國立虎尾科技大學 |

Applying Data Analytics to Improve Multi-Asset Portfolio Performance.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Applying Data Analytics to Improve Multi-Asset Portfolio Performance./
作者:	Akula, Amrith.
面頁冊數:	1 online resource (46 pages)
附註:	Source: Masters Abstracts International, Volume: 56-06.
Contained By:	Masters Abstracts International56-06(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355203752

Applying Data Analytics to Improve Multi-Asset Portfolio Performance.
Akula, Amrith.

Applying Data Analytics to Improve Multi-Asset Portfolio Performance. - 1 online resource (46 pages)

Source: Masters Abstracts International, Volume: 56-06.

Thesis (M.S.)

Includes bibliographical references

The number of casual investors allocating funds into financial exchanges has surged due to the increased availability of trading accounts on multiple platforms. These investors quite often invest only in one type of asset, stocks. Stocks are known to experience sudden market shifts and extreme volatility based on factors that the investors may not be able to control. Existing applications of data mining stocks perform well, but if the entire stock market performs poorly, investors can face severe losses. This study utilized a data mining tool that evaluates two other classes of investments: the commodities market and the currency exchange market. Three avenues of data mining were implemented as solutions, a neural network, logistic regression and a decision tree, to classify the buying and selling of investments. The results presented that unless in a bullish market scenario, utilizing a multi-asset portfolio with backed by a data mining tool can prove beneficial to an investor. In a bearish market, this study outlined how the performance of the multi-asset portfolio is drastically better than investing using a standalone stock classifier or investing in an index tracked product. In a volatile market, results showed that a multi-asset portfolio is competitive with a standalone stock classifier and in many scenarios even out performed. Overall, the data and resulting analysis provides a good basis for further research.

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

Mode of access: World Wide Web

ISBN: 9780355203752Subjects--Topical Terms:

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

554714
Electronic books.

Applying Data Analytics to Improve Multi-Asset Portfolio Performance.
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245 1 0 $a Applying Data Analytics to Improve Multi-Asset Portfolio Performance.
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500 $a Source: Masters Abstracts International, Volume: 56-06.
500 $a Adviser: Rajendra Raj.
502 $a Thesis (M.S.) $c Rochester Institute of Technology $d 2017.
504 $a Includes bibliographical references
520 $a The number of casual investors allocating funds into financial exchanges has surged due to the increased availability of trading accounts on multiple platforms. These investors quite often invest only in one type of asset, stocks. Stocks are known to experience sudden market shifts and extreme volatility based on factors that the investors may not be able to control. Existing applications of data mining stocks perform well, but if the entire stock market performs poorly, investors can face severe losses. This study utilized a data mining tool that evaluates two other classes of investments: the commodities market and the currency exchange market. Three avenues of data mining were implemented as solutions, a neural network, logistic regression and a decision tree, to classify the buying and selling of investments. The results presented that unless in a bullish market scenario, utilizing a multi-asset portfolio with backed by a data mining tool can prove beneficial to an investor. In a bearish market, this study outlined how the performance of the multi-asset portfolio is drastically better than investing using a standalone stock classifier or investing in an index tracked product. In a volatile market, results showed that a multi-asset portfolio is competitive with a standalone stock classifier and in many scenarios even out performed. Overall, the data and resulting analysis provides a good basis for further research.
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
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