It’s come to my attention that a number of firms are looking to adopt a model Marketplace. I suppose it seems obvious that it might be a good idea. a model Marketplace signals to the industry that your pointing towards an opening architecture, that you’re not seeking excessive control on the solutions that advisors implement, and in the wake of TD Ameritrade’s acquisition by Schwab who is mostly vanquishing the model Marketplace it seems that there is a void in the market. As the Veo platform was the standard in open architect and well liked by advisors.
firms that I see implementing this include:
| Name | Description |
| Altruist | Modern custodian with native model marketplace and TAMP-like functionality |
| Orion Portfolio Solutions | Integrated TAMP and model marketplace under Orion advisor tech stack |
| Morningstar Model Marketplace | Open architecture model delivery from third-party strategists |
| Envestnet | Largest model marketplace + UMA platform for enterprises |
| SmartX (SMArtX Advisory Solutions) | Model marketplace and UMA platform with deep billing and trade execution support |
| Advyzon TAMP | All-in-one advisor platform with integrated model marketplace (Advyzon Quantum) |
| AssetMark | Full-service turnkey platform with extensive model and strategist menu |
| Goldman Sachs Custody Solutions (FolioFN) | Sleeve-based model portfolio structure with fractional share execution |
| Interactive Brokers (IBKR) | Custodian offering simple model portfolio tools for RIAs and advisors |
| APEX Clearing | Digital-first custodian powering backend of many fintechs |
| CircleBlack | Advisor dashboard + data aggregation with light model marketplace integration |
| Pershing / Lockwood | Legacy UMA and model distribution via Pershing€™s NetX360 |
| Fidelity FMAX | Managed Account Xchange – Fidelity€™s advisor-facing model marketplace and SMA platform |
| Riskalyze (Nitrogen) | Risk profiling platform that now offers marketplace access to strategist models |
| Placemark | Pioneer of customizable UMA and tax-aware model portfolios |
| Tamarac (Envestnet) | Portfolio management and rebalancing platform with support for model portfolios |
| Schwab Model Marketplace (MDP) | Model Delivery Platform supporting third-party strategists post-TD merger |
| BlackRock Model Portfolios | BlackRock offers models through major custodians and tech platforms. |
| iCapital / SIMON | Alternative investments + structured note marketplace with some model delivery capabilities. |
| SMArtX | Model marketplace and UMA platform focused on integration and automation. |
| AssetMark | Leading TAMP with extensive strategist access and turnkey platforms. |
| Adhesion Wealth | Model marketplace (Manager Exchange) with UMA, SMAs, tax overlay. |
| TIFIN Wealth | Advisor platform with proposal gen, personalization, and integrations. |
| Betterment for Advisors | Robo-advisor with advisor-defined models and automation tools. |
| Schwab Intelligent Portfolios | In-house robo platform, closed architecture. |
This is a big cut of the advisor market.
These firms have substantial private equity and venture backing. In other words it is safe to say that they are well resourced. Despite this, I do not think that these marketplace models are enjoying the type of market traction one would expect given both their resources and the void left by TD Ameritrade.
Why is that?
I wonder that in the age of A.I., if a model Marketplace feels like skating to where the puck just was- Not to where it is going to be.
You can see the allure. Marketplace business models can be incredible. According to NFX, an early stage venture fund specializing in businesses with network effects 70% of the value created in the age of the internet – since 1994- has been in business with network effects; Think Facebook, TickToc, Ebay, Uber etc. A marketplace is a prime example of a business with network effects… at least sometimes. This figure includes marketplace models but also extends to other types of businesses with network effects.
Source: NFX – “Network Effects Manual”
Types of Marketplace Models
Drawing on NFX’s insights and broader marketplace expertise, here is an outline of the primary types of marketplace models:
1. Horizontal Marketplaces
- Definition: Serve a broad range of products or services across many categories.
- Examples: Amazon, eBay.
- Strengths:
- Huge TAM (Total Addressable Market) due to category breadth.
- Potential for cross-selling and upselling across categories.
- Applicability to Wealth Management: Sure, something like Envestnet perhaps although such marketplaces are limited to strategies only – not data or predictions, or idea generation or portfolio construction etc.
2. Vertical Marketplaces
- Definition: Focus on a specific niche or category.
- Examples: StockX (sneakers), Houzz (home improvement).
- Strengths:
- Deep understanding of category-specific needs.
- Easier to establish category dominance and defensibility through network effects.
- Applicability to Wealth Management: Modest
3. Service Marketplaces
- Definition: Match service providers with customers.
- Examples: Uber (rideshare), Fiverr (freelance work).
- Strengths:
- Asset-light model (no inventory).
- Often high-frequency use cases leading to engagement and stickiness.
- Applicability to Wealth Management: low, but most similar to a SAAS that is handing off the relationship with no monetization or fiduciary involvement
4. Product Marketplaces
- Definition: Facilitate the exchange of physical goods.
- Examples: Etsy, MercadoLibre,
- Strengths:
- Tangible goods offer potential for repeat business.
- Global scalability.
