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UID:pretalx-juliacon2023-GDRTNK@pretalx.com
DTSTART;TZID=EST:20230728T110000
DTEND;TZID=EST:20230728T113000
DESCRIPTION:This talk will demonstrate the use of the MLJ.jl package in bui
 lding a machine-learning pipeline for a dataset of property loans. The goa
 l is to predict which loans might default and build a strategy to minimize
  losses. Several machine learning models such as ElasticNet\, XGBoost\, an
 d KNN will be explored\, and then combined into a stacked model. I will al
 so show how the output of these models can be used to drive investment dec
 isions and the final results of the strategy. This talk will provide a
DTSTAMP:20260609T052145Z
LOCATION:26-100
SUMMARY:Machine Learning Property Loans for Fun and Profit - Dean Markwick
URL:https://pretalx.com/juliacon2023/talk/GDRTNK/
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UID:pretalx-juliacon2023-DBSAMT@pretalx.com
DTSTART;TZID=EST:20230728T115000
DTEND;TZID=EST:20230728T120000
DESCRIPTION:This talk will explore using Julia to simulate the Request for 
 Quote (RFQ) trading method. RFQ is a trading method that puts counterparti
 es in competition by asking banks for prices to buy or sell an asset. I wi
 ll simulate the Executing in an Aggregator model (Oomen 2017) and demonstr
 ate why Julia's high performance and ease of use make it a perfect choice 
 for simulating this type of trading. I'll finally show how we can learn fr
 om these simulations\, educate clients and guide pricing strategies.
DTSTAMP:20260609T052145Z
LOCATION:32-124
SUMMARY:Simulating RFQ Trading in Julia - Dean Markwick
URL:https://pretalx.com/juliacon2023/talk/DBSAMT/
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