Freddy Zea Restrepo

Background Equity-market concentration has reached historic levels, prompting warnings from regulators and asset managers that a market dominated by a handful of mega-capitalization stocks is structurally fragile. Whether concentration actually predicts market fragility, however, has rarely been tested over a sample long enough to contain multiple concentration cycles. Methods We hand-construct a thirty-year, semiannual series of S&P 500 portfolio concentration (48 observations, December 1995 – March 2026) from the complete portfolio schedules in SEC N-30D and NPORT-P filings of the SPDR S&P 500 Trust, cross-validated against independent NPORT-P parses to a mean absolute difference of 0.2%. We then run three independent tests: (i) Newey-West predictive regressions of four next-semester fragility measures (realized volatility, maximum drawdown, empirical VaR 1%, crash-day frequency) on concentration; (ii) a panel of 47 equity index funds (1,260 fund-quarters, 2019Q3–2026Q1) with fund and time fixed effects; and (iii) a walk-forward, out-of-sample evaluation of a non-circular composite financial-stress index against the VIX for crash prediction at 5- and 21-day horizons. Results Concentration is at an unprecedented level: the S&P 500 Herfindahl-Hirschman Index reached 0.0222 in September 2025, 2.1 times the dot-com peak of 0.0107, and the top-10 weight reached 38.9% versus 24.3% in 2000. Yet none of the three tests links concentration to subsequent fragility. Across all long-run specifications the HHI, CR10 and ΔHHI coefficients are statistically indistinguishable from zero (p = 0.28’0.95); the panel coefficient with two-way fixed effects is 0.27 (wild-cluster bootstrap p = 0.89); and the composite stress index underperforms the VIX alone out-of-sample (AUC 0.740 vs. 0.781 at t+5; 0.665 vs. 0.712 at t+21). Conclusions Portfolio concentration at the index level, however extreme by historical standards, is not by itself an early-warning indicator of market fragility. Volatility-based indicators dominate any concentration signal. Fragility channels associated with concentration, if present, do not operate at the aggregate index level over horizons of days to semesters — a well-identified null result with direct implications for concentration-based macroprudential proposals.

PISA reports how students’ sense of belonging at school changed between 2018 and 2022 in each education system. This data descriptor presents an atlas that tests, system by system, whether the six items behind that index measure the same two constructs in both cycles, for the 69 systems with at least 500 respondents in both. The items fit two factors separated by wording (CFI 0.997, RMSEA 0.041; one factor: 0.906 and 0.214). With WLSMV and the conventional cut-offs, scalar invariance holds in 65 systems and fails in France, Macao, Montenegro and Viet Nam. Freeing one intercept restores comparability in the first three; Viet Nam fails already at the threshold step, needs two freed items, and its estimates are not recommended. With a stricter cut-off, 20 systems fail, including all 7 atlas systems to which the OECD gave version-specific item parameters. Changes go in both directions and are mostly small (median absolute change 0.078 and 0.069 standard deviations on the two factors), with replicate-based standard errors, and in the invariant systems they are closely correlated with the OECD index (r = 0.84 and 0.93). The 33 files, the scripts and a data dictionary are deposited at Zenodo.

This article asks how United States asset prices respond to shocks in the federal deficit and whether the response is stable. We estimate a recursive VAR on 265 monthly observations, 2002–2024. A deficit shock of one standard deviation is followed on average over 12 months by rises of 0.55% in house prices and 1.97% in the S&P 500 and a narrower corporate spread; gold’s response is not significant. The equity average is not stable: a sup-F test places a break in the coefficients on the deficit in March 2009 (p = 0.002), the local projection response is not significant in the full sample (p = 0.153) and does not differ significantly between recessions and expansions, and it is imprecise without the crisis windows. House prices respond positively in local projections, but not in a VAR from 2009 alone. The fiscal dominance ratio is above one in nine of ten combinations of specification and metric, but housing accounts for most of the variance ratios. The deficit ratio is associated with the top 1% wealth share within the quarter, not cumulatively. The estimates are conditional associations; the deficit shock is not free of reactions to markets.

