Meituan Investment Thesis.

Overview and General Feedback

Fin 4716 – Meituan, Jason (Brian/Davin)

Note: I have a problem with your Excel. Your DCF value doesn’t seem to update when I change the variables (e.g., WACC).

There is good granularity for your revenue model, especially for food delivery. Good effort for justifying the assumptions. Your model makes it easier to understand the dynamics driving the business and to model inflation into the revenue number.

However, you did not break down the segments (i.e., Food, Hotel, New Initiatives). I think they have different dynamics:

• Your model cannot account for the difference in margins of the segments.
• For example, Hotel has no delivery cost (electronic fulfillment) so it should be a very high margin.
• There is disclosure here to help you break out the commission revenue for food delivery.

Revenue Drivers

Good slide on Revenue Drivers. You clearly defined how revenue is generated.

Your GTV for food delivery should be: Annual transacting users * Average food delivery transaction frequency per user in a year * Average GTV per food delivery transaction.

• This does not agree with your “Number of food delivery transactions” as you have estimated it using a universal growth rate from 2021.
• I think your bottom-up number seems more reasonable.
• But your Revenue from Food Delivery seems to be derived from bottom-up. Is the Number of food delivery transactions redundant?

Your model has “Other Services and Sales” and “Online Marketing Services” scaling rapidly:

• There is not enough granularity to understand why it will grow at a rapid rate.
• Not enough industry sizing (i.e., TAM? SAM?).
• I think this revenue profile will substantially change the business model and cost structure.
• Hence, I suspect there will be substantial changes to the opex, operational efficiency, and margins (i.e., EBITDA margins will change).
• I suspect the new business will involve different cost build-out, meaning the capex assumptions will have to change.
• AR2021: The increase in cost of revenues as a percentage of revenues was mainly due to the change in revenue mix as new initiatives with lower gross margin weighed heavier in our entire business portfolio.

Your model for others (e.g., Hotel) can be more granular (e.g., room nights).

Based on your number of food delivery, assuming the delivery rider is operating at optimal today, you will need to employ 19.5m delivery riders (today 5.27m riders). Does this make sense given the labor condition?

Your DCF(Base) has debt at 58.9bn but your WACC debt is 73bn. Why?

Discussion & Assessment Model

My long history in the market and being biased by a focus on “real asset”, I have missed many opportunities in the new economy sector. My real asset bias also makes it extremely difficult for me to project forward without anchoring on historical operating metrics. As you can see from my questionings, I am also biased towards “proof”. My sense is that with disruptive business models, there is an element of “hope and trust”.

The assessment model I am evolving has the following pieces:

1. Determine the cash burnt of the business: In this case, based on your projections and understanding of Meituan’s business model, it is highly cash generative at the operating level. Most of the cash burnt can be easily scaled back quickly as they are mostly capex in nature (R&D and marketing). However, one also has to be sensitive to the relationship between cash-burnt and brand building and traffic flow.
2. Assess business model scalability and equilibrium shifting: This is where the framing to potential market size comes in.
3. Determine management and team philosophy, adversity, and innovation intelligence: My sense is that this is where you need to spend the most amount of time to verify that management walks the talk. This is also a downside protection because the same management can pivot business models to new ones.
4. Determine Blue Sky scenarios and assess probability: This gives you a sense of the low probability but potential upside and helps to frame the business model financially. This is also where you identify the key variables to track and focus on when following the company.
5. How do I protect against the business risk inherent? In fact, once you determine the cash burnt and the equity structure, most of the time with platform businesses like Meituan, financial statement risk is actually quite small. In Meituan’s case, they have a large current liability from merchants and advances from transacting users, but they hardly have any inventory or receivables to fund!

It is almost like a bank—taking in lots of cash but not having to spend any. In fact, you can almost call it MeiTuan Bank. Cash is 90% of Current Assets and 56% of Total Assets.

Your DCF has the positive NWC contribution at almost 70% of FCF!

What I Like

I like the founder, Huang Xin’s unique and precise execution approach to business and his focus not only on the “bottom line” but looking long term. He has achieved in a very short period of time the following:

• Leader in service e-commerce with tremendous scale and network effects; cracked Alibaba and JD dominance.
• Household brands for high-frequency essential services.
• One-stop platform capturing consumer lifetime value.
• Management with a long-term vision and demonstrated execution capabilities.
• Started with a niche—food which is perishable—and built an extremely impressive city delivery logistic algorithm that outcompeted Alibaba.
• Highly analytical approach to building a business.
• Room for greater efficiency: better rider management can manage 10-20% gain in efficiency possible with an 8% increase in riders’ salary each year. Contactless delivery increases efficiency.
• SaaS approach and how he has built a highly impressive delivery algorithm.
• Shift from agency to franchise models.
• Wide range of solutions enabling merchants to succeed; largest intra-city on-demand delivery network.

What I Don’t Like

• Political risk has increased for the entire internet sector in China due to government intention to control data and socio-economic policies.
• Losses to date could impede MT’s ability to obtain sufficient capital on acceptable terms to fund operations.
• Ability to adopt new technologies to changing user requirements or emerging industry standards.
• Structural changes from the pandemic and the acceleration of online penetration.
• Alibaba waived platform and storefront fees for 6 months to help merchants cope.
• JD’s own logistic model is a winner because they were less affected by this crisis, which may trigger more investment in this area.
• Intense competition given the highly cash-generative business.

In terms of potential, if he can truly disrupt, MT is half of WeChat and Alipay in terms of users. The biggest challenge for MT is China’s population, which has started declining, especially the working population (i.e., cost and availability of labor). I also have some issues with the growth you are expecting across the board. Somehow, the numbers don’t add up.

