| 000 -LEADER |
|---|
| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
240318b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9783030472504 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Item number |
MOR |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Moraes Sarmento, Simao |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
A machine learning based pairs trading investment strategy |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc |
Springer, |
| Date of publication, distribution, etc |
2021 |
| Place of publication, distribution, etc |
Cham : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
ix, 104 p. ; |
| Other physical details |
ill., (some col.), |
| Dimensions |
24 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
59.99 |
| Price type code |
€ |
| Unit of pricing |
93.50 |
| 490 ## - SERIES STATEMENT |
|---|
| Series statement |
SpringerBriefs in Computational Intelligence, |
| Volume number/sequential designation |
2625-3704 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
This book investigates the application of promising machine learning techniques to address two problems: (i) how to find profitable pairs while constraining the search space and (ii) how to avoid long decline periods due to prolonged divergent pairs. It also proposes the integration of an unsupervised learning algorithm, OPTICS, to handle problem (i), and demonstrates that the suggested technique can outperform the common pairs search methods, achieving an average portfolio Sharpe ratio of 3.79, in comparison to 3.58 and 2.59 obtained using standard approaches. For problem (ii), the authors introduce a forecasting-based trading model capable of reducing the periods of portfolio decline by 75%. However, this comes at the expense of decreasing overall profitability. The authors also test the proposed strategy using an ARMA model, an LSTM and an LSTM encoder-decoder. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Machine learning |
|
|---|
| Topical term or geographic name as entry element |
Economics |
|
|---|
| Topical term or geographic name as entry element |
Trading |
|
|---|
| Topical term or geographic name as entry element |
Investment |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Horta, Nuno |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Books |