Hedge ratios, factor models and diversified portfolios all rest on an estimate of which firms move together. That estimate is not stable: firms migrate between the groupings the market treats as coherent, and when enough migrate the organizing axes of the cross-section turn. We measure the rate of that turning as the mean squared sine of the principal angles between subdominant eigenspaces of consecutive twelve-month S&P 500 correlation matrices. A typical month rewrites a fifth of the structure and carries four-fifths forward. That rate is priced: it couples to the aggregate variance risk premium at t = 5.40, no level measure correlates above 0.32, and the implied-correlation surface spans at most 6.7 percent of it. Only the persistent component is priced - the premium compensates the pace of revision, not the distance traveled. The mechanism is prepayment: implied variance rises on impact, volatility follows two to three quarters later (simulated-null p<0.03 at h = 1-9), and the premium converges as it arrives. Three pre-registered boundaries hold: no timing alpha, no crash protection, and a downside version inseparable from intensity. The premium is, in part, rent on exposure held over a map still being redrawn.
Markowitz defined portfolio risk as an internal property, built from the covariance among a book's own holdings rather than the distance to any index. Seventy years of simplification reversed that. The market beta of CAPM, the fixed style and industry axes of Barra-type models, and the promotion of benchmark deviation to the definition of risk all traded the inward view for an external one. Risk became distance from an index. For a fund that fits no benchmark, that trade fails. A global book concentrated in a few markets and a few innovation sectors has no natural index to deviate from, and the active-risk number it produces measures the mismatch, not the risk. We return to the covariance. Principal component analysis (PCA) recovers the systematic structure inside the portfolio directly from its own returns. PCA has always carried one cost: its factors resist a plain-English reading. We clear that with a generative-AI labelling layer. It names the leading factors, ranked by their actual contribution to risk rather than by universe variance, and a deterministic rubric keeps it from inventing structure the loadings do not contain. Around this sit four independent signals. Density-based clustering with a mismatch ratio flags groups whose risk outruns their capital. A sign-invariant PCA Risk Score (PRS) marks the names that build the dominant factor bets. A standalone Bleed score catches the slow capital destroyers PCA cannot see. A trailing-return timing gate routes disagreements between the risk signals and recent price action to human judgment. We run the full engine on a proxy global-innovation book of thirty names over one year.
Swaraj Gambhir, T. George, K. Sivasankar· 0 citations
We study the equity factor zoo using a duration-matching return-decomposition approach that adjusts factor returns by subtracting returns on duration-matched government bond portfolios. By doing so, we remove the component of factor returns attributable to interest rate movements while preserving shocks to expected growth and risk premia observed in the data. Among commonly used factors, the value, investment, and profitability premia increase after duration matching, while the market and size premia decrease, over the post-1981 sample period. Furthermore, the effect of duration matching on mean factor returns depends importantly on the interest rate environment, consistent with our return decomposition framework.
This paper was accepted by Kay Giesecke, finance.
Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.08851 .
The value-creation capacity of active asset management remains one of the most debated issues within modern portfolio theory. While the relationship between costs and performance is typically negative in developed markets, in smaller and less liquid markets—where information asymmetry is more pronounced—higher fees may also be interpreted as signals of managerial ability. This study investigates this apparent contradiction in the context of the Hungarian equity mutual fund market, using a panel dataset covering 79 funds over the period 2017–2024, with a particular focus on identifying non-linear effects among performance determinants. The methodological framework combines fixed-effects panel regression with Driscoll-Kraay robust standard errors, complemented by quantile regression estimates to examine different segments of the return and alpha distributions. The results indicate that growth dynamics (NAV_change) and cumulative historical performance (Yield_from_start) consistently enhance fund performance, while the negative effect of past returns suggests the dominance of mean reversion. The impact of the total expense ratio (TER) proves to be non-linear and specification-dependent—a finding con-firmed by an extensive battery of robustness checks—thereby rejecting the cost-signalling hypothesis with respect to risk-adjusted excess returns (Jensen’s alpha). Quantile estimates further reveal that the effects of economies of scale and cost structure differ significantly between underperforming and top-performing funds, confirming that analyses based on average effects obscure the heterogeneity of market dynamics. By jointly modelling returns and risk-adjusted performance across the full conditional distribution, the study contributes a distribution-sensitive theoretical account of active management’s limitations in a small, less liquid market, showing that these limitations are conditional on fund size and cost structure rather than uniform across the fund population.
