Alibaba Investment Case

October 24, 2021

Introduction

I think investors in Alibaba have overreacted to news around regulatory changes in China and the stock now discounts a very pessimistic scenario.

I do not believe that the largest e-commerce and cloud company in China could turn into a mediocre business overnight.

While it is possible that Alibaba has taken advantage of lax regulatory regime, I am quite sure its success has largely come from other factors which are hard to replicate (including first-mover advantage, entrepreneurial spirit and strong teamwork, focus on execution, innovative solutions and services, investments in online platform, employees, logistics and other critical segments).

Since late August, I have built c. 8% position in Alibaba at an average cost of $169.

What is my edge?

I admit that I have no edge in China. I have never used Alibaba's services or any of its competitors. My knowledge is limited to information that I have gathered from reading mostly Western media (WSJ, FT) as well as some other publications flagged by some Twitter users. I have also spoken to several friends who are either based in China or have invested in the country previously. I have also listened in on several past earnings calls and the 2020 Investor Day of Alibaba.

My level of knowledge about China is most likely below what a typical investor knows about this country. This is why I do not just compare valuation multiples, analyse competition and regulatory environment.

I believe my edge lies in a different method that I use to analyse the case and in a longer time horizon.

Current market views are broadly split into two opposing camps: 1. 'China (and Alibaba) is now simply un-investable' and 2. 'Alibaba is down about 50% in the past 12 months trading at 2017 level, yet its revenue is up 4.5x since that time. Stock has overreacted and is just too cheap'.

Instead of deciding which view is more likely to be correct, I try to assess what is the likelihood of a leading business like Alibaba running into deep trouble and how this probability might change based on new information received over the past 12 months.

I think my advantage is in applying common sense and especially Bayesian formula to evaluate how the situation has changed with taking into account the new information. More specifically, I try to understand what assumptions are priced in by the market and what needs to happen / how likely it is that those assumptions will hold true.

Another important source of my edge is my time horizon. I don't think many investors are willing to ride out the volatility and associate themselves with the stock unless they can see the light at the end of the tunnel in the next few months. I am happy to own it for at least a year, possibly 3-5 years. I would not 'fire' myself if my position is down 20%+ during that period.

Short summary of the Bayesian theorem

Bayesian theorem allows you to estimate to what extent you should change your original assumptions based on new evidence (condition). For example, your friend meets a girl whom he describes as "shy and introverted" and he tries to assess how likely it is that she works as a librarian. A typical approach would be to draw conclusions by focusing solely on the characteristics of this girl ('shy', 'introverted'). Experts on decision making and behavioural finance call this the Inside view.

In various similar studies students would rate the probability of this girl working as a librarian as quite high (20-30%).

The correct way of answering the question would consist of a three-step process. First, start with the Base Rate: see how many librarians there are and what is their overall share in the population. This is called the Outside View.

Based on my Google search, there are about 170 thousand librarians in the US (I take the US as an example). There were 258mn adults living in the US in 2020, so the share of librarians is about 0.07%.

The second step is to estimate how common the described characteristics are among all librarians. Essentially, we need to understand what proportion of librarians are women, who are also shy and introverted. Again, with the help of Google I can see that about 80% of librarians are women, but I would not know exactly the share of 'shy introverts' among them. If we assume 60%, then about half of all librarians would fit the description.

The third and last step is to estimate what proportion of the population who are not librarians are also shy, introverted women. Using just a general common sense (if people are either men or women and either extraverts or introverts as well as brave or shy), I would guess that this share is about 12.5% (50% x 50% x 50%). It is also possible that a person who is an introvert is also shy (meaning these two characteristics coincide). In this case, the share of the population who fits such description rises to 25%. I will assume that the rate is somewhere between 12.5% and 25%, but closer to 25% since such characteristics look quite similar. So, let's assume it is 20%.

Knowing all three parameters we can then estimate that the probability that the girl is a librarian is about 0.18%. This probability is dramatically lower than the initial estimate made intuitively using the Inside View approach.

The formula to estimate this (Bayesian theorem) is the following one:


P (H | E) = P (H) x P (E | H) / P (E ¬ H)

  • P (H | E) is the Probability of the Hypothesis if we observe Evidence (E). It is often called Posterior probability.

  • P (H) is the overall Probability of the Hypothesis without the Evidence. It is also called the Base Rate or Prior probability (before the Evidence).

  • P (E | H) is the Probability that the Evidence (E) is observed when Hypothesis (H) is correct. It is also called Likelihood.

