SOURCE: Devonshire Research Group, LLC

Devonshire Research Group, LLC

May 20, 2016 08:30 ET

Devonshire Research Group Will Issue Part Two of Tesla Motors Presentation on May 24, 2016

NEW YORK, NY--(Marketwired - May 20, 2016) - Devonshire Research Group announced today that it will release part two of the Tesla Motors presentation on May 24, 2016. "Tesla Motors - Part II" is a continuation of extensive research issued by the firm and provides additional arguments and analyses substantiating the firm's belief that Tesla's shares are overvalued.

Devonshire Research Group released "Tesla Motors - Part I" on March 22, 2016, to showcase an in-depth analysis of Tesla's technology and intellectual property. "Tesla Motors - Part I," explores three distinct arguments for why Devonshire Research Group believes Tesla's share price is overvalued: abnormally high revenue multiples, overview of Tesla's existing intellectual property and its weaknesses, and analysis of the environmentally-friendly nature of the company and its product.

Tesla has succeeded in creating a unique product that appeals to a specific sector of mostly American consumers. Although there is demand for Tesla vehicles and the brand has gained significant value, Tesla has yet to generate profit since its IPO in 2010. Yet, it is trading at revenue multiples many times higher than its competitors and/or comparables. The only companies trading at multiples comparable to Tesla's are in the biotechnology, software, and social media sectors, all of which have starkly different financial profiles. Although the rate of innovation within Tesla is impressive, Devonshire Research Group argues that neither substantial R&D spending, nor the ever-increasing brand value can justify Tesla's current valuation.

"Tesla Motors - Part I" next includes an overview of patents held by various automotive companies both domestic and international. Tesla's patent holdings are dwarfed by its technology competitors, limiting its freedom to operate in the electric vehicle market where its technology position is modest. Research shows Tesla lagging far behind General Motors, Toyota, Ford, Daimler, BMW, and others. Moreover, Tesla's decision to make its intellectual property public has eliminated its competitive advantage. It has also enabled Tesla's competitors to readily copy Tesla's valuable inventions without the threat of being litigated against. Finally, Tesla's cost position is heavily dependent on its bargaining positions with strategic suppliers who have an incentive to price aggressively for their advanced technologies. Devonshire Research Group believes that these decisions are bound to have a negative impact on Tesla and its ability to remain competitive in the future.

Tesla gained popularity due to its environmentally-friendly status. Research released by Devonshire Research Group, however, points to the toxicity of Tesla batteries and argues that Tesla's electric vehicle production has a heavier carbon footprint than traditional vehicles. Devonshire Research Group emphasizes the risk that the disposal of Tesla batteries at large scale will create a heavy toxic burden. "Tesla Motors - Part I" also argues that battery charging shifts the carbon burden to electric power plants, thus, rather than eliminating the environmentally harmful impact, it augments the negative impact of the grid.

Hype surrounding Tesla's success has translated into significant overvaluation of the stock. Fundamental analysis of Tesla's financials, technology, and intellectual property, however, paint a different picture and show that the company's value is significantly lower than the current market valuation. "Tesla Motors - Part II" will present additional research and analyses that could point to Tesla's vulnerability to remain competitive, profitable, and relevant in the marketplace.

"Tesla Motors - Part I" is available on the firm's website at

About Devonshire Research Group

Devonshire Research Group, LLC is an investment firm specialized in using large scale data mining, analytical methods, and semantic mining across 1,000's of datasets to determine the long-term viability of technologies, and to assess their competitive advantages.