Moore Threads Buy-Side Investment Analysis Report

Witness the journey of a middle-aged man in the stock market—transforming his Range Rover into a Volkswagen, his black hair into white, and fresh seafood into pickled vegetables! (There’s a bonus at the end—you know what I mean.) ************************************************** Moore Threads (688795.SH) Buy-Side Investment Analysis Report Report Date: September 7, 2026 | Market Data as of: Close on 2026-09-07 | Data Sources: IPO Prospectus / 2026 Semi-Annual Report / Broker Research Reports / IDC Module I: Executive Summary (Conclusions First) 【Investment Rating】Avoid (Do not buy; do not participate) 【Core Conclusion】This is a textbook case of a “good company, bad price”: its technical roadmap is scarce, its founder’s capabilities are top-tier, and its financial inflection point is genuine—but its current market cap of ~¥195.3 billion corresponds to only ~¥1.5 billion in 2025 revenue (P/S ratio ~130x), implying a negative margin of safety. Coupled with an impending flood of share unlocks and dilution from a potential Hong Kong IPO, buying now amounts to carrying the IPO pre-money shareholders’ burden. 【Fair Valuation Range】Based on broker consensus 2026 revenue forecast of ¥3.46 billion and a P/S multiple of 40–50x (midpoint between Hygon’s 36x and Cambricon’s 88x), fair market cap is estimated at ~¥138–173 billion, translating to a fair share price of ~¥295–368. The current share price of ¥415.48 remains ~13% above the upper bound of this fair range. 【Core Investment Thesis】In one sentence: domestic GPU substitution is among the most certain, decade-long trends in China’s tech sector—and Moore Threads is the sole domestic player capable of delivering full-featured GPUs. Yet alignment with a promising sector does not guarantee alignment with attractive stock valuation. It has yet to build a “real moat” (CUDA’s ecosystem remains insurmountable), remains unprofitable (cumulative losses ~¥5.9 billion), exhibits extreme customer concentration (91%), and faces sustained selling pressure over the next 12 months from IPO-pre-money shareholders exercising unlock rights. 【Key Catalysts】① Accelerated domestic substitution and ramp-up of large-scale AI computing orders from key customers; ② Achievement of quarterly profitability in 2027; ③ Deployment of a 100,000-GPU cluster. 【Major Risks】① Share unlock-related selling pressure (today’s unlock of 5.48% triggered a 20% daily limit-down); ② Dilution from a potential Hong Kong IPO; ③ Competition (Cambricon is already profitable; Huawei Ascend dominates the training segment); ④ Extreme customer concentration; ⑤ Persistently negative operating cash flow. 【Expected Return & Time Horizon】Buying at today’s price implies substantial downside risk—even under the most optimistic 2027 broker forecast of ¥501 million net profit and a generous 100x P/E multiple, implied market cap would be only ~¥50 billion, far below the current ¥195.3 billion, indicating significantly greater near-to-medium-term downside than upside. Module II: Company Overview (Understanding the Target) I. Core Business & Positioning Moore Threads Intelligent Technology (Beijing) Co., Ltd., founded in October 2020, is a fabless GPU (graphics processing unit) and related product R&D, design, and sales enterprise. Centered on its self-developed full-featured GPU, the company launched MUSA (Meta-computing Unified System Architecture)—a unified platform integrating scientific computing, 3D graphics rendering, AI training & inference, high-performance computing, and physics simulation. It is among the very few domestic enterprises capable of developing full-featured GPUs, thereby filling a critical technological gap in China’s high-end GPU domain.