  • P (E ¬ H) is the Probability of Evidence (E) when the Hypothesis is false. It shows how common or unique is the Evidence.
Using the numbers from our example we get 0.07% (Base Rate) X 50% (the share of shy, introverted women among all librarians) / 20% (the share of shy, introverted women in total population excluding librarians).


There are two key conclusions from this.

Firstly, Base rate (initial estimate of probability before your new evidence becomes available) is very important and often determines the final result.

Secondly, new evidence makes an impact only when it is rare (likelihood of evidence is low in a situation when our hypothesis is false) or when it is very common in a situation the hypothesis is true (share of shy, introverted women among librarians). Although, the impact in the latter case is lower than in the former.

There are many interesting resources in the Internet to learn more about Bayesian theory. I personally learnt a lot about it from the books by Nate Silver and Philip Tetlock.

Applying Bayesian theorem to Alibaba case

1. Base rate

In the case of Alibaba, Base rate would be the share of businesses that are 'destroyed' in China. There are no precise statistics on this. Also the definition of 'destroyed' matters.

I also think my approach or assumptions can easily draw criticism from mathematicians (partially because of limited historical data).

However, I think such an approach can at least help to better understand what the market is pricing in and whether these assumptions are realistic (or rather under what conditions they are realistic). I am convinced it would help to better understand the overall situation.

So, the base rate, in my view, should be between 1% (quite optimistic) to 10% (quite pessimistic), I will assume 5%.

It is important that we make this estimate before events have started to unfold not least because of Availability bias. This happens when we pick the most recent and easy-to-recall example from our memory (e.g., an unfortunate person whose partner has just cheated on him would overestimate the probability of partners cheating on each other).

If we imagine that we are in 2019 and decide how likely a business would fail in China in a given year, we would probably say such probability was not too high. Optimists could even say that the probability was lower than in the West as the regulator in China was not influenced by various lobbying groups and did not depend on election cycles. Judging purely by the pace of economic growth and the scale of the economy, Chinese government could have been seen as one of the most efficient regulators supportive of business.

It is easy to recall companies that got into trouble thanks to media focus (negative titles are well known for increasing readership). But it is more important to compare this number with the overall number of businesses that have operated in China.

According to Statista, there have been, on average, about 5,000 bankruptcies per year in China during 2007-2019. Taking into account the fact that there are about 35 million companies in China (I have also seen over 70 million in other sources), this puts the probability of a default by a Chinese company at any given year at just 0.01%.
A chart shows the number of insolvencies in China from 2007 till 2019
The alternative approach to estimating probability of a business failure is to look at an average lifespan of a company. I have not seen official data for China, but for a S&P 500 company it is about 18 years which implies about 5.6% chance of going out of business for a company at any given year.

I also think that if one were to look at how many millionaires there are in China, one could conclude that China is one of the best places to accumulate wealth.

The point I am trying to make here is not that China is a great place for doing business, but that problems of individual companies should not be extrapolated and should be viewed in perspective. China is not as bad as it may appear by simply reading media headlines.
2. Likelihood of regulatory changes applied to companies that fail

This sub-title may sound a little complicated, but what I really need to estimate next is the probability that if the government is going after a particular business (or business is on its way to fail), then how often does the government apply regulatory changes in such cases in China. This is hard to estimate using official statistics and therefore requires some judgement call.

To answer it, I think it is important to consider alternative ways of 'killing' a business or for a business to fail. An autocratic government could just outlaw certain activities, put executives into jail, come up with an unbearable tax or penalty, try to acquire it and come up with ways to destroy the shareholder value. A business could fail due to its product becoming obsolete, due to high debt, misuse of capital, much stronger competition and other factors.

Taking this into account I think it is reasonable to say that if the government decides to go after a company it would apply regulatory changes probably not more than in 50% of cases (but of course the real number could fall within a wider range of about 20%-70%).
3. How unique is the Evidence?

In our case, the third parameter shows how often regulatory changes take place in China. I think this is quite an important point and also where the major difference comes with the West. In China, like in many other fast-changing emerging markets, regulatory changes happen much more often than in Europe or US. In the latter, regulators can discuss certain changes for many years before making the final decision which is often a watered-down version of the original proposal.

In China such decisions are made faster and more frequently.

Since early 2000, China has experienced certain regulatory changes almost every year. Studies of the past campaigns suggest that adverse changes (crackdowns) take place once every three years.
A graph contains track records of regulatory interventions in China from 2006 till 2019
I will assume that a probability of severe regulatory changes that affect a particular industry or a business in China is about 33% (1 out of 3 years).

