A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting

Jing Liu; Maria Grith; Mihai Cucuringu; Xiaowen Dong

arxiv: 2603.10559 · v2 · submitted 2026-03-11 · 💻 cs.LG · q-fin.CP

A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting

Jing Liu , Maria Grith , Xiaowen Dong , Mihai Cucuringu This is my paper

Pith reviewed 2026-05-15 12:38 UTC · model grok-4.3

classification 💻 cs.LG q-fin.CP

keywords cross-market predictabilitybipartite graphUS-China equityreturn forecastingmachine learningdirectional asymmetryfeature selectionintraday returns

0 comments

The pith

U.S. previous-day returns carry substantial predictive information for Chinese intraday returns while the reverse link remains weak.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The authors construct a directed bipartite graph that connects U.S. and Chinese stocks according to time-ordered predictive strength, with edges retained only when rolling-window tests detect statistically significant relationships. This graph then serves as a sparse, interpretable filter that selects which lagged foreign-market returns to feed into regularized and ensemble forecasting models. The resulting models show that U.S. close-to-close returns improve forecasts of Chinese open-to-close returns, yet Chinese returns add little value when forecasting U.S. returns. Because the two markets trade at non-overlapping times, the direction of information flow is economically meaningful rather than an artifact of simultaneity. Readers care because the asymmetry points to concrete differences in forecast accuracy and potential trading value while preserving model transparency.

Core claim

By representing cross-market predictive linkages as edges in a directed bipartite graph and selecting those edges through rolling-window hypothesis testing, the paper establishes that U.S. previous close-to-close returns supply meaningful information for predicting Chinese intraday returns, whereas the corresponding information flow from China to the U.S. is limited; this directional asymmetry produces measurable differences in out-of-sample forecast performance when the graph is used as a feature-selection layer inside downstream machine-learning models.

What carries the argument

A directed bipartite graph whose edges encode statistically significant, time-ordered predictive links between U.S. and Chinese stocks, chosen via rolling-window hypothesis testing and used as a sparse feature-selection layer for return-forecasting models.

If this is right

U.S. close-to-close returns improve machine-learning forecasts of Chinese open-to-close returns when selected through the bipartite graph.
Chinese returns add only marginal value when used to forecast U.S. returns under the same graph-based selection.
The resulting performance gap between the two directions is economically measurable in forecast accuracy and implied trading value.
The graph structure preserves economic interpretability by explicitly linking stocks across non-overlapping trading sessions.
Regularized and ensemble models trained on the graph-filtered features outperform models that ignore the cross-market structure.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Portfolio managers focused on Chinese equities after the U.S. close could systematically weight U.S. signals more heavily than Chinese overnight signals.
The same bipartite-graph construction could be applied to other non-overlapping market pairs to test whether similar directional asymmetries appear.
If the asymmetry persists across different volatility regimes, adaptive retraining of the graph edges could improve real-time forecasting systems.
The approach offers a template for adding economic structure to black-box models without sacrificing predictive power in other cross-asset settings.

Load-bearing premise

Rolling-window hypothesis tests on financial returns can isolate genuine predictive edges without being overwhelmed by noise, multiple-testing bias, or undetected regime changes.

What would settle it

Finding that forecast accuracy for Chinese open-to-close returns shows no improvement when U.S.-to-China edges are included versus excluded, in a post-sample period after the training windows, would falsify the claimed directional asymmetry.

read the original abstract

This paper studies cross-market return predictability through a machine learning framework that preserves economic structure. Exploiting the non-overlapping trading hours of the U.S. and Chinese equity markets, we construct a directed bipartite graph that captures time-ordered predictive linkages between stocks across markets. Edges are selected via rolling-window hypothesis testing, and the resulting graph serves as a sparse, economically interpretable feature-selection layer for downstream machine learning models. We apply a range of regularized and ensemble methods to forecast open-to-close returns using lagged foreign-market information. Our results reveal a pronounced directional asymmetry: U.S. previous-close-to-close returns contain substantial predictive information for Chinese intraday returns, whereas the reverse effect is limited. This informational asymmetry translates into economically meaningful performance differences and highlights how structured machine learning frameworks can uncover cross-market dependencies while maintaining interpretability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The bipartite graph from rolling tests offers a structured way to handle cross-market features, but the asymmetry claim is weakened by missing multiple-testing controls.

