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.
This research examines a naive rolling Mean-Variance Optimization (MVO) strategy for multi-stock allocation, in which expected returns and covariances are estimated solely from trailing historical data and re-estimated on a monthly basis. The central aim is to test empirically whether this deliberately simple approach can remain competitive despite the classical concern that MVO's sensitivity to estimation error makes it impractical relative to simpler heuristics such as trend following and momentum ranking. Expected returns and covariance are re-estimated monthly from a common trailing window, and a long-only, max-Sharpe portfolio is held until the next rebalance - deliberately simple, with no trend filter, momentum ranking, or volatility-targeting overlay. It is benchmarked against SPY buy-and-hold, a 60/40 portfolio, QQQ buy-and-hold, and the original design (a trend-filtered, risk-adjusted momentum strategy with volatility targeting and drawdown control). The strategy is evaluated over two independent, non-overlapping periods rather than a single blended sample: an in-sample window (2010-2019) used to fix the design, and a strict out-of-sample holdout (2020-2025) touched only once. On real market data for a nine-stock universe, the naive MVO strategy outperformed all four benchmarks on annual return and Sharpe ratio in both the in-sample (29.11% return, 1.19 Sharpe) and out-of-sample (36.53%, 1.16) periods - though not on maximum drawdown, where the momentum benchmark risk overlay produced a shallower worst-case decline in both periods, highlighting a trade-off between simplicity and downside protection that practitioners should weigh explicitly.
Yu-Tong Li· Advances in Economics, Manag...· 0 citations
Predicting asset price movements is relevant for investment analysis, risk monitoring, and economic forecasting. This study evaluates whether a cross-sectional representation of the market state can improve short-horizon index forecasting relative to a univariate historical-price benchmark. The paper is framed as a forecasting-comparison study, while market-efficiency testing, asset-pricing restrictions, portfolio efficiency, and trading profitability are treated as complementary evaluation layers. We compare market state data and historical price data for the Dow Jones Industrial Average and the Nasdaq-100. The dataset spans 2005 to 2024, and the empirical evaluation focuses on two single-year experiments: 2019, as the base pre-COVID implementation period, and 2024, as a post-COVID robustness check in a bullish market environment. We introduce a Kalman-inspired linear forecasting framework that uses deterministic matrix estimation and dimensionality reduction. Three market state estimation variants are compared with a univariate time series Kalman benchmark. The findings show that the relative usefulness of historical price data and market state data depends on the forecasting target and the market context, consistent with the no free lunch intuition that performance depends on the match between method, target, and data context.
Josué Bustarviejo, C. Bousoño-Calzón, Pedro Aceituno-Aceituno et al.· Financial Innovation· 0 citations
Commodity futures are shaped by harvest cycles, weather shocks, storage conditions, and seasonal demand, but it remains unclear whether recurring patterns yield robust out-of-sample trading profits. Existing research documents return seasonality in commodity futures as well as more complex seasonal structure, while leaving less evidence on how alternative seasonal models compare under common implementation constraints. This article compares dummy-variable regression (DVR), Singular Spectrum Analysis (SSA), and robust low-rank SSA (RLSSA) within a unified trading framework, including a volatility-normalised specification. Using monthly delivery-avoidance returns for 15 liquid commodity futures, the models are estimated on rolling ten-year windows and evaluated from 2016 to 2024 with transaction costs, an equal-weight long benchmark, and Maximum Entropy Bootstrap (MEB) assessment. Across 500 MEB paths, the benchmark has the strongest average full-period profile, with a cumulative return of 16.81%, a Sharpe ratio of 0.191, and a maximum drawdown of -0.414. Classical SSA has the strongest average model outcomes, but negative median cumulative returns and deep drawdowns indicate substantial path sensitivity. Volatility normalisation reduces the average DVR short loss but generally weakens SSA-based portfolios. None of the 18 approximate paired MEB Sharpe tests rejects after within-family Holm adjustment. Because MEB preserves each contract's temporal rank ordering, the assessment is conditional on observed timing rather than a timing-randomised seasonal null. The evidence does not establish robust benchmark outperformance and shows that model performance varies materially across market subperiods.
Managing drawdown, the peak-to-trough decline in an investment portfolio's value, is a precondition for long-term survival in practical investment management. However, mainstream stock forecasting methods predominantly optimize returns or Sharpe ratios under the independent and identically distributed (i.i.d.) assumption. Real markets do not follow this assumption, triggering catastrophic drawdowns. We propose a cross-asset state-space trading system (CAST), consisting of two components: The predictor, Cross-Asset Collaborative Kalman Filter (CoKF), estimates each asset's latent state online, coupling all assets through their correlations and adaptively fusing multiple integrated-random-walk orders. The controller, Model Predictive Control (MPC), converts the predictor's forecast into trades, using forecast uncertainty as an explicit risk penalty that controls drawdown. We evaluate CAST on four real-world stock markets over a 15-year test window and show that it consistently occupies the return-drawdown Pareto frontier, achieving strong risk-adjusted performance while maintaining substantially lower maximum drawdown than competitive baselines. A stress test across crisis periods further demonstrates robust behavior under market shocks and distribution shift. Because the predictor and controller interact only through the predicted price path, both are plug-and-play, making CAST a modular, interpretable trading system. The code is available at https://github.com/FanBroWell/CAST
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.
We propose a new model of expected stock returns that incorporates quantity information from market trading activities into the factor pricing framework. We posit that the expected return of a stock is determined by not only its factor risk exposures (beta) but also the factor's quantity fluctuations (q) induced by trading flows, and hence term the model beta times quantity (BTQ). The rationale is that sophisticated investors should demand a higher factor premium when they have absorbed noise trading flows of stocks with high loadings to that factor. The BTQ model provides a compelling risk-based explanation for stock returns, which is otherwise obscured without considering the quantity information. The cross-sectional risk-return association, which is nearly flat unconditionally, strongly depends on the quantity variable. The structured BTQ model reliably predicts monthly stock returns out of sample, and addresses the factor zoo problem by selecting a small number of factors.
Yu An, Yi-Kai Su, Chen Wang· 0 citations
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