“The Squawk Bot”The name of this work is inspired by the popular financial commentary show called “Squawk Box” on CNBC (CNBC. Squawk Box: Business, Politics, Investors and Traders, https://www.cnbc.com/squawk-box-us/ , accessed May 23rd, 2019): Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering
Table 5: Performance of all models on forecasting market movement. Precision and recall are reported w.r.t. two prediction of up and down market respectively.
['Models', 'Acc.', 'Pre.', 'Rec.']
Two values in each entry of precision and recall columns are respectively reported for the forecasting of up and down market on the next day respectively. As observed, there is not much difference in the prediction accuracy of GRUtxt and CNNtxt though they analyze the textual news with different network topologies. Without exploiting the temporal order in the time series and the semantic dependencies of textual news, SVM’s performance seems less comparable to its counterpart neural networks. Out of all models, our MSIN achieves higher prediction accuracy. Its solution also converges to a more balanced classification boundary as reflected in the precision and recall measures.