國立虎尾科技大學 |

Forecasting asset price direction through sentiment.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Forecasting asset price direction through sentiment./
作者:	Houlihan, Patrick J.
面頁冊數:	1 online resource (144 pages)
附註:	Source: Dissertation Abstracts International, Volume: 78-05(E), Section: A.
標題:	Finance. -
電子資源:	click for full text (PQDT)
ISBN:	9781369369663

Forecasting asset price direction through sentiment.
Houlihan, Patrick J.

Forecasting asset price direction through sentiment. - 1 online resource (144 pages)

Source: Dissertation Abstracts International, Volume: 78-05(E), Section: A.

Thesis (Ph.D.)--Stevens Institute of Technology, 2016.

Includes bibliographical references

This research investigates both the individual and combined predictive capability of two investor sentiment indicators; one extrapolated from social media, text based, and one extrapolated from derivative data, market data based. Our findings show: 1) both microblogging message volume and sentiment can be used as features to predict continuation and reversal effects in asset prices; 2) specific market participant option trading volume is shown to be a predecessor to asset price movements; 3) short positions from specific market participants are a proxy of future performance; 4) combining both textual and market data features improves overall model performance. A significant contribution of this research to existing literature is made through the aggregation of two main sources of measurable sentiment, social media and market data. In addition, this research adjusts returns for risk, momentum and actual transaction costs (as a function of shares bought and sold) to properly capture a more realistic alpha. We use a predefined number of stocks (not company specific) which allows for a more practical approach that confines the number of daily positions to a reasonable count versus a large number that a quantile count would yield. We make no assumption that firms have unlimited capital or the means to invest in hundreds of stocks daily. Another contribution of our research is the use of a more recent data set that includes pre, during and post financial crisis, bringing us through varying market conditions. Such volatile market conditions (financial crisis) were not tested in previous research. The findings of this research also indicate investor overreaction to significant changes in crowd-sourced negative sentiment.

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

Mode of access: World Wide Web

ISBN: 9781369369663Subjects--Topical Terms:

559073
Finance.
Index Terms--Genre/Form:

554714
Electronic books.

Forecasting asset price direction through sentiment.
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500 $a Source: Dissertation Abstracts International, Volume: 78-05(E), Section: A.
500 $a Adviser: German G. Creamer.
502 $a Thesis (Ph.D.)--Stevens Institute of Technology, 2016.
504 $a Includes bibliographical references
520 $a This research investigates both the individual and combined predictive capability of two investor sentiment indicators; one extrapolated from social media, text based, and one extrapolated from derivative data, market data based. Our findings show: 1) both microblogging message volume and sentiment can be used as features to predict continuation and reversal effects in asset prices; 2) specific market participant option trading volume is shown to be a predecessor to asset price movements; 3) short positions from specific market participants are a proxy of future performance; 4) combining both textual and market data features improves overall model performance. A significant contribution of this research to existing literature is made through the aggregation of two main sources of measurable sentiment, social media and market data. In addition, this research adjusts returns for risk, momentum and actual transaction costs (as a function of shares bought and sold) to properly capture a more realistic alpha. We use a predefined number of stocks (not company specific) which allows for a more practical approach that confines the number of daily positions to a reasonable count versus a large number that a quantile count would yield. We make no assumption that firms have unlimited capital or the means to invest in hundreds of stocks daily. Another contribution of our research is the use of a more recent data set that includes pre, during and post financial crisis, bringing us through varying market conditions. Such volatile market conditions (financial crisis) were not tested in previous research. The findings of this research also indicate investor overreaction to significant changes in crowd-sourced negative sentiment.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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710 2 $a Stevens Institute of Technology. $b School of Systems and Enterprises. $3 1185313
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