A Peak Price Tracking-Based Learning System for Portfolio Selection

We propose a novel linear learning system based on the peak price tracking (PPT) strategy for portfolio selection (PS). Recently, the topic of tracking control attracts intensive attention and some novel models are proposed based on backstepping methods, such that the system output tracks a desired trajectory. The proposed system has a similar evolution with a transform function that aggressively tracks the increasing power of different assets. As a result, the better performing assets will receive more investment. The proposed PPT objective can be formulated as a fast backpropagation algorithm, which is suitable for large-scale and time-limited applications, such as high-frequency trading. Extensive experiments on several benchmark data sets from diverse real financial markets show that PPT outperforms other state-of-the-art systems in computational time, cumulative wealth, and risk-adjusted metrics. It suggests that PPT is effective and even more robust than some defensive systems in PS.

[1]  Bin Li,et al.  CORN: Correlation-driven nonparametric learning approach for portfolio selection , 2011, TIST.

[2]  Steven C. H. Hoi,et al.  PAMR: Passive aggressive mean reversion strategy for portfolio selection , 2012, Machine Learning.

[3]  Robert E. Schapire,et al.  Algorithms for portfolio management based on the Newton method , 2006, ICML.

[4]  Steven C. H. Hoi,et al.  Online portfolio selection: A survey , 2012, CSUR.

[5]  Saeid Nahavandi,et al.  Fuzzy Portfolio Allocation Models Through a New Risk Measure and Fuzzy Sharpe Ratio , 2015, IEEE Transactions on Fuzzy Systems.

[6]  Avimanyu Sahoo,et al.  Near Optimal Event-Triggered Control of Nonlinear Discrete-Time Systems Using Neurodynamic Programming , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[7]  Yoram Singer,et al.  Efficient projections onto the l1-ball for learning in high dimensions , 2008, ICML '08.

[8]  R. Thaler,et al.  Does the Stock Market Overreact , 1985 .

[9]  E. Thorp.  OPTIMAL GAMBLING SYSTEMS FOR FAVORABLE GAMES , 1969 .

[10]  M. Shirosaki.  Another proof of the defect relation for moving targets , 1991 .

[11]  Cun-Hui Zhang,et al.  The multivariate L1-median and associated data depth. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[12]  T. Cover,et al.  Asymptotic optimality and asymptotic equipartition properties of log-optimum investment , 1988 .

[13]  Eduardo Bayro-Corrochano,et al.  Geometric Bioinspired Networks for Recognition of 2-D and 3-D Low-Level Structures and Transformations , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[14]  J. Lintner.  THE VALUATION OF RISK ASSETS AND THE SELECTION OF RISKY INVESTMENTS IN STOCK PORTFOLIOS AND CAPITAL BUDGETS , 1965 .

[15]  Vikas Jain.  From Efficient Markets Theory to Behavioral Finance , 2003 .

[16]  Fischer Black,et al.  How to Use Security Analysis to Improve Portfolio Selection , 1973 .

[17]  R. Grinold,et al.  Active portfolio management : a quantitative approach for providing superior returns and controlling risk , 2000 .

[18]  Jun Zhang,et al.  Adaptive Filter Design Using Type-2 Fuzzy Cerebellar Model Articulation Controller , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[19]  Harry M. Markowitz,et al.  Mean‐Variance Model for Portfolio Selection , 2012 .

[20]  Narasimhan Jegadeesh,et al.  Evidence of Predictable Behavior of Security Returns , 1990 .

[21]  Narasimhan Jegadeesh,et al.  Seasonality in Stock Price Mean Reversion: Evidence from the U.S. and the U.K. , 1991 .

[22]  Bin Li,et al.  Robust Median Reversion Strategy for Online Portfolio Selection , 2013, IEEE Transactions on Knowledge and Data Engineering.

[23]  Allan Borodin,et al.  Can We Learn to Beat the Best Stock , 2003, NIPS.

[24]  Moncef Gabbouj,et al.  Training Radial Basis Function Neural Networks for Classification via Class-Specific Clustering , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[25]  Bin Li,et al.  Moving average reversion strategy for on-line portfolio selection , 2015, Artif. Intell..

[26]  Giordano Pola,et al.  A Stochastic Reachability Approach to Portfolio Construction in Finance Industry , 2012, IEEE Transactions on Control Systems Technology.

[27]  G. Lugosi,et al.  NONPARAMETRIC KERNEL‐BASED SEQUENTIAL INVESTMENT STRATEGIES , 2006 .

[28]  Enriqueta Vercher,et al.  A Possibilistic Mean-Downside Risk-Skewness Model for Efficient Portfolio Selection , 2013, IEEE Transactions on Fuzzy Systems.

[29]  T. Cover.  Universal Portfolios , 1996 .

[30]  Irene Aldridge,et al.  High-frequency Trading High-frequency Trading Industry Strategy Project Engineering Leadership Program , 2022 .

[31]  Narasimhan Jegadeesh,et al.  Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency , 1993 .

[32]  Matthew R. McKay,et al.  A Robust Statistics Approach to Minimum Variance Portfolio Optimization , 2015, IEEE Transactions on Signal Processing.

[33]  E. Fama.  EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[34]  Jack L. Treynor,et al.  MUTUAL FUND PERFORMANCE* , 2007 .

[35]  Tingwen Huang,et al.  Model-Free Optimal Tracking Control via Critic-Only Q-Learning , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[36]  Yong Chen,et al.  Adaptive Neural Control for a Class of Pure-Feedback Nonlinear Systems via Dynamic Surface Technique , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[37]  J. Mossin.  EQUILIBRIUM IN A CAPITAL ASSET MARKET , 1966 .

[38]  Bin Li,et al.  Confidence Weighted Mean Reversion Strategy for Online Portfolio Selection , 2011, TKDD.

[39]  Dapeng Oliver Wu,et al.  Why Deep Learning Works: A Manifold Disentanglement Perspective , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[40]  Tingwen Huang,et al.  A One-Layer Recurrent Neural Network for Real-Time Portfolio Optimization With Probability Criterion , 2013, IEEE Transactions on Cybernetics.

[41]  A. Tversky,et al.  Prospect theory: an analysis of decision under risk — Source link , 2007 .

[42]  William N. Goetzmann,et al.  Active Portfolio Management , 1999 .

[43]  Sarunas Raudys,et al.  Portfolio of Automated Trading Systems: Complexity and Learning Set Size Issues , 2013, IEEE Transactions on Neural Networks and Learning Systems.