RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting

Tong Liu; Lanmiao Liu; Xiang Hu

cs.LGSep 27, 2026

RICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting

Authors: Tong Liu, Lanmiao Liu, Xiang Hu

Organizations: Zircon Security · Utrecht University · The Max Planck Institute for Psycholinguistics · China Life R&D Center

Abstract

Equity-relevant news evolves through temporally dependent corporate events, making historical information useful only when event continuity, information availability, and transition reliability are modeled. Existing LLM-based financial agents incorporate historical evidence, yet they provide limited support for preserving issuer-specific chronology under point-in-time constraints and for identifying when historical transitions contribute information beyond the current forecast. We present RICE-Alpha (Reliability-Informed Correction with Event Graphs), a point-in-time stock-scoring framework that separates a history-aware multi-view Base Alpha from a reliability-calibrated residual correction derived from historical event continuation. A Multi-Tier Memory Layer grounds news interpretation in temporally eligible issuer-specific history, while a Typed Event Agent constructs event states whose successor relations are formed within issuers and pooled across firms only after valid local pairing. Matured transitions are calibrated by their empirical reliability, and the resulting graph signal is residualized against the Base Alpha and technical view to obtain the RICE Delta. On daily Nasdaq-100 and Hang Seng Index panels from 2024 to 2026, RICE-Alpha achieves the strongest results among the evaluated LLM-based agents and momentum across four predictive and four portfolio-level metrics. Its ICIR more than doubles that of the strongest baseline, while net Sharpe ratios reach 1.656 and 1.725 in the U.S. and Hong Kong, respectively. U.S. ablations further show significant reductions in IC and RankIC after Holm adjustment when major components are removed. These results indicate that historical event continuation adds incremental information when it is temporally grounded, reliability-calibrated, and introduced as a residual correction to a multi-view forecast.

Figures & tables

Figure 1: Overview of the RICE-Alpha framework. A point-in-time multi-view Base Alpha is refined by a reliability-calibrated residual derived from historical event propagation.

Panel A: Prediction quality
	U.S. (Nasdaq-100)				Hong Kong (HSI)
Method	IC $\uparrow$	ICIR $\uparrow$	RankIC $\uparrow$	RankICIR $\uparrow$	IC $\uparrow$	ICIR $\uparrow$	RankIC $\uparrow$	RankICIR $\uparrow$
MEME	0.0216	0.1135	0.0254	0.1228	0.0013	0.0067	0.0156	0.0794
R&D-Agent-Quant	0.0188	0.0796	0.0177	0.0706	0.0011	0.0048	0.0125	0.0630
AI Hedge Fund	0.0077	0.0595	0.0094	0.0845	$-0.0018$	$-0.0121$	0.0033	0.0264
Momentum (12–1)	0.0316	0.1274	0.0322	0.1324	0.0160	0.0645	0.0327	0.1312

Table 1: Main results on common data and dated universes. ICIR and RankICIR are not annualized; ARR and MDD (a positive loss) are in %. Bold: market best. All strategies use the long-only, equal-weighted top-10% weekly rule and costs of Section 4.1 ; NDX/HSI exclude costs. † / ‡ : lag-5 Newey–West

t>1.96

2.58

Figure 4: Net asset value of Rice-Alpha , the baselines, and the index in (a) the U.S. and (b) Hong Kong from 2024-01-03; models are net of costs, indices exclude costs.

Configuration	IC	ICIR	RankIC	RankICIR
Rice-Alpha (complete)	0.0350	0.2928	0.0360	0.2927
w/o C3 (direct)	0.0260	0.2203	0.0290	0.2437
Reliability only	0.0326	0.2706	0.0330	0.2665
Residualization only	0.0208	0.1777	0.0242	0.2040
w/o C2–C3 (memory)	0.0191	0.1469	0.0205	0.1531
w/o C1–C2–C3	0.0064	0.0472	0.0090	0.0641

Table 2: U.S. ablations and two separate references. C1 is retrieved memory, C2 numerical graph evidence, and C3 reliability weighting plus residualization. All component rows use multi-agent stock scoring; the single-agent row reports that run’s stored final score. Component paired tests are in Table 3 ; the single-agent test is in the text.

Panel A: Rice-Alpha means
Market		Dates	IC ( $t$ )	RankIC ( $t$ )
U.S.		562	0.0350 (4.31)	0.0360 (4.24)
Hong Kong		551	0.0283 (2.48)	0.0395 (3.10)
Pooled		534	0.0317 (4.17)	0.0377 (4.41)
Panel B: paired increments of Rice-Alpha over each comparator
Market	Comparator	Metric	Increment	$t_{\mathrm{diff}}$	$p_{\mathrm{boot}}$ ( $p_{\mathrm{Holm}}$ )

Table 3: Daily signal significance and mechanism contrasts. Panel A gives mean IC and RankIC with Newey–West

t

-statistics (lag 5; 20-lag IC values 4.72 and 2.28). Pooled means weight the two markets equally on 534 shared valid dates. Panel B compares the complete system with six U.S. rows or Hong Kong AI Hedge Fund on common valid dates;

p

-values use one-sided 20-session paired block bootstrap (5,000 replications), with Holm adjustment by market and metric. Panel C reports the separate unadjusted comparison of reliability-only against residualization-only; it is outside the Panel B Holm family.

Method	Capture	Crash	Down mo.	Capture	Crash	Down mo.
	U.S. (Nasdaq-100)			Hong Kong (HSI)
Index (NDX / HSI)	1.00/1.00	$-24.4$	$-4.5$	1.00/1.00	$-21.2$	$-3.3$
MEME	0.28/0.33	$-20.8$	$-4.1$	0.02/0.11	+3.5	$+0.2$
R&D-Agent-Quant	0.29/0.35	$-24.9$	$-4.9$	0.25/0.33	$-11.8$	$-1.8$
AI Hedge Fund	0.73/0.71	$-17.0$	$-3.2$	0.93/0.97	$-20.1$	$-2.8$
Momentum (12–1)	0.83/0.85	$-16.8$	$-4.1$	0.58/0.64	$-9.3$	$-1.4$

Table 4: Behavior in falling and rising markets. Capture: mean return on index-down/up sessions relative to the index. Crash: return (%) in the index’s largest drawdown. Down mo.: mean return (%) in the 7 and 13 months the index fell. Bold: best Crash and Down mo.

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