Asset Allocation Performance Research Articles

Is OCIO superior in asset allocation performance?

This study aims to empirically analyze the asset allocation capabilities of Outsourced Chief Investment Officers (OCIOs) in Korea. The empirical analysis used data from 35 funds that were evaluated by the Ministry of Strategy and Finance from 2012 to 2020. The results of the analysis are summarized as follows. First, this study found that funds that adopted OCIO improved their asset allocation performance. Second, the sensitivity between risk-taking and performance decreased for funds that adopted OCIO. Third, it is found that OCIO adoption improves a fund's asset management execution (tactical capabilities). This study has methodological limitations in which the methodology used in this study is not based on theoretical prior research, but on practical applications. However, considering the need to clearly analyze the capabilities of OCIO and the timeliness of the topic, this study is valuable and can provide meaningful information to funders who are considering adopting OCIO in the future.

Weighted Least Squares Realized Covariation Estimation

We introduce a novel weighted least squares approach to estimate daily realized covariation and microstructure noise variance using high-frequency data. We provide an asymptotic theory and conduct a comprehensive Monte Carlo simulation to demonstrate the desirable statistical properties of the new estimator, compared with existing estimators in the literature. Using high-frequency data of 27 DJIA constituting stocks over a period from 2014 to 2020, we confirm that the new estimator performs well in comparison with existing estimators. We also show that the noise variance extracted based on our method can be used to improve volatility forecasting and asset allocation performance.

회귀 강화학습을 이용한 포트폴리오 자산 배분의 성능 향상 연구

The present invention relates to a portfolio asset allocation performance increasing method using regression enhancement learning. In addition, in order to increase the performance of a regression enhancement learning model for portfolio asset allocation, the present invention presents a concrete implementation model for a method of generating and utilizing asset prediction value data and artificially generated data in addition to past asset data, and proves that such a model is effective through experiments. The regression enhancement learning model generating and utilizing the asset prediction data and the artificially generated data created by the present invention is implemented by long short-term memory (LSTM). The asset prediction value data is generated by generating and using a virtual prediction value for the rise and fall of asset prices based on prediction accuracy over the period of operation and the artificially generated data is generated by using a Gaussian process.

THE TIMING OF PORTFOLIO ADJUSTMENTS: A REGIME-SWITCHING APPROACH

The fact that the relationships among the returns of financial assets tend to be nonlinear and time-varying has important implications for asset allocation. To describe these two features, this paper first combines a copula function with the Markov switching technique to model the dependence structure across assets and then builds on this Markov Switching Copula model to present a procedure for the timing of portfolio adjustments. Our empirical evidence confirms that the dependence structure between high-risk and low-risk stocks in the Shanghai and Shenzhen markets is not static but switches between regimes over the course of the sample horizon considered in this paper. More importantly, as a result of such regime-switching characteristics of their dependence structure, our analysis of the out-of-sample asset allocation performance indicates that employing the procedure proposed in this paper to identify regime changes and decide when to adjust portfolio weights allows investors with the Constant Relative Risk Aversion utility to achieve both higher realized returns and higher certainty equivalent rate of returns than does the use of strategies based on static models.

The Alpha Bias in Asset Allocation Performance Measurement

Professional asset allocators frequently report positive alphas, and the generation of alpha is widely discussed in the context of asset allocation. This paper demonstrates that two-fund asset allocation strategies contain a positive-alpha bias and derives an expression for the alpha of an asset allocator. An examination of the properties of the alpha of an asset allocator show that while it does capture an element of asset allocation ability, it is influenced by a factor unrelated to ability and subject to manipulation so that randomly chosen positions can lead to long-term positive estimated alphas. Only extremely large but impractical sample sizes can eliminate the bias, which can add as much as 25 basis points to the performance of a manager using a random number generator. Artificially setting the beta to the proportion of periods in the market can eliminate this bias, but alpha still cannot separate capable asset allocators from those using random number generators. Widely-used alternatives such as Henriksson-Merton, Treynor-Mazuy, and the Sharpe ratio show some ability to discriminate but have unacceptably large Type I errors. Thus, this paper provides new results that show why asset allocators are so enthusiastic about reporting alphas, why such results must be looked upon with skepticism, and why alternatives do not perform any better. In general, alpha and these performance evaluation techniques are able to recognize good managers, but bad managers are lumped in with good ones.

The Alpha Bias in Asset Allocation Performance Measurement

The Alpha Bias in Asset Allocation Performance Measurement