K e FVP : Knowledge-enhanced Financial Volatility Prediction (2023)

Abstract: Financial volatility prediction is vital for indicating a company’s risk profile. Transcripts of companies’ earnings calls are important unstructured data sources to be utilized to access companies’ performance and risk profiles. However, current works ignore the role of financial metrics knowledge (such as EBIT, EPS, and ROI) in transcripts, which is crucial for understanding companies’ performance, and little consideration is given to integrating text and price information. In this work, we statistic common financial metrics and make a special dataset based on these metrics. Then, we introduce a knowledge-enhanced financial volatility prediction method (KeFVP) to inject knowledge of financial metrics into text comprehension by knowledge-enhanced adaptive pre-training (KePt) and effectively incorporating text and price information by introducing a conditional time series prediction module. We conduct extensive experiments on three real-world public datasets, and the results indicate that KeFVP is effective and outperforms all the state-of-the-art methods.

## introduction the volatility of financial asset prices is typically considered a valid proxy for the risk of financial assets and plays an essential role in evaluating the risk of financial assets and their derivatives @xcite . predicting the volatility of financial assets is therefore of great significance to market participants. meanwhile, in addition to asset price information, a wealth of unstructured data (e.g., news, social media, etc.) @xcite @xcite @xcite can also reflect potential changes in the future volatility of assets, which is also vital information that the market participants should be aware of. one such unstructured data source is earnings calls, which are quarterly conferences held by public company management to explain the latest performance, offer guidance on their expectation for the coming future, and answer questions raised by investors and analysts @xcite . the information conveyed by the conference provides investors and analysts with valuable insights into the company's current state and future prospects. hence, the goal of this task is to predict future stock price volatility after the earnings call announcement by combining historical prices and earnings call transcripts. recent works @xcite @xcite have also embarked on exploring the approaches of utilizing these earnings calls to improve financial volatility predictions. prior works based on earnings calls pay attention to multitask architecture for predicting volatility and price movement @xcite , correlations between stocks @xcite , and the impact of numeric features @xcite . however, these studies either completely overlooked the role of price @xcite @xcite , or did not consider the combination of price and text information elaborately @xcite . for instance, voltage @xcite encodes price information by a vanilla lstm, while ensemble @xcite uses support vector regression to predict volatility based on historical price information. thus, based on the preceding review, we propose two existential challenges in existing studies: (1) financial metric (fm) knowledge is not concerned; (2) combining text and price information is rarely considered elaborately. specifically, there are a large number of fms (e.g., eps, ebitda foot_0 , etc. as shown in firstly, the text information is directed into the ie module, composed of multiple transformer blocks. following the ie module's processing, the refined text information, along with historical prices, is fed into the ctsp module to carry out volatility predictions. our main contributions are as follows: ## conclusion and future work in this work, we present kefvp and systematically illustrate the fm knowledge introduction into predictions. the kept method is proposed and the kept dataset is concurrently constructed to serve it. the fvp equipped with ie and ctsp modules to integrate text and price information drives results to sota. further, this method is also informative for research based on other financial text (e.g. news, tweets, etc.). in the future, we will explore ways to incorporate other financial knowledge (e.g., financial analysis formulas) into financial applications.