کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4943057 1437619 2017 42 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An adaptive portfolio trading system: A risk-return portfolio optimization using recurrent reinforcement learning with expected maximum drawdown
ترجمه فارسی عنوان
یک سیستم معاملاتی انعطاف پذیر: بهینه سازی نمونه کارها با ریسک و بازگشت با استفاده از یادگیری تقویتی مجدد با حداکثر رسیدن به حد انتظار
کلمات کلیدی
یادگیری تقویتی مجدد، حداکثر تخلیه مورد انتظار، بهینه بازنشستگی نمونه کارها، خطر ضعف
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
Dynamic control theory has long been used in solving optimal asset allocation problems, and a number of trading decision systems based on reinforcement learning methods have been applied in asset allocation and portfolio rebalancing. In this paper, we extend the existing work in recurrent reinforcement learning (RRL) and build an optimal variable weight portfolio allocation under a coherent downside risk measure, the expected maximum drawdown, E(MDD). In particular, we propose a recurrent reinforcement learning method, with a coherent risk adjusted performance objective function, the Calmar ratio, to obtain both buy and sell signals and asset allocation weights. Using a portfolio consisting of the most frequently traded exchange-traded funds, we show that the expected maximum drawdown risk based objective function yields superior return performance compared to previously proposed RRL objective functions (i.e. the Sharpe ratio and the Sterling ratio), and that variable weight RRL long/short portfolios outperform equal weight RRL long/short portfolios under different transaction cost scenarios. We further propose an adaptive E(MDD) risk based RRL portfolio rebalancing decision system with a transaction cost and market condition stop-loss retraining mechanism, and we show that the proposed portfolio trading system responds to transaction cost effects better and outperforms hedge fund benchmarks consistently.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 87, 30 November 2017, Pages 267-279
نویسندگان
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