Our founder's experience at Google, overseeing multi-billion dollar capital flows and managing advanced AI systems, instilled rigorous data-driven decision-making and the discipline to build robust, scalable algorithmic systems. This engineering rigor is the bedrock of Jaspil Capital, minimizing human error and emotional bias while maximizing performance consistency.

Unlike many traditional quantitative models that rely on predefined rules or human-calibrated parameters, our jQuant multi-state model, built upon advanced AI architectures, operates autonomously. It processes essential market indicators, ensuring direct correlation between market dynamics and our trading signals. This fully automated, market-agnostic approach allows the model to continuously adapt and react to market fluctuations without human intervention or bias. This objective, data-driven decision-making ensures consistent alpha generation regardless of market direction, which is a significant differentiator in consistency and objectivity.

The competitive landscape is evolving rapidly, but few firms possess our unique combination of deep AI/engineering expertise, a truly market-agnostic and fully automated model, and a validated track record of consistent outperformance across diverse market conditions since 2018. Our SCA lies in the jQuant model's unique ability to systematically generate alpha irrespective of market direction, delivering superior risk-adjusted returns with low trade frequency. Our industry-leading record of outperforming benchmarks in 99.5% of tested historical scenarios further substantiates this advantage.

Our core trading logic enables our model to effectively implement strategies that capitalize on market fluctuations and enhance benchmark performance to achieve consistent positive returns. For trade execution, the model processes essential market data points. The jQuant model focuses on the most critical market inputs for real-time decision-making. This adaptive approach allows our products to perform effectively across various market conditions.

Our jQuant model's dynamic capital allocation and sophisticated mechanisms are critical during volatile periods. Unlike strategies that simply ride market momentum, our AI identifies and capitalizes on market dynamics. During downturns, the model actively adjusts exposure and can implement defensive strategies to mitigate losses. This adaptive nature, as evidenced by our rapid recovery from the April 2025 tariff shock, is fundamental to our downside protection and ability to deliver superior risk-adjusted returns. For example, in April, our proprietary account tracking the US SOX index experienced a significant gain while the index itself declined, showcasing our model's resilience.

Our rigorous cross-validation process is designed to ensure the model's future robustness. We conduct extensive backtesting across diverse instruments like SPY, QQQ, and SOXX, validating thousands of parameter combinations. This has consistently shown a parameter effectiveness rate of 99.5%, meaning our model outperformed the index in 99.5% of historical scenarios, and significantly outperformed in 60% of cases – an industry-leading record. To further ensure future validity and adaptability, we even apply the model to fundamentally different asset types and market conditions. This cross-asset and cross-market validation confirms that our model has truly grasped internal market dynamics, ensuring stable relative returns regardless of future market shifts.

"Fearless of market volatility" signifies our model's ability to achieve profitability even in declining or highly volatile markets. This is because our strategy can dynamically adjust its internal components. While our early live data showed some volatility due to an initially aggressive parameter configuration, which also led to outperformance exceeding our PPT projections, our drawdown control has since been stabilized through optimized tuning. Importantly, even within that aggressive phase, during black swan events like the Trump tariff war, our strategy demonstrated rapid recovery. Leverage is used judiciously and infrequently, with usage strictly controlled by the model itself, not human discretion.

Our maturity lies in the stability and consistency of our core jQuant model. The "mature model" implies that the fundamental parameters have been definitively established through extensive research and validation. This ensures inherent consistency between our simulated strategy performance and actual live trading. Furthermore, ongoing fine-tuning of subset parameters within the established framework allows us to continually optimize for even better risk-adjusted returns. We are confident that future product performance can match, and potentially even exceed, our historical strategy simulations.

When our performance has slightly lagged the index, it has typically occurred during years characterized by exceptionally stable, low-volatility upward trends. In such markets, our strategy's configuration, which is designed to ensure substantial outperformance during unfavorable market conditions or periods of significant volatility, might result in a slightly lower total annual return compared to a pure index tracker. However, a discerning investor recognizes that this prioritizes long-term, risk-adjusted returns over capturing every basis point in highly stable bull markets.

The primary distinction between our aggressive and conservative strategies lies in their risk parameters and dynamic allocation profiles. These configurations are tailored to accommodate varying investor risk appetites. For instance, a conservative strategy would typically involve parameters designed to achieve very low drawdowns, resulting in a correspondingly lower, but highly stable, return. This allows investors to select their preferred balance between scale, return, and drawdown.

Our jQuant model is designed to be highly versatile and performs effectively in both volatile (oscillating) and one-sided trending markets. While our ability to profit from market fluctuations is key, our current product offerings are primarily index-enhanced. This means that when the underlying index rises, our products are engineered to deliver superior returns, and when the index falls, our decline is designed to be less severe. However, in extreme short-term index crashes (e.g., a drop exceeding 30%), our products may still experience temporary losses, though our model's resilience aims for rapid recovery.

