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A Twitter-Based Prediction Tool for ...

ProQuest Information and Learning Co.

A Twitter-Based Prediction Tool for Digital Currency.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	A Twitter-Based Prediction Tool for Digital Currency./
作者:	McCoy, Mason.
面頁冊數:	1 online resource (59 pages)
附註:	Source: Masters Abstracts International, Volume: 58-01.
Contained By:	Masters Abstracts International58-01(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780438162266

A Twitter-Based Prediction Tool for Digital Currency.
McCoy, Mason.

A Twitter-Based Prediction Tool for Digital Currency. - 1 online resource (59 pages)

Source: Masters Abstracts International, Volume: 58-01.

Thesis (M.S.)--Southern Illinois University at Carbondale, 2018.

Includes bibliographical references

Digital currencies (cryptocurrencies) are rapidly becoming commonplace in the global market. Trading is performed similarly to the stock market or commodities, but stock market prediction algorithms are not necessarily well-suited for predicting digital currency prices. In this work, we analyzed tweets with both an existing sentiment analysis package and a manually tailored "objective analysis," resulting in one impact value for each analysis per 15-minute period. We then used evolutionary techniques to select the most appropriate training method and the best subset of the generated features to include, as well as other parameters. This resulted in implementation of predictors which yielded much more profit in four-week simulations than simply holding a digital currency for the same time period--the results ranged from 28% to 122% profit. Unlike stock exchanges, which shut down for several hours or days at a time, digital currency prediction and trading seems to be of a more consistent and predictable nature.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438162266Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

A Twitter-Based Prediction Tool for Digital Currency.
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500 $a Source: Masters Abstracts International, Volume: 58-01.
500 $a Adviser: Shahram Rahimi.
502 $a Thesis (M.S.)--Southern Illinois University at Carbondale, 2018.
504 $a Includes bibliographical references
520 $a Digital currencies (cryptocurrencies) are rapidly becoming commonplace in the global market. Trading is performed similarly to the stock market or commodities, but stock market prediction algorithms are not necessarily well-suited for predicting digital currency prices. In this work, we analyzed tweets with both an existing sentiment analysis package and a manually tailored "objective analysis," resulting in one impact value for each analysis per 15-minute period. We then used evolutionary techniques to select the most appropriate training method and the best subset of the generated features to include, as well as other parameters. This resulted in implementation of predictors which yielded much more profit in four-week simulations than simply holding a digital currency for the same time period--the results ranged from 28% to 122% profit. Unlike stock exchanges, which shut down for several hours or days at a time, digital currency prediction and trading seems to be of a more consistent and predictable nature.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Artificial intelligence. $3 559380
650 4 $a Finance. $3 559073
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
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710 2 $a Southern Illinois University at Carbondale. $b Computer Science. $3 1190734
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