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This study aims to examine whether mutual funds can earn daily alpha and time daily market return.

Based on the Treynor and Mazuy (1966) model and the Henriksson and Merton (1981) model, the author tests the daily market-timing ability of actual mutual funds and bootstrapped mutual funds.

The author finds that daily alpha and daily market-timing ability can come from pure luck. In addition, the relation between fund alpha and market-timing ability is at best minimal.

Using bootstrapped funds as the benchmark, this study shows that daily fund market is overall efficient.

The purpose of this paper is to develop a more robust methodology for asset allocation for the property investment market which takes into account inherent valuation and data issues.

The methodology applied is that of a bootstrap, borrowed from Carlstein, and is applied to an investment universe consisting of UK equities, gilts and property. The bootstrap selectively re‐samples the return time series by maintaining the economic cycle. The resulting return series is then used in the standard mean‐variance optimisation (MVO) on an unconstrained basis. Finally, a “sanity” test is applied on the correlation matrix to ensure that spurious instances do not skew the results.

The bootstrapped optimisation provides a range within which the portfolio weights can be manoeuvred instead of a static point under the standard MVO. It provides a more robust methodology for asset allocation and without giving any undue significance to one year of extreme result.

The current analysis is based on unconstrained portfolio optimisation, with a very limited investment universe. Additionally, by conforming with the MVO methodology, normality of asset returns is implicitly assumed, which is clearly not the case in the data used. Future work will also focus on an all‐property portfolio.

The proposed methodology will prove to be useful for making asset allocation decisions, particularly in turbulent financial markets.

The paper focuses solely on bootstrapping with the IPD UK annual index and is particularly significant after one year of extremely poor performance of UK property. The results will be of use to fund managers and portfolio analysts.

The standard market models assume that all investors are rational with the same level of risk aversion, whereas investors in the real world are neither rational nor homogeneous. This contrast makes these models inappropriate for evaluating manager skill. The purpose of this paper is to attempt to bridge the gap between model assumption and fund investment practice.

This study proposes a series of modified models using the excess return of peer funds to estimate fund alpha. In these models, the market excess return in the standard market models is replaced with the average excess return of bootstrapped funds. In addition, the author examines the reasons for the difference between the modified models and the standard models.

The modified models better explain the variation of fund returns, and they exhibit that a considerably higher percentage of funds can earn positive alpha, thus the skill of fund managers is underestimated based on the standard market models.

The proposed models provide a more reliable method for investors to identify skilled fund managers, and they can also serve as an objective benchmark in evaluating fund performance and in designing manager compensation packages.

The purpose of this paper is to examine managerial skill of US equity mutual funds in the context of both abnormal return and risk.

The authors evaluate manager skill based on the outperforming probability and cumulative distribution function of the actual funds and the bootstrapped funds. And the authors recognize the role of fund life cycle and use different evaluation horizons to control for fund age and the overall state of the market.

The authors find that a small percentage of equity funds can beat the market, and the percentage is overall higher than what the control group would predict. The authors find no evidence of persistence. The authors also document that the chance of underperformance is much higher than what the authors had expect from the control group. Taking the risk-return tradeoff into account, any performance advantage of actual funds over bootstrapped funds is correlated with tail risk, and a robustness check confirms this finding.

The authors find that the outperforming probability itself is not enough to confirm the existence of manager skill. The complete story of mutual fund alpha, should it exist, would not be complete without incorporating both risk and luck.

Germany has a polycentric city structure. This paper aims to reduce complexity of this structure and to find a reliable classification scheme of German housing markets at city level based on 17 relevant market parameters.

This paper uses a two-step clustering algorithm combining k-means with Ward’s method to develop the classification scheme. The clustering process is preceded by a principal component analysis to merely retain the most important dimensions of the market parameters. The robustness of the results is investigated with a bootstrapping method.

It is found that German residential markets can best be segmented into four groups. Geographic contiguity plays a specific role, but is not a main factor. Our bootstrapping analysis identifies the majority of pairwise city relations (88.5%) to be non-random.

A deeper discussion concerning the most relevant market parameters is required. The stability of the clusters is to be re-investigated in the future, as the bootstrapping analysis indicates that some clusters are more homogeneous than others.

The developed classification scheme provides insights into opportunities and risks associated with specific city groups. The findings of this study can be used in portfolio management to reduce unsystematic investment risks and to formulate investment strategies.

To the best of the authors’ knowledge, this is the first paper to offer insights into the German housing markets which applies principal component, cluster and bootstrapping analyses in a sole integrated approach.

In capital markets, research risk factor loadings and characteristics are considered as opposing explanations for the cross-sectional dispersion in average stock returns. However, there is little known about the performance an investor would obtain who believes either in the characteristics explanation (CB-investor) or in the risk factor loadings explanation (RB-investor). The purpose of this paper is to compare the performance of CB- and RB-investors.

To compare the competing strategies, the authors propose a simple new approach to equity portfolio optimization in the style of Brandt et al. (2009) by modeling the portfolio weight in each asset as a function of the asset's risk factor loadings or characteristics. The authors perform an empirical analysis on the German stock market, exploiting the risk factor loadings from the Carhart (1997) four-factor model and the respective characteristics size, book-to-market equity ratio and momentum.