So, having estimates for three parameters, I can calculate the probability that Alibaba would fail at about 7.6% (I multiplied Base rate [5%] by Probability of regulatory changes in case of failure [50%] and divided by Probability of adverse regulatory changes in any given year [33%]).

I would like to point out that the ratio 50% / 33% equals 1.5 which means that the new evidence (Regulatory changes) increase initial estimate by 50%. And since the Base rate is relatively low (5%), the updated probability remains quite low as well – 7.5%.

The only situation when the new probability rises to a dangerous level (over 30%) is when the Base Rate is high (companies often fall victim to government intervention) or such regulatory changes are unique (for example, there is just 1% chance of them taking place).

This is the crux of the matter, in my view. It is possible that the market mis-interprets the news by either artificially boosting the Base rate or overemphasising the importance of regulatory changes. The Base rate is easy to overestimate if you fall victim to Availability bias. If you had been asked about the probability of terrorist hijacking the plane on 12 September 2001, you would have most likely significantly overestimated.

At the same time, reading about all the details of the latest changes with various comments, views and background stories make you think that this is an exceptional / unique event (1 out of 100 years).

To be sure, it is probably harsher and more rapid than previous campaigns and maybe the political situation in China is different than 10 or 20 years ago. But after all, China remains a Communist country with one party officially running the country since 1949 (and the country has been run by a non-elected leader for centuries if not thousands of years).

I could be wrong and what we observe is very different to what has happened before. It could be that the latest developments are the game changes and maybe China indeed is departing from its reliance on private business and moving towards relying more on state controlled entities. I think this is what market consensus believes judging by the share price.

However, I think the probability of this is quite low based on the assumptions and reasons I discussed earlier.

Game Theory approach

So far I have only used Bayesian method. Another way could be to use Game Theory (developed by a famous mathematician John Nash). In this approach you put yourself into a position of key parties (in our case – Chinese government and Alibaba management).

I am not a big expert in applying this method and do not have a long track record. I would probably guess that for both sides the best outcome would be to de-escalate. The government, for example, seems to be resolved to consolidate political power, but it is also focused on maintaining economic growth and is even more likely interested in growing 'local champions'.

Other reasons to be optimistic about Alibaba

Strong moat

Alibaba is the largest e-commerce platform in China with over $1.1tn of GMV. About a fifth of all consumer spending in China goes through Alibaba's services. Alibaba's online marketplaces have 828mn active customers, while its international platforms have 265mn customers (as of 30 June 2021). Together with other formats, Alibaba's retail business serves 912mn people in China alone (65% of total population and close to 100% of adult population). Other businesses such as Cloud and partially owned Alipay (Alibaba's stake in Alipay is 33%) are also dominant platforms in China.

This is an important competitive advantage especially for e-commerce platforms which have clear positive network effects – the more users sell and buy products on a platform, the more valuable it is. If a new platform is created with just 10 sellers and 100 buyers, then neither sellers nor buyers will be satisfied due to limited assortment and weak demand (it would take a long time for merchants to sell their products on such platform).

There are competing platforms emerging in China, of course, but Alibaba cannot lose its advantage overnight.
A graph shows Alibaba Retail GMV (CNY mn) and effective Take Rate (%) from FY-15 till FY-21
A graph contains the number of Alibaba Active Customers (mn) and ARPU (CNY) from FY-15 till FY-21
Alibaba is not just about Jack Ma

I would like to highlight that Alibaba's success does not depend on Jack Ma currently. The market may be overestimating his current role. He should be credited for what he has done, but he has stepped down from all executive roles since September 2019. Jack Ma remains a member of Alibaba Partnership, but is not involved in running the business. There are over 30 members of the Alibaba Partnership.

Jack Ma has been gradually reducing its interest in Alibaba starting from direct sale at IPO in 2014. Based on the latest filings, his total interest in the company is only 4.3%.
Innovative culture

Alibaba has a strong track record of successful innovation with many services and products launched besides e-commerce. For example, Alibaba has developed a leading financial services business (Ant Financial), it is the largest provider of Cloud services in China (and one of the few providers globally that are already profitable), the company has also launched media entertainment services, logistics, offline retail and other businesses and ventures.
An image illustrates the track record of Business Innovation from 1999 till 2019
An image shows Alibaba's Business Innovations for Future Growth including different phases of maturing: seeds - traction - profitability
Team culture

I think human factor is harder to estimate because it is not regularly recorded in financial accounts, unlike fixed assets or financial debt. But it is important to note that Alibaba has a unique work environment with a less formal hierarchy, more dynamic and entrepreneurial, than many traditional corporations.
Early team of Alibaba with Jack Ma is on the photo
Conservative balance sheet, strong margins

The company has been generating positive net income and FCF since its 2014 IPO. Its current net cash position is $49.6bn (10% of the market cap).