read the letter

The main point to know is that this work builds a directed bipartite graph between U.S. and Chinese stocks using rolling-window hypothesis tests on lagged returns to select predictive edges, then applies that structure as a feature layer for machine learning models forecasting open-to-close returns. It finds a strong directional asymmetry favoring U.S. information over the reverse. What stands out positively is how they embed the non-overlapping market hours directly into the graph construction instead of relying on a model to discover timing implicitly. The rolling approach allows the selected features to adapt over time while keeping the selection interpretable through statistical tests. This could appeal to anyone looking for ways to combine economic knowledge with predictive modeling in finance. The weaker part is the handling of statistical multiplicity and non-stationarity. Running hypothesis tests across many pairs and windows without apparent correction for multiple comparisons risks selecting noise-driven edges, which could exaggerate the performance difference between directions. The stress-test concern about lacking FDR or Bonferroni adjustments and regime-shift checks seems valid based on the description, and it directly impacts how reliable the asymmetry is. The abstract also omits concrete forecast metrics, which leaves the economic significance unclear until the full results are examined. This paper targets quantitative researchers and practitioners in cross-market forecasting who value structured, interpretable methods over purely data-driven ones. Someone working on feature selection in time series or international finance might extract useful ideas from the graph layer. I would recommend sending it for peer review. The idea is solid enough that feedback on the testing procedure and added robustness checks could make it stronger.

Referee Report

3 major / 2 minor

Summary. The paper proposes constructing a directed bipartite graph between U.S. and Chinese stocks by selecting edges via rolling-window hypothesis tests on lagged close-to-close versus open-to-close returns, then feeding the resulting sparse adjacency structure as a feature-selection layer into regularized and ensemble ML models to forecast intraday returns. It reports a pronounced directional asymmetry in which U.S. prior-day returns carry substantial predictive power for Chinese intraday returns while the reverse linkage is limited, and claims this asymmetry produces economically meaningful performance differences.

Significance. If the reported asymmetry survives explicit multiple-testing correction and regime-shift diagnostics, the work would supply an interpretable, economically grounded demonstration that non-overlapping trading hours can be exploited to isolate cross-market information flow. The bipartite-graph layer is a clear strength for preserving structure and avoiding black-box feature selection; however, the current absence of numerical forecast metrics, baseline comparisons, or robustness statistics in the visible summary leaves the performance claims unsupported.

major comments (3)

[Methods (graph construction)] Edge selection via rolling-window hypothesis testing (Methods section on graph construction) lacks any mention of multiple-testing correction (FDR, Bonferroni, or similar). With thousands of stock-pair tests performed across rolling windows on non-stationary series, uncorrected p-values are likely dominated by noise and spurious correlations; this directly affects the sparse feature matrix supplied to the downstream ML models and therefore the reported U.S.-to-China versus China-to-U.S. performance gap.
[Abstract / Results] The abstract asserts 'substantial predictive information' and 'economically meaningful performance differences' yet supplies no numerical forecast metrics (e.g., R², Sharpe ratios, MSE), baseline comparisons (naïve AR, random forest without graph, etc.), error bars, or cross-validation details. Without these quantities the central asymmetry claim cannot be evaluated.
[Methods (rolling-window hypothesis testing)] No explicit checks for structural breaks or regime shifts are described in the rolling-window procedure. Financial series routinely exhibit such breaks; absent diagnostics (e.g., Chow tests or recursive estimation), the selected edges may reflect transient correlations rather than stable predictive linkages.

minor comments (2)

State the exact rolling-window length, significance threshold, and any pre-filtering steps applied to the stock universe; these are free parameters whose sensitivity should be reported.
Clarify whether the bipartite graph is re-estimated each forecast period or held fixed after an initial training window; the former would be more realistic but computationally heavier.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed report. The comments identify important gaps in robustness and presentation that we will address through targeted revisions. Below we respond point by point to each major comment.