No, it is not 100% directly invested in ETF indices. Our strategy primarily invests in the core ETF indices we track, with a portion dynamically allocated to other liquid instruments. This flexible allocation allows for enhanced alpha generation within the index-tracking framework, adapting to market conditions based on the jQuant model's signals.

The "unprecedented ETF scalability" is a direct outcome of our AI strategy's adaptability, enabling us to generate alpha across a vast array of large, liquid financial instruments. Each new ETF essentially acts as a unique wrapper around our core jQuant strategy applied to a specific benchmark. Crucially, the AI's fully automated nature minimizes the incremental human capital required for each additional fund. Our initial capital allocation will prioritize scaling our core operational and distribution capabilities in collaboration with a white-label partner, who provides the existing infrastructure essential for efficient product launches and global market access.

Our large fund capacity, projected at $20-30B per JQQQ and JSPY, is rooted in our low-frequency trading approach and our exclusive focus on major, highly liquid US equity indices and large financial instruments. Unlike high-frequency or niche strategies limited by market microstructure, our methods minimize market impact. While our strategies do have upper limits, Jaspil is committed to responsible growth. Once these capacities are approached, we will, in accordance with our fiduciary duty, cease accepting further capital to preserve the integrity and performance for existing investors.

The jQuant model's inherent market-agnosticism allows for significant adaptability. It has already demonstrated efficacy across diverse asset classes, including stock indexes, super & large caps, commodities, bonds, forex, and crypto. We are confident in its ability to generate alpha for new or custom indices requested by a white-label partner's client base, provided the underlying instruments possess sufficient liquidity and relevant market data.

Our strategies are designed for low trade frequency, which simplifies integration. We provide clear, actionable trading signals. The integration process would primarily involve establishing secure, robust data pipelines for signal transmission and performance reporting. We are committed to collaborating closely with your technical and operations teams to ensure a seamless integration that minimizes disruption and leverages your existing infrastructure efficiently. Our dedicated engineering expertise is prepared to facilitate this process.

Data security and the protection of our intellectual property are paramount. All sensitive model data, proprietary algorithms, and core jQuant IP will reside securely within Jaspil's controlled environment. We will transmit only the necessary, sanitized signals required for trade execution. A comprehensive Non-Disclosure Agreement (NDA) will form the foundational legal framework for any collaborative validation or integration phase, clearly defining data usage protocols and IP boundaries to ensure mutual protection.

We envision a highly collaborative partnership where the white-label partner plays a significant and leading role in distribution and marketing. Your established brand, extensive distribution channels, and experienced sales teams are precisely the leverage we seek for a successful global rollout. Jaspil will consistently deliver the underlying AI strategy, provide compelling performance data, and offer thought leadership, while the partner drives the product launch and client acquisition efforts.

Our jQuant model was initially developed and rigorously tested on US equity markets, where we observed higher returns and optimal performance characteristics. US markets offer distinct advantages, including trading mechanisms, short selling availability, and significantly larger trading volumes, which allows us to achieve far greater scale. While our current focus is on US indices, we are actively modeling and developing strategies for A-shares, but these products require additional time for thorough validation before release.

Our strategic objective is global market reach for our products. Our jQuant strategies are engineered for seamless implementation across major regulatory frameworks, including the US, EU, and APAC. Therefore, we are open to establishing fund structures in jurisdictions that offer the most efficient and scalable pathway for a global rollout into every developed country. The precise domicile (e.g., US for 40 Act funds, Ireland or Luxembourg for UCITS funds) will be determined in collaboration with our white-label partner based on their existing infrastructure and target distribution markets.

Yes, we are fully cognizant of the substantial legal, compliance, and operational costs inherent in establishing and maintaining international investment funds. This awareness is a primary driver behind our pursuit of a world-class white-label partner. By leveraging an established partner's existing fund infrastructure, regulatory licenses, and operational expertise, we can significantly mitigate these upfront expenditures and accelerate the global deployment of our products. This approach ensures capital efficiency and allows us to prioritize resources on continuous enhancement of our jQuant strategy and product development.

We are actively preparing for adherence to all major regulatory frameworks necessary for a global ETF rollout. This includes comprehensive understanding and compliance with the specific requirements for 40 Act funds in the US, UCITS directives in the EU, and equivalent regulatory mandates across the APAC region. Our strategy involves close collaboration with specialized external legal counsel and, critically, with a white-label partner possessing deep expertise in these areas, to ensure full and timely compliance as we navigate these complex regulatory landscapes.

