FISH : A Financial Interactive System for Signal Highlighting (2023)

Abstract: In this system demonstration, we seek to streamline the process of reviewing financial statements and provide insightful information for practitioners. We develop FISH, an interactive system that extracts and highlights crucial textual signals from financial statements efficiently and precisely. To achieve our goal, we integrate pre-trained BERT representations and a fine-tuned BERT highlighting model with a newly-proposed two-stage classify-then-highlight pipeline. We also conduct the human evaluation, showing FISH can provide accurate financial signals. FISH overcomes the limitations of existing research andmore importantly benefits both academics and practitioners in finance as they can leverage state-of-the-art contextualized language models with their newly gained insights. The system is available online at https://fish-web-fish.de.r.appspot.com/ , and a short video for introduction is at https://youtu.be/ZbvZQ09i6aw .

## introduction financial statements document the business activities and financial performance of a company. for example, the 10-k fillings required by sec 1 are regulatory documents required of all public companies and are typically composed of several sections each. considerable time and human resources are needed to digest such long and complicated texts. accordingly, efficient analysis of complex and condensed documents is critical for financial practitioners. in this work, we introduce fish-a financial interactive system for signal highlighting-as an effective and efficient system to review financial reports. one common scenario in practice is when a company's report has just been released: financial professionals such as financial analysts and accountants must skim through the report and quickly pre-our most critical accounting policies relate to revenue recognition, inventory, pension and other postretirement benefit costs, goodwill, other intangible assets and long-lived assets and income taxes. ## (reference) our most critical accounting policies relate to revenue recognition, inventory, pension and other postretirement benefit costs, goodwill, other intangible assets and long-lived assets and income taxes. we first recognize two challenges in the literature concerning textual information in long and complicated financial reports: (1) many parts of a financial report are minor or even trivial; (2) it is difficult to utilize coarse information in empirical applications. to address these challenges, we propose a multi-stage financial analysis pipeline composed of two modules: a segment classifier and a segment highlighter. to tackle the first one, we leverage the year-toyear structure of the annually released financial statements of a company. for example, as shown in for the second challenge, the segment highlighter module provides straightforward and finegrained signals in segments identified as the third type-revised segments-which are considered those revised from segments in the reports of the reference year. specifically, this module highlights words in such segments by predicting the word importance based on the semantic context of the financial report and the differences between the two segments in a year-to-year pair. to accomplish this, we adopt contextualized representations from the pre-trained language model @xcite and further fine-tune the proposed module with a supervised token classification task. in this demonstration, we showcase fish, an interactive system to help financial professionals effectively and efficiently skim through financial reports in a straightforward manner. fish is technically supported by the proposed two-module pipeline. in particular, we use financial 10-k reports collected by @xcite to demonstrate our idea. fish better visualizes the segment classifications in a target-year report and provides fine-grained information highlighting the essential information for the revised segments for financial professionals to review and analyze carefully. ## background and related work traditionally, research on financial statements focuses on quantitative data such as stock prices or other financial metrics. textual information such as operation calls and forward-looking statements in reports are rarely carefully considered in conventional finance literature. pioneering studies in both finance and computer science literature first adopted statistical or machine learning methods to identify crucial information in text data in financial reports. for example, @xcite compile a large amount of 10-k reports and construct a finance-specific sentiment lexicon. moreover, @xcite ; @xcite leverage the sentiment signals in textual data to investigate relations between quan-titative and textual information. more recently, distributed representation techniques have been introduced to analyze financial reports @xcite @xcite . recent advancements in natural language processing (nlp) techniques have made it possible to develop useful information systems that can analyze textual information in financial reports. for example, @xcite leverage variants of pretrained word embedding models to identify financial risks and cues to support financial analysis. @xcite integrate multiple representations of 10-k reports and further infuse financial sentiment aspects into word and sentence representations. hive @xcite ) is an interactive system utilizing an attention mechanism to explore insights from financial reports. however, existing systems do not effectively address the two challenges mentioned earlier, nor do they utilize stateof-the-art and dominant deep contextualized language models such as bert @xcite and its variants as their back-end engine. ## financial data and pre-processing the form 10-k financial statements. we used the form 10-k filings collected from the software repository for accounting and finance, foot_0 where a form 10-k is an annual report required by the u.s. sec. specifically, we used the 10-k filings ranging from 2011 to 2018, which comprise 63,336 filings from 12,960 public companies. to make the best use of the year-to-year information, we discarded companies for which the reports in some years are missing during the period; 3,849 companies (3,849×8 = 30,792 reports in total) remained after this filtering. note that in this study, we randomly sample 200 companies from the 3,849 companies with their annual reports for demonstration purpose. coherent text segments. every 10-k annual report contains 15 schedules (e.g., items 1, 1a, 1b, 2, 3, . . . , 7, 7a, . . . , 15). 3 each item section in a report is typically composed of multiple paragraphs, to which we first applied the spacy api foot_2 to divide each paragraph into sentences as our smallest unit of text. moreover, as coherent text segments have been claimed to be beneficial to some downstream tasks such as information re- ## the multi-stage pipeline the proposed multi-stage pipeline is composed of the segment classifier and the segment highlighter modules, both of which leverage contextualized text representations from bert-based models @xcite . with this pipeline we seek to examine year-to-year signals from the 10-k filings of each given company. specifically, our interactive system targets each company's 10-k filings for a certain year; the company's report from the previous year is regarded as the reference document (see ## demonstration figure 1 shows the main interactive interface of the proposed fish, a web-based interactive analysis system for financial reports. for a better user experience, we lay out a concise system interface with user-friendly shortcuts to meet the needs of financial practitioners. in addition, we use both coarse-grained (segment-level) and fine-grained (word-level) signal highlighting features and interactive functions based on the proposed pipeline. fish thereby facilitates more effective and efficient reviewing of financial reports. ## empirical evaluation in this section, we report real-world cases that fish captured and evaluate fish's highlighting results with human judgement. case studies we take the financial report of estée lauder companies inc in 2016 for example. we also plan to continue to build upon and leverage our history of outstanding creativity, innovation and entrepreneurship in high quality products and services and engaging communications. ## conclusion we propose fish, a financial statement signalhighlighting system integrated with a two-stage pipeline architecture, including a segment classifier and a segment highlighter. both utilize bert contextualized representations to strengthen the semantic comprehension of texts. notably, our pipeline leverages the relationship of text segments between the target year and the previous year for automatic and interactive signal highlighting for financial professionals. the segment classifier first narrows the focus to new or revised segments instead of the entire report. as for the revised segments, we integrate a word-level highlighter to provide finegrained financial signals via transfer learning on an external dataset. in addition, our human evaluation also suggests that fish can provide effective highlighting results for empirical applications. to the best of our knowledge, fish is the first interactive system not only made for practical financial applications but also leverages state-of-the-art contextualized language models, which shall greatly benefit both academics and finance practitioners to yield new insights.