Počet záznamů: 1
Deep learning, predictability, and optimal portfolio returns
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$a 336.7 $x Finance $9 4 $2 Konspekt 080 $a 336.76 $2 MRF 080 $a 336.763.2 $2 MRF 080 $a 336.7:519.86 $2 MRF 080 $a 004.852 $2 MRF 080 $a (048.8) $2 MRF 100 1-
$a Babiak, Mykola $7 kv2018988748 $4 aut 245 10
$a Deep learning, predictability, and optimal portfolio returns / $c Mykola Babiak, Jozef Baruník 264 -1
$a Prague : $b CERGE-EI, $c 2020 300 $a 43 stran : $b ilustrace ; $c 21 cm 336 $a text $b txt $2 rdacontent 337 $a bez média $b n $2 rdamedia 338 $a svazek $b nc $2 rdacarrier 490 1-
$a Working paper series, $x 1211-3298 ; $v 677 504 $a Obsahuje bibliografii a bibliografické odkazy 520 3-
$a We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty equivalent returns and Sharpe ratios. We demonstrate that a long-short-term-memory recurrent neural network, which excels in learning complex time-series dependencies, generates a superior performance among a variety of networks considered. Return predictability via deep learning generates substantially improved portfolio performance across different subsamples, particularly during recessionary periods. These gains are robust to including transaction costs, short-selling and borrowing constraints $c okcz $u https://www.obalkyknih.cz/view?isbn=9788073434847 $2 OKCZ - ANOTACE Z WEBU 546 $a Anglické a české resumé 650 07
$a finanční investování $7 ph137283 $2 czenas 650 07
$a kapitálové trhy $7 ph114867 $2 czenas 650 07
$a akcie $7 ph118318 $2 czenas 650 07
$a teorie portfolia $7 ph180952 $2 czenas 650 07
$a hluboké učení $7 ph1042930 $2 czenas 655 -7
$a studie $7 fd133597 $2 czenas 700 1-
$a Baruník, Jozef $7 pna20211118168 $4 aud 830 -0
$a Working paper series (CERGE-EI) ; $0 677 910 $a CBA001 FMT BK
Počet záznamů: 1