A simple nonparametric approach to low-dimension, shortfall-based portfolio selection

Haley, M. Ryan

doi:10.1016/j.frl.2008.04.002

Cited by 8 publications

(4 citation statements)

References 6 publications

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“…29 We point out that shortfall in Haley (2008) is indeed the meaning of loss (truncated) probability but not the same meaning of expected shortfall (CVaR) as in many papers. Haley (2008) in fact considers the safety-first portfolio selection problem but not the expected shortfall (CVaR) portfolio selection problem. 30 For the definition of complementary Heaviside function, please see Equation (4-4).…”

Section: Related Literature Reviewmentioning

confidence: 87%

“…First, we study two safety-first portfolio optimization problems under the nonparametric kernel estimation framework. Second, simulation and empirical results based on the CNSSF strategic stocks show that our NPK approach outperforms the EMD estimation method (Haley, 2008). Compared with the EMD estimation method, the main advantage is that our NPK portfolio optimization method is smooth, retains convergence properties and is computationally tractable.…”

Section: Paper 3: Contributionmentioning

confidence: 93%

“…As noted by Haley (2008), his approach is only suitable in the case where the number of the assets, n , in the optimization problem, is small (i.e., 8 n  ). However, in most realistic settings, such a small portfolio size is far too restrictive for investors, especially for financial institutions such as pension funds, banks and mutual funds.…”

Section: Related Literature Reviewmentioning

confidence: 99%

“…We compare our nonparametric method with the empirical distribution (EMD) estimation approach (Haley, 2008), numerically and empirically, to demonstrate its accuracy and effectiveness.…”

Section: Introductionmentioning

confidence: 99%

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Three essays on the financial analysis of the political actions of the Chinese National Social Security Fund

Li¹

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In this thesis, using a finance lens, we investigate various aspects of political actions of the Chinese National Social Security Fund (CNSSF). The thesis comprises three empirical papers. In the first essay, we analyze the trading behavior of the CNSSF that operates in a highly political environment. We show that the CNSSF adopts a rebalancing strategy that maintains both portfolio liquidity and growth opportunities. Further, we find direct and indirect evidence that the CNSSF actively intervenes in the stock market by providing liquidity to mutual funds in distress. This liquidity provision can be primarily explained by public information quality. Most notably, CNSSF can profit by assisting distressed mutual funds, especially if such liquidity provision relates to private information. In addition, this bailout-like behavior is not speculative and can positively improve the performance of the distressed mutual funds.In essay 2, we propose a new measure of the policy information advantage available to the CNSSF. Specifically, we assess the impact of this policy-linked "information advantage" on stock performance. Our results show that in the short run, there is a positive and significant information advantage-stock performance linkage.Moreover, our findings support the view that CNSSF promotes the absorption of inside information into prices. In contrast, in the long run, the policy information advantage of the CNSSF negatively decreases the firm operation performance. Finally, we document that there is an information network spillover of the information advantage across the Chinese mutual fund industry, which also affects mutual fund performance.In the third essay, we propose a smooth non-parametric estimation to explore the safety-first portfolio optimization problem. As an empirical application, we simulate optimal portfolios and display return-risk characteristics using the CNSSF strategic stocks. We obtain a non-parametric estimation calculation formula for loss (truncated) probability using the kernel estimator of the portfolio returns' cumulative distribution function, and embed it into two types of safety-first portfolio selection models. We numerically and empirically test our non-parametric method to demonstrate its accuracy and efficiency. Cross-validation results show that our non-parametric kernel estimation method outperforms the empirical distribution method.

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Section: Related Literature Reviewmentioning

confidence: 87%

Section: Paper 3: Contributionmentioning

confidence: 93%

Section: Related Literature Reviewmentioning

confidence: 99%

“…We compare our nonparametric method with the empirical distribution (EMD) estimation approach (Haley, 2008), numerically and empirically, to demonstrate its accuracy and effectiveness.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations