25 citations · 45 across the 6 of their papers we have counts for
6 papers
Machine Learning Modeling to Evaluate the Value of Football Players
Chenyao Li, Stylianos Kampakis, Philip Treleaven
In most sports, especially football, most coaches and analysts search for key performance indicators using notational analysis. This method utilizes a statistical summary of events…
QuantNet: Transferring Learning Across Systematic Trading Strategies
Adriano Koshiyama, Sebastian Flennerhag, Stefano B. Blumberg +2
Systematic financial trading strategies account for over 80% of trade volume in equities and a large chunk of the foreign exchange market. In spite of the availability of data from…
Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination
Adriano Koshiyama, Nick Firoozye, Philip Treleaven
Systematic trading strategies are algorithmic procedures that allocate assets aiming to optimize a certain performance criterion. To obtain an edge in a highly competitive environm…
A Machine Learning-based Recommendation System for Swaptions Strategies
Adriano Soares Koshiyama, Nick Firoozye, Philip Treleaven
Derivative traders are usually required to scan through hundreds, even thousands of possible trades on a daily basis. Up to now, not a single solution is available to aid in their…
In Quest of Significance: Identifying Types of Twitter Sentiment Events that Predict Spikes in Sales
Olga Kolchyna, Th'arsis T. P. Souza, Tomaso Aste +1
We study the power of Twitter events to predict consumer sales events by analysing sales for 75 companies from the retail sector and over 150 million tweets mentioning those compan…
Twitter Sentiment Analysis Applied to Finance: A Case Study in the Retail Industry
Thársis Tuani Pinto Souza, Olga Kolchyna, Philip C. Treleaven +1
This paper presents a financial analysis over Twitter sentiment analytics extracted from listed retail brands. We investigate whether there is statistically-significant information…