Financial Risk Analysis

• Tasks:
  • Volatility Prediction
  • Other Tasks

Volatility Prediction

(sort in chronological order)

Reference	Paper	Source	Model Type	Evaluation Metric(s)	Time Span	Contributions	Venue
Theil et al. (2018)	Word Embeddings-Based Uncertainty Detection in Financial DisclosuresChristoph	CRSP & EDGAR	Jrip	Precision, Recall, F1, Accuracy, p-value	1994-2015	Expand the L&M uncertainty dictionary by adding scematically close terms. Investigated manual filtering by expert and specific versus general-domain embedding model. New dataset of annotated annual reports.	ECONLP-2018
Theil et al. (2019)	PRoFET: Predicting the Risk of Firms from Event Transcripts	They present a new dataset of 90K earnings call transcripts (open source)	BiLSTM	Linear correlation coefficient Pearson’s r, the Non-linear rank correlation coefficients Spearman’s ρ and Kendall’s τ, and the MSE	2002–2017	They introduce PRoFET, the first neural model for volatility prediction jointly exploiting both semantic language representations and a comprehensive set of financial features	IJCAI-19
Theil et al. (2020)	Explaining Financial Uncertainty through Specialized Word Embeddings	EDGAR	Regression	t-statistic of coefficients	1994-2015	Developed the first word embedding models accounting for industry-specific vocabulary. Statistically explain drift in returns and volality of stock price.	ACM Transactions on Data Science-20
Qin and Yang et al. (2019)	What You Say and How You Say It Matters: Predicting Stock Volatility Using Verbal and Vocal Cues	Dataset	MDRM	MSE	S&P 500 companies in 2017	First work to extend the earnings conference call analysis as a multimodal problem by incorporating textual and audio information in the same model.	ACL-19
Yang et al. (2020)	HTML: Hierarchical Transformer-based Multi-task Learning for Volatility Prediction	Code	HTML (Hiererachical Transformer with Multi-task Learning)	MSE	S&P 500 companies in 2017	Yang et al. (2020) is the first approach that can deal with the long-form financial documents for volatility prediction. This paper demonstrates very significant improvements in prediction accuracy, in the range 17% - 49% compared to the current state-of-the-art.	WWW-20
Ye et al. (2020)	Financial Risk Prediction with Multi-Round Q&A Attention Network	6494 text-only samples	Bidirectional LSTM	MSE	2015-2018	Instead of word-level or document level feature extraction, they focus on dialogues in the conference. Through extracting features of each round of dialogue, the model predicts the financial volatility. Compared to the most common baseline, past volatility, proposed model achieves 47% improvement in 3-days span, 31% in 7-days span, and 23% in 15-days span.	IJCAI-20
Li et al. (2020)	MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction	MAEC Dataset	Multimodal Alignment	MSE	S&P 1500 companies from 2015 to 2018	This is a data resource paper that is more than six times larger than those currently available to the research community.	CIKM-20

OtherTasks

(sort in chronological order)

Reference	Paper	Source	Model Type	Evaluation Metric(s)	Time Span	Contributions	Venue
Yang et al. (2020)	Financial Risk Analysis for SMEs with Graph-based Supply Chain Mining	Alipay Ant SME Lending (NOT open-sourced)	Spatial-temporal aware Graph Neural Network	Supply Chain Mining task, Loan Default Prediction task	\	Analyze financial risk through mining the supply chain between Small and Medium-size Enterprises	IJCAI-20
Bisi et al. (2020)	Risk-Averse Trust Region Optimization for Reward-Volatility Reduction	\	\	Reward Volatility, Most of proof is given in theoretical derivation.	\	In many cases, the risk is measured not only on a long-term perspective, but also on a step-wise reward. (like on a daily base)	IJCAI-20
Cheng et al. (2020)	Risk Guarantee Prediction in Networked-Loans	A real-world dataset from a major institution in East Asia (NOT open-sourced)	Graph convolutional network, Graph recurrent network	On datasets in 2014,2015 and 2016, got 84.6%, 84,2% and 85% accuracy respectively.	01/01/2013-31/12/2016	Detect and predict the risk in a guaranteed loan	IJCAI-20
Ting-Wei et al. (2020)	“The Cat is Out of the Bag” Explainable Risk Ranking with Financial Reports	\	WRR(self-constructed) (RankSVM + FastText + HAN)	Spearman’s Rho (ρ), Kendall’s Tau (τ)	01/01/1996-31/12/2013	Propose an eXplainable Risk Ranking (XRR) model that uses multilevel encoders and attention mechanisms to analyze financial risks among companies	AAAI-20
Masson & Montariol (2020)	Detecting Omissions of Risk Factors in Company Annual Reports	DoRe Corpus (Masson and Paroubek, 2020)	FlauBERT	Accuracy, F1, Precision, Recall	2009-2019	First to look at risk omission in annual reports	FinNLP-2020