Eero Pätäri

Enhancement of equity portfolio performance using data envelopment analysis

This paper examines the applicability of data envelopment analysis (DEA) as a basis of selection criteria for equity portfolios. It is the first DEA application for constructing a combined equity investment strategy that aims to integrate the benefits of both value investing and momentum investing. The 3-quantile portfolios are composed of a comprehensive sample of Finnish non-financial stocks based on their DEA efficiency scores that are calculated using three variants of DEA models (the constant returns-to-scale, the super-efficiency, and the cross-efficiency models). The performance of portfolios is evaluated on the basis of the average return and several risk-adjusted performance metrics throughout the 1994–2010 sample period.

Enhancement of value portfolio performance using data envelopment analysis

Purpose - The purpose of this paper is to examine the applicability of data envelopment analysis (DEA) as a basis of value portfolio selection criterion. Design/methodology/approach - The portfolios are composed of the comprehensive sample of Finnish non-financial stocks based on their DEA scale efficiency scores. The performance of portfolios is evaluated on the basis of average return and several risk-adjusted performance metrics. Moreover, the impact of holding period length on the results is examined by varying the portfolio reformation frequency from one to five years at annual frequency. Findings - The results show that the DEA scale efficiency scores add value to portfolio selection. Though outperformance of the DEA value portfolios in contrast to both comparable glamour portfolio and the stock market average is most evident for shorter (i.e. annual and biannual) holding periods, the absolute performance of the DEA value portfolio can be enhanced by using longer reformation intervals. Research limitations/implications - The sample of stocks is not large in spite of its comprehensiveness from the local stock market aspect. Future studies can apply DEA approach to other stock markets to examine whether the results are parallel to this study. Practical implications - The DEA is particularly useful as a multicriteria methodology in cases in which the number of stocks in the sample is large. Originality/value - This paper is the first attempt to form value portfolios using DEA models. The proposed methodology provides an interesting alternative to detect undervalued stocks by capturing several dimensions of relative value simultaneously. It provides also useful implications in portfolio management.

Performance of moving average trading strategies over varying stock market conditions: the Finnish evidence

This article examines the profitability of dual moving average crossover (DMAC) trading strategies in the Finnish stock market over the period 1996 to 2012. It contributes to the existing technical analysis literature by comparing for the first time the performance of DMAC trading portfolios of individual stocks to the performance of index trading strategies based on trading on an index that consists of the same stocks. The results show that their relative performance varies over time, whereas previous studies have documented outperformance of index trading strategies over trading strategies of stock portfolios. Moreover, the great majority of 3020 DMAC strategies examined in this article outperform the corresponding buy-and-hold (B and H) strategy for both trading targets (i.e., OMX Helsinki 25 index and individual stocks included in the index) in out-of-sample tests. In addition, the decomposition of the full-sample-period performance into separate bull- and bear-period performance shows clearly that the outperformance of DMAC strategies over B and H strategy is mostly attributable to their better performance during bearish periods.

Comparison of the multicriteria decision-making methods for equity portfolio selection: The U.S. evidence

This paper compares the efficacy of four multicriteria decision-making (MCDM) methods in identifying the future best-performing stocks in two comprehensive samples of U.S. stocks. This is the first time that median-scaling (MS), the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), the Analytic Hierarchy Process (AHP), and the additive Data Envelopment Analysis (add.DEA) have been used to combine value and momentum indicators into a single efficiency score. The results show that the MCDM methods examined can successfully be applied to equity portfolio selection. As a robustness check, we repeat all the main sample tests for the sample of the largest-cap stocks included in the two biggest size quintiles (i.e., stocks above 40% NYSE market-cap breakpoint) and find that the overall results are surprisingly robust to size effect. However, the best-performing portfolios formed on the basis of different MCDM methods have remarkably different exposures to the style factors that are commonly used to explain the abnormal returns of active equity portfolios. As a practical implication of this study, investors following certain investing styles could take these different style exposures into account when choosing the MCDM criteria that best fit their portfolio-selection purposes.

Can size-, industry-, and leverage-adjustment of valuation ratios benefit the value investor?

This paper introduces a new and innovative methodology for value portfolio selection by adjusting the conventional valuation ratios on the basis of firm size, financial leverage and industry classification and combining them as single selection criteria. The tercile portfolios are composed of a comprehensive sample of Finnish non-financial stocks based on their adjusted valuation. The performance of portfolios is evaluated on the basis of the average return and several risk-adjusted performance metrics throughout the 1996-2013 sample period. The results show that the suggested multidimensional combination criteria can add value to equity portfolio selection. The outperformance of such top-tercile portfolios in contrast to both the comparable bottom portfolios and the stock market portfolio is statistically significant on the basis of all performance metrics employed. The methodology employed offers an interesting alternative for identifying undervalued stocks by capturing both several dimensions of relative value and several peer-group comparisons at the same time.

Performance of Moving Average Trading Rules in a Volatile Stock Market: The Russian Evidence

ABSTRACT This article examines the profitability of dual moving average crossover (DMAC) trading strategies in the Russian stock market over the 2003–12 period. It contributes to the existing technical analysis (TA) literature by testing, for the first time, the applicability of ordered weighted moving averages (OWMA) as an alternative calculation basis for determining DMACs. In addition, this article provides the first comprehensive performance comparison of DMAC trading rules in the stock market that is known as one of the most volatile markets in the world. The results show that the best trading strategies of the in-sample period can also outperform their benchmark portfolio during the subsequent out-of-sample period. Moreover, the outperformance of the best DMAC strategies is mostly attributable to their superior performance during bearish periods and, particularly, during stock market crashes.

The anatomy of returns from moving average trading rules in the Russian stock market

ABSTRACT This paper examines the profitability of index trading strategies that are based on dual moving average crossover (DMAC) rules in the Russian stock market over the 2003–2012 period. It contributes to the existing technical analysis (TA) literature by comparing for the first time in emerging markets the relative performance of individual stocks’ trading portfolios with that of trading strategies for the index that consists of the same stocks (i.e., the most liquid stocks of the Moscow Exchange). The results show that the best trading strategies of the in-sample period can outperform buy-and-hold strategy during the subsequent out-of-sample period, although with low statistical significance. In addition, we document the benefits of using DMAC combinations that are much longer than those employed in previous TA literature. Moreover, the decomposition of the full-sample-period performance into separate bull- and bear-period performances shows that the outperformance of the best past index trading strategies over is mostly attributable to the fact that they managed to stay mostly out of the stock market during a dramatic crash caused by the global financial crisis.