Abstract: A system configured to: (a) retrieve structured and unstructured data from one or more external data sources, the structured data including time-series data on a financial instrument and the unstructured data including words; (b) analyze the unstructured data to determine a sentiment measure for the financial instrument; (c) analyze the structured data to obtain a training dataset; (d) train a neural network model with the training dataset such that the neural network can provide a predicted price of the financial instrument for a future timestamp; and (e) provide a decision for managing the financial instrument based at least in part on the sentiment measure for the financial instrument, the predicted price of the financial instrument, and a current holding of the financial instrument.

Abstract: A system configured to: (a) retrieve structured and unstructured data from one or more external data sources, the structured data including time-series data on a financial instrument and the unstructured data including words; (b) analyze the unstructured data to determine a sentiment measure for the financial instrument; (c) analyze the structured data to obtain a training dataset; (d) train a neural network model with the training dataset such that the neural network can provide a predicted price of the financial instrument for a future timestamp; and (e) provide a decision for managing the financial instrument based at least in part on the sentiment measure for the financial instrument, the predicted price of the financial instrument, and a current holding of the financial instrument.

Abstract: Methods, systems, computer-readable media, and apparatuses for optimizing and distributing discounts are presented. In some embodiments, a computing device may receive a request for a randomized discount. In response to receiving the request for the randomized discount, the computing device, may select a merchant from a hub that includes at least two merchants grouped based on geographical proximity of business locations of the at least two merchants. Subsequently, the computing device may determine a discount for the selected merchant. The computing device then may cause the determined discount for the selected merchant to be presented.

Abstract: Methods, systems, computer-readable media, and apparatuses for optimizing and distributing discounts are presented. In some embodiments, a computing device may receive a request for a randomized discount. In response to receiving the request for the randomized discount, the computing device, may select a merchant from a hub that includes at least two merchants grouped based on geographical proximity of business locations of the at least two merchants. Subsequently, the computing device may determine a discount for the selected merchant. The computing device then may cause the determined discount for the selected merchant to be presented.