Using insights from social media for making investment decisions has become mainstream. However, in the current era of information ex- plosion, it is essential to mine high-quality so- cial media posts. The FinNLP-2022 ERAI task deals with assessing Maximum Possible Profit (MPP) and Maximum Loss (ML) from social me- dia posts relating to finance. In this paper, we present our team LIPI’s approach. We ensem- bled a range of Sentence Transformers to quan- tify these posts. Unlike other teams with vary- ing performances across different metrics, our system performs consistently well. Our code is available here https://github.com/sohomghosh/LIPI_ERAI_ FinNLP_EMNLP- 2022/

Understanding Environmental, Social, and Governance (ESG) factors related to financial products has become extremely important for investors. However, manually screening through the corporate policies and reports to understand their sustainability aspect is extremely tedious. In this paper, we propose solutions to two such problems which were released as shared tasks of the FinNLP workshop of the IJCAI-2022 conference. Firstly, we train a Sentence Transformers based model which automatically ranks ESG related concepts for a given unknown term. Secondly, we fine-tune a RoBERTa model to classify financial texts as sustainable or not. Out of 26 registered teams, our team ranked 4th in sub-task 1 and 3rd in sub-task 2. The source code can be accessed from https://github.com/sohomghosh/Finsim4_ESG

In today’s world, the advancement and spread of the Internet and digitalization have resulted in most information being openly accessible. This holds true for financial services as well. Investors make data driven decisions by analysing publicly available information like annual reports of listed companies, details regarding asset allocation of mutual funds, etc. Many a time these financial documents contain unknown financial terms. In such cases, it becomes important to look at their definitions. However, not all definitions are equally readable. Readability largely depends on the structure, complexity and constituent terms that make up a definition. This brings in the need for automatically evaluating the readability of definitions of financial terms. This paper presents a dataset, FinRAD consisting of financial terms, their definitions and embeddings. In addition to standard readability scores (like “Flesch Reading Index (FRI)”, “Automated Readability Index (ARI)”, “SMOG Index Score (SIS)”,“Dale-Chall formula (DCF)”, etc.), it also contains the readability scores (AR) assigned based on sources from which the terms have been collected. We manually inspect a sample from it to ensure the quality of the assignment. Subsequently, we prove that the rule-based standard readability scores (like “Flesch Reading Index (FRI)”, “Automated Readability Index (ARI)”, “SMOG Index Score (SIS)”,“Dale-Chall formula (DCF)”, etc.) do not correlate well with the manually assigned binary readability scores of definitions of financial terms. Finally, we present a few neural baselines using transformer based architecture to automatically classify these definitions as readable or not. Pre-trained FinBERT model fine-tuned on FinRAD corpus performs the best (AU-ROC = 0.9927, F1 = 0.9610). This corpus can be downloaded from https://github.com/sohomghosh/FinRAD_Financial_Readability_Assessment_Dataset.

While reading financial documents, investors need to know the causes and their effects. This empowers them to make data-driven decisions. Thus, there is a need to develop an automated system for extracting causes and their effects from financial texts using Natural Language Processing. In this paper, we present the approach our team LIPI followed while participating in the FinCausal 2022 shared task. This approach is based on the winning solution of the first edition of FinCausal held in the year 2020.

Simplified definitions of complex terms help learners to understand any content better. Comprehending readability is critical for the simplification of these contents. In most cases, the standard formula based readability measures do not hold good for measuring the complexity of definitions of financial terms. Furthermore, some of them works only for corpora of longer length which have at least 30 sentences. In this paper, we present a tool for evaluating readability of definitions of financial terms. It consists of a Light GBM based classification layer over sentence embeddings (Reimers et al., 2019) of FinBERT (Araci, 2019). It is trained on glossaries of several financial textbooks and definitions of various financial terms which are available on the web. The extensive evaluation shows that it outperforms the standard benchmarks by achieving a AU-ROC score of 0.993 on the validation set.