World Business StrategiesServing the Global Financial Community since 2000
08.30 - 09.00
Morning Welcome Coffee
09.00 - 09.45
Keynote: Applying Machine Learning to Investment and Wealth Management: Opportunities and Challenges

Cristian Homescu:

Director, Portfolio Analytics Bank of America Merrill Lynch

09.45 - 10.30
Actionable Artificial Intelligence

Abstract: Artificial intelligence (AI) algorithms typically operate in high-dimensional spaces, are trained on vast and often heterogeneous data sets, and involve non-linear models that attempt to capture previously unknown patterns. In areas where the results of such algorithms are filtered through human decisions, insight into the pattern generation processes is key to fully capitalizing on the power of AI. Actionable AI (AAI) aims to build trust between collaborating human and software agents. In Finance, AAI is paramount not only to addressing regulatory requirements for transparency, but also to the widespread adoption of AI methods in areas largely driven by human decisions. In this presentation, we delve into the challenges and state-of-the-art solutions for AAI in the context of quantitative modeling for investment management decisions.

Ioana Boier:

10.30 - 11.00
Morning Break and Networking Opportunities
11.00 - 11.45
Using Bayesian Machine Learning as an Investment Strategy

Alexander Fleiss:

CEO, Rebellion Research – The Ai Machine Learning Robo Advisor

11.45 - 12.30
Big Data and Machine Learning for Global Macro & FX Strategies

Richard V. Rothenberg:

Global AI Corporation & Research Affiliate, Lawrence Berkeley National Laboratory

12.30 - 13.30
Lunch
13.30 - 14.15
“AI-Powered Algorithmic Trading”

Yves Hilpisch:

CEO Of The Python Quants

14.15 - 15.00
Investigating what volatility of news sentiment - and other NLP-driven measures - can tell us about market volatility

Abstract:

An investigation of how aggregate measures of news article sentiment can prefigure changes in market volatility. This will use a variety of natural language processing techniques – including Sentiment Analysis and doc2vec – to determine aggregate measures of sentiment for an individual stock. The measures are used as an input to a neural network classifier to predict whether the article will lead to market changes.

James Baker:

Product Manager, Suite, LLC

15.00 - 15.05
Afternoon Coffee
15.05 - 15.50
Applications of Artificial Intelligence in Algorithmic Trading

An introduction of computing has revolutionized the financial trading industry, making it possible to track market changes and execute an enormous number of orders at a speed of light. Today, AI-driven trading systems are launching the next wave of innovation that will result in the most significant transformation in finance history. In this talk, Vladyslav Ivanov is going to introduce the audience to the development and production level algorithmic trading pipelines and their primary components. The talk will cover how the three primary subsets of Artificial Intelligence: Machine Learning, NLP, and Vision are being used in an effort to gain better market insights and ultimately a trading advantage. Topics as fetching and storing the time-series financial data, application of alternative data, and aspects of the financial markets will also be discussed. Finally, we will conclude by talking about using AI in conjunction with traditional models for describing market volatility, such as GARCH.

Vladyslav Ivanov:

Quantitative Researcher, Outremont Technologies

15.50 - 16.30
Mining News Topics With pICA

Abstract:

A suitably ICA-corrected Latent Semantic Analysis (pICA) is shown to produce linear factors on text data which are maximally parsimonious according to a specific criterion. The model produces stable and mostly interpretable unsupervised factors for Bloomberg Story-Level data for both News and Twitter feeds. A wide variety of applications of the model are discussed, ranging from factor-specific sentiment aggregation to theme discovery and tracking. Connections to neural net methods are clarified.

Ivailo Dimov:

Quant and Data Science Research, Bloomberg LP – Adjunct Professor, NYU Courant

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    When two colleagues attend the 3rd goes free!

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    (FULL-TIME Students Only)

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