The results show that investment strategies relying on characteristics (particularly on momentum) outperform risk-based investment strategies in horse races. These findings hold in- and out-of-sample. Furthermore, the characteristics-based investment strategies outperform a value-weighted market portfolio strategy in- and out-of-sample.

The authors introduce a portfolio optimization approach that enables investors to directly link portfolio decisions to the firm’s characteristics or risk factor loadings.

The purpose of this paper is to examine residual value risk modelling issues with a focus on automotive lease portfolios. Residual value risk is approached through a re‐sampling technique that provides the probability density function of losses and VaR measures for credit portfolios.

The methodology is applied to a portfolio of 37,523 operating leases issued between 1989 and 2001 by a major European financial institution.

The results show that residual value losses are low and sometimes non‐existent. Moreover, the major part of residual value risk is idiosyncratic and can thus be eliminated through adequate diversification. Additionally, this internal model seems to prove that capital requirements stemming from the Basel Committee's proposed new framework are somehow overestimated.

This paper advocates determining a more accurate risk weight for residual value risk in order to better reflect this relatively low‐risk part of leasing activities.

Considering the standards developed by the Sustainability Accounting Standards Board (SASB), this study aims to examine whether the link between material sustainability and financial performance depends on the extent to which the company is oriented toward stakeholders.

To test the predictions, 13,942 firm-year observations from 43 different countries are used, covering the period from 2010 to 2019. Using a hand-mapping approach to match the indicators suggested by the SASB with those of the ASSET4, the authors realize that there are 170 material sustainability indicators among 466 indicators of the ASSET4. The authors use three different methods to verify if the materiality matters, including the alphas obtained from the Fama and French factor models, comparing the average abnormal returns of the portfolios and the bootstrapped Cramer technique.

The findings show that companies investing in material sustainability activities perform better than those investing in immaterial activities. Also, consistent with the theoretical foundations, the authors find that the effect of investing in material sustainability activities is more pronounced in stakeholder-oriented countries than that in shareholder-oriented countries. The results are robust to a battery of sensitivity tests.

Owing to COVID-19 in late 2019, data from 2020 to 2022 have not been used to obtain reliable results.

The results obtained in the current research provide valuable guidance for investors to make investments considering the degree of materiality of sustainability activities in different industries. It also helps managers to increase the company’s financial performance, make efficient decisions related to investment in sustainability activities and find investment strategies on the material sustainability issues in their industries.

This study provides a clearer understanding of investment in sustainability activities in different industries by separating material and immaterial sustainability activities in stakeholder and shareholder-oriented countries, and the results obtained can change the perspective of investors and company managers regarding investing in such activities in different countries. Investing in more materiality sustainability activities than the immateriality dimension can be new opportunities for companies to achieve predetermined goals, help retain and attract business partners or be a source of innovation for new product lines or services. Internal morale and employee engagement may increase while increasing productivity and firm performance. This discussion opens the way for future research.

This study provides insight into the effect of investing in material and immaterial sustainability activities in different industries on the company’s performance in shareholder and stakeholder-oriented countries.

The purpose of this paper is to compare the risk and return characteristics as well as the allocation of mean‐variance (MV) and downside risk (DR) optimized portfolios of international real estate stock markets and to discuss implications for portfolio management.

The analysis focuses on real estate markets only and examines the appropriateness of the Markowitz approach based on MV optimization in comparison to the DR framework suggested by Estrada. Therefore, the two frameworks are presented before the properties of the return distributions are analyzed. Afterwards, the risk and return characteristics as well as the allocation of the efficient portfolios in both frameworks and the divergences are analyzed.

Because of non‐normally distributed returns, negative skewness, and probably non‐quadratic utility functions of investors, MV optimization is not appropriate and the alternative approach by Estrada has its merit compared with other DR frameworks. Furthermore, MV‐efficient and DR‐efficient portfolio allocation differ, as shown by a similarity index. Summarizing, MV optimization is inherent with misleading results and DR optimization shows stronger out‐of‐sample performance – at least during time periods characterized by high market volatility and financial market turmoil.

This study provides some interesting and valuable insights into the DR of international securitized real estate portfolios and the limitations for portfolio management based on MV optimization.

The purpose of this paper is to introduce error ellipse into the bootstrap method to improve the reliability of small samples and the credibility of the S-N curve.

Based on the bootstrap method and the reliability of the original samples, two error ellipse models are proposed. The error ellipse model reasonably predicts that the discrete law of expanded virtual samples obeys two-dimensional normal distribution.

By comparing parameters obtained by the bootstrap method, improved bootstrap method (normal distribution) and error ellipse methods, it is found that the error ellipse method achieves the expansion of sampling range and shortens the confidence interval, which improves the accuracy of the estimation of parameters with small samples. Through case analysis, it is proved that the tangent error ellipse method is feasible, and the series of S-N curves is reasonable by the tangent error ellipse method.

The error ellipse methods can lay a technical foundation for life prediction of products and have a progressive significance for the quality evaluation of products.