Measuring Consistency in Text-based Financial Forecasting Models
This addresses the need for robust and trustworthy financial forecasting models for investors and analysts, but it is incremental as it focuses on evaluation rather than a new forecasting method.
The authors tackled the problem of inconsistency in text-based financial forecasting models by proposing FinTrust, an evaluation tool that assesses logical consistency, and found that state-of-the-art models perform poorly with significant performance degradation under meaning-preserving alterations.
Financial forecasting has been an important and active area of machine learning research, as even the most modest advantage in predictive accuracy can be parlayed into significant financial gains. Recent advances in natural language processing (NLP) bring the opportunity to leverage textual data, such as earnings reports of publicly traded companies, to predict the return rate for an asset. However, when dealing with such a sensitive task, the consistency of models -- their invariance under meaning-preserving alternations in input -- is a crucial property for building user trust. Despite this, current financial forecasting methods do not consider consistency. To address this problem, we propose FinTrust, an evaluation tool that assesses logical consistency in financial text. Using FinTrust, we show that the consistency of state-of-the-art NLP models for financial forecasting is poor. Our analysis of the performance degradation caused by meaning-preserving alternations suggests that current text-based methods are not suitable for robustly predicting market information. All resources are available at https://github.com/yingpengma/fintrust.