Core Concepts
This work proposes a novel Natural Language Processing (NLP) system to assist investors in the detection of relevant financial events in unstructured textual sources by considering both relevance and temporality at the discursive level.
Abstract
The authors present a novel NLP system for the automatic detection of relevant financial events in unstructured textual sources. The system comprises the following key steps:
Multi-paragraph topic segmentation: The TextTiling algorithm is used to group closely related text into coherent segments.
Co-reference resolution: The Neural Network by Clark et al. (2016) is employed to replace references with meaningful words, improving the performance of the subsequent LDA stage.
Tag processing: Financial terms are detected, homogenized and replaced with appropriate tags to prepare the input for the LDA stage.
Relevant text detection with LDA topic modelling: LDA is used to differentiate between relevant and less relevant information in the text segments. A topic score ρ is defined to represent the density of significant tags, and an LDA score threshold δ is introduced to improve the precision of the LDA algorithm in detecting relevant text.
Temporal analysis: Dependency and proximity analyses are performed to extract temporal features, which are then used to train a Linear Support Vector Classifier (SVC) to estimate the temporality (past, present, future) of a segment.
The proposed solution was evaluated on a data set of 2,158 financial news items manually labeled by NLP researchers. The results show that the system outperformed a rule-based baseline, with ROUGE-L values of 0.662 and 0.982 for the identification of relevant text and predictions/forecasts, respectively.
Stats
ticker (stock:ticker_abr) is worth at least 55% more than ticker_abr stock price today
ticker reported "boring" earnings, according to Barron's magazine for Q2 on July 24
ticker earnings on a non-fin_abr adjusted basis was num per share
ticker generated higher cash flow
first-half 2020 cash flow from operations of $23.6 billion, an increase of $7.7 billion from first-half of 2019
this free cash flow (ticker_abr) in the first half was $13.7 billion, an increase of 74.1 percent year over year
ticker trades for a paltry 12.3 times this year's expected earnings and just 12 times next year
Quotes
"boring is good" in this market
"The communications business is not a bad place to be in a pandemic."