“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 6: Performance of all models on forecasting market movement as varying the daily latest time up to which news headlines are collected.
['Models', '9:00', '15:40', '19:00', '23:59']
In order to further observe the impact of textual news on the models’ classification accuracy, we vary the latest time, up to which the daily news headlines are collected, to 9:00 (before market open), 15:40 (before market close), 19:00, and 23:59 on the same day the market performance is predicted. A clear trend is seen that, the prediction accuracy is higher as the news articles are collected closer to the market closing time, which confirms the indicative information embedded in the textual news toward predicting the S&P500 performance. While most of neural models tend to perform feature engineering better than that of SVM, only our MSIN can further offer better interpretation due to its join-training of both evolving time series and the news articles. It is noted that evaluations at the time stamp of 19:00 (or 23:59) will shift a model from a predictive system to a purely explanatory one.