Reweighted Price Relative Tracking System for Automatic Portfolio Optimization
In this paper, we propose a novel reweighted price relative tracking (RPRT) system for automatic portfolio optimization (APO). In the price prediction stage, it automatically assigns separate weights to the price relative predictions according to each asset's performance, and these weights will also be automatically updated. In the portfolio optimizing stage, a novel tracking system with a generalized increasing factor is proposed to maximize the future wealth of next period. Besides, an efficient algorithm is designed to solve the portfolio optimization objective, which is applicable to large-scale and time-limited situations. Extensive experiments on six benchmark datasets from real financial markets with diverse assets and different time spans are conducted. RPRT outperforms other state-of-the-art systems in cumulative wealth, mean excess return, annual percentage yield, and some typical risk metrics. Moreover, it can withstand considerable transaction costs and runs fast. It indicates that RPRT is an effective and efficient APO system.
Code (1)
Tasks
Portfolio OptimizationSimilar Papers 제목 키워드 기반
Unifying Tree-Reweighted Belief Propagation and Mean Field for Tracking Extended Targets
This paper proposes a unified tree-reweighted belief propagation (BP) and mean field (MF) approach for scalable detection and tracking of extended targets within the framework of factor graph. The factor graph is partiti…
Efficient Modeling and Forecasting of the Electricity Spot Price
The increasing importance of renewable energy, especially solar and wind power, has led to new forces in the formation of electricity prices. Hence, this paper introduces an econometric model for the hourly time series o…
Time SeriesTime Series AnalysisSRAW-Attack: Space-Reweighted Adversarial Warping Attack for SAR Target Recognition
Synthetic aperture radar (SAR) imagery exhibits intrinsic information sparsity due to its unique electromagnetic scattering mechanism. Despite the widespread adoption of deep neural network (DNN)-based SAR automatic targ…
Adversarial RobustnessIteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes
Shrinkage algorithms are of great importance in almost every area of statistics due to the increasing impact of big data. Especially time series analysis benefits from efficient and rapid estimation techniques such as th…
Time SeriesTime Series AnalysisA Vietnamese information retrieval system for product-price
A price information retrieval (IR) system allows users to search and view differences among prices of specific products. Building product-price driven IR system is a challenging and active research area. Approaches entir…
Information RetrievalRetrieval