Forecasting Firm Material Events from 8-K Reports

Proceedings of the Second Workshop on Economics and Natural Language Processing, pages 22–30 Hong Kong, November 4. c⃝2019 Association for Computational Linguistics 22 Forecasting Firm Material Event Sequences from 8-K Reports Shuang Zhai Iowa State University szhai@iastate.edu Zhu Zhang Iowa State University zhuzhang@iastate.edu Abstract In this paper, we show deep learning mod- els can be used to forecast firm material event sequences based on the contents in the com- pany’s 8-K Current Reports. Specifically, we exploit state-of-the-art neural architectures, in- cluding sequence-to-sequence (Seq2Seq) ar- chitecture and attention mechanisms, in the model. Our 8K-powered deep learning model demonstrates promising performance in fore- casting firm future event sequences. The model is poised to benefit various stake- holders, including management and investors, by facilitating risk management and decision making. 1 Introduction One Corporate Event Sequence (CES) is a se- quence of events that take place at one company during a period of time. A series of company events can represent corporate strategy and future plans. Therefore, CES can be used as a tool to probe corporate strategy and decision-making be- haviors. Investors can use existing CES to project a com- pany’s future CES. For instance, an acquisition is a sign for financing, and insufficient funding is a vane for refinancing. Similarly, a failing opera- tional decision can bring an executive personnel change, and a new senior-level appointment can be expected after that. Since CES embodies con- sistency (to corporate strategy) and continuity (to time), it is equipped to illuminate a maze pathway for organizational strategy evaluation. Researches increasingly reveal the merit of tex- tual data in financial studies. Many studies are centered on the financial market, such as a firm’s stock price, return, and volatility (Fang and Peress, 2009; Tetlock, 2010; Edmans, 2011). Meanwhile, the high dimensionality characteristic of textual data presents challenges to traditional econometric models. Therefore, they render themselves to ma- chine learning and deep learning models naturally. Deep learning models have become gradually pop- ular in finance applications recently, and many of them have focused on market-related tasks as well (Ding et al., 2014, 2015). These models, how- ever, didn’t take corporate strategy into account and didn’t focus on the sequence nature of corpo- rate events. U.S. Security and Exchange Commission (SEC) requires the publicly-traded company to file Form 8-K (also called ’Material Event Report’ or ’Cur- rent Report’) when certain types of the corporate event take place. In general, an 8-K report should be filed when the company has an event that its shareholders should be aware of. A material event is defined as a matter if there is a substantial like- lihood that a reasonable person would consider it important 1, and a ”rule of thumb” impact scale of 2.a material event is five to ten percent of net income Although public companies are also required to file Form 10-K (annual report), and Form 10-Q (quarterly report), 10-K/Qs have apparent and sig- nificant drawbacks compared to 8-Ks. 10-K/Qs are designed to cover a mixed category of infor- mation. …

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