Your Investment Thesis

• Strong Management Team with superb execution ability.
• Emotional Brand with a wide Economic Moat.
• Positive outlook for China Tech.
• MT strategy misunderstood and Upside will be higher than expected.

I think your base model is quite aggressive. Your bear case looks more like a base to me. The share price is reflecting your bear case.

Conclusion

I am also unsure how the non-food business plays out as competition is much more intense. I am very impressed with MT’s management ability to execute and to disrupt Alibaba and also Ctrip. However, I have issues valuing MT. My sense is that the market is very bullish on MT and the market-implied growth is very high.

The market is also highly sensitive to quarterly revenue growth. Finally, with Tencent’s distribution of nearly 20% of its holdings to shareholders, there will be near-term pressure on the MT price.

Industry & Framing Market

Superficially, the industry framing seems simple for Meituan—it’s the Chinese consumers and the rate at which they are converting to online for food delivery and other eCommerce transactions. I have made a fatal error in Alibaba by underestimating the conversion to online, but I also underestimated the conversion of some “service” sales online (e.g., education, health). Hence, framing around China’s GDP growth, personal consumption growth, and the rate at which transactions are moved online is crucial.

Four years ago, the Prospectus noted the following trends in China:

• Mass adoption of consumer service e-commerce.
• Mobile-savvy consumers with increasing demand for consumer service e-commerce.
• Consumer service merchants are moving online rapidly.
• Food consumption is the most frequented category in consumers’ daily lives.
• China’s food retail and service industry is expected to grow to RMB14,132 billion with an online penetration rate of 29.5% by 2023, according to the iResearch Report.

I suspect these low-hanging fruits have been mostly tapped out. Consumption is slightly less than 40% of the Rmb120tn GDP (2022). Food service is estimated at about $4tn in 2021 or about 10% of consumption. This is expected to grow to 6.5tn in 2027. Assuming 30% online delivery penetration, this implies about 2tn in online business. However, your model has MT at GTV of 4.2tn in 2027…

I suspect the eCommerce players will have to become more sophisticated with verticals to reach deeper into demand and to change habits. This explains MT’s aggressive move into hotel, ticketing, grocery, and e-commerce.

Business Model

From a financial perspective, this is fundamentally a crazily cashflow-positive business. The financial statements show that merchants provided the bulk of the funding for MT in the form of trade payables, deposits from transacting users, and other payables. It’s a massively scalable business model where, once the platform is built, all revenue contributes to the bottom line with very little non-discretionary cash spent apart from the delivery riders. But they are not employed by MT and hence constitute a variable cost.

I think MT is like a bank taking in lots of short-term deposits. The questions here are:

• How is MT developing a moat in this highly lucrative business opportunity?
• How is it deepening its stickiness with its customers, both merchants and consumers?
• How does this show through in the financial statement? (Monetisation rate? Average spend/frequency of purchase? Advertising and promotion?)

Management and Corporate Governance

I believe assessing management is highly critical in this business. There is no hard asset for you to fall back on. When the business goes downhill, there is literally no salvage value.

• How would you judge the Management?
• How would you rate its financial disclosure? I find it rather poor and late.
• How would you rate management’s vision statement? Are they executing?
• How would you rate the CEO, Huang Xin? Xing seems socially awkward but very intrigued by social networks. His belief is that core advantage is not only people and IT systems but a mix of talent and technology.

Weighted Voting Right Structure: The Company is proposing to adopt a weighted voting rights structure… Each Class A Share will entitle the holder to exercise 10 votes, and each Class B Share will entitle the holder to exercise one vote.

Financial Statement Observations

• Note 16 IR2021, Intangible Asset: What is the large intangible asset of 33bn?
• Note 13 and 16 IR2021, Taxation/Deferred Income Tax: What is the applicable tax rate for MT? Why is it not recognising the deferred tax benefit of its massive losses?
• Note 22 IR2021, Prepayments/Deposit/Other Assets: How would you regard the risk of micro-loans? Why did they make such a large tax prepayment?
• Note 29 IR2021, Trade Payable: What is the nature of this trade payable? What are advances from transacting users and deposits from transacting users?
• Note 31/32 IR2021, Borrowings/Notes Payable: Why does it need to borrow?

Financial Model Assumptions

Revenue & Operating Metrics

• GTV of Food Delivery: Your 2023 food delivery GTV is 1.3tn. Your forecast for 2027 is 4.3tn, implying MT has a 50% market share. Most estimates I see suggest MT has 60-70% of the food delivery market.
• Growth % – Annual Transacting Users: Your CAGR 22-27 is 18%, which seems aggressive with a higher base now. By 2025, it would be bigger than the population of China? How to understand this concept?
• Number of food delivery transactions: By 2027, you are expecting 69bn food deliveries. Assuming Meituan has a 50% market share, you are expecting the 828m users to make an order every 2.25 days. Are food orders family-based or individual-based?

Valuation (DCF & Beta)

• Beta: How did Yahoo derive a beta of 0.81? Refinitiv has it at 1.16. Why the difference?
• Cost of Debt: How did you get the 4.4%? AR2021 notes average interest rate was 1.70%.
• Tax Rate: China’s corporate rate is 25%. You are using 19%. Why? Since MT is scaling to profitability rapidly on your model, I think it’s fairer to use a normalized tax rate.
• Cost of Equity: Your formula: (Rf + ERP + Country Risk + Industry Risk) * Beta. How is this formula derived?