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A Self-Rewarding Mechanism in Deep Reinforcement Learning for Trading Strategy Optimization
MathematicsReinforcement Learning (RL) is increasingly being applied to complex decision-making tasks such as financial trading. However, designing effective reward functions remains a significant challenge.
Yuling Huang +3 more
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Tail-Risk Protection Trading Strategies [PDF]
SSRN Electronic Journal, 2015 Starting from well-known empirical stylised facts of financial time series, we develop dynamic portfolio protection trading strategies based on econometric methods. As a criterion for riskiness we consider the evolution of the value-at-risk spread from a GARCH model with normal innovations relative to a GARCH model with generalised innovations.
N. Packham +3 more
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HETEROGENEOUS TRADING STRATEGY ENSEMBLING FOR INTRADAY TRADING ALGORITHMS
South African Journal of Industrial Engineering, 2023 Since the inception of algorithmic trading during the mid-1970s, considerable resources and time have been committed by the financial sector to the development of trading algorithms in the hope of obtaining a competitive advantage over human contenders.
Koegelenberg, D.J.C, van Vuuren, J.H.
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Testing the performance of technical trading rules in the Chinese market
, 2015 Technical trading rules have a long history of being used by practitioners in financial markets. Their profitable ability and efficiency of technical trading rules are yet controversial. In this paper, we test the performance of more than seven thousands
Jiang, Zhi-Qiang +3 more
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DEEP LEARNING FOR STOCK MARKET TRADING: A SUPERIOR TRADING STRATEGY?
Neural Network World, 2019 Deep-learning initiatives have vastly changed the analysis of data. Complex networks became accessible to anyone in any research area. In this paper we are proposing a deep-learning long short-term memory network (LSTM) for automated stock trading.
D. Fister +3 more
semanticscholar +1 more source
Trading strategies of institutional investors in a limit order book market
MATEC Web of Conferences, 2016 The study aims to examine the trading strategies of institutional investors in limit order book market. The study modifies assumptions of prior studies [1,2] to match actual situations or facilitate calculations.
chen Naiwei, Peng Mingxu
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Equity Returns Around Extreme Loss: A Stochastic Event Approach
American Business ReviewWe define an extreme loss event as a daily return at the left tail of negative two standard deviations of all daily returns for a specific stock. Prior studies focus on the relationship between extreme losses and specific anticipated announcements.
Xiaomin Guo +2 more
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Advanced Investment Strategy for Trading Major Currency Pairs
Littera Scripta, 2013 In this paper there is a description of one of the possible approaches to investing in the currency market, which is based on the statistical analysis of price movements of major currency pairs. It is the currency pairs EUR/USD, GBP/USD and USD/JPY which
Jan Budík, Lenka Smolíková
doaj
Practical Deep Reinforcement Learning Approach for Stock Trading
, 2018 Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy
Liu, Xiao-Yang +4 more
core
Pricing of Proactive Hedging European Option with Dynamic Discrete Position Strategy
Discrete Dynamics in Nature and Society, 2019 Proactive hedging European option is an exotic option for hedgers in the options market proposed recently by Wang et al. It extends the classical European option by requiring option holders to continuously trade in underlying assets according to a ...
Meng Li, Xuefeng Wang, Fangfang Sun
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