Nicolás Magner

Grounded in signaling and disclosure theory, we examine when and which voluntary ESG disclosure pillars are associated with firms' cost of debt in an emerging market setting characterized by voluntary reporting, concentrated ownership, and debt centered corporate finance. Using firm‐ and year‐fixed‐effects models and several robustness checks on a panel of Chilean listed firms from 2015 to 2022, we find that environmental and governance disclosures are more consistently associated with lower debt costs, whereas social disclosure shows weaker and less stable associations. Higher operational profitability strengthens the negative association between pillar disclosure and the cost of debt, consistent with a credibility‐based mechanism in which creditors appear to place greater weight on voluntary disclosure when it is anchored in observable operational capacity. By contrast, additional information‐channel tests show no robust direct or moderating role for institutional ownership or stock‐market liquidity. Robustness tests using alternative measures and sample restrictions preserve the operational profitability interaction patterns; dynamic specifications suggest that pillar main effects are estimated less precisely, supporting a cautious interpretation centered on credibility‐contingent associations rather than unconditional disclosure effects. Finally, reverse‐causality diagnostics provide no systematic evidence that lower borrowing costs precede higher subsequent ESG disclosure. Overall, the evidence is consistent with pillar specific differences in materiality and verifiability under voluntary disclosure regimes in debt financing centered markets with concentrated ownership. Our study contributes to the literature by clarifying that voluntary ESG disclosure is more likely to be credit‐relevant when creditors can interpret the pillar‐specific signal and when operational profitability enhances its credibility.

The relevant empirical question about momentum in exchange-traded funds (ETFs) is not whether it works, but whether simple operational rules can extract value from it. We show that pure momentum over a broad ETF universe creates no value: replicated across 3,215 investable U.S. ETFs, it delivers Sharpe ratios between −0.17 and +0.22 with drawdowns of 69% to 82%, systematically below passive investing. Yet three layers of rules available to any investor fully reverse that conclusion. We evaluate the 2001–2026 period (1,289 weeks) across the investable universe, charging a 20-basis-point transaction cost per side at every stage of the analysis. On top of replicating classic cross-sectional momentum, we add technical entry signals, a multi-horizon momentum ranking with mandatory diversification across super-categories within an eight-position portfolio, and an expanded family of 144 dynamic stop-loss and take-profit exit rules, all under an asymmetric rebalancing regime of biweekly purchases and weekly exit monitoring. The complete strategy delivers a Sharpe ratio above 1.3 and a maximum drawdown of −7%, beating passive investing by close to a full Sharpe point; the alpha concentrates in tail-risk reduction and is impaired only during the high-implied-volatility regime. The central implication for researchers and practitioners is that the value comes not from the technical signal but from the operational management layer that surrounds it.

We document the determinants of early collapse in a panel of 5,559 cryptoassets recorded in CoinMarketCap between 2014 and 2026. One in four launches collapses within the first six months, and the incidence varies systematically with ex ante observable dimensions: memecoins deployed on Solana show failure rates close to forty-four percent, compared with rates below two percent for stablecoins and tokenized real-world assets. A random forest trained exclusively on attributes available at launch achieves a test AUC of 0.78; adding price behavior over the first thirty days lifts the AUC to 0.84 and yields 73.6% precision in the top-risk decile. This specification generalizes out-of-sample to the 2024 and 2025 cohorts, whereas purely structural specifications lose more than ten AUC percentage points when evaluated on those cohorts. The pattern reveals that the ex ante signature of collapse is unstable: the structural correlates of failure shift with the recent memecoin cycle, while early market dynamics remain a substantially more stable predictor. A Cox model confirms the sectoral hierarchy: membership in the memecoin sector doubles the instantaneous failure rate, while stablecoins and tokenized real-world assets exhibit instantaneous failure rates between one-fifth and one-eighth of the baseline.

In conventional markets, volatility admits a decomposition into a systematic component and an idiosyncratic residual. No such decomposition yet exists for a contemporary cryptocurrency universe at the individual-asset level. We build it by applying to a panel of 38 cryptoassets (91% of market capitalization, October 2018 to February 2026) the asymptotic principal components factor model of Connor et al. (2006) and the residual COVOL framework of Engle & CamposMartins (2023), and we obtain a daily per-asset decomposition into common and idiosyncratic components. The cross-section alternates between two regimes that a Markov-switching regression separates sharply: systemic stress with a common share above 55%, and speculative below 30%. Within that architecture, the two largest single-day shocks in the sample do not occur during the crypto-native failures, but at the SEC v. Ripple ruling and the spot Bitcoin ETF approval; LUNA/ Terra is the only episode with sustained elevation. Macro-financial transmission turns out to be selective: crypto-native sentiment, captured by the Fear & Greed index, reaches the idiosyncratic volatility of nearly the entire cross-section, while the VIX, the EPU, the NFCI, the GPR, the dollar and oil add no explanatory power at the daily frequency. Applied to 38 S&P 500 stocks and 11 SPDR sector ETFs under the same procedure, the exercise yields an indistinguishable regime architecture; the differential concentrates in the upper tail and in crypto-native events. In the space of volatility, this time is not so different.

Despite four decades of research, the profitability of breakout trading remains unresolved. We argue the central question is mis-specified. Breakout profitability decomposes into two separable margins: the macroeconomic regime governs the magnitude of the return, while firm-level characteristics govern the win rate. Because a tradeable strategy needs both, no single-dimensional screen suffices. We establish this on 453,856 breakouts from the 656 historical S&P 500 constituents (1990–2026), screening thirteen candidate filters (five firm-level, eight macroeconomic) through a two-phase protocol: an in-sample fixed-effects panel followed by a walk-forward out-of-sample battery under multiplicity control. Five filters survive and span both families: BAA–10Y and the 10Y–2Y yield-curve spread (macro); RSI, volume acceleration, and Amihud illiquidity (firm-level). Out of sample, only filters that identify a recurring regime, rather than a calibrated level, persist. A rank-based composite of the survivors delivers an annual out-of-sample excess return of +6.7 pp over the cap-weighted S&P 500 in 10 of 12 folds.

In conventional markets, volatility admits a decomposition into a systematic component and an idiosyncratic residual. No such decomposition yet exists for a contemporary cryptocurrency universe at the individual-asset level. We build it by applying to a panel of 38 cryptoassets (91% of market capitalization, October 2018 to February 2026) the asymptotic principal components factor model of Connor et al. (2006) and the residual COVOL framework of Engle & CamposMartins (2023), and we obtain a daily per-asset decomposition into common and idiosyncratic components. The cross-section alternates between two regimes that a Markov-switching regression separates sharply: systemic stress with a common share above 55%, and speculative below 30%. Within that architecture, the two largest single-day shocks in the sample do not occur during the crypto-native failures, but at the SEC v. Ripple ruling and the spot Bitcoin ETF approval; LUNA/ Terra is the only episode with sustained elevation. Macro-financial transmission turns out to be selective: crypto-native sentiment, captured by the Fear & Greed index, reaches the idiosyncratic volatility of nearly the entire cross-section, while the VIX, the EPU, the NFCI, the GPR, the dollar and oil add no explanatory power at the daily frequency. Applied to 38 S&P 500 stocks and 11 SPDR sector ETFs under the same procedure, the exercise yields an indistinguishable regime architecture; the differential concentrates in the upper tail and in crypto-native events. In the space of volatility, this time is not so different.