Artificial Intelligence is deemed to be the main driver of the 4th Industrial Revolution. Investment in AI has grown at a phenomenal rate with companies investing $26-39bn in 2016. Adoption in 2017, however, remains low. As a result, this has spurred companies from every industry 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.
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. Application of AI in the financial service industry is fundamental to the competitiveness and automation process for years to come. This event is a sophisticated conference that not only interrogates and explores the implications of AI in the financial services industry but also goes on to identify the regional business and investment opportunities.
Additionally, 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 Covered Include:
Professor Pascale Fung, Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong
“Sorry I didn’t hear you” may be the first empathetic utterance by a commercial machine. As people increasingly interact with voice and gesture controlled machines, they expect the machines to recognize different emotions, and understand other high-level communication features such as humor, sarcasm and intention. To make such communication possible, the machines need an empathy module - a software system that can extract emotions from human speech and behavior and accordingly decide the correct response. This talk presents our work in the areas of deep learning of emotion and sentiment recognition, as well as humor recognition, using signal processing techniques, sentiment analysis and machine learning. It gives an overview of the future direction of android development and how it can help improve people's lives.
Xiang Yu, Business Development Techno Executive, and Gautam Mitra, CEO/Director, OptiRisk Systems/UCL, UK
We have created an innovative and dynamic trading strategy for equities, with a particular focus on controlling downside risk. The mathematical concept behind the approach is called stochastic dominance, where investment decisions are based on distributions rather than moments. A major contribution of news sentiment is in the prediction of future distributions. Regression analysis on news sentiment and regime switching models are employed to digest market moods and account for changing market situations.
Richard Peterson, CEO, MarketPsych Data, USA
Dr. Peterson will describe the unique characteristics of media sentiment data and approaches to financial price prediction with this data. The basics of media sentiment data, various modeling approaches, and their results (including live trading results) will be described in this talk. Viewers will gain an understanding of real-world modeling tips and techniques when dealing with noisy and inconsistent data such as media sentiment streams.
Juho Kanniainen, Professor of Financial Engineering at the Tampere University of Technology
Gurvinder Brar, Macquarie Research
Recent academic research (and our own work on US data) have found that analyst conference calls convey useful information not contained in earnings numbers and analyst forecasts. The slow reaction of markets to that type of information implies that sentiment, as expressed by analysts and management during the call, predicts returns. This effect is distinct from the well known post earnings announcement drift. We collected call transcripts for global companies from Factset going back to 2002. Using text mining techniques, we measure the tone of the management discussion and Q&A session of each call, with a goal of developing an alpha signal at low frequency. This presentation describes the strategy and findings of our research.
Arun Verma, Quantitative Researcher, Bloomberg LP
♦ 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
Barbara Fusinska, Data Scientist
Deep learning is the area that wins over the field of Artificial Intelligence. By using libraries like TensorFlow, it is now available to the wider audience. In this tutorial, Barbara will walk the audience through the process of creating several types of neural networks. The session will start with explaining key concepts of deep learning and introducing datasets the computation will be performed on. Along the way, attendees will have the practical opportunity to use TensorFlow to build deep networks, train them and evaluate the results. After the session, participants will become familiar with how to use TensorFlow when shaping the architecture of neural networks. By the hands-on form of the tutorial, the audience will have the chance to gain some first hand experience of how to apply deep learning to computer vision and natural language processing tasks.
Humberto Brandão, Data scientist
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.
Sebastien Lleo, NEOMA Business School