- Applicability to Wealth Management: None
5. Managed Marketplaces
- Definition: Provide additional services to enhance trust, quality, or convenience (e.g., payment processing, guarantees).
- Examples: Opendoor (real estate), Turo (car rentals).
- Strengths:
- Higher control over customer experience.
- Monetization opportunities through added value services.
- Applicability to Wealth Management: Yes. Like a less open architecture where the marketplace is controlled and monetized by the sponsor
6. B2B Marketplaces
- Definition: Connect businesses instead of consumers.
- Examples: Faire (wholesale), Alibaba.
- Strengths:
- Large transaction sizes.
- Fragmented industries create opportunities for consolidation and standardization.
- Applicability to Wealth Management: have not seen it
7. Two-Sided Marketplaces
- Definition: Facilitate transactions between two distinct groups (e.g., buyers and sellers).
- Examples: Airbnb, Upwork.
- Strengths:
- Creates natural flywheel effects.
- Network effects grow stronger as each side scales.
- Applicability to Wealth Management: Low because the model managers run the money but usually do not interact with the investors or advisors otherwise
8. One-Sided Marketplaces
- Definition: Network participants both buy and sell (e.g., peer-to-peer).
- Examples: Craigslist, Facebook Marketplace.
- Strengths:
- Easier to scale supply-side as participants fulfill both roles.
- Lower dependency on segment-specific growth.
- Applicability to Wealth Management: Reminds me of copy trading. I don’t think this is for professional investors and see serious risks for investors copying others without a fiduciary relationship, track record or regulatory oversight / licensure.
Reasons Marketplace Models Can Be Great Businesses (in Theory)
- Network Effects:
- Each new user on one side increases the value of the marketplace for the other side, creating a self-reinforcing growth loop.
- Strong network effects make marketplaces difficult to displace once scale is achieved.
- Economies of Scale:
- As more users join, the marginal cost of acquiring and serving additional customers decreases.
- Increased efficiency in matching buyers and sellers improves the experience and reduces churn.
- High Margins:
- Marketplaces often take a percentage of each transaction, leading to high margins without the burden of owning inventory.
- Examples include Airbnb (up to 15% fee) or Etsy (~5% fee).
- Winner-Takes-All Dynamics:
- Due to strong network effects, marketplaces tend to consolidate around a few dominant players in each category.
- Examples: Amazon in e-commerce, Uber in ridesharing.
- Data Moats:
- Marketplaces amass proprietary data on user behavior, pricing trends, and preferences.
- This data can be leveraged to optimize matching, predict demand, or improve monetization strategies.
- Scalability:
- Asset-light nature of many marketplaces allows for rapid expansion across geographies or categories without heavy CAPEX.
- Example: Uber scaled globally without owning a single car.
- Trust and Brand Equity:
- Once a marketplace becomes the default for a category, it can command significant pricing power.
- Trusted brands reduce friction for both sides of the market.
- Multiple Revenue Streams:
- Transaction fees, advertising, subscription models, premium listings, and value-added services all provide diverse monetization opportunities.
- Example: Etsy generates revenue from both seller fees and promoted listings.
- Fragmented Supply or Demand:
- Marketplaces thrive in industries with fragmented supply or demand by aggregating and standardizing the offering.
- Example: Airbnb aggregated short-term rental hosts worldwide.
- Low Customer Acquisition Costs:
- Flywheel effects reduce the cost of acquiring new users over time.
- Word-of-mouth and virality are common due to network effects.
This is powerful stuff and when I am doing an old school analysis of a business, whether public or private they are things that should be considered.
However, a manager or model marketplace for investors lacks most of these benefits.
and because of the lack of interactions between the money managers, advisors and investors the absence of any cross selling there has never been and is unlikely to be now any type of flywheel effect.
There is little reason why an advisor with investors in one model marketplace cannot uproot to any other system or marketplace. This is called switching costs and in general, apart from the friction of re-papering investors to a new custodian bank, advisors can switch freely between marketplaces, potentially without even a client conversation.
The worst part about these marketplaces is that the investors get a definitively inferior product.
This is what people don’t get, unless they get it and choose to offer a marketplace anyway, let’s let that one slide for now. Fiduciary minded enterprises will win the day….eventually.
Model marketplaces suffer from layered fees. Investors in any marketplace are almost certain to be paying a three or even four layer fee:
- The Advisor that opened the account, performs the financial planning, serves as the front line support and financial psychiatrist: that advisor probably expects a splash under 1% for their contributions. Let’s call it 85 bps.
- The money managers used in the platform – this has some variance by strategy complexity but lets average it out to 45 bps.
- The platform provider or sponsor – usually a broker dealer, but could be a bank or tech platform too, lets estimate that they are in it for 25 bps.
- God help you if the model manager invests in funds adding a hidden 4th layer.
So, all in here the investor has account expenses of 155 Bps. That’s too many mouths to feed.
Too many hogs at the trough
So if the investors are paying top dollar they must be getting a top solution right? Wrong.
Usually, in these systems, advisors will allocate to a handful of different managers that are naively assumed to be complementary. It is more arbitrary than science.
