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.
Katharina Schwaiger, Investment Researcher, BlackRock
Environmental, Social, and Governance (ESG) signals are an important part of factor-based investing strategies as they can stem from the same economic rationales as general factor premiums. Because factors are broad and diversified, building portfolios by jointly optimizing factor exposures with ESG and carbon outcomes results in similar historical performance as benchmark factor portfolios which do not include those considerations. We show how sustainable signals, which often involve alternative data, can be integrated in the definitions of factors themselves: we offer two examples on green intangible value and corporate culture quality which enhance traditional financial value and quality factors, respectively.
Dan Joldzic, Alexandria Technology
♦ Technology borrowed from the domain of DNA identification
♦ Design to identify cause and effect in large datasets
♦ Analysis of immense quantity of genomic information
♦ This AI&ML base technology applied to financial analytics
Factor investing is an investment approach that involves targeting specific drivers of return across asset classes. There are two main types of factors: macroeconomic and style. Investing in factors can help improve portfolio outcomes, reduce volatility and enhance diversification. Factors has the transformative ability to change the way that we efficiently invest, deliberately manage risk and holistically build portfolios.
Investment management, is it discretionary or systematic, can benefit from insights gained in behavioral finance. Markus will highlight why professional investors tend to talk more about behavioral finance in investment management than actually make use of its practical takeaways in favor of more rational decision making.
On a single day, humans across the globe produce 500 million tweets, 4 million blogs and 2 million online news.
That’s why in the age of big data, the real challenge is to make sense of it by filtering out the noise and finding relevant signals. In this session, we show you how we extract actionable insights and how these help you to stay ahead of the curve.
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.
Moderator: Alexander Eisele, Analytics & Quant Modelling, UBS
Dan Joldzic, CEO, Alexandria
Lucas Bruggeman, Partner, Sentifi
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.
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.
Local source, native publishers may offer an information advantage compared to publications in English. Translation services have typically been sub-optimal for character-based languages, but machine learning allows for classification in the native form, which can lead to significant alpha in forward periods.
Sovereign bond spreads are modelled taking into account macroeconomic news sentiment. We investigate sovereign bonds spreads of European countries and enhance the prediction of spread changes by including news sentiment. We conduct a correlation and rolling correlation analysis between sovereign bond spreads and accumulated sentiment series and analyse changing correlation patterns over time. These findings are utilised to monitor sovereign bonds, predict spread changes in an ARIMAX model and highlight changing risks. The results are integrated in the SENRISK tool, a DSS for Bond Risk Assessment.
Institutional multi-asset portfolios are often managed with significant constraints on turnover, tracking errors and the investable asset universe. Does news sentiment add any value to a portfolio when such constraints are taken into account? In this session we provide and discuss evidence suggesting that it does. Furthermore, we decompose news sentiment into different components to learn more about the drivers of its value-added.
European sovereign bonds are especially sensitive to the political news flow. Consistent to the current sentiment, market makers adjust factor models in their quotation systems to be prepared for short-term market reactions in the most liquid instruments. We present a correlation influence network case study to make the signs of these factor betas transparent using intraday data analysis. This shows the sentiment of the most active market participants.
If India is rapidly emerging as a world power in FinTech, full credit must be given to the digital payments revolution unleashed by the Unified Payment Interface (UPI). Designed and launched by the National Payments Corporation of India (NPCI), the volume of UPI transactions has exploded in the last one year, surpassing all expectations. What is fuelling the UPI revolution? Why are Banks and FinTechs adopting UPI at an unprecedented rate? What do consumers like so much about it? This presentation will provide a bird’s eye view of the UPI growth machine.
To gain an edge in the markets quantitative hedge fund managers require automated processing to quickly extract actionable information from unstructured and increasingly non-traditional sources of data. The nature of these “alternative data” sources presents challenges that are comfortably addressed through machine learning techniques. We illustrate use of AI and ML techniques that help extract derived signals that have significant alpha or risk premium and lead to profitable trading strategies.
