Has the Japanese Auto Components Industry valuation changed over the past few years?

Investors are pessimistic on the Japanese Auto Components industry, indicating that they anticipate long term growth rates will be lower than they have historically. The industry is trading at a PE ratio of 12.3x which is lower than its 3-year average PE of 13.9x. The industry is trading close to its 3-year average PS ratio of 0.56x.

Which industries have driven the changes within the Japanese Consumer Discretionary industry?

Investors are most optimistic about the Auto Parts and Equipment industry even though it's trading below its 3-year average PE ratio of 14.5x. Analysts are expecting annual earnings growth of 8.1%, which is lower than the prior year's growth of 27.3% per year. Investors are most pessimistic about the Tires and Rubber industry, which is trading below its 3-year average of 12.6x.

Japanese (NIKKEI) Auto Components Industry Analysis

Date	Market Cap	Revenue	Earnings	PE	Absolute PE	PS
Fri, 21 Aug 2026	JP¥32.3t	JP¥47.8t	JP¥2.6t	9.8x	12.3x	0.7x
Sun, 19 Jul 2026	JP¥31.9t	JP¥46.7t	JP¥2.4t	9.5x	13.3x	0.7x
Tue, 16 Jun 2026	JP¥34.1t	JP¥46.7t	JP¥2.4t	9.4x	14.2x	0.7x
Thu, 14 May 2026	JP¥34.5t	JP¥46.6t	JP¥2.3t	9.8x	14.8x	0.7x
Sat, 11 Apr 2026	JP¥32.0t	JP¥46.0t	JP¥2.1t	10.1x	14.9x	0.7x
Mon, 09 Mar 2026	JP¥32.3t	JP¥46.0t	JP¥2.1t	10.4x	15.1x	0.7x
Wed, 04 Feb 2026	JP¥30.4t	JP¥45.6t	JP¥1.9t	10.3x	15.7x	0.7x
Fri, 02 Jan 2026	JP¥29.7t	JP¥45.8t	JP¥1.9t	10.5x	15.3x	0.6x
Sun, 30 Nov 2025	JP¥29.6t	JP¥46.5t	JP¥2.0t	9.9x	15.1x	0.6x
Tue, 28 Oct 2025	JP¥28.8t	JP¥46.1t	JP¥1.7t	12.2x	16.8x	0.6x
Thu, 25 Sep 2025	JP¥27.5t	JP¥46.1t	JP¥1.7t	11.4x	16x	0.6x
Sat, 23 Aug 2025	JP¥26.0t	JP¥45.5t	JP¥1.7t	10.7x	15.4x	0.6x
Mon, 21 Jul 2025	JP¥22.8t	JP¥45.6t	JP¥1.8t	9.3x	12.9x	0.5x
Wed, 18 Jun 2025	JP¥21.9t	JP¥45.6t	JP¥1.8t	8.9x	12.3x	0.5x
Fri, 16 May 2025	JP¥21.8t	JP¥45.6t	JP¥1.8t	9.1x	11.9x	0.5x
Sun, 13 Apr 2025	JP¥19.1t	JP¥45.2t	JP¥1.8t	9x	10.5x	0.4x
Tue, 11 Mar 2025	JP¥22.2t	JP¥45.2t	JP¥1.8t	10.3x	12.2x	0.5x
Thu, 06 Feb 2025	JP¥22.3t	JP¥45.3t	JP¥1.8t	10.1x	12.3x	0.5x
Sat, 04 Jan 2025	JP¥23.1t	JP¥45.4t	JP¥1.7t	10.1x	14x	0.5x
Mon, 02 Dec 2024	JP¥22.1t	JP¥45.4t	JP¥1.7t	9.6x	13.4x	0.5x
Wed, 30 Oct 2024	JP¥21.8t	JP¥45.6t	JP¥1.9t	8.4x	11.7x	0.5x
Fri, 27 Sep 2024	JP¥22.2t	JP¥45.6t	JP¥1.9t	8.1x	11.9x	0.5x
Sun, 25 Aug 2024	JP¥22.4t	JP¥45.5t	JP¥1.9t	7.8x	12x	0.5x
Tue, 23 Jul 2024	JP¥23.7t	JP¥45.0t	JP¥1.8t	9x	13.2x	0.5x
Thu, 20 Jun 2024	JP¥24.0t	JP¥45.0t	JP¥1.8t	8.6x	13.3x	0.5x
Sat, 18 May 2024	JP¥25.2t	JP¥44.9t	JP¥1.8t	8.8x	14.3x	0.6x
Mon, 15 Apr 2024	JP¥27.0t	JP¥44.5t	JP¥1.7t	9.5x	15.8x	0.6x
Wed, 13 Mar 2024	JP¥24.6t	JP¥44.7t	JP¥1.7t	9.1x	14.3x	0.6x
Fri, 09 Feb 2024	JP¥24.2t	JP¥44.5t	JP¥1.7t	10.1x	14.5x	0.5x
Sun, 07 Jan 2024	JP¥22.1t	JP¥43.8t	JP¥1.7t	10x	13.2x	0.5x
Tue, 05 Dec 2023	JP¥22.5t	JP¥43.8t	JP¥1.7t	10x	13.5x	0.5x
Thu, 02 Nov 2023	JP¥22.4t	JP¥43.2t	JP¥1.6t	9.8x	14.3x	0.5x
Sat, 30 Sep 2023	JP¥22.9t	JP¥42.8t	JP¥1.5t	10.4x	15.1x	0.5x
Mon, 28 Aug 2023	JP¥22.1t	JP¥42.8t	JP¥1.5t	10.2x	14.6x	0.5x


