Standard
Deep Learning Approach for Prognoses of Long-Term Options Behavior. / Bogdanov, A. V.; Bogdanov, S. A.; Rukovchuk, V. P.; Khmel, D. S.
Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings. ed. / Sanjay Misra; Osvaldo Gervasi; Beniamino Murgante; Elena Stankova; Vladimir Korkhov; Carmelo Torre; Eufemia Tarantino; Ana Maria A.C. Rocha; David Taniar; Bernady O. Apduhan. Springer Nature, 2019. p. 631-640 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11620 LNCS).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Harvard
Bogdanov, AV, Bogdanov, SA, Rukovchuk, VP & Khmel, DS 2019,
Deep Learning Approach for Prognoses of Long-Term Options Behavior. in S Misra, O Gervasi, B Murgante, E Stankova, V Korkhov, C Torre, E Tarantino, AMAC Rocha, D Taniar & BO Apduhan (eds),
Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11620 LNCS, Springer Nature, pp. 631-640, 19th International Conference on Computational Science and Its Applications, ICCSA 2019, Saint Petersburg, Russian Federation,
1/07/19.
https://doi.org/10.1007/978-3-030-24296-1_51
APA
Bogdanov, A. V., Bogdanov, S. A., Rukovchuk, V. P., & Khmel, D. S. (2019).
Deep Learning Approach for Prognoses of Long-Term Options Behavior. In S. Misra, O. Gervasi, B. Murgante, E. Stankova, V. Korkhov, C. Torre, E. Tarantino, A. M. A. C. Rocha, D. Taniar, & B. O. Apduhan (Eds.),
Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings (pp. 631-640). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11620 LNCS). Springer Nature.
https://doi.org/10.1007/978-3-030-24296-1_51
Vancouver
Bogdanov AV, Bogdanov SA, Rukovchuk VP, Khmel DS.
Deep Learning Approach for Prognoses of Long-Term Options Behavior. In Misra S, Gervasi O, Murgante B, Stankova E, Korkhov V, Torre C, Tarantino E, Rocha AMAC, Taniar D, Apduhan BO, editors, Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings. Springer Nature. 2019. p. 631-640. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
https://doi.org/10.1007/978-3-030-24296-1_51
Author
Bogdanov, A. V. ; Bogdanov, S. A. ; Rukovchuk, V. P. ; Khmel, D. S. /
Deep Learning Approach for Prognoses of Long-Term Options Behavior. Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings. editor / Sanjay Misra ; Osvaldo Gervasi ; Beniamino Murgante ; Elena Stankova ; Vladimir Korkhov ; Carmelo Torre ; Eufemia Tarantino ; Ana Maria A.C. Rocha ; David Taniar ; Bernady O. Apduhan. Springer Nature, 2019. pp. 631-640 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
BibTeX
@inproceedings{418f0cccb117478b8dd836f7cc5a5ed7,
title = "Deep Learning Approach for Prognoses of Long-Term Options Behavior",
abstract = "Since the inception of asset pricing models, starting as far back as beginning of XX century, and moreover after the fundamental work of Black and Scholes (1973), there has been considerable interest in analytical research of stock exchange equities behavior. Still up to nowadays it remains a critical task for participants engaged in the field of “financial mathematics”. The reason of such an undying interest is that to adequately assess investments risks the stock exchange actors (brokers, investors, traders, et. al) need still more and more accurate prediction results obtained as fast as possible. It is a matter of fact proven by numerous researchers that assets derivatives behave differently being observed in small, medium, and long-term frames. Algorithms for predicting the dynamics of stock options and other assets derivatives for both small times (where one plays on market fluctuations), and medium ones (where trade is stressed at the beginning and closing moments) are well developed, and trading robots are actively used for these purposes. Analysis of the dynamics of assets for very long time-frames (of order of several months and years) is still beyond the scope of analysts as it is expensively prohibited, although this issue is extremely important for hedging the investments portfolios. The present paper focuses on construction of an effective and resource-intensive model for predicting the behavior of financial instruments, trends and price movements based upon the principles of deep learning. The forecasts obtained by the model showed an almost acceptable compliance with the true prices of the S&P500.",
keywords = "Deep learning, LSTM, Recurrent neural networks, S&P500, TensorFlow",
author = "Bogdanov, {A. V.} and Bogdanov, {S. A.} and Rukovchuk, {V. P.} and Khmel, {D. S.}",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; 19th International Conference on Computational Science and Its Applications, ICCSA 2019 ; Conference date: 01-07-2019 Through 04-07-2019",
year = "2019",
doi = "10.1007/978-3-030-24296-1_51",
language = "English",
isbn = "9783030242954",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature",
pages = "631--640",
editor = "Sanjay Misra and Osvaldo Gervasi and Beniamino Murgante and Elena Stankova and Vladimir Korkhov and Carmelo Torre and Eufemia Tarantino and Rocha, {Ana Maria A.C.} and David Taniar and Apduhan, {Bernady O.}",
booktitle = "Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings",
address = "Germany",
}
RIS
TY - GEN
T1 - Deep Learning Approach for Prognoses of Long-Term Options Behavior
AU - Bogdanov, A. V.
