Equity research: Shopify Inc.
Bibliographic record
Abstract
Shopify is a Canadian company that caught the eye of worldwide investors due to its fast-growing tech-enabled business. Within the core of its operations, we find software that enables almost any merchant to build an online shop with little to no previous knowledge of programming. Its subscriber base is diverse and the services offered are tailored to serve small, medium-sized and big-sized merchants. Its growth strategy is based on increasing its subscriber base and then generating additional revenue through the tools they provide to its merchants to manage their daily operations. In 2021, the global pandemic directly affected the demand for Shopify shares. Shopify’s stock rose to record high levels as investors saw in Shopify´s online network of merchants, a good retail hedge for the impacts caused by the government closing of all non-essential businesses. Across 2022 and as we move further away from the pandemic the stock has been steadily decreasing from its all-time high of USD 176.3 to USD 34.2 as of 30th October 2022. The main goal of this equity research is to reach the fair value of Shopify. Several valuation methodologies were carried out to value Shopify: the Discounted Cash Flow model through the FCFF and FCFE methods and the multiple-based valuation through EV/EBITDA and the Forward P/E ratio. Complementary to the 5-Year Discounted Cash Flow Method, a sensitivity analysis table was also included to understand the impacts of some of the most critical assumptions considered in the presented valuation such as the WACC, the growth rate and the cost of equity. The valuation based on the FCFF results in a fair value for Shopify stock at USD 20.6, followed by the value obtained through the FCFE methodology at USD 21.4. The valuation through the multiples resulted in a share price of USD 18.3 and USD 33.7 corresponding to the EV/EBITDA and Forward P/E ratio respectively. The value at which the stock is currently trading above the fair values through the application of the models and as result the final recommendation is to sell.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".