Portfolio construction using bootstrapping neural networks: evidence from global stock market
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Portfolio construction using bootstrapping neural networks: evidence from global stock market Hsiao-Fen Hsiao1 · Jiang-Chuan Huang2 · Zheng-Wei Lin3
© Springer Science+Business Media, LLC, part of Springer Nature 2019
Abstract The study investigates the investment value of global stock markets by a portfolio construction method combined with bootstrapping neural network architecture. A residual sample will be generated from bootstrapping sample procedure and then incorporated into the estimation of the expected returns and the covariant matrix. The outputs are further processed by the traditional Markowitz optimization procedure. In order to examine the efficacy of the proposed approach, the illustrated case was compared with traditional Markowitz mean–variance analysis, as well as the James–Stein and minimum-variance estimators. From the empirical results, it indicated that this novel approach significantly outperforms most of benchmark models based on various riskadjusted performance measures. It can be shown that this new approach has great promise for enhancing the estimation of the investment value by Markowitz mean— variance analysis in the global stock markets. Keywords Bootstrap · Elman neural network · Portfolio construction · International diversification JEL Classification G11 · G12 · G15 · G17
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Jiang-Chuan Huang [email protected]
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Newhuadu Business School, Minjiang University, No. 200, Xiyuangong Road, Shangjie Town, Minhou County, Fuzhou City, Fujian Province, China
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School of Business, Putian University, No. 1133 Xueyuan Middle Street, Chengxiang District, Putian 351100, Fujian, China
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Graduate School of Management, National Yunlin University of Science and Technology, No. 123, University Road, Sec. 3, Douliu City 640, Yunlin County, Taiwan
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H.-F. Hsiao et al.
1 Introduction A tenet of modern financial theory related to the pioneering works of Markowitz (1952, 1959) is that an investor should construct a diversified portfolio of investments to achieve the most favorable tradeoff between risk and return. Markowitz-Sharpe portfolio theory provides the standard normative criteria for forming securities portfolios, and although mean–variance efficiency already represents well received wisdom within the field of finance, it is difficult to understand why, for almost half a century, mean–variance optimization methodology has not met with widespread acceptance within the investment community. Michaud (1989) clearly demonstrates the Markowitz optimization enigma under many rationalized settings, systematically pointing out that the major problem with mean–variance optimization is its tendency to maximize the effects of errors within the input assumptions. In particular, mean–variance optimizers are essentially estimationerror maximizers, in the sense that mean–variance optimization results in significant overweighting of those securities with large estimated returns, negative correlations and small variance. The end result is the greater likelihood of these securities being subject
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