An Adaptive Box-Normalization Stock Index Trading Strategy Based on Reinforcement Learning

2018 ◽  
pp. 335-346
Author(s):  
Yingying Zhu ◽  
Hui Yang ◽  
Jianmin Jiang ◽  
Qiang Huang
Keyword(s):  
Reinforcement Learning ◽  
Trading Strategy ◽  
Stock Index

scholarly journals Optimizing the Pairs-Trading Strategy Using Deep Reinforcement Learning with Trading and Stop-Loss Boundaries

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-20 ◽  
Author(s):  
Taewook Kim ◽  
Ha Young Kim
Keyword(s):  
Reinforcement Learning ◽  
Trading Strategy ◽  
Trading Strategies ◽  
Cointegration Test ◽  
Optimum Level ◽  
Pairs Trading ◽  
Proposed Model ◽  
The Mean ◽  
Stop Loss ◽  
The Given

Many researchers have tried to optimize pairs trading as the numbers of opportunities for arbitrage profit have gradually decreased. Pairs trading is a market-neutral strategy; it profits if the given condition is satisfied within a given trading window, and if not, there is a risk of loss. In this study, we propose an optimized pairs-trading strategy using deep reinforcement learning—particularly with the deep Q-network—utilizing various trading and stop-loss boundaries. More specifically, if spreads hit trading thresholds and reverse to the mean, the agent receives a positive reward. However, if spreads hit stop-loss thresholds or fail to reverse to the mean after hitting the trading thresholds, the agent receives a negative reward. The agent is trained to select the optimum level of discretized trading and stop-loss boundaries given a spread to maximize the expected sum of discounted future profits. Pairs are selected from stocks on the S&P 500 Index using a cointegration test. We compared our proposed method with traditional pairs-trading strategies which use constant trading and stop-loss boundaries. We find that our proposed model is trained well and outperforms traditional pairs-trading strategies.

Do the Option Prices Forecast Spot Price? Evidence from the KOSPI 200 Index Option Market

2011 ◽  
Vol 19 (3) ◽  
pp. 251-280
Author(s):  
Byungwook Choi
Keyword(s):  
Implied Volatility ◽  
Parametric Method ◽  
Trading Strategy ◽  
Stock Index ◽  
Rate Of Return ◽  
Spot Price ◽  
Option Prices ◽  
Daily Rate ◽  
Risk Neutral ◽  
Index Option

This study investigates a forecasting power of volatility curvatures and risk neutral densities implicit in KOSPI 200 option prices by analyzing minute by minute historical index option intraday trading data from January of 2007 to January of 2011. We begin by estimating implied volatility functions and risk neutral price densities based on non-parametric method every minute and by calculating volatility curvature and skewness premium. We then compare the daily rate of return of the signal following trading strategy that we buy (sell) a stock index when the volatility curvature or skewness premium increases (decreases) with that of an intraday buy-and-hold strategy that we buy a stock index on 9:05AM and sell it on 2:50PM. We found that the rate of return of the signal following trading strategy was significantly higher than that of the intraday buy-and-hold strategy, which implies that the option prices have a strong forecasting power on the direction of stock market. Another finding is that the information contents of option prices disappear after three or four minutes.

Trading Strategy for Stock Index Futures Based on Disposition Effect

2014 ◽  
Vol 22 (1) ◽  
pp. 25-44
Author(s):  
Seung Hyun Oh ◽  
Sang Buhm Hahn
Keyword(s):  
Investment Strategy ◽  
Trading Strategy ◽  
Capital Gains ◽  
Stock Index ◽  
Expected Returns ◽  
Stock Index Futures ◽  
Investment Strategies ◽  
Positive Relation ◽  
Index Futures ◽  
The U.S

Grinblatt and Han (2005) argued that unrealized capital gains and expected returns are positively related in the U.S. stock markets. This study investigates the possibility of developing investment strategies for stock index futures using the positive relation. Probing the trading data of futures on KOSPI200 during the period of 1995~2013, several interesting results are obtained. First, the strategy of building long positions when the unrealized capital gain is greater than zero produces positive profit which is statistically significant. Second, the profitability of this strategy during December is significantly positive while the profitability during January is insignificant. Third, the strategy generates positive profit during the second half year while the profitability of the first half year is insignificant. These results imply that it is possible to develop investment strategy by extracting some information from the unrealized capital gains.

Pairs trading strategy optimization using the reinforcement learning method: a cointegration approach

2016 ◽  
Vol 20 (12) ◽  
pp. 5051-5066 ◽  
Author(s):  
Saeid Fallahpour ◽  
Hasan Hakimian ◽  
Khalil Taheri ◽  
Ehsan Ramezanifar
Keyword(s):  
Reinforcement Learning ◽  
Trading Strategy ◽  
Learning Method ◽  
Pairs Trading

Development of Machine Learning Software for High Frequency Trading in Financial Markets

2009 ◽  
pp. 664-683
Author(s):  
Andrei Hryshko ◽  
Tom Downs
Keyword(s):  
Machine Learning ◽  
Genetic Algorithms ◽  
Reinforcement Learning ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Trading Strategy ◽  
Significant Activity ◽  
Trading System ◽  
Learning Software ◽  
Foreign Exchange Trading

Foreign exchange trading has emerged in recent times as a significant activity in many countries. As with most forms of trading, the activity is influenced by many random parameters, so that the creation of a system that effectively emulates the trading process will be very helpful. This chapter presents a novel trading system using Machine Learning methods of Genetic Algorithms and Reinforcement Learning. The system emulates trader behavior on the Foreign Exchange market and finds the most profitable trading strategy.

Acquiring a Government Bond Trading Strategy Using Reinforcement Learning

2009 ◽  
Vol 13 (6) ◽  
pp. 691-696 ◽  
Author(s):  
Tohgoroh Matsui ◽  
◽  
Takashi Goto ◽  
Kiyoshi Izumi ◽  
◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Trading Strategy ◽  
Japanese Government ◽  
Government Bond

This paper proposes using reinforcement learning to acquire a government bond trading strategy. We applied this method to the 10-year Japanese government bond (JGB) market and confirmed that it acquires profitable trading even in extrapolation.

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