candidate for the application of machine learning techniques, being such a sizeable market with vast amounts of data open to the public. In the US, mortgages are bundled, sold by the Government Sponsored Agencies (GSEs), who guarantee the investors against mortgage defaults. Typical agency prepayment models include many effects, but do not implicitly consider defaults due to this guarantee. We have shown that applying machine learning techniques produces good predictive power on defaults in agency pools. In this talk I will briefly introduce the mortgage markets, define the problem that we are solving, and show the results of the study. Given the successful outcomes, more ambitious projects are planned and will be discussed.
Finding an edge is hard because traditional data sources are widely available, have time lags and in some instances are manually aggregated. In addition to that, asset valuations shift constantly, and those who have the earliest information signals benefit the most. We address these challenges by commingling traditional and alternative data – enabling investors to identify market outliers and early indicators for future price movements.
This talk will answer the following questions:
A very valuable feature of the Self-Organising Map, a method of Machine Learning, is its visualisation capabilities. We show how the Self-Organising Map can be deployed to visualise the risk structure in a portfolio, in particular for assets for which no risk models exist. Some examples to this end are the visualisation of risk concentrations, identifying diversifiers and scenario analysis. Real-world applications are the selection of hedge fund managers or the analysis of a portfolio of Alternative Risk Premia.
We present a framework for ex-ante analysis of the USD denominated EM Sovereign spreads and respective currencies based on the flow of news in global and local media. The public domain GDELT data is currently being used, but the framework is agnostic to data source, and can be adopted to any data source. We will discuss use cases pertaining to selected countries and spearhead the discussion about predictive qualities of the produced analytics.
In December of 2017 Northfield introduced the first commercially available factor risk models that incorporates computerized analysis of news text directly into volatility risk forecasts for individual stocks, corporate bonds, industry groups and ETFs based on market indices. Market events in early 2018 provided several excellent examples of why we believe that Risk Systems That Read® is the most significant innovation in factor risk models in more than three decades. We will illustrate show how recent news events drove financial market outcomes for Wynn Resorts, Wynn Macau, Facebook and Wanda Hotels (HK). Each day the content of thousands of news articles are now part of the input for the full range of models available from Northfield. The line of research that led to this innovation stretches back to 1997, and includes five published papers by Northfield staff [diBartolomeo and Warrick (2005), diBartolomeo, Mitra, Mitra (2009), diBartolomeo (2011,2013,2016)]. Beyond the obvious improvement in risk estimation, the method has important implications for alpha generation by both quant and traditional for active managers.
In this talk Dr. Richard Peterson describes how media analytics are providing new insights into the origins and topping process of asset price bubbles. Examples from price bubbles including the China Composite, cryptocurrencies, housing, and many others will be explored. Recent mathematical models of bubble price action will be augmented with sentiment analysis. Attendees will leave with new models for identifying and taking advantage of speculative manias and panics.
Based on our proprietary Augmented Language Intelligence Technology we are empowering Financial Experts to excel in their jobs by always being on top of the latest news development around their investment and customers.
We present Brain proprietary signals based on the application of machine learning and natural language processing techniques:
1) The BSR signal is a daily stock ranking based on a supervised machine learning model that uses an ensemble of features related to market regimes, stock fundamentals, prices and volumes, calendar anomalies. The model can be customized with the specific investable universe, the rebalancing frequency and the investment style.
2) The Brain Sentiment Indicator (BSI) is a daily stock ranking based on the natural language processing analysis and sentiment extraction from the financial news on a global scope.
AI and Machine Learning methods can be used to generate investment decisions successfully. A clever combination of Data Science methods with methods from the field of Decision Science (Prescriptive Analytics) may lead to even more successful models. In this talk a general outline for such a successful methodological combination will be presented as well as a concrete novel Deep Learning investment model which is based on graphical TTR series representations instead of using time-series directly. It will be shown how important Feature Engineering for Deep Learning in Finance actually is.
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