This session will cover the following topics:
– Reformulate deep learning as an optimisation problem
– Discuss the importance of stability for robust solutions.
– Illustrate the use of deep learning to solve high dimensional (more than 100 dimensions) nonlinear parabolic PDEs (Black&Scholes, Hamilton-Jacobi Belman)
– Provide code and some examples for participants to experiment with.
Markets around the globe exhibit strong varying intraday characteristics. As a consequence, modelling the underlying intraday market dynamics is crucial in optimising trading execution. In this talk, we discuss the effect that modelling intraday flow co-movement and intraday risk have in creating optimal trade schedules, while also taking into consideration the individual stock’s market microstructure, providing useful insights. Our methodology relies on unsupervised learning techniques to identify the most important drivers of intraday market dynamics at stock level.
Established nowcasting techniques generally use as inputs two types of economic data: directly quantifiable variables, mostly referred to as hard data, and survey-based measures of the economy, or soft data. In this presentation, we increase the dimensionality of the nowcasting input data set by introducing a third type of data: machine-readable news analytics based on textual analysis. We propose to exploit the quantitative information conveyed by machine-readable news, transforming these analytics into proxies for macro-driven sentiment values. To retain tractability and facilitate the interpretation of this novel type of data, we aggregate the high–dimensionality of this information set into an established taxonomy of economically identifiable sentiment proxies; through this, we are able to map them into the same categories in which we group hard and soft data. We concentrate on U.S. economy as the focus of our analysis. The wider objective here is to extract and incorporate additional information relevant for tracking current and future economic conditions, but also and foremost to provide an empirical method to shed light on the interaction between macro news-flow sentiment, the real economy and asset prices fluctuations.
We analyze how AI and Machine Learning and Sentiment Analysis of News and Micro-blogs are and impacting the two rapidly expanding markets, namely, Financial market and Retail market. We support our analysis by a few Use Cases for these markets.
StockTwits is the largest independent social network setup for investors and traders to talk about investing. In addition to covering 8,300 stocks per year, the network also discusses 1,500+ alternative assets, including FX, futures, fixed income, privative companies, ETFs/indexes, and cryptoassets. With a dataset that stretches back to 2009, the network becomes a rich dataset for both quantitative investing as well as model development. In this talk, we will discuss the methodology behind developing an NLP-based social signal, as well as some of the academic studies run in parallel with this research. We will also discuss some of the ways in which it is being deployed in markets today.
Moderator: Enza Messina, Professor in Operations Research, University of Milano-Bicocca
Dorothy Ruderman, Head of Data Partnerships, StockTwits
Katharina Schwaiger, Investment Researcher, BlackRock
Erica Stanford, CEO, Crypto Curry Club
Monica Summerville, Director of FinTech Research, Tabb Group
We will present results of the Kaggle Two Sigma News Analytics Competition. This competition provides both prices and Thomson-Reuters news sentiment data as input to predict the future returns of US stocks. We want to answer questions such as whether machine learning techniques truly outperform simple factor models, and whether news sentiment truly adds value. Our presentation will not only include our own research, but also highlights other high-performing competitors research as well.
As a generic technology, machine learning has numerous secondary innovations in a lot of sectors. In this talk I will discuss the innovations emerging (or to appear) for financial markets. I will provide one example of online learning on trading flows (for real-time Dark Pools selection), another one of now casting (satellite images processing for crop quality prediction), and the last one will be on human-machine interface (decision support to monitor hundreds of algorithms). This last example is significant of one of the greatest challenge linked to the use of AI: when and how to give back the control to humans, so that then can really take an enlighten decision.
Moderator: Monica Summerville, Director of FinTech Research, Tabb Group
Arun Verma, Quantitative Research Solutions, Bloomberg
Francesco Cricchio, Executive Chairman, Brain
We present Brain proprietary solutions to some common financial problems using machine learning techniques.
In this talk Anthony Luciani 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.
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