ADMIRAL: A Data Mining Based Financial Trading System

Rachlin, G.; Alberg, Dima; Kandel, Abraham

doi:10.1109/cidm.2007.368947

Cited by 35 publications

(28 citation statements)

References 12 publications

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“…For numeric data, depending on its closing price we assign trend like up, down, expected, slight-up, and slight-down [1]. Example of Historical data of Reliance is shown in fig [2] below:…”

Section: Pre-processingmentioning

confidence: 99%

“…When news articles are collected, the first step is to extract key word and key phrases from predefined Window of Influence [1]. Depending on it, TermDictionary is automatically created.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…But only K highest words will be used from term dictionary. Score is calculated by using following equation [1] Score= ( …”

Section: Figure 2 Example Of Historical Data Of Reliancementioning

confidence: 99%

“…This weight is calculated by dividing number of occurrence of word during particular period by the occurrence of that word in the term dictionary. [1] …”

Section: Calculate Term Weightmentioning

confidence: 99%

“…They do this by combining both technical and fundamental analysis strategies. In technical analysis, it performs the prediction based on past price, while in fundamental analysis; it is based on real economy factors, such as trading volume, organizational changes in the company, etc [1]. Therefore stock market data or financial news articles can be used to get data required by these two strategies.…”

Section: Introductionmentioning

confidence: 99%

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Financial Trading System using Combination of Textual and Numerical Data

Dange¹,

Argiddi²,

Apte³

2012

IJCA

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There is large amount of financial data that are generated and evaluated at a high speed. These financial data is coming continuously, changing with time and may be unpredictable. Therefore there is a critical need for automated approaches to effective and efficient utilization of large amount of data to support companies and individuals for decision-making. Data mining techniques can be used to uncover hidden patterns, to discover the behavior of the stock market, to find out the trends in financial markets and so on. For predicting stock trends and making financial trading decisions, a new model is presented. It is based on combination of data and text mining techniques which takes the textual contents of time-stamped web documents along with numerical time series data and performs the future prediction. By using this model, we will show that the accuracy of result will be improved.

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“…For numeric data, depending on its closing price we assign trend like up, down, expected, slight-up, and slight-down [1]. Example of Historical data of Reliance is shown in fig [2] below:…”

Section: Pre-processingmentioning

confidence: 99%

“…When news articles are collected, the first step is to extract key word and key phrases from predefined Window of Influence [1]. Depending on it, TermDictionary is automatically created.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…But only K highest words will be used from term dictionary. Score is calculated by using following equation [1] Score= ( …”

Section: Figure 2 Example Of Historical Data Of Reliancementioning

confidence: 99%

“…This weight is calculated by dividing number of occurrence of word during particular period by the occurrence of that word in the term dictionary. [1] …”

Section: Calculate Term Weightmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Financial Trading System using Combination of Textual and Numerical Data

Dange¹,

Argiddi²,

Apte³

2012

IJCA

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Asset pricing via deep graph learning to incorporate heterogeneous predictors

Jiwen

Xing

2022

Int J of Intelligent Sys

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Tradition financial studies on asset pricing focused on the economic indicators and media information of a stock. Recent financial studies found that the momentum spillovers of relevant firms are salient as well for measuring asset risk. However, previous studies on asset pricing via machine learning only relied on partial of these market information types. In this study, a deep learning framework is proposed to combine these three market information types with different data structures, that is, numerical economic indicators represented as scalars, media represented as textual vectors, and the influences of related firms captured by graphs. More importantly, the unique data characteristics brought by such data fusion are well addressed in the proposed learning framework. Specifically, a matrix-based module is first proposed to fuse numerical economic data and textual media, which specifically considers the interactions of the fused features. Such fused information, along with the firm relevance represented in graphs, is further integrated by a novel self-adaptive graph neural network that can address the dynamic merging of multilinked listed firms. Experiments performed on real market data demonstrate the effectiveness of the proposed approach over state-of-the-art algorithms, including eLSTM, RGCN, and TGC.

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Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research

Fisher

Garnsey

Hughes

2016

Intell. Sys. Acc. Fin. Mgmt.

145

112

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Natural language processing (NLP) is a part of the artificial intelligence domain focused on communication between humans and computers. NLP attempts to address the inherent problem that while human communications are often ambiguous and imprecise, computers require unambiguous and precise messages to enable understanding. The accounting, auditing and finance domains frequently put forth textual documents intended to communicate a wide variety of messages, including, but not limited to, corporate financial performance, management's assessment of current and future firm performance, analysts' assessments of firm performance, domain standards and regulations as well as evidence of compliance with relevant standards and regulations. NLP applications have been used to mine these documents to obtain insights, make inferences and to create additional methodologies and artefacts to advance knowledge in accounting, auditing and finance. This paper synthesizes the extant literature in NLP in accounting, auditing and finance to establish the state of current knowledge and to identify paths for future research.of achieving human-like language processing for a range of tasks or applications' (Liddy, 2001). This implies, as demonstrated throughout this paper, that 'there are multiple methods or techniques from which to choose to accomplish a particular type of language analysis' (Liddy, 2001). We reference a variety of analytical methods that incorporate NLP data, at various 'levels of linguistic analysis' (Liddy, 2001). However, since 'NLP is considered a discipline within Artificial Intelligence (AI)' we focus most heavily on the AI-related applications of NLP (Liddy, 2001). Despite the plethora of analytical methods that are associated with and use data produced by NLP, it comprises a unique set of 'computational techniques' that should not be confused or conflated with the many analytical tools referenced in the text that follows.Fifty years ago Goldberg (1965) stated: 'it is scarcely an exaggeration to say that the problem of communication is the axial problem in accounting'. Accounting practice is replete with written documents intended to communicate such messages as, but not limited to: current accounting standards, past and expected future corporate performance (along multiple dimensions), policies and practices embodied by financial statements and the results of financial statement audits. The growth in digital and social media usage by businesses has further increased the volume of unstructured text documents. Since the dissemination of these documents and messages is now largely automated by and through computers, we believe that NLP research and applications have considerable potential to enhance communication in the areas of accounting, auditing and finance. Our paper is motivated by the desire to determine the state of the extant literature in NLP in accounting, auditing and finance so as to inform and guide future research efforts. ObjectivesThis paper examines the NLP literature in accounting, auditing and fi...

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ADMIRAL: A Data Mining Based Financial Trading System

Cited by 35 publications

References 12 publications

Financial Trading System using Combination of Textual and Numerical Data

Financial Trading System using Combination of Textual and Numerical Data

Asset pricing via deep graph learning to incorporate heterogeneous predictors

Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research

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