Knowing which factors are significant in credit rating assignment leads to
better decision-making. However, the focus of the literature thus far has been
mostly on structured data, and fewer studies have addressed unstructured or
multi-modal datasets. In this paper, we present an analysis of the most
effective architectures for the fusion of deep learning models for the
prediction of company credit rating classes, by using structured and
unstructured datasets of different types. In these models, we tested different
combinations of fusion strategies with different deep learning models,
including CNN, LSTM, GRU, and BERT. We studied data fusion strategies in terms
of level (including early and intermediate fusion) and techniques (including
concatenation and cross-attention). Our results show that a CNN-based
multi-modal model with two fusion strategies outperformed other multi-modal
techniques. In addition, by comparing simple architectures with more complex
ones, we found that more sophisticated deep learning models do not necessarily
produce the highest performance; however, if attention-based models are
producing the best results, cross-attention is necessary as a fusion strategy.
Finally, our comparison of rating agencies on short-, medium-, and long-term
performance shows that Moody's credit ratings outperform those of other
agencies like Standard & Poor's and Fitch Ratings.

本研究对于公司信用评级的深度学习模型进行结构化数据和非结构化数据的多模态融合，其中以卷积神经网络为基础的模型与两种融合策略的效果最佳；此外，我们还发现更复杂的深度学习模型未必能够带来最好的性能，然而，如果注意力机制为性能最佳，那么交叉注意力机制为必要的融合策略；最后，我们比较了短期、中期和长期表现，并得出 Moody's 信用评级超过标普和惠誉等其他机构的结论。

利用文本和数值数据流的多模态深度学习进行信用评级预测

Multi-Modal Deep Learning for Credit Rating Prediction Using Text and  Numerical Data Streams

Cryptocurrencies (or digital tokens, digital currencies, e.g., BTC, ETH, XRP,
NEO) have been rapidly gaining ground in use, value, and understanding among
the public, bringing astonishing profits to investors. Unlike other money and
banking systems, most digital tokens do not require central authorities. Being
decentralized poses significant challenges for credit rating. Most ICOs are
currently not subject to government regulations, which makes a reliable credit
rating system for ICO projects necessary and urgent.
In this paper, we introduce IcoRating, the first learning--based
cryptocurrency rating system. We exploit natural-language processing techniques
to analyze various aspects of 2,251 digital currencies to date, such as white
paper content, founding teams, Github repositories, websites, etc. Supervised
learning models are used to correlate the life span and the price change of
cryptocurrencies with these features. For the best setting, the proposed system
is able to identify scam ICO projects with 0.83 precision.
We hope this work will help investors identify scam ICOs and attract more
efforts in automatically evaluating and analyzing ICO projects.

本文介绍了 IcoRating，这是第一个基于学习的加密货币评级系统。利用自然语言处理技术对 2251 种数字货币的各个方面进行分析，例如白皮书内容、创始团队、Github 仓库、网站等。借助监督学习模型将数字货币的寿命和价格变化与这些特性相关联。该系统能够以 0.83 的精度识别骗局 ICO 项目。我们希望这项工作能帮助投资者识别诈骗 ICO，并吸引更多精力自动评估和分析 ICO 项目。