Alibaba has one of the highest profit margins among peers (31% EBITA in Commerce in FY21, 51% average for the past 7 years), while its take rate is one of the lowest (4.1% in FY21).
A graph shows Alibaba Net Cash and Free Cash Flow from FY-15 till FY-21

Alibaba business model and financial performance

Alibaba has historically focused on an online Marketplace – a platform to connect merchants and buyers. There are at least two main formats of the Marketplace – Taobao (C2C), which is similar to eBay and TMall (B2C), which is focused more on branded goods. Alibaba charges merchants commissions based on their turnover which mostly cover advertising services to promote merchants on those platforms and help them find more buyers.

Traditionally, Alibaba's model has been quite capital light as most other services (such as fulfilment and delivery for example) were performed either by merchants themselves or outsourced to third-party providers.

As a result, Alibaba's commission (or Take rate) has been quite low (at just 2-4%).

However, over time Alibaba started adding more services for merchants and consumers including logistics (Cainiao) as well as offline formats. It has also launched and expanded financial services which is now run as a separate company (Ant Financial) where Alibaba holds 33% interest.

This has helped the company to raise its Take rate to over 4% last year but also reduced its margins. It is important to note that historically exceptional margins (over 50% at EBITA level) were partially boosted by low revenue line. Had Alibaba included total GMV of its merchants (rather than its Take on the GMV) in its revenue, company's EBITDA margin would fall to 1-2%.

Most new services are recorded on gross basis which automatically lowers margins. In addition to that, Alibaba has been recently focused on expanding in lower-income rural areas of China with lower sales per one customer. This has also negatively impacted the margins.

Below I provide historical financial performance of Alibaba by its key segment.
A graph contains Alibaba Valuation scenarios ($ bn except per-share data): Bear Case, Base Case, Bull Case
A graph contains Alibaba Valuation scenarios ($ bn except per-share data): Bear Case, Base Case, Bull Case
There are several takeaways from these historical financials.

  • Alibaba's Core Commerce business has grown at 41% annualised growth rate during FY2015-21 period.

  • Other Commerce has grown at 44% rate as Alibaba launched new services such as Logistics (Cainiao) and others.

  • Cloud business has been the fastest business segment within Alibaba growing at 90% CAGR, although its share still remains relatively low (just 8% of total revenue in FY2021).

  • The company's Commerce margin has been steadily declining as the market has been maturing pushing Alibaba into rural lower-income regions. Rising competition has also forced Alibaba to step up its investments in new formats and products. Commerce EBITDA has declined from 65%-62% level in FY2015-17 to 38%-31% in FY2020-21. It is likely that the margins will remain under pressure as competition remains high and Alibaba continues to expand in lower-income areas.

  • The trend in Cloud margins has been opposite to that of Commerce. The business segment turned profitable during FY21 and will likely enjoy rising margins in the medium-term.

  • Digital media has seen gradual reduction in relative losses from -42% in FY2015 to -20% in FY2021.



How can Alibaba's growth and margins look like in the future?

I think it is reasonable to expect Alibaba's growth to slow down and its margins to continue to decline in the future. The company can probably maintain higher margins in the short-term if it does not expand into regions, but this may create longer-term problems especially as new rivals are gaining strength in those regions and could pose a challenge to Alibaba in core markets.

Its revenue is driven by four factors:

  • Overall growth of Chinese consumer market
  • Alibaba's market share
  • Alibaba's Take rate
  • Launch of new services outside of retail


I think the Chinese retail market could be growing at 10-15% over the medium-term (in nominal terms) on the back of 5% GDP growth, increasing a share of consumption in the GDP (currently 39% compared to 60% for richer countries) and some inflation on top of this.

It will be hard for Alibaba to increase its market share even if it expands in new regions or experiments with new formats. At the same time, I do not think that it can start losing its market share quickly given its scale advantages and focus on innovation. So, probably, a growth in line with the overall Chinese market or just slightly below it is the most likely scenario (base rate).

