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Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing [article]

Qiong Wu, Christopher G. Brinton, Zheng Zhang, Andrea Pizzoferrato, Zhenming Liu, Mihai Cucuringu
2021 arXiv   pre-print
To this end, we propose an end-to-end deep learning framework to price the assets.  ...  Pricing assets has attracted significant attention from the financial technology community.  ...  We thank anonymous reviewers for helpful comments and suggestions. Christopher G.  ... 
arXiv:1909.04497v2 fatcat:2lfpciy2s5abbf7h5mpitmoztu

Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems

2019 Applied Sciences  
The author proposes in this work the use of an algorithm based both on supervised Deep Learning and on a Reinforcement Learning algorithm for forecasting the short-term trend in the currency FOREX (FOReign  ...  by mathematical algorithms, that act on markets for shares, options, bonds, derivative instruments, commodities, and so on.  ...  The authors analyzed the application of deep learning approaches in the A-trader framework for making profitable trading strategies in the forex market.  ... 
doi:10.3390/app9204460 fatcat:qzzo3bru3nasfonnhuth456qa4

Financial Portfolio Optimization with Online Deep Reinforcement Learning and Restricted Stacked Autoencoder - DeepBreath

Farzan Soleymani, Eric Paquet
2020 Expert systems with applications  
In this paper, a portfolio management framework is developed based on a deep reinforcement learning framework called Deep-Breath.  ...  The framework consists of both offline and online learning strategies: the former is required to train the CNN while the latter handles concept drifts i.e. a change in the data distribution resulting from  ...  Declaration of Competing Interest All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important  ... 
doi:10.1016/j.eswa.2020.113456 fatcat:pntmi7qawrdcdnvfgqzybqr5gu

Deep Reinforcement Learning in Agent Based Financial Market Simulation

Iwao Maeda, David deGraw, Michiharu Kitano, Hiroyasu Matsushima, Hiroki Sakaji, Kiyoshi Izumi, Atsuo Kato
2020 Journal of Risk and Financial Management  
In this study we propose a framework for training deep reinforcement learning models in agent based artificial price-order-book simulations that yield non-trivial policies under diverse conditions with  ...  Prediction of financial market data with deep learning models has achieved some level of recent success.  ...  Proposal and verification of a deep reinforcement learning framework that learns meaningful trading strategies in agent based artificial market simulations 2.  ... 
doi:10.3390/jrfm13040071 fatcat:6rpzflkjxramvntlhv6py5tuc4

Financial Time Series Prediction Using Deep Learning [article]

Ariel Navon, Yosi Keller
2017 arXiv   pre-print
In this work we present a data-driven end-to-end Deep Learning approach for time series prediction, applied to financial time series.  ...  A Deep Learning scheme is derived to predict the temporal trends of stocks and ETFs in NYSE or NASDAQ.  ...  presented a Deep Learning approach for financial time series forecasting.  ... 
arXiv:1711.04174v1 fatcat:yauhe4wqznf4jasataugnriani

A Reinforcement Learning Based Encoder-Decoder Framework for Learning Stock Trading Rules [article]

Mehran Taghian, Ahmad Asadi, Reza Safabakhsh
2021 arXiv   pre-print
In this paper, a novel end-to-end model based on the neural encoder-decoder framework combined with DRL is proposed to learn single instrument trading strategies from a long sequence of raw prices of the  ...  A wide variety of deep reinforcement learning (DRL) models have recently been proposed to learn profitable investment strategies.  ...  of a specific financial instrument and learn a profitable trading strategy.  ... 
arXiv:2101.03867v1 fatcat:pohr6kvczbc7pm5b3cn7d6rj7a

Managing Editor's Letter

Francesco A. Fabozzi
2021 The Journal of Financial Data Science  
This allows good hedges to be developed for asset price processes that are not associated with analytic pricing models. Deep sequence models have been applied to predicting asset returns.  ...  The authors suggest an approach where a relatively simple valuation model is used in conjunction with more complex models for the evolution of the asset price.  ...  In particular, the authors apply deep learning models to predict changes in the Eurodollar futures curve, as a function of a recent history of asset price data, within a high-frequency domain.  ... 
doi:10.3905/jfds.2021.3.1.001 fatcat:rlm4fkjcjbd35bza3xvlj5lsay

Capturing Financial markets to apply Deep Reinforcement Learning [article]

