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Quantitative Trading Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Quantitative Trading Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network./
Author:	Hao, Ruizhi.
Description:	1 online resource (57 pages)
Notes:	Source: Masters Abstracts International, Volume: 83-01.
Contained By:	Masters Abstracts International83-01.
Subject:	Business administration. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28498566click for full text (PQDT)
ISBN:	9798534672107

Quantitative Trading Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network.
Hao, Ruizhi.

Quantitative Trading Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network. - 1 online resource (57 pages)

Source: Masters Abstracts International, Volume: 83-01.

Thesis (Master's)--Stevens Institute of Technology, 2021.

Includes bibliographical references

Stock prediction is critical in quantitative trading for creating an efficient trading strategy that yields a high return. The ability to predict outcomes is also needed for successful portfolio construction and optimization. Stock prediction, on the other hand, is a difficult task due to the numerous factors involved, such as uncertainty and instability. Deep learning techniques, especially the recurrent neural network (RNN), have recently been developed for sequence prediction. A long short-term memory (LSTM) network is proposed in this paper to predict market movement using historical data. Multiple portfolio optimization techniques, such as equal-weighted modeling (EQ) and optimization modeling maximizing Sharpe ratio, are used to optimize portfolio efficiency in order to build an effective portfolio. The results showed that our proposed LSTM prediction model is effective in predicting stock prices with high accuracy. In addition, using maximizing Sharpe ratio method to rebalance the allocation strategy every three months showed a significant improvement in the cumulative return of the constructed portfolios. Furthermore, our constructed portfolios beat the benchmark Sector ETF index in both XLU and XLB.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798534672107Subjects--Topical Terms:

3168311
Business administration.
Subjects--Index Terms:

Stock predictionIndex Terms--Genre/Form:

542853
Electronic books.

Quantitative Trading Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network.
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100 1 $a Hao, Ruizhi. $3 3701881
245 1 0 $a Quantitative Trading Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network.
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300 $a 1 online resource (57 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Masters Abstracts International, Volume: 83-01.
500 $a Advisor: Bozdog, Dragos.
502 $a Thesis (Master's)--Stevens Institute of Technology, 2021.
504 $a Includes bibliographical references
520 $a Stock prediction is critical in quantitative trading for creating an efficient trading strategy that yields a high return. The ability to predict outcomes is also needed for successful portfolio construction and optimization. Stock prediction, on the other hand, is a difficult task due to the numerous factors involved, such as uncertainty and instability. Deep learning techniques, especially the recurrent neural network (RNN), have recently been developed for sequence prediction. A long short-term memory (LSTM) network is proposed in this paper to predict market movement using historical data. Multiple portfolio optimization techniques, such as equal-weighted modeling (EQ) and optimization modeling maximizing Sharpe ratio, are used to optimize portfolio efficiency in order to build an effective portfolio. The results showed that our proposed LSTM prediction model is effective in predicting stock prices with high accuracy. In addition, using maximizing Sharpe ratio method to rebalance the allocation strategy every three months showed a significant improvement in the cumulative return of the constructed portfolios. Furthermore, our constructed portfolios beat the benchmark Sector ETF index in both XLU and XLB.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2023
538 $a Mode of access: World Wide Web
650 4 $a Business administration. $3 3168311
650 4 $a Finance. $3 542899
650 4 $a Artificial intelligence. $3 516317
650 4 $a Decision making. $3 517204
650 4 $a Neural networks. $3 677449
653 $a Stock prediction
653 $a Quantitative trading
653 $a Trading strategies
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690 $a 0800
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710 2 $a Stevens Institute of Technology. $b School of Business. $3 3428368
773 0 $t Masters Abstracts International $g 83-01.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28498566 $z click for full text (PQDT)