Optimize stock and ETF portfolios with AI-driven personalization
Sooper's AI algo deeply analyzes an investor's equity holdings across apps and customizes the entire portfolio for optimal return versus return.
The algo called “StockVector” uses LLM text embeddings to represent stocks as multidimensional vectors.
This enables it to uncover deep levels of hidden risks in equity portfolios which was not previously possible.
| Stock | Suggested action | Index allocation | Modified allocation |
|---|---|---|---|
| TSLA | Reduce | 10.4% | 8.2% |
| ROKU | Reduce | 7.6% | 6.3% |
| COIN | Reduce | 7.4% | 5.9% |
| PLTR | Increase | 4.7% | 5.8% |
| SHOP | Increase | 4.4% | 5.2% |
Portfolio Analysis
Detailed reports that analyze any portfolio and suggest actions
Direct Indexing
Customize any ETF index before buying to optimize return v/s risk
APIs
APIs are available for integration into other investment apps
How it works
1. Portfolio information aggregation
Sooper is able to pull any individual investor's stock holdings across different popular apps. This provides a consolidate view of all stocks in the entire portfolio.
2. Risk assessment
Sooper's robust risk assessment algorithm then analyses the portfolio for various types of risk. These include parameters such as – theme clustering, volatility, market dissonance, sector skew etc. Based on these, it arrives at a risk score.
3. Suggested action
Sooper then suggests actions such as which stocks could be considered for paring and which ones could be added for improving the return to risk profile of the portfolio.
4. Detailed reports
Detailed reports with explanations for all suggestions are generated. This helps investors and advisors to make informed decisions with full understanding and conviction.
For RIAs
Let’s face it –proprietary systems like TAMPs come with a huge tradeoff. Adopting them means being locked in to closed systems and complete loss of control. Sooper empowers RIAs to be independent, completely seamlessly with their current operating set up. This includes full compatibility with popular PMSes such as Orion, Black Diamond etc.
| Problems with TAMPs | How Sooper solves them |
|---|---|
Hard to set up and manage Rules based customization involves considerable effort | AI based automation Operator needs to just define the desired theme, and AI does the heavy lifting |
Advisors lose control Portfolios are rebalanced automatically by the TAMP without advisor input | Advisors' permission needed Operators can override any suggestions and thus approval needed for any trades |
No trade transparency Rebalances happen silently; advisors often can't explain the rationale | Explainable AI reports Detailed reports generated for all suggested actions |
Fee justification becomes harder Clients question why they're paying the advisor if the TAMP does the investing | Advisors can also modify the suggested portfolios Advisors retain discretion over any suggested actions |
Incompatible with other tools such as Portfolio Management Systems TAMPs are closed systems which cannot work with other PMSes | Advisor continues to use PMS seamlessly Sooper is completely compatible with other PMSes as Orion, Black Diamond etc. |
Custody lock-in TAMPs often require moving assets to their custodian | Custodian neutral Sooper works via APIs (e.g., BridgeFT) with any advisor workflow or custodian |
StockVector Algo
Overview
StockVector is the beating heart of our offering. It is at the absolute cutting edge of AI research in the asset management space. And that's not just a marketing statement. It rethinks the traditional “Markowitz Efficient Frontier” theory for the current AI world we live. Following is a comparison of Markowitz's approach versus that of StockVector
| Markowitz Efficient Frontier theory primer | Advantages of vector framework |
|---|---|
Bedrock of finance and investments industry | More aligned to contemporary digital and AI world |
First proposed in 1952 and won Nobel Prize in 1990 | Rethinks classical theoretical concepts with analogous AI ones |
Has the following shortcomings since it was built in pre IT / AI era | Leverages the advances in AI and LLM technology |
Only incorporates quantitative but not qualitative data | Can handle both quantitative and qualitative information |
Backward looking - relies on historical returns and co-variances | Dynamic and forward looking - incorporates real-time news flows |
Assumes simplistic linear relationships between assets | Captures non-linear relationships and complex patterns |
Cannot directly account for specific news events that drive stocks | Allows for a granular, event level cause-effect modeling |
Application To Risk Modeling
Between two pairs of stocks with similar return profile, the pair with maximum cosine distance will be less risky
Portfolio 2 will be less risky than Portfolio 1 (because Θ2 > Θ1)
The efficient frontier in Markowitz's theory is computed by using covariance of stock returns. In Sooper, it is computed by using the cosine distance between the stock vectors. Also, historical mean and variance is replaced by expected mean and variance.
API Suite
Plug and play approach
For app owners, Sooper offers a suite of powerful, easy-to-integrate APIs that bring personalized portfolio construction, dynamic rebalancing, and intelligent investment recommendations directly into your existing app or platform.
Designed for seamless compatibility with the tools RIAs, wealth managers, and digital advisors already use — including Orion, Black Diamond, and more — Sooper enables you to deliver sophisticated direct indexing and investment intelligence without building the infrastructure yourself.
With minimal lift, you can activate fully compliant, white-labeled workflows that empower agents to deliver next-gen portfolio personalization — all within their familiar environment.