Background Information and communication technology (ICT) infrastructure drives firm performance in emerging markets, yet institutional voids and security vulnerabilities often undermine its efficacy. The mechanisms linking ICT endowment, organizational security investment, and firm-level outcomes remain underexplored, particularly in developing economies. Methods We tested a recursive three-block structural equation model (SEM) on nationally representative microdata from Peru’s Annual Economic Survey 2024 (N = 9,966 firms across 14 sectors). The model integrated the Resource-Based View (RBV) and Routine Activity Theory (RAT) to specify pathways from ICT infrastructure, internet access, and digital human capital through security management investment to firm performance (large vs. medium/small firms, by INEI survey format). Parameters were estimated by maximum likelihood; robustness checks re-estimated the model without small firms and with continuous log sales as the outcome. Mediation analysis decomposed total effects via bootstrap confidence intervals (5,000 replications). Results ICT infrastructure was the dominant predictor across all equations (β = 0.356 in security management; β = 0.327 in firm performance, both p < 0.001). Security management significantly mediated 17.6% of ICT’s total effect on performance (indirect β = 0.070, p < 0.001). Criminal victimization exposure directly predicted security investment (β = 0.060, p < 0.001), establishing a protective response mechanism predicted by Routine Activity Theory. The main paths hold without small firms and with continuous log sales as the outcome. Model fit indices confirmed excellent fit (CFI = 0.988, RMSEA = 0.029, GFI = 0.987). Conclusions In volatile emerging market environments, security management functions as a strategic resource that transforms raw ICT connectivity into sustainable competitive advantage — a phenomenon we term “Digital Resilience.” These findings challenge technological determinism and provide a blueprint for firms and policymakers in developing economies to integrate security as a core dynamic capability rather than a cost center.

Background The post-pandemic financial landscape presents a paradox: record asset price highs coexist with mounting systemic vulnerabilities. Market concentration in equity indices has reached levels comparable to the dot-com era, driven by passive investment inflows and the dominance of technology mega-caps. Yet the relationship between this concentration and systemic risk remains poorly quantified. Methods Using daily data from January 2020 to October 2024 (1,218 observations) across equity, fixed income, commodity, and cryptocurrency markets, we develop a novel econometric framework combining a Vector Error Correction Model (VECM) with a Markov-Switching Regime model. We construct three concentration measures—the Herfindahl-Hirschman Index (HHI), Concentration Ratio (CR10), and Entropy-Based Concentration Index (ECI)—and introduce a composite Market Fragility Index (MFI) derived via Principal Component Analysis (PCA). Results The HHI for the top-10 S&P 500 holdings reached 0.18. A 1% increase in HHI corresponds to a 2.31% increase in tail risk (Value-at-Risk at 1%), rising to 2.67% in high-volatility regimes. The MFI achieves an Area Under the Curve (AUC) of 0.891 in predicting market stress events, with an average lead time exceeding seven days. Volatility spillover analysis yields a Total Connectedness Index of 40.6%, with the S&P 500 as the primary risk transmitter and the cryptocurrency market as the largest net receiver. Conclusions Market concentration is a significant nonlinear amplifier of systemic risk in post-pandemic financial markets. The MFI provides superior early-warning capability over traditional indicators. These findings support concentration-adjusted portfolio strategies and enhanced macroprudential oversight, including mandatory stress testing when HHI exceeds 0.18. Methods Using daily data from January 2020 to October 2024 (1,218 observations) across equity, fixed income, commodity, and cryptocurrency markets, we develop a novel econometric framework combining a Vector Error Correction Model (VECM) with a Markov-Switching specification and a composite Market Fragility Index (MFI) built via Principal Component Analysis (PCA). Results The HHI for the top-10 S&P 500 holdings reached 0.18. A 1% increase in HHI corresponds to a 2.31% increase in tail risk (Value-at-Risk at 1%), rising to 2.67% in high-volatility regimes. The MFI achieves an AUC of 0.891 in-sample and 0.843 in walk-forward validation, outperforming all single-variable benchmarks. Total Connectedness Index (TCI) across ten asset series is 40.6%, with equities as net risk transmitters and cryptocurrencies as net receivers. Conclusions Market concentration is a significant nonlinear amplifier of systemic risk in post-pandemic financial markets. The MFI provides superior early-warning capability over traditional indicators. Portfolio optimization under high-concentration regimes requires defensive reallocation toward gold and fixed income.

