Artificial Intelligence is deemed to be the main driver of the 4th Industrial Revolution. IDC predicts that investment in AI will grow from $12bn in 2017 to $57.6 by 2021, while Deloitte Global predicts the number of machine learning pilots and implementations will double in 2018 compared to 2017. As a result, companies from every industry have been spurred on to seize the trend and innovate – from virtual assistants to cyber security to fraud detection and much more. The majority of C-level executives have identified and agree that AI will have an impact on their industry. However, only 20% of C-level executives admit they have already adopted AI technology in their businesses, according to research conducted by McKinsey. So, there is plenty of scope for change and improvement. The Finance industry is anticipated to lead the way in adoption of AI with a significant projected increase in spending over the next three years.
Until recently, practitioners have faithfully relied upon neo-classical models to measure performance, whether it’s in financial organisations or marketing corporations. AI is the new technology that offers an automated solution to these processes. It has the capability to replicate cognitive decisions made by humans and also remove behavioural bias adherent to humans.
Machine learning and sentiment analysis are specific techniques that are applied in AI. These techniques are maturing and rapidly proving their value within businesses. In order to process and understand the masses of data out there, machine learning and sentiment analysis have become essential methods that open the gateway to data analytics. To keep up with the ever-expanding datasets, it is only natural that the techniques and methods with which to analyse them must also improve and update.
This conference will help you to demystify the buzz around AI and differentiate the reality from the hype. Learn about how you can benefit from the unprecedented progress in AI technologies at this conference. Participants will be presented with real insights on how they can exploit these technological advances for themselves and their companies.
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 GARP CPD credit hours. If you are a Certified Financial Risk Manager (FRM®), please record this activity in your Credit Tracker.
Topics to be covered include:
Fundamentals and applications of machine learning and deep learning
Pattern classifiers, Natural Language Processing (NLP) and AI applied to data, text, and multi-media
Sentiment scores combined with neo-classical models of finance
Financial analytics underpinned by qualitative and quantitative methods
Predictive and normative analysis applied to finance
Behavioural and cognitive science
The future of AI and its impact on industries
Hear from leading international subject experts
Programme includes the latest state-of-the-art research, practical applications and case studies
Expect technical and in-depth presentations and discussions; we like to stimulate your brain cells!
Excellent networking opportunities throughout the days with all participants, including presenters, investors and exhibitors.
Meet your peers from…
UNICOM’s Code of Conduct & Views on Diversity
We at UNICOM strive to be a leading provider of knowledge to the business community and to engage the global business community as a specialised provider of knowledge. We strive to do this maintaining a culture of co-operation, commitment and trust. We want every UNICOM conference and training day to be a safe and productive environment for everyone – a place to share research and innovation and to build professional networks. To that end, we will enforce a code of conduct throughout all our events. We expect cooperation from all participants to help ensure a safe environment for everybody.
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 & Visiting Professor, UCL
Matthias Uhl, Executive Director in Analytics & Quantitative Modelling at UBS Asset Management
We show that sentiment from news articles can explain and predict movements in the term structure of U.S. government bonds. This effect is stronger at the short end of the curve, coinciding with greater volatility and investors’ need to continually reassess the Fed’s reaction function. Facing such uncertainty, market participants rely on news and sentiment as a central element in their decision-making process. Considering this dependence, we propose a new yield curve factor-news sentiment-that is distinct from the 3 established yield curve factors (level, slope, and curvature) as well as from fundamental macroeconomic variables.
Christina Erlwein-Sayer, Senior Quantitative Analyst, OptiRisk Systems
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.
Peter Schwendner, Professor, Zurich University of Applied Sciences
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.
Arun Verma, Ph.D, Senior Quantitative Researcher & Quant Solutions Team Lead, Bloomberg L.P.