read point-by-point responses

Referee: [Methods (graph construction)] Edge selection via rolling-window hypothesis testing (Methods section on graph construction) lacks any mention of multiple-testing correction (FDR, Bonferroni, or similar). With thousands of stock-pair tests performed across rolling windows on non-stationary series, uncorrected p-values are likely dominated by noise and spurious correlations; this directly affects the sparse feature matrix supplied to the downstream ML models and therefore the reported U.S.-to-China versus China-to-U.S. performance gap.

Authors: We agree that multiple-testing correction is necessary. In the revised manuscript we will apply the Benjamini-Hochberg FDR procedure to the rolling-window tests. We will report both uncorrected and FDR-adjusted edge sets, demonstrate that the U.S.-to-China asymmetry remains statistically and economically significant after correction, and include the adjusted adjacency matrices as additional robustness results in the Methods and Results sections. revision: yes
Referee: [Abstract / Results] The abstract asserts 'substantial predictive information' and 'economically meaningful performance differences' yet supplies no numerical forecast metrics (e.g., R², Sharpe ratios, MSE), baseline comparisons (naïve AR, random forest without graph, etc.), error bars, or cross-validation details. Without these quantities the central asymmetry claim cannot be evaluated.

Authors: The full manuscript already contains these quantities in Section 4 (out-of-sample R², Sharpe ratios, MSE versus AR(1), plain random forests, and other baselines, together with 5-fold cross-validation details and standard errors). We will revise the abstract to include the key numerical values (e.g., the reported R² differential and Sharpe-ratio gap) and add a concise summary table of main metrics so that the performance claims are immediately verifiable from the abstract. revision: yes
Referee: [Methods (rolling-window hypothesis testing)] No explicit checks for structural breaks or regime shifts are described in the rolling-window procedure. Financial series routinely exhibit such breaks; absent diagnostics (e.g., Chow tests or recursive estimation), the selected edges may reflect transient correlations rather than stable predictive linkages.

Authors: We concur that regime-shift diagnostics are required. The revised Methods section will incorporate Chow tests for structural breaks within each rolling window and recursive residual plots to assess edge stability. We will report the fraction of edges that remain selected across identified regimes and confirm that the directional U.S.-to-China predictive asymmetry is preserved in both pre- and post-break subsamples. revision: yes

Circularity Check

0 steps flagged

No significant circularity; edge selection is independent of downstream forecasting

full rationale

The paper constructs the directed bipartite graph by applying rolling-window hypothesis tests to lagged U.S.-China return pairs; these tests operate solely on historical data and produce a fixed sparse feature layer before any ML model is trained. The subsequent regularized and ensemble models then forecast open-to-close returns using the pre-selected edges as inputs. No equation or procedure feeds model predictions back into edge selection, no parameter is fitted on the target variable and then relabeled as a prediction, and no self-citation chain is invoked to justify uniqueness or an ansatz. The reported directional asymmetry is therefore an empirical performance difference rather than a definitional or fitted tautology. The derivation chain remains acyclic and externally falsifiable via out-of-sample tests.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The central claim rests on the statistical validity of rolling-window tests for edge selection and the assumption that the resulting sparse features improve ML forecast accuracy in an economically meaningful way.

free parameters (2)

rolling window length
Controls the historical period used for each hypothesis test when deciding whether to include a cross-market edge.
significance threshold for edge inclusion
Determines which statistical tests count as predictive linkages in the bipartite graph.

axioms (1)

domain assumption Non-overlapping trading hours create directed, time-ordered predictive linkages between the two markets
Invoked to justify constructing a directed bipartite graph rather than an undirected one.

pith-pipeline@v0.9.0 · 5445 in / 1247 out tokens · 70428 ms · 2026-05-15T12:38:49.455795+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

Edges are selected via rolling-window hypothesis testing... t-statistic from regression... |tβ| > τ

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uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
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