AU - Bogdanov, S. A.
AU - Rukovchuk, V. P.
AU - Khmel, D. S.
N1 - Conference code: 19
PY - 2019
Y1 - 2019
N2 - Since the inception of asset pricing models, starting as far back as beginning of XX century, and moreover after the fundamental work of Black and Scholes (1973), there has been considerable interest in analytical research of stock exchange equities behavior. Still up to nowadays it remains a critical task for participants engaged in the field of “financial mathematics”. The reason of such an undying interest is that to adequately assess investments risks the stock exchange actors (brokers, investors, traders, et. al) need still more and more accurate prediction results obtained as fast as possible. It is a matter of fact proven by numerous researchers that assets derivatives behave differently being observed in small, medium, and long-term frames. Algorithms for predicting the dynamics of stock options and other assets derivatives for both small times (where one plays on market fluctuations), and medium ones (where trade is stressed at the beginning and closing moments) are well developed, and trading robots are actively used for these purposes. Analysis of the dynamics of assets for very long time-frames (of order of several months and years) is still beyond the scope of analysts as it is expensively prohibited, although this issue is extremely important for hedging the investments portfolios. The present paper focuses on construction of an effective and resource-intensive model for predicting the behavior of financial instruments, trends and price movements based upon the principles of deep learning. The forecasts obtained by the model showed an almost acceptable compliance with the true prices of the S&P500.
AB - Since the inception of asset pricing models, starting as far back as beginning of XX century, and moreover after the fundamental work of Black and Scholes (1973), there has been considerable interest in analytical research of stock exchange equities behavior. Still up to nowadays it remains a critical task for participants engaged in the field of “financial mathematics”. The reason of such an undying interest is that to adequately assess investments risks the stock exchange actors (brokers, investors, traders, et. al) need still more and more accurate prediction results obtained as fast as possible. It is a matter of fact proven by numerous researchers that assets derivatives behave differently being observed in small, medium, and long-term frames. Algorithms for predicting the dynamics of stock options and other assets derivatives for both small times (where one plays on market fluctuations), and medium ones (where trade is stressed at the beginning and closing moments) are well developed, and trading robots are actively used for these purposes. Analysis of the dynamics of assets for very long time-frames (of order of several months and years) is still beyond the scope of analysts as it is expensively prohibited, although this issue is extremely important for hedging the investments portfolios. The present paper focuses on construction of an effective and resource-intensive model for predicting the behavior of financial instruments, trends and price movements based upon the principles of deep learning. The forecasts obtained by the model showed an almost acceptable compliance with the true prices of the S&P500.
KW - Deep learning
KW - LSTM
KW - Recurrent neural networks
KW - S&P500
KW - TensorFlow
UR - http://www.scopus.com/inward/record.url?scp=85069150435&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-24296-1_51
DO - 10.1007/978-3-030-24296-1_51
M3 - Conference contribution
AN - SCOPUS:85069150435
SN - 9783030242954
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 631
EP - 640
BT - Computational Science and Its Applications – ICCSA 2019 - 19th International Conference, 2019, Proceedings
A2 - Misra, Sanjay
A2 - Gervasi, Osvaldo
A2 - Murgante, Beniamino
A2 - Stankova, Elena
A2 - Korkhov, Vladimir
A2 - Torre, Carmelo
A2 - Tarantino, Eufemia
A2 - Rocha, Ana Maria A.C.
A2 - Taniar, David
A2 - Apduhan, Bernady O.
PB - Springer Nature
T2 - 19th International Conference on Computational Science and Its Applications, ICCSA 2019
Y2 - 1 July 2019 through 4 July 2019
ER -
