Selection of Related Stocks using Financial Text Mining

Hirano, Minoru; Sakaji, Hiroki; Kimura, Shoko; Izumi, Kiyoshi; Matsushima, Hiroyasu; Nagao, Shintaro; Kato, Atsuo

doi:10.1109/icdmw.2018.00036

Cited by 4 publications

(8 citation statements)

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“…We now explain our scheme for selecting stocks related to a fund's theme, which is an extension of our previous scheme [1]. By inputting a word for a fund's theme in our scheme, a ranking of related companies appears with similarity scores and reasons for extracting each companies.…”

Section: Our Extended Scheme: Related-word-based Stock Extraction Usimentioning

confidence: 99%

“…This ensembling is based on a previous study [3], in which they used multiple word2vec models with different settings for deciding whether two words are similar words or not by a majority vote of multiple results. However, we extended their study and use harmonic average newly since our previous study [1].…”

Section: Word2vec Model and Similaritiesmentioning

confidence: 99%

“…We evaluated our proposed scheme using the same hyperparameters in our previous study [1]. There are two hyperparameters in our scheme.…”

Section: Evaluation Of Extended Scheme Using Hyperparameters From Ourmentioning

confidence: 99%

“…As the method, we propose a scheme extended from our previous scheme [1]. We developed the previous scheme to handle this task mainly using natural language processing (NLP).…”

Section: Introductionmentioning

confidence: 99%

“…In our previous scheme [1], we used top-10 (topn mode) with no evidence. However, since we thought there are some possible parameters or modes, we also extended this part of the scheme.…”

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confidence: 99%

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Related Stocks Selection with Data Collaboration Using Text Mining

et al. 2019

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We propose an extended scheme for selecting related stocks for themed mutual funds. This scheme was designed to support fund managers who are building themed mutual funds. In our preliminary experiments, building a themed mutual fund was found to be quite difficult. Our scheme is a type of natural language processing method and based on words extracted according to their similarity to a theme using word2vec and our unique similarity based on co-occurrence in company information. We used data including investor relations and official websites as company information data. We also conducted several other experiments, including hyperparameter tuning, in our scheme. The scheme achieved a 172% higher F1 score and 21% higher accuracy than a standard method. Our research also showed the possibility that official websites are not necessary for our scheme, contrary to our preliminary experiments for assessing data collaboration.

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Section: Our Extended Scheme: Related-word-based Stock Extraction Usimentioning

confidence: 99%

Section: Word2vec Model and Similaritiesmentioning

confidence: 99%

“…We evaluated our proposed scheme using the same hyperparameters in our previous study [1]. There are two hyperparameters in our scheme.…”

Section: Evaluation Of Extended Scheme Using Hyperparameters From Ourmentioning

confidence: 99%

“…As the method, we propose a scheme extended from our previous scheme [1]. We developed the previous scheme to handle this task mainly using natural language processing (NLP).…”

Section: Introductionmentioning

confidence: 99%

“…In our previous scheme [1], we used top-10 (topn mode) with no evidence. However, since we thought there are some possible parameters or modes, we also extended this part of the scheme.…”

mentioning

confidence: 99%

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