Souradeep Chakraborty
2019 arXiv   pre-print
In this paper we explore the usage of deep reinforcement learning algorithms to automatically generate consistently profitable, robust, uncorrelated trading signals in any general financial market.  ...  In order to do this, we present a novel Markov decision process (MDP) model to capture the financial trading markets.  ...  This motivated researchers and financial institutions to try to come up with a machine/deep learning framework for trading.  ... 
arXiv:1907.04373v3 fatcat:veoevks3xjcv3ak65qky5tvy2m

Deep Learning with Recursive Neural Network for Temporal Logic Implementation

Ajeet K. Jain
2020 International Journal of Advanced Trends in Computer Science and Engineering  
Stock prediction in financial market is one of the exciting applications of deep learning (DL).  ...  Stock values fluctuate in accordance with time and hence suitability of Recursive Neural Network (RNN) as one of the model for prediction of stocks is a niche choice as a predictor.  ...  ABSTRACT Stock prediction in financial market is one of the exciting applications of deep learning (DL).  ... 
doi:10.30534/ijatcse/2020/383942020 fatcat:f3p3mzdqavb6jfgo37fxe3wx3q

Deep Replication of a Runoff Portfolio [article]

Thomas Krabichler, Josef Teichmann
2020 arXiv   pre-print
To the best of our knowledge, the application of deep learning in the field of quantitative risk management is still a relatively recent phenomenon.  ...  This article presents the key notions of Deep Asset Liability Management (Deep~ALM) for a technological transformation in the management of assets and liabilities along a whole term structure.  ...  framework), they will never object to use deep learning. 5.7.  ... 
arXiv:2009.05034v1 fatcat:nnboim65nbfovbhbgiucoiwrle

SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems [article]

Pooja Gupta, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia
2020 arXiv   pre-print
This work proposes a supervised multi-channel time-series learning framework for financial stock trading.  ...  Numerical experiments confirm that the proposed model yields considerably better results than state-of-the-art deep learning techniques for real-world problem of stock trading.  ...  Introduction Financial time series forecasting, and particularly stock price forecasting, requires to determine the future value of a company's stock or any other form of a financial instrument traded  ... 
arXiv:2011.04364v1 fatcat:oig4t5iarnaudkvvsevvicntfy

Timeseries Forecasting using Long Short-Term Memory Optimized by Multi Heuristics Algorithm

2019 International journal of recent technology and engineering  
Forecasting future price of financial instruments (such as equity, bonds and mutual funds) has become an ongoing effort of financial and capital market industry members.  ...  This paper presents optimization solution for a deep learning model in forecasting selected Indonesian mutual funds' Net Asset Value (NAV).  ...  The experiments implements KERAS, the python deep learning library [32] as a framework. Fig 13.  ... 
doi:10.35940/ijrte.d4260.118419 fatcat:iatg2rgchbfyrma7ajd626ezsq

Stock Price prediction with LSTM Based Deep Learning Techniques

2021 Zenodo  
for each data point using a stochastic gradient.  ...  A proposed algorithm that uses market data to predict the share price using machine learning strategies such as a repetitive neural network called Long Short Term Memory, in which process weights are adjusted  ...  In [12] , a combination of financial time analysis and NLP has been compiled. In [9] and [7] , deep learning structures have been used for modeling the multivariate financial timeline series.  ... 
doi:10.5281/zenodo.5327646 fatcat:vouiunnitrarroevm3mshk6ytm

YOLO Object Recognition Algorithm and "Buy-Sell Decision" Model over 2D Candlestick Charts

Serdar Birogul, Gunay Temur, Utku Kose
2020 IEEE Access  
INDEX TERMS YOLO, object detection and classification, decision support systems, deep learning, finance, trend decision.  ...  Those who can generate a high income in financial markets and even be more successful than large companies are actually the ones interpreting the data in a different way.  ...  APPENDIX The authors would like to thank BIST for providing past stock price data used in the study free of charge.  ... 
doi:10.1109/access.2020.2994282 fatcat:2jcqapiry5bgrhwfg3m7ikcpyi

Automated trading systems statistical and machine learning methods and hardware implementation: a survey

Boming Huang, Yuxiang Huan, Li Da Xu, Lirong Zheng, Zhuo Zou
2018 Enterprise Information Systems  
Automated trading, which is also known as algorithmic trading, is a method of using a predesigned computer program to submit a large number of trading orders to an exchange.  ...  It is substantially a real-time decision-making system which is under the scope of Enterprise Information System (EIS).  ...  Feuerriegel and Fehrer (2016) Headlines of adhoc announcement 2004-2011 daily Deep learning, recursive autoencoder model Deep learning Presents novel research for deep learning application  ... 
doi:10.1080/17517575.2018.1493145 fatcat:iu2ik77qkfcx5jl2vqyotthpc4
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