Economics and Mathematical MethodsEconomics and Mathematical MethodsЭкономика и математические методы0424-73883034-6177The Russian Academy of Sciences65330610.31857/S0424738824020102Mathematical analysis of economic modelsМатематический анализ экономических моделейResearch ArticlePortfolio constructions in the stock market based on data envelopment analysis and stochastic frontier analysisПортфельные построения на рынке акций на основе методов оболочечного анализа и стохастической границыTeplovaT. V.ТепловаТ. В.
Russian Federation
tteplova@hse.ruSokolovaT. V.СоколоваТ. В.
Russian Federation
tv.sokolova@hse.ruHanievA. I.ХаниевА. И.
Russian Federation
ahaniev@hse.ruNational Research University “Higher School of Economics” (HSE University)Национальный исследовательский университет «Высшая школа экономики»0409202460212313803022025Copyright ©; 2024, Russian Academy of SciencesCopyright ©; 2024, Российская академия наук2024Russian Academy of SciencesРоссийская академия наукhttps://journals.eco-vector.com/0424-7388/article/view/653306

The study compares the results of applying the parametric method of Stochastic Frontier Analysis (SFA) and the non-parametric Bias-corrected Data Envelopment Analysis (DEA) for forming integrated stock selection metrics in portfolios based on diverse financial and non-financial indicators of U.S. issuing companies. The authors implement a novel approach in which “input” and “output” indicators for both stochastic frontier analysis and data envelopment analysis models are pre-selected using regression analysis. Deviations of identified company indicators from median industry values are considered. Significant characteristics in explaining stock returns include board size, proportion of independent directors, board meetings attendance, and among financial and market characteristics — the net debt to EBITDA ratio and past stock returns (momentum-effect). It is demonstrated that portfolios consisting of 20–30 securities, constructed on the authors’ integrated metrics, outperform in terms of returns and risk–return ratio compared to the S&P 500 index and an equal-weighted portfolio of all considered stocks. The stability of conclusions is verified through comparison with randomly generated portfolios (Monte Carlo method). The obtained results remain stable for both the pre-Covid-19 pandemic period (2008–2019) and the period including the pandemic and geopolitical tensions from 2020 to 2022. From 2008 to 2019, portfolios created using the data envelopment analysis method were more effective than those based on stochastic frontier analysis models. Conversely, during the period from 2020 to 2022, the latter demonstrated superior performance.

В работе сопоставлены результаты применения параметрического метода анализа стохастической границы (Stochastic Frontier Analysis, SFA) и непараметрического метода оболочечного анализа данных с корректировкой на асимметричное смещение (Bias-corrected Data Envelopment Analysis, DEA) для формирования интегральных метрик отбора акций в портфель на основе разноплановых финансовых и нефинансовых показателей компаний-эмитентов США. Реализован авторский подход, при котором «входные» и «выходные» показатели для моделей анализа стохастической границы и оболочечного анализа данных предварительно отбираются с помощью регрессионного анализа. Учитывается отклонение выявленных показателей компаний выборки от медианных отраслевых значений. Выявлено, что значимыми характеристиками корпоративного управления в объяснении доходности акций являются размер совета директоров, доля независимых директоров, посещаемость заседаний совета директоров, а среди финансовых и биржевых характеристик — отношение чистого долга к EBITDA и прошлая доходность акций (эффект моментума). Показано, что портфели из 20–30 ценных бумаг, построенные на основе авторских интегральных метрик, оказались более эффективными по доходности и по соотношению риск–доходность, чем индекс S&P 500 и равно-взвешенный портфель всех рассматриваемых акций. Проверка устойчивости выводов проведена путем сопоставления с портфелями, построенными случайным образом (метод Монте-Карло). Полученные результаты устойчивы как для периода до пандемии коронавируса COVID-19 (2008–2019 гг.), так и для периода пандемии и роста геополитической напряженности в 2020–2022 гг. С 2008 по 2019 г. портфели, созданные с применением метода оболочечного анализа данных, были более эффективными, чем те, которые основаны на моделях анализа стохастической границы. В период с 2020 по 2022 г. — наоборот; последние продемонстрировали лучшие результаты.

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