Title
Indicator selection for daily equity trading with recurrent reinforcement learning
Abstract
Recurrent reinforcement learning (RRL), a machine learning technique, is very successful in training high frequency trading systems. When trading analysis of RRL is done with lower frequency financial data, e.g. daily stock prices, the decrease of autocorrelation in prices may lead to a decrease in trading profit. In this paper, we propose a RRL trading system which utilizes the price information, jointly with the indicators from technical analysis, fundamental analysis and econometric analysis, to produce long/short signals for daily trading. In the proposed trading system, we use a genetic algorithm as a pre-screening tool to search suitable indicators for RRL trading. Moreover, we modify the original RRL parameter update scheme in the literature for out-of-sample trading. Empirical studies are conducted based on data sets of 238 S&P stocks. It is found that the trading performance concerning the out-of sample daily Sharpe ratios turns better: the number of companies with a positive and significant Sharpe ratio increases after feeding the selected indicators jointly with prices information into the RRL system.
Year
DOI
Venue
2013
10.1145/2464576.2480773
GECCO (Companion)
Keywords
Field
DocType
trading analysis,recurrent reinforcement learning,rrl trading system,daily trading,indicator selection,trading performance,out-of-sample trading,trading profit,proposed trading system,rrl system,rrl trading,high frequency trading system,daily equity trading,fundamental analysis,technical analysis,genetic algorithm
Trading strategy,Econometrics,Mathematical optimization,Actuarial science,High-frequency trading,Computer science,Sharpe ratio,Equity (finance),Stock (geology),Empirical research,Technical analysis,Reinforcement learning
Conference
Citations 
PageRank 
References 
3
0.46
3
Authors
2
Name
Order
Citations
PageRank
Jin Zhang130.46
Dietmar Maringer215611.35