Rules-Based Integration of News-Trading Algorithms

Gagnon, Stéphane

doi:10.3905/jot.2012.8.1.015

Cited by 5 publications

(9 citation statements)

References 66 publications

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“…Based on our findings, one can build algorithmic trading systems around our text-based prediction methodology, which are capable of executing potentially profitable trading strategies [60,22]. In this regard, text mining in particular has recently received great traction and is propelling the automated interpretation of the linguistic content in corporate disclosures [61].…”

Section: Business Implications For Financial Decision-making and Deci...mentioning

confidence: 93%

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

Feuerriegel

Gordon

2018

Decision Support Systems

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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 ...

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Section: Business Implications For Financial Decision-making and Deci...mentioning

confidence: 93%

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

Feuerriegel

Gordon

2018

Decision Support Systems

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“…This section introduces a short description of components that are relevant for a news trading system. For a thorough review and taxonomy, we refer to the reference [15].…”

Section: Related Workmentioning

confidence: 99%

“…Related to our research is [15], who propose an architecture for a rulebased news trading system. This system screens events with the help of different data mining algorithms and recommends trading strategies.…”

Section: Trading Strategiesmentioning

confidence: 99%

“…Different data sources might provide further insights into the costs of additional noise. For example, innovative news sources [15,28], such as social media like Twitter, offer a massive volume of new textual materials, which are published quickly and without quality control. Related approaches also incorporate Google query volume for search terms related to finance [29] and Internet stock message boards [11].…”

Section: Managerial Implicationsmentioning

confidence: 99%

“…News trading combines real-time market data and natural language processing to detect suitable news announcements in order to trigger transactions. Its mechanisms are often part of an algorithmic trading system, while many regard it as an enabling Decision Support System (DSS) for use in banking and financial markets [15].…”

Section: Introductionmentioning

confidence: 99%

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News-Based Trading Strategies

Feuerriegel,

Prendinger

2015

SSRN Journal

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The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets.Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted by investors. Quite recently, researchers have started to automatically determine news sentiment in order to explain stock price movements. Interestingly, this so-called news sentiment works fairly well in explaining stock returns. In this paper, we design trading strategies that utilize textual news in order to obtain profits on the basis of novel information entering the market. We thus propose approaches for automated decisionmaking based on supervised and reinforcement learning. Altogether, we demonstrate how news-based data can be incorporated into an investment system.

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Evaluation of News-Based Trading Strategies

Feuerriegel

Neumann

2015

Lecture Notes in Business Information Processing

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Rules-Based Integration of News-Trading Algorithms

Cited by 5 publications

References 66 publications

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

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

News-Based Trading Strategies

Evaluation of News-Based Trading Strategies

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