♦ Extracting actionable information in the high volume, time-sensitive environment of news and social media stories
♦ Using machine learning to address the unstructured nature of textual information
♦ Techniques for identifying relevant news stories and tweets for individual stock tickers and assigning them sentiment scores
♦ Demonstrating that using sentiment scores in your trading strategy ultimately helps in achieving higher risk-adjusted returns
Gautam Mitra, CEO, OptiRisk & Visiting Professor, UCL, and Xiang Yu, Chief Business Development Officer, OptiRisk
We compute daily trade schedules using a time series of historical equity price data and applying the powerful mathematical concept of Stochastic Dominance. In contrast to classical mean-variance method this approach improves the tail risk as well as the upside of the return. In our recent research we have introduced and combined market sentiment indicators and technical indicators to construct enhanced RSI and momentum filters. These filters restrict the choice of asset universe for trading. Consistent performance improvement achieved in back-testing vindicates our approach.
Humberto Brandão, Head of R&D Lab, Federal University of Alfenas
It is not difficult to find academic papers showing how to make money easily using algorithmic trading, which includes graphs, statistical tests, etc. However, in real markets, the majority of them cannot be replicated. In this presentation, I will discuss some reasons for this problem and try to explain how to improve validation processes before applying an algotrader in real stock exchanges.
Kamel Nebhi, Head of NLP, AIEVE - PECULIUM
In this context, AIEVE is based on cutting-edge NLP techniques to extract semantic meaning and sentiment from large volumes of unstructured text from multiple sources such as social media or RSS feeds. In this presentation, we will present a Twitter sentiment analysis pipeline based on CNNs and LSTMs networks using fine-tuned word embeddings. We will show how these techniques help AIEVE to predict the cryptocurrency market with a higher level of accuracy to increase users savings. Peculium is the first Crypto-Savings platform that combines traditional savings, blockchain technology, cryptocurrency, and artificial intelligence. Indeed, Peculium is a platform that makes use of the AIEVE Artificial Intelligence technology to forecast the market price of several cryptocurrencies and giving real-time saving-portfolios advices.
Claus Huber, Founder and Managing Director, Rodex Risk Advisers
This presentation describes the application of Kohonen’s Self-Organising Maps (SOM), a method of Machine Learning, to the problem of selecting hedge funds to achieve stable portfolio performance. SOM can help to identify similarities in return structures of hedge fund managers and hence to avoid concentrations in a portfolio. The core question is if SOM can add any value for manager selection. Two novel yet simple methods to select hedge funds based on the specific properties of SOM are proposed, that both aim to identify unique investment strategies. To evaluate their performance relative to other, simpler benchmark methods of portfolio selection, a simulation study finds both SOM-based methods proposed enhance risk/return profiles and drawdown patterns.
Tobias Setz, CTO, OpenMetrics
Artificial Intelligence is about replacing human decision-making with sophisticated technologies. We will show how tactical asset management is being done by an AI instance based upon a real word application. The investment manager can therefore focus on designing the strategic asset allocation and new product development. It frees investment management from emotions and makes its results reproducible.
Ronald Hochreiter, WU Vienna University of Economics and Business & Academy of Data Science in Finance
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.
Anders Bally, Sentifi
This presentation is about how new AI methodologies like Deep Learning, the maturing Big Data Technologies and the fast emerging Information Sharing Culture can help investors to more efficiently discover, monitor and potentially predict Asset Valuation Drivers.
Francesco Cricchio, CEO, Brain and Matteo Campellone, Executive Chairman, Brain
Brain has developed a set of models based on machine learning methods to statistically classify assets that are more likely to have a positive/negative return over the following time period. Input data can be conventional series (fundamentals, financial time series) or non conventional series such as, for instance, sentiment indicators or signals coming from other proprietary models. This approach can be used for multi-stock trading strategies as well as for tactical asset allocation models.
Stefan Woerner, Research Staff Member and Technical Leader, Quantum Optimization, IBM Research - Zurich
In five years, the effects of quantum computing will reach beyond the research lab to solve problems once considered unsolvable.
In this talk I will introduce quantum computing and its applications in optimization and machine learning with a particular focus on the financial industry, where quantum computers could eventually help to speed up solving problems such as pricing, risk analysis, or portfolio optimization.
Ashish Kishore Bindal, CTO, Deeption SA
Alternative data sources and machine learning technologies can be used to understand the relationship between companies. In this study, we show how NLP and network analysis can help to discover the subtle signals to anticipate the movement in price of related securities.