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  <title>EconStor Collection:</title>
  <link rel="alternate" href="https://hdl.handle.net/10419/211500" />
  <subtitle />
  <id>https://hdl.handle.net/10419/211500</id>
  <updated>2026-04-28T15:29:35Z</updated>
  <dc:date>2026-04-28T15:29:35Z</dc:date>
  <entry>
    <title>Forecasting inflation: The sum of the cycles outperforms the whole</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/335013" />
    <author>
      <name>Verona, Fabio</name>
    </author>
    <id>https://hdl.handle.net/10419/335013</id>
    <updated>2026-01-17T02:04:34Z</updated>
    <published>2026-01-01T00:00:00Z</published>
    <summary type="text">Title: Forecasting inflation: The sum of the cycles outperforms the whole
Authors: Verona, Fabio
Abstract: Inflation dynamics reflect forces operating at different cycles, from short-lived shocks to longterm structural trends. We introduce the sum-of-the-cycles (SOC) method, which exploits this multifrequency structure of inflation for forecasting. SOC decomposes inflation into cyclical components, applies forecasting models suited to their persistence, and recombines them into an aggregate forecast. Across U.S. inflation measures and horizons, SOC consistently outperforms leading time-series benchmarks, reducing forecast errors by about 25 percent at short horizons and nearly 50 percent at long horizons. During the 2020-21 inflation surge, when many models - including advanced machine-learning methods - struggled, SOC retained strong performance by incorporating shortage indicators. Beyond accuracy, SOC enhances interpretability: financial variables dominate high- and business-cycle frequencies, Phillips Curve models are most informative at medium frequencies, and factor-based methods, forecast combinations, and shortage indices prevail at low frequencies. This combination of accuracy and transparency makes SOC a practical complement to existing tools for inflation forecasting and policy analysis.</summary>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Pyrrhic diversification: Foreign institutional ownership and stock return sensitivity to the global financial cycle</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/335890" />
    <author>
      <name>Ambrocio, Gene</name>
    </author>
    <author>
      <name>Bui, Dien Giau</name>
    </author>
    <author>
      <name>Hasan, Iftekhar</name>
    </author>
    <author>
      <name>Lin, Chih-Yung</name>
    </author>
    <id>https://hdl.handle.net/10419/335890</id>
    <updated>2026-02-04T02:05:27Z</updated>
    <published>2026-01-01T00:00:00Z</published>
    <summary type="text">Title: Pyrrhic diversification: Foreign institutional ownership and stock return sensitivity to the global financial cycle
Authors: Ambrocio, Gene; Bui, Dien Giau; Hasan, Iftekhar; Lin, Chih-Yung
Abstract: We demonstrate that foreign institutional ownership (FIO) is associated with stronger stock return sensitivity to the Global Financial Cycle (GFC), indicating greater global co-movement among stocks selected by FIOs compared to those not selected. We conjecture that this may be because (i) FIOs tend to pick ex-ante very similar firms when investing abroad, or (ii) FIO investments itself makes firms ex-post more similar and more sensitive to the GFC. We find evidence in support of both hypotheses: that the increased co-movement may be due to FIO's selecting more homogeneous firms and that the sensitivity to the GFC increases after FIO investment. However, we find no significant difference between firms that have longer exposure to FIO investors and those that have only recently obtained FIO investment. Our results indicate that diversification gains are left on the table when FIOs select firms to invest in.</summary>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Quantifying Minsky cycles</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/340165" />
    <author>
      <name>Ristolainen, Kim</name>
    </author>
    <id>https://hdl.handle.net/10419/340165</id>
    <updated>2026-04-25T01:04:55Z</updated>
    <published>2026-01-01T00:00:00Z</published>
    <summary type="text">Title: Quantifying Minsky cycles
Authors: Ristolainen, Kim
Abstract: We develop a novel sentiment measure from survey forecasts that captures the component of beliefs arising from the systematic misaggregation of public information relative to a machine benchmark based on the same information set. We extend this sentiment measure historically for a panel of 78 countries using machine learning models trained on BERT embeddings of historical news articles (1903-2020). The backcasted sentiment shows that shocks in median sentiment predict credit booms in the non-tradable corporate sector, which prior research has linked to financial crises. We further find that this sentiment component is shaped by memory-related dynamics, as the time elapsed since major crises and the share of young-to-old people in the population predict surges in optimism even when recent economic developments are controlled for. Taken together, the findings provide new historical evidence consistent with the Minsky-Kindleberger view on financial crises.</summary>
    <dc:date>2026-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>From waves to rates: Enhancing inflation forecasts through combinations of frequency-domain models</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/308098" />
    <author>
      <name>Verona, Fabio</name>
    </author>
    <id>https://hdl.handle.net/10419/308098</id>
    <updated>2025-01-10T02:11:32Z</updated>
    <published>2025-01-01T00:00:00Z</published>
    <summary type="text">Title: From waves to rates: Enhancing inflation forecasts through combinations of frequency-domain models
Authors: Verona, Fabio
Abstract: This paper addresses the challenge of inflation forecasting by adopting a thick modeling approach that integrates forecasts from time- and frequency-domain models. Frequency-domain models excel at capturing long-term trends while also accounting for short-term fluctuations. Combining these models with traditional approaches leverages their complementary strengths, resulting in forecasts that consistently outperform individual methods, especially during periods of heightened inflation volatility. By pooling insights from diverse modeling frameworks, this study provides a robust and effective strategy for improving inflation forecasts across different horizons.</summary>
    <dc:date>2025-01-01T00:00:00Z</dc:date>
  </entry>
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