یادگیری عمیق با شبکه های حافظه کوتاه مدت
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یادگیری عمیق با شبکه های حافظه کوتاه مدت

عنوان فارسی مقاله: یادگیری عمیق با شبکه های حافظه کوتاه مدت برای پیش بینی بازارهای مالی
عنوان انگلیسی مقاله: Deep learning with long short-term memory networks for financial market predictions
مجله/کنفرانس: مجله اروپایی تحقیق در عملیات - European Journal of Operational Research
رشته های تحصیلی مرتبط: مهندسی کامپیوتر، مدیریت
گرایش های تحصیلی مرتبط: هوش مصنوعی، مدیریت مالی
کلمات کلیدی فارسی: دارایی، معامله آماری، LSTM، یادگیری ماشین، یادگیری عمیق
کلمات کلیدی انگلیسی: Finance، Statistical arbitrage، LSTM، Machine learning، Deep learning
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journal List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.ejor.2017.11.054
دانشگاه: Department of Statistics and Econometrics - University of Erlangen-Nürnberg - Germany
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2018
ایمپکت فاکتور: 3/632 در سال 2017
شاخص H_index: 211 در سال 2019
شاخص SJR: 2/437 در سال 2017
شناسه ISSN: 0377-2217
شاخص Quartile (چارک): Q1 در سال 2017
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 16
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: بله
کد محصول: E10789
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Data, software, hardware


3- Methodology


4- Results


5- Conclusion


References

نمونه متن انگلیسی مقاله

Abstract


Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the constituent stocks of the S&P 500 from 1992 until 2015. With daily returns of 0.46 percent and a Sharpe ratio of 5.8 prior to transaction costs, we find LSTM networks to outperform memory-free classification methods, i.e., a random forest (RAF), a deep neural net (DNN), and a logistic regression classifier (LOG). The outperformance relative to the general market is very clear from 1992 to 2009, but as of 2010, excess returns seem to have been arbitraged away with LSTM profitability fluctuating around zero after transaction costs. We further unveil sources of profitability, thereby shedding light into the black box of artificial neural networks. Specifically, we find one common pattern among the stocks selected for trading – they exhibit high volatility and a short-term reversal return profile. Leveraging these findings, we are able to formalize a rules-based short-term reversal strategy that yields 0.23 percent prior to transaction costs. Further regression analysis unveils low exposure of the LSTM returns to common sources of systematic risk – also compared to the three benchmark models.


Introduction


Prediction tasks on financial time series are notoriously difficult, primarily driven by the high degree of noise and the generally accepted, semi-strong form of market efficiency (Fama, 1970). Yet, there is a plethora of well-known capital market anomalies that are in stark contrast with the notion of market efficiency. For example, Jacobs (2015) or Green, Hand, and Zhang (2013) provide surveys comprising more than 100 of such capital market anomalies, which effectively rely on return predictive signals to outperform the market. However, the financial models used to establish a relationship between these return predictive signals, (the features) and future returns (the targets), are usually transparent in nature and not able to capture complex non-linear dependencies. In the last years, initial evidence has been established that machine learning techniques are capable of identifying (nonlinear) structures in financial market data, see Huck (2009, 2010), Takeuchi and Lee (2013), Moritz and Zimmermann (2014), Dixon, Klabjan, and Bang (2015), and further references in Atsalakis and Valavanis (2009) as well as Sermpinis, Theofilatos, Karathanasopoulos, Georgopoulos, and Dunis (2013).

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