Long-term stock index forecasting based on text mining of regulatory disclosures

Abstract

Share valuations are known to adjust to new information entering the market, such as regulatory disclosures. We study whether the language of such news items can improve short-term and especially long-term (24 months) forecasts of stock indices.For this purpose, this work utilizes predictive models suited to high-dimensional data and specifically compares techniques for data-driven and knowledge-driven dimensionality reduction in order to avoid overfitting. Our experiments, based on 75,927 ad hoc announcements from 1996-2016, reveal the following results: in the long run, text-based models succeed in reducing forecast errors below baseline predictions from historic lags at a statistically significant level. Our research provides implications to business applications of decision-support in financial markets, especially given the growing prevalence of index ETFs (exchange traded funds). reflect publicly-available information. Based on this premise, one can expect price changes whenever new information enters the market. In practice, regulations ensure that stock-relevant information is revealed primarily via regulatory disclosures in order to provide equal access for all market participants. Such materials disclose, for instance, quarterly earnings, but also management changes, legal risks and other events deemed important [2]. Accordingly, financial disclosures present an alluring and potentially financially-rewarding means of forecasting changes in stock valuations [3].In this respect, corporate news conveys a broad spectrum of information concerning the past performance and current challenges of the business [4], as well as frequently hinting at the future outlook. Research has followed this reasoning and empirically quantified the impact of the narrative content on the subsequent stock market responses [cf. 5, 6, 7]. Moreover, researchers have also demonstrated the prognostic capability of financial disclosures with respect to individual stock market returns in the short term [e. g. 8, 9, 10]. Accordingly, news-based forecasting has received considerable traction and, as a result, various publications have evaluated different news datasets, forecasted indicator/markets, preprocessing operations from the field of natural language processing and forecasting algorithms.Here we refer to the literature, which provides a thorough overview [3].Forecasting the development of stock indices is highly demanded by multiple stakeholders in financial markets. The underlying reason is that households are investing their money no only in individual stocks, government bonds or savings accounts; rather, they increasingly prefer exchange-traded funds (ETFs). These ETFs replicate the movements of marketable securities, with stock indices being the most prominent example. As part of their benefits, ETFs are traded on stock exchanges but often with higher liquidity and lower fees. Hence, private investors demand for decision support in better understanding the development of markets, as well as for obtaining prognostic support. For ...

Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions

“…Considering a decision support required for investment in financial markets, future indices on short-term as well as long-term horizons were predicted using regulatory disclosures in Ref. [45] . The proposed text mining approach collected ad-hoc announcements from the publishing service provider and integrated machine learning techniques under the decision support system.…”

“…The proposed text mining approach collected ad-hoc announcements from the publishing service provider and integrated machine learning techniques under the decision support system. The German prime index (DAX), German composite index (CDAX), and STOXX Europe (STOXX) indices were targeted and their stock data were fused with the derived sentiments from timely disclosures in both languages, English and German; the results indicated the significance of financial decision-making as well as decision support for index forecasting of exchange-traded funds (ETFs) [45] .…”

The interconnectedness of the economic content in the speeches of the US Presidents

“…Nowadays, textual analysis is employed in a wide variety of studies, for example in medicine Lee et al (2019), in tourism management analysis Cheng and Jin (2019), in designing recommendation systems Ji et al (2019), in analyzing countries' foreign policies Cannon et al (2018), in investigating the blog users' sentiments during rainstorm and waterlogging disasters Wu et al (2018) or in understanding the potential applications and users of augmented reality tools Li et al (2018). Feuerriegel and Gordon (2018) highlight the importance of the information contained in written documents to analyse the economic paths and to forecast economic and financial variables. Among the latest works, we include Feuerriegel and Gordon (2019) in which text mining techniques are applied for reducing forecast errors of the macroeconomic indicators by analyzing news.…”

Website Usability Differences between Males and Females: An Eye-Tracking Evaluation of a Climate Decision Support System

“…One way to disseminate tailored climate information to decision-makers is through a decision support system (DSS), a collection of information located in a central place that decision-makers can access and consult before making a decision. DSSs and similar products are widely used in many fields, including water resource management (Andreu et al 1996), stock forecasting (Feuerriegel and Gordon 2018), and humanitarian relief management (Sahebjamnia et al 2017). As DSSs become increasingly common, they are rarely rigorously evaluated for usability and user understanding and therefore do not always meet the needs of the decision-makers using them (Pyke et al 2007).…”