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Automatic Detection of Relevant Information, Predictions and Forecasts in Financial News through Topic Modelling with Latent Dirichlet Allocation


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."

Deeper Inquiries

How can the proposed system be extended to analyze financial news in multiple languages?

To extend the proposed system to analyze financial news in multiple languages, several steps can be taken. Firstly, the system can be adapted to incorporate language detection capabilities to identify the language of the incoming news articles. This would allow for the implementation of language-specific processing pipelines tailored to each language. Additionally, the system can be trained on multilingual datasets to improve its ability to handle diverse languages. Utilizing pre-trained multilingual models for tasks such as co-reference resolution and named entity recognition can also enhance the system's language versatility. Moreover, incorporating translation services or APIs to translate news articles into a common language before analysis can further expand the system's language coverage.

What are the potential limitations of using LDA for relevance detection, and how could alternative topic modeling approaches be explored?

While LDA is a powerful tool for topic modeling, it has certain limitations when it comes to relevance detection in financial news. One limitation is that LDA assumes a fixed number of topics in the documents, which may not always align with the dynamic nature of financial news. Additionally, LDA may struggle with capturing subtle nuances and context-specific information present in financial texts. Alternative topic modeling approaches that could be explored include Latent Semantic Analysis (LSA), Non-Negative Matrix Factorization (NMF), and Hierarchical Dirichlet Process (HDP). These models offer different perspectives on topic modeling and may be better suited for capturing relevance in financial news by considering factors like word co-occurrence patterns, semantic relationships, and hierarchical topic structures.

How could the temporal analysis component be further improved to better capture the nuances of speculative language in financial news?

To enhance the temporal analysis component for capturing speculative language in financial news, several strategies can be implemented. Firstly, incorporating sentiment analysis techniques to assess the sentiment of speculative statements can provide valuable insights into the tone and context of the language used. Additionally, leveraging contextual embeddings or transformer-based models like BERT or GPT to understand the context of speculative language within the news articles can improve the accuracy of temporal analysis. Fine-tuning the temporal analysis model on a larger and more diverse dataset of financial news articles containing speculative language can also enhance its ability to identify and interpret such nuances effectively. Furthermore, integrating domain-specific lexicons or dictionaries related to financial speculation can aid in recognizing key terms and phrases indicative of speculative language.
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