Abstract
We explore the predictive power of historical news sentiments based on financial market performance to forecast financial news sentiments. We define news sentiments based on stock price returns averaged over one minute right after a news article has been released. If the stock price exhibits positive (negative) return, we classify the news article released just prior to the observed stock return as positive (negative). We use Wikipedia and Gigaword five corpus articles from 2014 and we apply the global vectors for word representation method to this corpus to create word vectors to use as inputs into the deep learning TensorFlow network. We analyze high-frequency (intraday) Thompson Reuters News Archive as well as the high-frequency price tick history of the Dow Jones Industrial Average (DJIA 30) Index individual stocks for the period between 1/1/2003 and 12/30/2013. We apply a combination of deep learning methodologies of recurrent neural network with long short-term memory units to train the Thompson Reuters News Archive Data from 2003 to 2012, and we test the forecasting power of our method on 2013 News Archive data. We find that the forecasting accuracy of our methodology improves when we switch from random selection of positive and negative news to selecting the news with highest positive scores as positive news and news with highest negative scores as negative news to create our training data set.
Highlights
With the latest technological developments and advancement in data analytics, financial professionals and economists have increasingly explored new artificial intelligence and machine learning approaches to enhance financial market forecasting results
The promising results obtained using artificial intelligence and deep learning have attracted the attention of the finance and economics researchers in a quest to improve economic forecasting results
Among the pre-trained word vectors provided by global vectors for word representation method (GloVe) [5], we choose the one created from Wikipedia 2014 and Gigaword 5 [4] corpus, which contains 400K words represented by vectors in 200 dimensions
Summary
With the latest technological developments and advancement in data analytics, financial professionals and economists have increasingly explored new artificial intelligence and machine learning approaches to enhance financial market forecasting results. Qualitative inputs such as the news, corporate earnings’ reports, corporate press releases, and regulatory announcements play an important role in shaping the decisions of central bankers, economic strategists, investment professionals, securities traders, and portfolio managers regarding global investment decisions, portfolio re-balancing, as well as exploring new investment products and opportunities. The promising results obtained using artificial intelligence and deep learning have attracted the attention of the finance and economics researchers in a quest to improve economic forecasting results
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
