title: Risk-adjusted equity valuation of Tesla Motors: a practical application of Monte Carlo simulation to calibrate risk and uncertainty of risk in a Discounted Cash Flow valuation
Creators: name: Zaitsev, Maxim
Publisher: name: TU Wien
Year: 2017
Subjects: subject: Tesla
subject: Discounted Cash Flow
subject: DCF
subject: Monte-Carlo
subject: Equity
subject: Valuation
subject: Risk
subject: Forecasting

description: Financial equity valuation of public companies is a complex and daunting task. In a tech start-up arena, promising companies are generally evaluated against expected future potential of their innovation and/or ability to capitalize on those expectations. In these cases, financial historical data is generally of limited value for evaluating future financial projections, thus analysts often rely on hypothetical inputs to valuate equity. Tesla Motors (NASDAQ: TSLA) is a notable example in this category- a Silicon Valley “poster child” in the automotive space with a vision to advance the adoption of electric mobility across the world. Historically, the automotive industry has been considered rather predictable due to its capital-intense requirements for heavy expenditures for manufacturing, infrastructure, as well as research and development. Yet, while Tesla Motors operates in the same industry, the company’s hefty capital outflows, shadowed by consistent annual losses and missed delivery targets (typically a recipe for a financial meltdown) is contrasted by rather exceptional stock performance since its IPO in 2010. This phenomenon draws a fine line between the two opposing investment camps on Wall Street. The bulls, in the optimistic corner are charged by fanatic optimism on Tesla’s hyper-growth and industry-disruption potential. On the other side of the financial ring, conservative bears are considerably less optimistic about Tesla’s future and are often appalled by Tesla CEO’s often “far-fetched” aspirations. In addition, the bears also stress the dangers of blatantly ignoring substantial down-side risks in Musk’s masterplan that could quickly spiral into a financial disaster for its stakeholders. Historically, equity valuations for companies in the early stages of development, such as Tesla Motors require analysts to resort to hypothetical valuation techniques to model financial circumstances and assess “what-if” scenarios. In fact, one of the most widely used techniques is a Discounted Cash Flow model (DCF). This approach focuses on calculating the present value (PV) of the future cash flows of the firm, which is then discounted by the cost of capital or the discount rate to compute equity valuation in today’s terms. However, while the mathematical formula unarguably conveys confidence, these models are often plagued by hypotheticals and analysts’ biases at the core of its forecasted inputs. In other words, the risk of “guestimates” is that even the slightest deviation from these estimations may often lead to significant errors in calculation of the value and consequently impact stock price targets. In this research, the discussion is focused on readily-available and widely-used methods that could be applied to improve the accuracy of financial valuation. These procedures such as the Discounted Cash Flow model and the Monte-Carlo simulation of risk factors are valuable assets for analysts in equity valuation of companies such as Tesla Motors. The methodical approach will be supported by the review of current literature, discounted cash flow analysis of Tesla Inc, as well as risk modeling using ModelRisk analysis software for Excel spreadsheets. The further application of Monte Carlo simulation in this step will model the risk around the uncertainties in the DCF model and derive a probability distribution with a range of possible outcomes for the variables bearing the highest uncertainty risk. The model will produce a risk-adjusted valuation and stock price that falls within the 95% confidence interval and is expected to be lower than current market valuation relying on Discounted Cash Flow valuation alone. The reason for this expectation is attributed to the modeled key risk factors that significantly impact the probability of the expected outcomes of the inputs at the core of the DCF model.