September 16-17, 2020  |  Virtual Conferece

 

 

 
2000+
Attendees
250+
Speakers
75+
Exhibitors
120+
Sessions
 
1500+
Meetings

WHAT IS DATA & AI?

 

As more data becomes available, firms are finding unique ways to implement datasets into alpha generating strategies and as these datasets become more complex the use of AI and Machine Learning techniques are instrumental to avoid overfitting. Our Data & AI portions of the show will focus on proper use of data, standardization, and various use cases for AI and Machine Learning.

 

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DATA & AI AGENDA

 

Data, Thursday 3 September 2020

Panel discussion
Data
11:20

The Data Process – how data moves from research to production

·Sourcing – receiving data from research teams and outside vendors·Onboarding – having the right team and technology to bring in new data·Data monitoring – ensuring the highest quality input throughout the process·Execution – turning alpha signals into usable models that make trades
Data
12:00

Data Standardization – setting up the industry for easier use of market information

·Data descriptions – better understanding of datasets prior to vendor calls·Structured API – finding a common format for easy access to data to save time and reduce costs·Compliance – covering best practices with vendor onboarding
round tables
Panel discussion
Data
14:20

Data Sourcing – separating the relevant from the noise

·Web-scrapping – how is online behavior helping to develop untapped alphas?·Identifying credibility – finding appropriate sources to avoid ‘garbage in garbage out’·Vendor solutions – working with data providers to customize datasets
Panel discussion
Data
15:00

Data Infrastructure – determining between in-house and outsourced data

·In-house Storage – building data management systems that meet the demand of alpha generation·Outsourcing – which vendors have the best server connectivity for efficient analysis?·Integration – creating clear channels for new datasets to be most useful in conjunction with less recent data
Eric Taylor, Senior Systems Architect, Rotella Capital Management, Inc.
last published: 11/May/20 20:45

 

 

Data, Friday 4 September 2020

Panel discussion
Data
11:40

Smart Beta – identifying true factor exposure and understanding factor correlation

·Factor selection – deciding which inputs matter most in your analysis and how do you know if they are performing well?·Risk horizon – how do you allocate based on risk premia?·Keeping your edge - how can a strategy that everyone knows about still work?
Data
12:20

4) How to launch a CTA or fund

Data
12:20

5) Stat Arb – what factors matter most?

Data
12:20

6) Event-based Trading – running models based on current events and other macro issues

last published: 11/May/20 20:45

 

 

A.I., Thursday 3 September 2020

Panel discussion
A.I.
11:20

Testing fairness – finding the difference between alpha and coincidence

·Reading the data - what causes the positive performance?·Reliable algos – creating predesign equations to make your model reproducible·Finding the root cause – developing layers of AI to create confidence levels
Jim Creighton
A.I.
12:00

Measuring Prediction Accuracy – And Its Impact on Portfolio Construction

A.I.
12:20

7) Humans and Machines – how supervised/unsupervised should ML algos be?

A.I.
12:20

8) AI/ML for Forecasting – using new technologies for better price prediction models

A.I.
12:20

9) Variable Selection – influencing your models before inserting the dataset

Zak David, Machine Learning Editor, Algorithmic Finance
Panel discussion
A.I.
14:20

Transparency – how can algos using AI explain blackbox trading?

·TCA – discovering what factors are going into the decision-making process·Loopholes – how to address ML that takes advantage of grey areas and the dangers of doing so·Staying in charge – how do you make sure that your evolving AI doesn’t change the rules on you?
Panel discussion
A.I.
15:00

More than trading – other use cases for AI and machine learning

·Market Data Predictions – developing models to predict financial factors for trade-making decisions·Asset allocation – how can machine learning minimize risk and maximize returns?·Trade strategy layering – adding an AI-based layer designed to mitigate risk
last published: 11/May/20 20:45

 

 

Data, Friday 4 September 2020

Panel discussion
Data
11:40

Smart Beta – identifying true factor exposure and understanding factor correlation

·Factor selection – deciding which inputs matter most in your analysis and how do you know if they are performing well?·Risk horizon – how do you allocate based on risk premia?·Keeping your edge - how can a strategy that everyone knows about still work?
Data
12:20

4) How to launch a CTA or fund

Data
12:20

5) Stat Arb – what factors matter most?

Data
12:20

6) Event-based Trading – running models based on current events and other macro issues

last published: 11/May/20 20:45

 

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