Whether an entity seeks to create trading algorithms or mitigate risk, predicting trade volume is an important task, but one that comes with a number challenges—one of which is the sheer size of the data. Still, companies seek meaningful insight that can be used to forecast price volatility using trade volume and understand whether trade volume versus price volatility relationships support the theories of market agents such as speculators and hedgers. Focusing on futures trading that relies on Apache Spark for processing the large amount data, Tobi Bosede considers the use of penalized regression splines for trade volume prediction and the relationship between price volatility and trade volume.
Tobi Bosede is a machine learning engineer at Johns Hopkins University. Previously, she was a reviewer for Bayesian Methods for Hackers and taught R programming at Johns Hopkins University and Python programming for General Assembly. Tobi’s professional work spans multiple industries, from telecom at Sprint to finance at JPMorgan. She holds a bachelor’s degree in mathematics from the University of Pennsylvania and a master’s in applied mathematics and statistics from Johns Hopkins University.
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