Background Information and communication technology (ICT) infrastructure drives firm performance in emerging markets, yet institutional voids and security vulnerabilities often undermine its efficacy. The mechanisms linking ICT endowment, organizational security investment, and firm-level outcomes remain underexplored, particularly in developing economies. Methods We tested a recursive three-block structural equation model (SEM) on nationally representative microdata from Peru’s Annual Economic Survey 2024 (N = 9,966 firms across 14 sectors). The model integrated the Resource-Based View (RBV) and Routine Activity Theory (RAT) to specify pathways from ICT infrastructure, internet access, and digital human capital through security management investment to firm performance (measured as firm size category: micro vs. medium/large). We employed full-information maximum likelihood (FIML) estimation with Satorra–Bentler robust standard errors to account for non-normal distributions in e-commerce adoption measures. Mediation analysis decomposed total effects via bootstrap confidence intervals (5,000 replications). Results ICT infrastructure was the dominant predictor across all equations (β = 0.356 in security management; β = 0.327 in firm performance, both p < 0.001). Security management significantly mediated 17.6% of ICT’s total effect on performance (indirect β = 0.070, p < 0.001). Criminal victimization exposure directly predicted security investment (β = 0.060, p < 0.001), establishing a protective response mechanism predicted by Routine Activity Theory. Model fit indices confirmed excellent fit (CFI = 0.988, RMSEA = 0.028, GFI = 0.986). Conclusions In volatile emerging market environments, security management functions as a strategic resource that transforms raw ICT connectivity into sustainable competitive advantage — a phenomenon we term “Digital Resilience.” These findings challenge technological determinism and provide a blueprint for firms and policymakers in developing economies to integrate security as a core dynamic capability rather than a cost center.

Background The post-pandemic financial landscape presents a paradox: record asset price highs coexist with mounting systemic vulnerabilities. Market concentration in equity indices has reached levels comparable to the dot-com era, driven by passive investment inflows and the dominance of technology mega-caps. Yet the relationship between this concentration and systemic risk remains poorly quantified. Methods Using daily data from January 2020 to October 2024 (1,218 observations) across equity, fixed income, commodity, and cryptocurrency markets, we develop a novel econometric framework combining a Vector Error Correction Model (VECM) with a Markov-Switching Regime model. We construct three concentration measures—the Herfindahl-Hirschman Index (HHI), Concentration Ratio (CR10), and Entropy-Based Concentration Index (ECI)—and introduce a composite Market Fragility Index (MFI) derived via Principal Component Analysis (PCA). Results The HHI for the top-10 S&P 500 holdings reached 0.18. A 1% increase in HHI corresponds to a 2.31% increase in tail risk (Value-at-Risk at 1%), rising to 2.67% in high-volatility regimes. The MFI achieves an Area Under the Curve (AUC) of 0.891 in predicting market stress events, with an average lead time exceeding seven days. Volatility spillover analysis yields a Total Connectedness Index of 40.6%, with the S&P 500 as the primary risk transmitter and the cryptocurrency market as the largest net receiver. Conclusions Market concentration is a significant nonlinear amplifier of systemic risk in post-pandemic financial markets. The MFI provides superior early-warning capability over traditional indicators. These findings support concentration-adjusted portfolio strategies and enhanced macroprudential oversight, including mandatory stress testing when HHI exceeds 0.18.

Ha parecido claro que las organizaciones de Economía Social y Solidaria impactan positivamente el desarrollo de las comunidades; sin embargo, no existe mayor evidencia empírica, basada en ejercicios de carácter cuantitativo. Se quiso establecer la relación existente entre la Economía Social y Solidaria y el desarrollo en el Departamento de Antioquia; para ello, se tomó la variable de pobreza de Oxford como indicador de desarrollo; los datos están disponibles en las bases de datos otorgadas por la Secretaría de Planeación departamental de Antioquia y de la Unidad Administrativa Especial de Organizaciones Solidarias -UAEOS- dependiente del Ministerio de Trabajo de Colombia, por municipio a excepción de Medellín, con una muestra que representa el 84% del Producto Interno Bruto departamental. Se realizaron regresiones lineales simples en mínimos cuadrados ordinarios de corte transversal. Puede decirse que existe una relación inversa entre la cantidad de entidades de Economía Social y Solidaria y el nivel de pobreza, donde a mayor cantidad de entidades, menor es el índice de pobreza en los municipios analizados.

El presente artículo cuantifica el impacto en la rentabilidad del activo (ROA) y la rentabilidad del Patrimonio (ROE) de la implementación de las NIF en las empresas del sector curtido y recurtido de cueros, y fabricación de calzado y productos de cuero. Además, se identificaron los rubros contables más relevantes y su impacto en el ROE y el ROA de las Pymes; igualmente, se analizan los principales cambios en la estructura financiera de las empresas Pymes de Colombia.