The third factor (Take rate) can make a big impact. Currently at 4.08%, I think it can grow higher in the medium term (in line with the management's goals) due to additions of new services for merchants. At the same time, stricter regulation and rising competition will limit the upside.

I think reaching a 5-6% level of Take rate is achievable for Alibaba taking into account that some other e-commerce players enjoy over 10% take rates in other geographies.

The fourth and final factor – a launch of new services can add about 10-15% to the overall growth rate of Alibaba (in line with its historical contribution to group growth). Other services such as Cloud and Digital media have been growing at an annual rate of 90% and 44% since FY15 (faster than retail operations of Alibaba), but since they account for just 13% of the total revenue, their overall impact on the company's growth rate is limited.

Taking all four factors together, I think a growth rate of 30-40% is reasonable over the medium term with a likely slowdown in core Commerce segment partially offset by new segments.

My expectations for margins are more negative. Rising margins at Cloud and eventual breaking even of Digital media should positively impact group margins, but the bulk of profits are coming from the Commerce segment which will likely face a continued decline in margins (as discussed above).

Margins of offline retailers in mature markets are in 2-5% range. The more modern Chinese retailers with dominant Marketplace business would probably maintain higher margins, so, perhaps, a 10-15% range is plausible (over the medium to long-term).

Despite such deterioration in margins, the overall EBITA of the Commerce segment could still deliver about 10-15% growth rate per year if the overall sales in that segment continue to grow at 20-30%, while the margins fall from 31% (in FY21) to 12% in 10 years.

Group EBITA could rise a little faster than that (15-20%) due to growing contributions from other business segments.

Valuation scenarios: Bear / Base / Bull Cases

A graph contains Alibaba Valuation scenarios ($ bn except per-share data): Bear Case, Base Case, Bull Case
Bear Case

The downside case for Alibaba depends on several key questions.

Firstly, has the government decided to fully 'destroy' the company? If the answer is Yes, then the company's value would be zero. I think it is very unlikely based on the arguments I presented earlier.

Secondly, how much would Alibaba's business model change due to regulatory pressure?

Thirdly, can penalties and charity contributions to 'Common prosperity' become the recurring item?

Fourth, will the company's culture change as a result of recent events?

Fifth, how stronger can the competition become and whether consumer preferences would change in favour of competitors' products?

While the answer to the first question looks to be most definitely No, other questions seem less obvious.

It is possible that with less monopoly on data Alibaba could lose some of its competitive edge. It is also possible that with no exclusivity imposed on its merchants, Alibaba would have to increase marketing and promotion expenses. Its customer acquisition costs could also rise.

However, being the leading platform with almost a billion local users, Alibaba could also benefit if other players increase their marketing expenses and it is also unlikely that the whole billion customers would leave the platform overnight. As for merchants, they would always be attracted to places with the highest number of buyers.

In short, with strong network effects due to its already large scale, Alibaba has a strong moat which should cushion the blow.

It is also possible that the company's cost base could rise due to its efforts to support 'Common prosperity' policy. Higher wages and growing social contributions cannot be completely ruled out.

I do not think that all these negative factors are likely to play out. Perhaps, a probability of 25-30% for the Bear case is warranted. I consider the Chinese government toe be generally rational and economy savvy, they are likely to be interested in strengthening political control over the country and may prefer to pursue their long-term goals at some economic cost in the short-term, but this does not mean that they would act completely irrationally.

To be absolutely honest, though, I should reiterate that my overall knowledge of China is limited and I have no special edge in this part of the market.

For someone who believes in Smith's 'Invisible hand', as long as a business delivers value to buyers and sellers and continues to innovate, it would create more jobs for a local economy, increase overall efficiency and allow the economy to grow faster. Of course, a communist government may view the situation differently, but so far the track record of the Chinese government in delivering economic growth and creating conditions for entrepreneurs to prosper has been quite strong.

To take all these possible risk factors into account I applied a fairly conservative 7x EV/EBITDA multiple to company's marketplace EBITDA (which is my estimate for FY22). I assign no value to Other segments within Commerce business unit, I value Cloud business at just $50bn (less than 5x EV/revenue), I completely write off the value of Alibaba's stake in Ant Financial and apply further discounts to company's investments and net cash position.

My Bear Case valuation is $150/ADS. The share price has fallen below that level and can still go down further. I view this $150 estimate as some conservative reference point, but not as a precise level. There is small probability that Marketplace EBITDA would decline and/or the company could face new penalties which would reduce its cash position. I doubt, however, that all negative factors could occur simultaneously. It is more likely that there could be positive surprises from other segments, which would offset earlier issues.