Paper Reading | Hermes: Integrating Multi-Scale Spatio-Temporal Hypergraphs with Industry-Leading-Lagging Relationships for Stock Time-Series Forecasting

Paper Link: arxiv.org/pdf/2509.2366... Stock time-series forecasting provides critical decision support for investors, regulators, and analysts in finance. Unlike multivariate time series in other domains, stock time series exhibit pronounced industry-level correlations; uncovering such correlations can significantly improve prediction accuracy. However, existing hypergraph-based methods only superficially capture industry associations, suffering from two major limitations: (i) insufficient modeling of inter-industry leading-lagging interactions, and (ii) failure to model multi-scale information both within and across industries. To address these issues, this paper proposes the Hermes framework, which integrates moving aggregation and multi-scale fusion modules into a hypergraph network. Specifically, Hermes designs a hyperedge-based moving aggregation module (combining sliding windows and dynamic temporal aggregation operations to capture inter-industry leading-lagging dependencies) and a multi-scale fusion module (enabling cross-scale edge-to-edge message passing to fuse multi-scale information while preserving consistency across scales). Experiments on multiple real-world stock datasets demonstrate that Hermes outperforms existing state-of-the-art (SOTA) methods in both efficiency and accuracy. 1 Background Stock time series typically comprise key indicators (e.g., opening price, closing price) for multiple stocks, and their forecasting is vital for investment decisions. Yet existing studies often treat stocks as independent entities, overlooking the intrinsic interdependencies embedded in market dynamics. In reality, rich industry-level associations exist among stocks: stocks within the same industry are jointly influenced by common macroeconomic conditions and policy factors (e.g., synchronized price movements of Apple and Microsoft in the technology sector); meanwhile, causal and temporal dependencies exist across industries (e.g., technological breakthroughs in the tech sector driving growth in the energy sector, forming leading-lagging relationships). Although traditional hypergraph methods can capture intra-industry associations via hyperedges (connecting multiple stocks within the same industry), they face two key challenges: • Insufficient modeling of inter-industry leading-lagging interactions: Financial markets feature complex causal and temporal dependencies across industries (e.g., tech sector development driving energy sector growth), yet existing methods inadequately model such dynamic relationships. • Lack of multi-scale information modeling: Stock time series exhibit multi-scale characteristics (e.g., coarse-grained long-term trends vs. fine-grained cyclical patterns); single-scale analysis fails to capture scale-dependent association differences (e.g., two industries may be positively correlated at the coarse scale but negatively correlated at the fine scale). 2 Problem Definition This paper tackles two core problems in stock time-series forecasting: 1. Deep modeling of inter-industry leading-lagging relationships: How to capture dynamic temporal dependencies (e.g., changes in one industry preceding or lagging behind another) within a hypergraph framework; 2. Effective fusion of multi-scale information: How to decompose and integrate industry-level associations across different time scales (coarse-grained vs. fine-grained), while maintaining consistency across scales. 3 Methodology The Hermes framework comprises five core components: multi-scale feature extraction, adaptive spatio-temporal hypergraph construction, hyperedge-based moving aggregation module, hyperedge-based multi-scale fusion module, predictor, and optimization objective. 3.1 Multi-Scale Feature Extraction • Multi-scale decomposition: Apply 1D convolutional layers of varying kernel sizes to downsample the raw time-series input X, generating S distinct-scale time-series representations. • Causal mixing: Apply MLP layers along the time dimension to ensure each time step depends only on historical information (enforcing causality), yielding enhanced representations.

Methanex Corporation (MEOH) Stock Forecasts

Oct 18, 2026

Argus Research has raised its target price for Methanex Corporation to $66.00. The company manufactures methanol, a key feedstock used in various end-products.

Yahoo! Finance Canada

Methanex Corporation (MEOH) Stock Price and News

Syfe

2026-01-30

Eastman Chemical, Kolmar Korea Forge Partnership to Advance Biodegradable Personal Care Solutions

2026-04-06

Eastman Chemical Co (EMN) Shares Drop 3.04% on April 6

2026-04-06

Eastman Chemical Co. Stock Underperforms Monday Compared to Competitors

2026-04-08

Global Amines Market Growth Driven by Arkema, Eastman Chemical in North America and Europe Expansion