Big data analysis of futures trades
Whether an entity seeks to create trading algorithms or mitigate risk, predicting trade volume is an important task. 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.
| Talk Title | Big data analysis of futures trades |
| Speakers | Tobi Bosede (Johns Hopkins) |
| Conference | Strata Data Conference |
| Conf Tag | Make Data Work |
| Location | New York, New York |
| Date | September 26-28, 2017 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
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.
