Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review

Hu, Yong; Liu, Kang; Zhang, Xiangzhou; Su, Lijun; Ngai, Eric W.T.; Liu, Mei

doi:10.1016/j.asoc.2015.07.008

Cited by 159 publications

(67 citation statements)

References 105 publications

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“…Technical analysis on the other hand focuses solely on historical price and volume. Hu et al [13] did a comprehensive literature review in 2015 and concluded that most existing studies which apply soft computing and neural network for financial prediction are based on technical analysis. An earlier literature review by Atsalakis et al [14] shows the same pattern.…”

Section: Related Workmentioning

confidence: 99%

Neural Network Models for Stock Selection Based on Fundamental Analysis

Huang

Capretz

Ho³

2019

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)

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Application of neural network architectures for financial prediction has been actively studied in recent years. This paper presents a comparative study that investigates and compares feed-forward neural network (FNN) and adaptive neural fuzzy inference system (ANFIS) on stock prediction using fundamental financial ratios. The study is designed to evaluate the performance of each architecture based on the relative return of the selected portfolios with respect to the benchmark stock index. The results show that both architectures possess the ability to separate winners and losers from a sample universe of stocks, and the selected portfolios outperform the benchmark. Our study argues that FNN shows superior performance over ANFIS. Keywords-Neural network, Feed-forward neural network (FNN), Adaptive neural fuzzy inference system (ANFIS), Stock portfolio selection, Fundamental analysis (FA), Stock predictionI.

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Section: Related Workmentioning

confidence: 99%

Neural Network Models for Stock Selection Based on Fundamental Analysis

Huang

Capretz

Ho³

2019

2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)

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“…This includes techniques for data-driven and knowledge-driven dimensionality reduction. We thereby contribute to the growing use of data mining techniques in financial forecasting [63].…”

Section: Business Implications For Financial Decision-making and Decimentioning

confidence: 99%

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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“…Others have used grammatical evolution, e.g. [32] to evolve rules for collective behaviours in robots or genetic programming to create rules for stock-trading [33].…”

Section: Bio-inspired Computationmentioning

confidence: 99%

For Flux Sake: The Confluence of Socially- and Biologically-Inspired Computing for Engineering Change in Open Systems

Pitt¹,

Hart²

2017

2017 IEEE 2nd International Workshops on Foundations and Applications of Self* Systems (FAS*W)

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This position paper is concerned with the challenge of engineering multi-scale and long-lasting systems, whose operation is regulated by sets of mutually-agreed, conventional rules. The core of the problem is that there are multiple, inter-dependent dimensions of flux, with numerous contextual factors to take into account. These dimensions of flux include, on the one hand, the set of rules itself; and on the other, the system components (population), their social network, and the operating environment. However, there appears to be no 'one size fits all' optimum ruleset for all combinations of population, social network and environment; nor (given the contextual factors) is there a planning-type algorithm that can compute an 'ideal' ruleset for any particular combination of population, social network and environment. These features of the problem suggest that recent advances in machine learning and evolutionary computation can provide the instruments for facilitating self-adaptation of a rule-based system over different timescales. This paper proposes that the integration of concepts from socially-and biologically-inspired computing can pave the way for eventual development of a computational framework that will enable principled (methodological) development of sustainable adaptive rule-based systems.

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Application of evolutionary computation for rule discovery in stock algorithmic trading: A literature review

Cited by 159 publications

References 105 publications

Neural Network Models for Stock Selection Based on Fundamental Analysis

Neural Network Models for Stock Selection Based on Fundamental Analysis

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

For Flux Sake: The Confluence of Socially- and Biologically-Inspired Computing for Engineering Change in Open Systems

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