László Vancsura, Tibor Tatay, Tivadar Zakár et al.· Economies· 0 citations
Among the four factors of the Carhart four-factor model, which premium is regime-dependent —
and can that dependence be exploited in real time? A factor-attribution methodology isolates each
factor’s regime contribution across 18 size-sorted portfolios and three evaluation windows (January
1927–November 2025). As a regime-identification exercise, the size factor (SMB) is uniquely regimedependent:
forecast improvement scales with SMB loading (Spearman ρ = 0.948; pooled dependencerobust
p = 0.12 at the automatically selected block length, falling to 0.03 at fixed 12-month blocks), and
regime parameters show the premium is approximately zero in the calm regime and concentrated in the
turbulent regime at 0.5–1.1% per month (calm-stress difference positive in all three windows, one-sided
bootstrap p ≤ 0.030, though the windows are nested and two lean heavily on one historical episode),
consistent with countercyclical risk compensation; a parametric Monte Carlo test against a single-state
GARCH null indicates this difference exceeds what a volatility-clustered process without switching
manufactures (p = 0.015–0.090 across windows); high-minus-low (HML) switching is counterproductive.
These identification results do not, however, translate into ex-ante forecasting skill. Under a fully ex-ante
design — parameters estimated on training data only, combined with one-step-ahead predicted regime
probabilities — the regime model’s out-of-sample R2 is 0.07–0.08% depending on the regime-mean
estimator, no portfolio-window test is significant even before adjustment (minimum Clark-West p =
0.081), and none survives a 5% false-discovery rate across the full 54-test family (minimum adjusted p =
0.709). Moving-block bootstrap inference preserving serial and cross-sectional dependence renders even
the strongest single-window cross-sectional correlation (2010, naive p ≈ 7×10−9) indistinguishable from
zero (bootstrap p = 0.40; pooled p = 0.86). A three-stage decomposition attributes the apparent gains
of less disciplined designs to parameter and probability-timing look-aheads, with the timing channel
dominating. The size premium is regime-dependent in identification but not exploitably forecastable.
Ethan Wuang· American Journal of Student...· 0 citations
This paper investigates how Economic Policy Uncertainty (EPU) affects the returns and volatility (proxied by squared returns) of 448 S&P 500 stocks over the period January 2010–December 2020, and whether volatility persistence is related to EPU sensitivity. Persistence is measured with three semiparametric estimators of the degree of fractional integration: the Geweke–Porter-Hudak log-periodogram regression, the local Whittle, and the exact local Whittle. The linear EPU–volatility link does not survive market-level controls, but extreme EPU shocks generate significant volatility responses in roughly one-fifth of the stocks examined, with a slightly greater impact of adverse (bad) news. Long memory in volatility is pervasive. Persistence and EPU sensitivity are negatively related at the standard bandwidth (ρ = −0.182, p = 0.0001), a link that survives controls for size, liquidity, market beta, and sector fixed effects, and that holds for the subsample of stocks whose long memory is statistically significant according to the Qu (2011) test. This association is mainly concentrated at low frequencies, consistently with a low-frequency phenomenon possibly caused by level shifts.
G. Caporale, L. Gil-Alana, Jesus Pantoja Cárdenas· CESifo working papers· 0 citations
We present a simple framework for dynamic portfolio management that uses nothing but daily prices, trading volumes, and market capitalizations. Its state is three fixed-size matrices built from the price history: the distance matrix of the return correlations and the transition matrices of two Markov chains that rank the S\&P 500 names monthly by trailing return and by trailing volatility. These three matrices rest on the price history alone, the same information Markowitz mean-variance optimization draws on, but they replace its expected-return vector and covariance matrix. Our method requires no matrix inversion, works on outlier-robust cross-sectional ranks, and is dynamic rather than single-period. Empirically the volatility rank is forecastable one step ahead while the return rank stays close to unforecastable. A portfolio built on the forecasts, a market-neutral momentum long-short blended with an opportunistic long-only sleeve, beats the market on two non-overlapping out-of-sample test sets, January 2022 to December 2024 and January 2025 to July 2026, at Sharpes of $1.06$ and $1.32$ against the market's $0.78$ and $1.14$, respectively, net of a five-basis-point trading cost and marked to market daily. It also outperforms the classical minimum-variance and maximum-diversification portfolios. Diversifying the long sleeve by residual distance adds a further edge on both periods, lifting the Sharpe to $1.08$ and $1.44$ and the annualized return from $18\%$ to $20\%$ and from $44\%$ to $56\%$, respectively. A convex information-leader overlay separately insures the market-neutral sleeve, buying convexity and a shallower drawdown at a small cost in return, the Sharpe unchanged.