Base Case

I apply a more normal 10x EV/EBITDA to the Marketplace EBITDA in my Base Case which combined with net cash of $49.6bn equals to company's current market cap. In other words, if the business continues to operate normally, then the market is missing the value of Cloud, Ant and other segments. Together, they are worth about $83/ADS, on my estimates.

I think that Base Case is most the likely scenario with about 50% probability. It does not mean that from tomorrow Alibaba would go to business as usual, but most current issues will probably be left behind in 1-3 years from now.

I have also tried to factor in rising competition from other players like JD.com and PDD. This is why I used 10x EV/EBITDA multiple and not higher.

As an aside, with predominantly negative headlines, the market may be missing the upside from recent regulatory changes. For example, raising capital would be harder for new Chinese Tech companies. There will be less appetite by entrepreneurs to start a new business in the Consumer Tech business. New business will also likely face higher Customer acquisition costs (CAC) to attract new users especially if they have to compete with existing Internet giants like Alibaba and Tencent.

All this could reduce competition over the medium term and benefit existing players such as Alibaba.

Besides, with more transparent operations Alibaba's services could even become more attractive to consumers and merchants.
Bull Case

My Bull Case (20-25% chance) assumes that Alibaba remains the dominant e-commerce platform in China (deserving 12x EV/EBITDA multiple) and its other commerce operations are also successful (valued at $150bn). I also give credit to Ant Financial (still at 25% discount to last year's valuation), Cloud (15x EV / FY21 Revenue worth $150bn) and some premium to the value of investments.

My Bull Case valuation is $365/ADS. As in other cases, this is a rough estimate and the actual share price can deviate. This value could also change if the company launches new ventures which I have not taken into account (its track record suggests that this is possible).

My weighted average valuation is $255/ADS. I would caution that this is just a rough reference point that can change considering new probabilities or assumptions in particular cases.

Risks

There are three main groups of risks.

The first one is the usual group related to macro and market sentiment. Chinese economy is slowing down, its population growth is also quite worrying. There will be additional cyclicality in economic performance which will likely impact Alibaba's results (e.g. current power shortages).

I am also aware that Alibaba has broadly reached a critical size in China and will not be able to replicate its past growth rates in the future.

On top of that, the market may interpret actual results differently or start anticipating more troubles ahead applying a lower valuation multiple to company's financials.

All this will keep Alibaba's share price volatile.

Importantly, I have no control over these factors so when they happen, they should not be the reason to sell the stock.



Factors to watch out for

The second group of factors are the ones that can materially impact my estimates in the Bayesian theorem.

Firstly, it is critical to watch out for any signs confirming that the case against Alibaba is really unique. For example, should there be more outright intervention like a criminal case, a direct ban on certain operations, materially higher penalties (closer to $100bn), this would likely reduce P (E) parameter and / or increase P (E | H) in the formula above.

Other factors that are worth watching are the overall Chinese economic policy with a particular focus on any signs indicating the government's decision to move away from relying on private enterprises in favour of state companies ('Capitalism is outlawed in China').

Other possible risks to watch are signs of Alibaba's business model being broken, which could include a fast decline in customer base or customer spending, much faster rising costs, material success of competitors.

As I mentioned earlier, domestic competition is rising and the Chinese market is generally quite young with incumbents having weaker positions than in traditional markets. I think Chinese consumers have not developed loyalty which is as strong as that of US/European customers to existing retail companies and can switch to new formats faster.

WSJ had a good article on this subject recently.

I think these risks are broadly offset by Alibaba's innovative culture and continued strong operational performance. Its strong balance sheet and margins also increase its resources to fight back.

The final, third group of potential risks consists of those factors that I am not currently aware of. It could potentially include the devastating scenario of a large military conflict or other events which I simply cannot imagine today. A military conflict is unlikely to be the reason not to invest in Alibaba since it would affect most stocks globally. It impacts one's overall decision on how much capital should be invested in stocks.
Thank you for taking time to go through this piece. I would be interested to hear your feedback which you are welcome to send to ideas@HiddenValueGems.com.
DISCLAIMER: this publication is not investment advice. The main purpose of this publication is to keep track of my thought process to better assess future information and improve my decision making process. Readers should do their own research before making decisions. Information provided here may have become outdated by the time you read it. All content in this document is subject to the copyright of Hidden Value Gems. The author held a position in the stock discussed above at the time of writing. Please read the full version of Disclaimer here.