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 <front>
  <journal-meta>
   <journal-id journal-id-type="publisher-id">JOURNAL OF MONETARY ECONOMICS AND MANAGEMENT</journal-id>
   <journal-title-group>
    <journal-title xml:lang="en">JOURNAL OF MONETARY ECONOMICS AND MANAGEMENT</journal-title>
    <trans-title-group xml:lang="ru">
     <trans-title>JOURNAL OF MONETARY ECONOMICS AND MANAGEMENT</trans-title>
    </trans-title-group>
   </journal-title-group>
   <issn publication-format="print">2782-4586</issn>
   <issn publication-format="online">2949-1851</issn>
  </journal-meta>
  <article-meta>
   <article-id pub-id-type="publisher-id">103429</article-id>
   <article-id pub-id-type="doi">10.26118/2782-4586.2025.99.59.053</article-id>
   <article-categories>
    <subj-group subj-group-type="toc-heading" xml:lang="ru">
     <subject>Научные статьи</subject>
    </subj-group>
    <subj-group subj-group-type="toc-heading" xml:lang="en">
     <subject>SCIENTIFIC ARTICLES</subject>
    </subj-group>
    <subj-group>
     <subject>Научные статьи</subject>
    </subj-group>
   </article-categories>
   <title-group>
    <article-title xml:lang="en">Comparative analysis of data sources for back-testing cost estimation models</article-title>
    <trans-title-group xml:lang="ru">
     <trans-title>Сравнительный анализ источников данных для обратного тестирования моделей оценки стоимости</trans-title>
    </trans-title-group>
   </title-group>
   <contrib-group content-type="authors">
    <contrib contrib-type="author">
     <name-alternatives>
      <name xml:lang="ru">
       <surname>Лихенко</surname>
       <given-names>Иван Иванович</given-names>
      </name>
      <name xml:lang="en">
       <surname>Lihenko</surname>
       <given-names>Ivan Ivanovich</given-names>
      </name>
     </name-alternatives>
     <xref ref-type="aff" rid="aff-1"/>
    </contrib>
   </contrib-group>
   <aff-alternatives id="aff-1">
    <aff>
     <institution xml:lang="ru">Новосибирский государственный университет экономики и управления</institution>
    </aff>
    <aff>
     <institution xml:lang="en">Novosibirsk State University of Economy and Management</institution>
    </aff>
   </aff-alternatives>
   <pub-date publication-format="print" date-type="pub" iso-8601-date="2025-09-04T00:21:47+03:00">
    <day>04</day>
    <month>09</month>
    <year>2025</year>
   </pub-date>
   <pub-date publication-format="electronic" date-type="pub" iso-8601-date="2025-09-04T00:21:47+03:00">
    <day>04</day>
    <month>09</month>
    <year>2025</year>
   </pub-date>
   <issue>6</issue>
   <fpage>269</fpage>
   <lpage>274</lpage>
   <history>
    <date date-type="received" iso-8601-date="2025-08-21T00:00:00+03:00">
     <day>21</day>
     <month>08</month>
     <year>2025</year>
    </date>
   </history>
   <self-uri xlink:href="https://zhpi.ru/en/nauka/article/103429/view">https://zhpi.ru/en/nauka/article/103429/view</self-uri>
   <abstract xml:lang="ru">
    <p>В статье рассматриваются современные подходы к оценке стоимости активов с акцентом на выбор, классификацию и сопоставимость источников данных, применяемых при тестировании и валидации моделей. Предлагается концептуальная рамка, охватывающая как наблюдаемые рыночные показатели (котировки ликвидных публичных бумаг, агрегированные рыночные индикаторы), так и экспертные оценки (отчёты независимых оценщиков, оценки покупающей/продающей стороны), а также различные виды стоимости – рыночную, инвестиционную и ликвидационную. Вводится понятие акта оценки стоимости (АОС) как универсального элемента, фиксирующего результат действия по определению стоимости на конкретную дату и позволяющего унифицировать источники разнородной природы в едином массиве для обратного тестирования. Предложена классификация источников АОС по происхождению, наблюдаемости, частоте обновления, прозрачности методологии и рискам смещения, что облегчает сопоставление результатов исследований и практических приложений. Показано, что ни один из источников не является самодостаточным: рыночные данные обеспечивают высокую оперативность и объективность в условиях ликвидных рынков, но подвержены искажениям при низкой активности и шоках; экспертные оценки незаменимы для уникальных и нерыночных активов, но требуют контроля независимости, качества и воспроизводимости. Обосновывается необходимость стандартизации процедур бэктестинга (включая требования к метаданным АОС, правила отбора и агрегирования наблюдений, единые критерии качества и устойчивости оценок) и разработки формализованных критериев надёжности для разных типов АОС. Реализация предложенного подхода повышает прозрачность, сопоставимость и воспроизводимость оценочных исследований, создаёт основу для калибровки моделей и снижает долю субъективных допущений при принятии решений.</p>
   </abstract>
   <trans-abstract xml:lang="en">
    <p>The article examines modern approaches to asset valuation with a focus on the selection, classification and comparability of data sources used for model testing and validation. It proposes a conceptual framework spanning both observable market indicators (quotes for liquid public securities, aggregated market measures) and expert-based inputs (independent appraisal reports, buy-side/sell-side assessments), as well as different value notions market, investment and liquidation value. The paper introduces the Asset Valuation Act (AVA) as a universal unit that records the outcome of a valuation action at a specific date, enabling heterogeneous sources to be captured within a single dataset for backtesting. A taxonomy of AVA sources is outlined along the dimensions of origin, observability, update frequency, methodological transparency, and bias risk, which facilitates cross-study comparability and practical implementation. The analysis shows that no single source is sufficient: market data provide timeliness and objectivity in liquid markets but may be distorted under illiquidity and stress, while expert assessments are indispensable for unique and non-traded assets yet call for stricter controls on independence, data quality, and reproducibility. The paper argues for standardization of backtesting procedures–including AVA metadata requirements, sampling and aggregation rules, and common criteria for accuracy and robustness–and for the development of formal reliability criteria tailored to different AVA types. Implementing the proposed framework improves transparency, comparability, and reproducibility of valuation research, supports model calibration, and reduces subjective assumptions in decision-making.</p>
   </trans-abstract>
   <kwd-group xml:lang="ru">
    <kwd>оценка стоимости</kwd>
    <kwd>акт оценки стоимости</kwd>
    <kwd>бэктестинг</kwd>
    <kwd>валидация моделей</kwd>
    <kwd>обратное тестирование</kwd>
   </kwd-group>
   <kwd-group xml:lang="en">
    <kwd>valuation</kwd>
    <kwd>asset valuation act</kwd>
    <kwd>backtesting</kwd>
    <kwd>model validation</kwd>
    <kwd>reverse testing</kwd>
   </kwd-group>
  </article-meta>
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