JP Market	-2.32%
Consumer Discretionary	0.23%
Auto Components	-1.59%
Tires and Rubber	-1.06%
Auto Parts and Equipment	-1.75%

Company	Last Price	7D	1Y	Valuation
7282 Toyoda Gosei	JP¥5.32k	2.1% +JP¥13.0b	49.8%	PE9.4x
5985 Suncall	JP¥2.48k	15.9% +JP¥10.3b	176.7%	PE13.1x
7259 Aisin	JP¥2.28k	0.4% +JP¥7.0b	-3.7%	PE9.7x
5161 Nishikawa Rubber	JP¥3.55k	3.8% +JP¥4.7b	15.8%	PE11.3x
7280 Mitsuba	JP¥1.34k	6.9% +JP¥4.0b	42.1%	PE5.8x

Announcement • Aug 14

TIER IV Joins JST Next-Generation Edge Ai Semiconductor R&D Program to Open-Source Ai Chip Designs for Autonomous Driving

TIER IV, the pioneering force behind open-source software for autonomous driving, has joined the Next-Generation Edge AI Semiconductor Research and Development Program led by the Japan Science and Technology Agency (JST) to advance research and development of a software-defined system-on-chip (SoC) for Level 4 autonomous driving. Under the JST program, a research team led by Professor Yoshihiro Kawahara of Graduate School of Engineering, The University of Tokyo, will advance research into use-case-driven, functionally differentiated physical AI chip design. Meanwhile, TIER IV is developing the logic design of an AI chip designed to efficiently process inference for end-to-end (E2E) autonomous driving AI, with the goal of open-sourcing the design assets and associated toolchain. Through this initiative, TIER IV will independently design an AI chip for autonomous driving that supports Autoware, the world's leading open-source software for autonomous driving, and evaluate its effectiveness as part of an SoC. In addition to the chip's logic design, TIER IV will open-source the compiler and related toolchain. By making these technologies available, TIER IV aims to establish an open ecosystem in which semiconductor manufacturers can leverage its platform technologies to accelerate the commercialization of SoCs for Level 4 autonomous driving. Autonomous driving AI increasingly relies on large-scale Transformer models that process sensor inputs, such as camera images and point cloud data, in an integrated manner from perception through motion planning. This initiative will focus on Transformer inference and develop a dedicated architecture that simplifies the complex control mechanisms required for general-purpose computing. Data required for AI model execution will be efficiently placed in advance and repeatedly reused within the chip, reducing the power consumed by external memory transfers and computation control. The architecture will also incorporate dedicated compute circuits for operations frequently used in Transformers, including matrix multiplication and attention mechanisms. Rather than optimizing only the chip's peak performance, TIER IV will seek to improve performance per watt across the entire autonomous driving system, including Autoware. The design will support flexible deployment across a broad range of applications, from embedded devices operating at several watts to in-vehicle electronic control units operating at several tens of watts. The technology landscape surrounding autonomous driving AI is evolving rapidly, making it difficult for architectures designed around a specific model or hardware configuration to adapt continuously to change. To address this challenge, TIER IV will introduce the Tensor Operator Set Architecture (TOSA), a standardized intermediate representation, between AI models and the AI chip. Operations from AI models developed in frameworks such as PyTorch will be converted into a common TOSA representation. Optimization and code generation will then be performed through TOSA before execution on the AI chip, enabling loose coupling between AI frameworks and hardware. As a result, changes in AI model architectures or computational methods can be accommodated primarily through software updates to components such as the compiler and runtime, without requiring the entire chip to be redesigned from scratch. TIER IV aims to realize a software-defined SoC whose performance and power efficiency can be continuously optimized through software. In safety-critical applications such as Level 4 autonomous driving, it is important to understand not only the AI model itself, but also the internal architecture and processing flow of the computing system on which it runs. Under this initiative, TIER IV will open-source the logic design of its autonomous driving AI chip, together with the compiler and related toolchain. By making the design information publicly available, TIER IV will enable semiconductor manufacturers and developers to inspect the chip's internal architecture and software behavior, and to modify, extend, and reuse the technologies according to their vehicle platforms, AI models, performance requirements, and power constraints. Just as Autoware has enabled collaborative development of autonomous driving software, TIER IV will extend the same open-source philosophy to AI chip design and related toolchains. This will support an ecosystem in which autonomous driving AI technologies can be continuously improved and advanced without dependence on specific semiconductor products or closed development environments. The process of executing an AI model on a chip involves model-format conversion, computational optimization, quantization, rounding, and other transformations. These processes are essential to improving performance and power efficiency, but they may also introduce numerical differences that affect the final computation results. A mechanism is therefore needed to assess the impact of such differences. Using TOSA and its clearly defined operator specifications as a foundation, TIER IV will structure the compilation transformation process and introduce formal verification techniques. For selected transformations and operations, the initiative will establish mechanisms to mathematically verify numerical consistency before and after transformation, as well as compliance with predefined error tolerances. Through this approach, TIER IV will pursue not only high processing performance, but also an execution environment in which the transformations applied to an AI model before chip execution can be traced and the correctness of the resulting processing can be verified. This will contribute to a more reliable execution environment for autonomous driving AI.

Japanese (NIKKEI) Auto Components Industry Analysis

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