Quant Strats 2024 Blog » Session Preview
Session preview: Data, a new asset to your portfolio
Glen High, Quantitative Analyst at Ostrum Asset Management, provides a preview to his upcoming talk at on day 2 of AI & Data Science in Trading at 1:35pm.
Session preview: The appeal and perils of quant assisted social listening - Alt data’s ground zero for information discovery
Chris Camillo, Social Arbitrage Investor, Co-Founder/Advisor at TickerTags explains the three categories for which applications for a non-financial social mention frequency analysis generally fall into ahead of his upcoming talk at 1.35pm on Monday 16 September at AI & Data Science in Trading.
Session preview: When ML doesn't work as expected, what went wrong and how can you recover?
Maurizio Garro, Senior Manager, Market, Credit & Risk at Lloyds Banking Group gives us an insight into the panel session he's part of taking place on day 2 of AI & Data Science in Trading at 4:15pm.
Session preview: Data distortions, dangers, and disasters
This talk is about what might be called the Achilles heel of data science. It is a general talk, making reference to algorithmic trading, but applying much more generally to the applications of machine learning, AI, and statistics in the modern world of what is often called “big data”.
Session preview: Nimbly pushing boundaries: The role of FinTech in commodity trading
Tristen Fletcher, CEO of ChAI gives us a preview of what to expect from the upcoming panel session taking place 17 September at 9:50am.
Over the past decade, the financial world has familiarised itself with technologies such as Artificial Intelligence (AI) and Blockchain. Today, the two are integral to services like fraud detection in banking and share trading analytics. What is the next frontier for FinTech? The vote is still out, but polls point in the direction of commodity trading.
Session preview: Enabling value extraction from limit order book data
Hugh Christensen, Founder & Head of Research at BMLL Technologies gives us a preview of what to expect from his upcoming session at AI & Data Science in Trading, taking place 16 September at 4:20pm.
L3 limit order book (LOB) data contains 30% more price discovery relevant information than L1 data. The problem is this data is difficult to work with and the information is hard to extract. While a small handful of very well resourced leading market participants have capitalized on being able to use this data over the last decade, most major financial institutions are still not able to.