As far as I understand, none of these systems are providing any optimization opportunities, so the advisors are basically throwing darts at shiny things, completely devoid of any intelligent architecture or optimality.
Sadly, the worst thing is yet to come.
Because each manager is responsible for only their sleeve and there’s no interaction between the managers, each of the managers can use diversification but only to benefit its own strategy. This means that the greatest opportunity for diversification is basically indexed out.
The cost to investors is jarring.
As I have wrote before, this is Biggest Misconception on Diversification: Debunking the Myth
When advisors spread the account across a few different managers, typically those managers will have portfolios big enough to be a bit like a mutual fund. This means that the strategy is mostly a beta product; its return will be mostly derived from the performance from the market at large.
As you can see in the linked post above the correlations between funds are very high. So too are the correlations between fund managers models.
Without non-correlation there is no utility in rebalancing. There is no Diversification Return.
This matters far more than you might realize.
I just built a portfolio for a prospect showing what my system would do using their public company holdings. It obtained a Diversification Alpha of 10.85%
Granted, it is a big one, but I have seen bigger. Also, this strategy was optimized using a fairly vanilla set of investment policies. See for yourself in the up to date live link https://gsphere.net/view?ref=nqus3ulfgwxgoznpetgy or here in the Free lunch Diversification funnel graphic screenshot:
Do you assume that advisors are good at diversification? That’s a risky bet. Advisors probably think they are getting something like this:
In his 1996 book Against the Gods: The Remarkable Story of Risk, Peter L. Bernstein discusses the concept of a “rebalancing bonus,” which refers to the potential increase in returns achieved by periodically rebalancing a diversified portfolio. While Bernstein does not provide specific quantitative estimates in this book, he has addressed the rebalancing bonus in his other works.
In his article “The Rebalancing Bonus” on the Efficient Frontier website, Bernstein illustrates this concept with an example:
- A portfolio consisting of 50% U.S. common stocks and 50% long-term corporate bonds from 1926 to 1994.
- The arithmetic mean return for stocks was 10.19%, and for bonds, it was 5.51%.
- An equal mixture would suggest a “Markowitz return” of 7.85%.
- However, annually rebalancing to maintain the 50/50 allocation yielded an actual return of 8.34%, resulting in a rebalancing bonus of 0.49%.
Stepping up the rebalancing from yearly to monthly could likely approach twice that rebalancing bonus.
However, in a portfolio strategy using indexed funds, your average correlation is not the stock – bond correlation, but much higher, especially since most investors are skewed towards equity.
I just ran an example for this using a 3 model account, 2 equity and one fixed income using a 60-40 allocation. The portfolio data is below in pre-code
<pre><code>
# Portfolio data
weights = np.array([0.3, 0.3, 0.4])
std_devs = np.array([0.2, 0.15, 0.1])
correlation_matrix = np.array([
[1, 0.9, 0.6],
[0.9, 1, 0.4],
[0.6, 0.4, 1]
])
cov_matrix = np.outer(std_devs, std_devs) * correlation_matrix
portfolio_variance = np.dot(weights.T, np.dot(cov_matrix, weights))
portfolio_std_dev = np.sqrt(portfolio_variance)
</code></pre>
This gives comparisons that would fit into my funnel Graphic above
- Sum of Weighted Position Variances .02275
- Portfolio Variance .0164
- Variance Gap .00634
- Estimated Diversification Alpha .00317
- Estimated Diversification Alpha achieved (assuming ⅔, which is about ~ to quarterly rebalancing) .2124
So that is 21.24 basis points (.21%) of diversification alpha captured by the investors.
Not exactly a sufficient reward to justify the triple layered fee.
What Should the Ideal Solution Look Like?
Personalized – Move beyond outdated risk profiling to a modern, precise approach tailored to each investor’s objectives and constraints.
Optimized – Designed with institutional-grade portfolio science, as if Jim Simons himself were calibrating your allocations.
Tax-Efficient – Because smart investors don’t tip the IRS more than necessary.
Prudent – Systematic diversification enforces disciplined behavior—think fiduciary oversight built into the core.
Automated – Remove emotion and bias from the equation with intelligent automation that keeps portfolios aligned.
Diversified – Tuned, measured, and optimized diversification, developed by the inventor of diversification optimization itself.
Cost-Effective – Flexible pricing models that reduce total cost and deliver more value to investors.
Uncomplicated – Advanced machine learning runs quietly under the hood. All you do is steer.
Compelling – Investors need a reason to act. In the age of AI, data-driven solutions beat legacy product menus.
High-Performing – Is it too much to ask for strong outcomes? Not with the right architecture.
Flexible – Easily customized, adjusted, or evolved without requiring a complete rebuild.
Adaptive – Responsive to changing market conditions and investor preferences by design.
Advisor- or Investor-Led – Control resides where it should: with the advisor, the investor, or the home office—depending on the model.
Supported – Backed by infrastructure, technology, and people who understand what fiduciaries need.
It is possible. Maybe in 100 years? Or now. You got the puck on your stick.
Now Shoot!