Performance analysis of the integration between Portfolio Optimization and Technical Analysis strategies in the Brazilian stock market

Abstract This article proposes a fusion between Technical Analysis indicators and Multiobjective Portfolio Optimization. It considers four indicators and the optimization performed over two risk measures and the expected return, subject to cardinality constraint, self-financing, and investment limits. The fusion occurs using two scenarios. The first one generates an optimal investment portfolio at the beginning of each month and uses Technical Analysis indicators to carry out the transactions. The second one performs the optimization monthly, considering just the assets filtered by the indicators. Numerical simulations consider an insightful comparison concerning the performance of proposed approaches with indicators and optimization in isolation and with standard benchmarks, covering six years of data from the Brazilian Stock Exchange, as a robust analysis. The portfolios are evaluated under metrics return, maximum drawdown and drawup, Return Over Maximum Drawdown index, and the Draw Ratio index. The results show that these fusions can improve the portfolio performance, providing optimal strategies to the investor giving a higher return for a certain level of risk, even considering realistic constraints.

[1]  Alberto Bemporad,et al.  A stochastic model predictive control approach to dynamic option hedging with transaction costs , 2011, Proceedings of the 2011 American Control Conference.

[2]  Francesco Cesarone,et al.  Optimally chosen small portfolios are better than large ones , 2016 .

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

[4]  Shucheng Liu,et al.  Lagrangian relaxation procedure for cardinality-constrained portfolio optimization , 2008, Optim. Methods Softw..

[5]  William W. Hogan,et al.  Computation of the Efficient Boundary in the E-S Portfolio Selection Model , 1972, Journal of Financial and Quantitative Analysis.

[6]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[7]  R. Thaler Behavioral Economics: Past, Present and Future , 2016 .

[8]  Stoyan V. Stoyanov,et al.  Portfolio selection based on a simulated copula , 2010 .

[9]  Perry J. Kaufman,et al.  Trading Systems and Methods , 1997 .

[10]  Kalyanmoy Deb,et al.  Bi-objective Portfolio Optimization Using a Customized Hybrid NSGA-II Procedure , 2011, EMO.

[11]  J. Murphy Technical Analysis of the Futures Markets: A Comprehensive Guide to Trading Methods and Applications , 1986 .

[12]  F. Cesarone,et al.  Portfolio selection problems in practice: a comparison between linear and quadratic optimization models , 2011, 1105.3594.

[13]  Alberto Suárez,et al.  A memetic algorithm for cardinality-constrained portfolio optimization with transaction costs , 2015, Appl. Soft Comput..

[14]  R. Rockafellar,et al.  Optimization of conditional value-at risk , 2000 .

[15]  Pedro Godinho,et al.  Mean-semivariance portfolio optimization with multiobjective evolutionary algorithms and technical analysis rules , 2017, Expert Syst. Appl..

[16]  Rodrigo T. N. Cardoso,et al.  Decision-making for financial trading: A fusion approach of machine learning and portfolio selection , 2019, Expert Syst. Appl..

[17]  Karolina Koziorowska Conditional Value at Risk , 2009 .

[18]  Gerald Appel,et al.  Technical Analysis: Power Tools for Active Investors , 2005 .

[19]  W. Sharpe CAPITAL ASSET PRICES: A THEORY OF MARKET EQUILIBRIUM UNDER CONDITIONS OF RISK* , 1964 .

[20]  Merton H. Miller The Cost of Capital, Corporation Finance and the Theory of Investment , 1958 .

[21]  F. Black,et al.  The Pricing of Options and Corporate Liabilities , 1973, Journal of Political Economy.

[22]  G. Mitra,et al.  Portfolio selection models: a review and new directions , 2009 .

[23]  Rodrigo T. N. Cardoso,et al.  Parallel MOEAs for Combinatorial Multiobjective Optimization Model of Financial Portfolio Selection , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[24]  David R. Aronson Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals , 2006 .

[25]  J. Wilder New Concepts in Technical Trading Systems , 1978 .

[26]  V. Evangelos Efficient markets hypothesis in the time of COVID-19 , 2020 .

[27]  C. Hațiegan,et al.  Multifractal Detrended Fluctuation Analysis (MF-DFA) of Stock Market Indexes. Empirical Evidence from Seven Central and Eastern European Markets , 2020 .

[28]  W. Sharpe A Simplified Model for Portfolio Analysis , 1963 .

[29]  J. Bollinger Bollinger on Bollinger Bands , 2001 .

[30]  Ankit Thakkar,et al.  Fusion in stock market prediction: A decade survey on the necessity, recent developments, and potential future directions , 2020, Information Fusion.

[31]  Yazid M. Sharaiha,et al.  Heuristics for cardinality constrained portfolio optimisation , 2000, Comput. Oper. Res..

[32]  Dongxu Mo,et al.  Projecting Financial Technical Indicators Into Networks as a Tool to Build a Portfolio , 2021, IEEE Access.

[33]  Rodrigo T. N. Cardoso,et al.  Multi-attribute decision making applied to financial portfolio optimization problem , 2020, Expert Syst. Appl..

[34]  Federico Divina,et al.  Technical analysis strategy optimization using a machine learning approach in stock market indices , 2021, Knowl. Based Syst..

[35]  Rodrigo T. N. Cardoso,et al.  Analysis of risk measures in multiobjective optimization portfolios with cardinality constraint , 2019, Brazilian Review of Finance.