Which stocks do you firmly看好 following 2026?

We are relatively bullish on SDEK. 'SDEK has achieved substitution of Japanese industry giants—including Toray, Fujifilm, Nittodenko, and Mitsubishi—across multiple sectors through full-industry-chain vertical integration and continuous technological breakthroughs. It is one of the few domestic material platforms capable of competing head-to-head with Japanese firms in three strategic fields: optical display, electronic components, and new energy. With China’s countermeasures against Japan taking effect and domestic substitution accelerating, the company is poised for dual growth in both earnings and valuation—potentially becoming the 'Huawei of materials.' 'January 23, 2026: The company officially announced that its subsidiary has introduced a strategic shareholder (Huawei), signaling capital endorsement for accelerated commercialization of its magneto-electric storage business.' A market capitalization of RMB 50 billion appears quite certain at present; further upside will require additional catalysts or earnings releases. For the tech sector, it is genuinely difficult to discern whether a given opportunity is merely hype—or truly executable. Even if execution proves feasible, the magnitude of resulting earnings remains uncertain. Thus, buying tech stocks at low valuations inevitably involves an element of speculation. If one wishes to avoid speculation, waiting until outcomes are fully confirmed before entering positions makes achieving outsized returns extremely difficult.

Momenta found Fairy Island.

On January 29, 1886, German engineer Carl Benz patented his 'Benz Patent Motor Car,' marking the birth of the world’s first modern automobile possessing practical utility and commercial viability. Over the past 140 years, countless engineers have relentlessly refined and transformed the automobile. Every physical component—from engines to brakes—has undergone revolutionary evolution, propelled by advances in engineering and Moore’s Law. Yet the 'core component' seated behind the steering wheel—the human driver—has remained virtually unchanged since the automobile’s inception, constrained by innate physiological and cognitive limitations. This harsh reality is reflected in the approximately 1.3 million annual global road traffic deaths. Cao Xudong’s father was involved in a serious traffic accident—an experience that became one of Cao’s key motivations for entering the field of autonomous driving. At Momenta’s founding, he and his team established three decade-long visions; the top priority was: 'Within ten years, save one million lives.' It is precisely this profound reverence for life that has embedded safety into Momenta’s corporate DNA. Yet enabling true vehicle safety remains extraordinarily challenging—chiefly because it requires the vehicle to understand the physical world. In recent years, digital AI has advanced rapidly: large language models now absorb 'common sense' about the digital world from vast internet text corpora. The physical world, however, offers no ready-made corpus or neatly labeled datasets. The trajectory of a rolling apple, subtle shifts in lighting on a highway, or the minute sway of a cyclist’s shoulder—none of these phenomena appear in any textbook. To tackle this challenge, Momenta’s solution is: reinforcement learning + world models. Hawkins posits that intelligence fundamentally arises from prediction. This logic is straightforward: just as large language models compress knowledge of the digital world by predicting the next word—and thereby acquire textual and natural language understanding—breakthroughs in physical AI should similarly emerge from predicting 'what will happen next.' By compressing physical-world regularities into models via such predictive tasks, AI can learn object properties, causal motion relationships, and potential interaction outcomes. Lift a cup, release your hand—it falls. Humans intuitively grasp this due to millions of years of evolution hardwiring such intuition into our cerebral cortex; even without Newton or knowledge of gravitational formulas, we understand it instinctively. Machines lack this innate intuition and must instead acquire comparable common-sense reasoning through world models trained on massive real-world data. Xia Yan, Partner and SVP of R&D at Momenta, explains that during world model pre-training, the system first 'internalizes' physical laws, common sense, and causal relationships embedded in vast volumes of real-world driving data—forming the model’s foundational understanding of the physical world. Based on this foundation, the world model is then deployed within closed-loop autonomous driving simulations, enabling the system to project how the world would evolve in response to changes in its own behavior. Leveraging highly efficient scenario projection capabilities, the system evaluates performance on long-tail edge cases—and ultimately conducts reinforcement learning within this highly realistic virtual environment, allowing the system to evolve through repeated trial-and-error in near-real conditions. Sun Gang, Partner and SVP of R&D at Momenta—and Cao Xudong’s colleague—conducted a small-scale experiment a decade ago.

Hawkins, Inc. (HWKN) Stock Forecasts

Oct 8, 2026

Argus Research raised its price target for Hawkins, Inc. (HWKN) to $145.00 on October 7, 2026, citing strong performance in chemical and ingredient manufacturing.

Yahoo! Finance Canada

2026-04-02

Hawkins Inc. VP Withholds Shares for Tax Obligations

2026-04-05

Hawkins, Inc. Stock Valuation Revised Amid Strong Performance

2026-04-07

Ascent Group LLC Acquires 24,314 Shares in Hawkins, Inc.

2026-04-11

Wall Street Analysts Project 27.26% Upside for Hawkins (HWKN)

2026-04-15

Hawkins Inc Stock: Water Treatment Chemicals Drive Steady Investor Returns