We would require robust and comprehensive ongoing support, encompassing routine regulatory filings, continuous compliance monitoring, detailed reporting, and proactive adherence to evolving fund regulations across all jurisdictions where our products are offered. This holistic support from a proficient fund administrator and dedicated legal counsel is indispensable for maintaining operational integrity and ensuring full regulatory good standing.

While Jaspil Capital possesses internal expertise in general compliance principles and a strong understanding of regulatory requirements from a quantitative perspective, we strategically rely on external specialized legal counsel and experienced fund administrators for the intricate details of fund setup and ongoing regulatory compliance. Our core strength resides in our AI and investment strategy development; thus, we seek partners whose legal and operational capabilities complement our own.

We envision a highly collaborative and symbiotic ecosystem. Jaspil Capital, as the investment manager, will focus exclusively on generating alpha through our jQuant strategy and providing precise trading signals. The white-label issuer will assume responsibility for the fund's legal structure, distribution, and direct client interface. The fund administrator will diligently manage all back-office operations, accounting, and compliance functions. Clear lines of communication, well-defined roles, and regularly scheduled coordination meetings will be established to ensure seamless operational flow and complete strategic alignment among all parties involved.

As an AI-driven quantitative firm, our "deals" are fundamentally different from traditional private equity opportunities. Our 'deal flow' is defined by our continuous identification and validation of opportunities to apply our jQuant strategy to new large financial instruments and benchmarks. We possess a robust pipeline of strategies that are ready for deployment across diverse asset classes. The primary constraint we face is not a lack of viable opportunities, but rather the need for sufficient capital capacity and the necessary white-label partnership infrastructure to efficiently launch and scale these proven strategies into ETF products globally. This strategic objective is precisely why we are actively engaging with sophisticated partners like Savvy Capital.

Jaspil Capital Management, as a firm, is an emergent and innovative investment management entity primarily focused on productizing its proprietary AI strategies. Therefore, our "track record" is fundamentally defined by the extensive and consistent performance of our jQuant strategy across comprehensive back-tests, forward-tests, and live-tests conducted since 2018, demonstrating continuous outperformance. Additionally, our founder's previous experience at a leading technology company, encompassing the oversight of multi-billion dollar capital flows and the management of immense-scale AI systems, provides a strong foundation and a proven capacity for robust financial operations and algorithmic strategy deployment. Our current strategic endeavor represents a significant "deal" in the form of launching a new generation of highly scalable, AI-enhanced ETFs.

Yes, absolutely. Our core jQuant strategy has a validated track record of consistent outperformance spanning over six years, from 2018 to 2024, verified through extensive back-tests, forward-tests, and live-tests. Furthermore, our founder, Wang Chenyi, has been actively developing and refining the quantitative fluctuation theory for global financial markets since 2019. This includes hands-on experience in achieving substantial returns in futures and cryptocurrency markets prior to founding Jaspil Capital, indicating a significant and demonstrable investing experience well beyond the three-year minimum threshold.

This target valuation is currently illustrative and will be precisely finalized following comprehensive due diligence. The basis for this valuation is multifaceted, encompassing our team's unparalleled expertise, the vast addressable market opportunity for AI-enhanced index products, and detailed financial projections that demonstrate rapid AUM growth across our scalable ETF product suite. We firmly believe that the significant long-term equity appreciation potential of our operating entity, fundamentally driven by disruptive AI technology and its ability to capture substantial market share, justifies this early-stage valuation.

For strategic partners making significant equity investments, we are certainly open to discussing appropriate levels of governance and board representation. Our aim is to ensure strong alignment of interests while simultaneously preserving the operational agility and the distinct, unbiased nature of our AI-driven decision-making processes. Any such arrangements would be carefully structured to contribute positively to our strategic direction without impeding our core capabilities.

The seed capital is pivotal for accelerating our trajectory. Our deployment plan prioritizes critical areas: securing all necessary fund establishment approvals, finalizing ETF product structures for market readiness, achieving initial exchange listings, and robustly building out the essential compliance and back-office functions that are crucial for seamless global distribution with our white-label partner. Key milestones will include onboarding initial assets to demonstrate real-world operational scale and market acceptance, and successfully completing the first wave of ETF launches, setting the foundation for rapid AUM growth.

The "stringent high-water mark provision" is a critical protection for our seed investors, ensuring absolute alignment of interests. It dictates that performance fees will only be charged on new profits that exceed the fund's previous highest net asset value. This means if the fund experiences any losses, we are obligated to recover those losses entirely before any new performance fees can be applied. This mechanism directly incentivizes us to prioritize capital preservation and recovery, as we only profit when our investors achieve new highs, offering a higher degree of investor protection compared to structures without this provision.

A loan would primarily be serviced and repaid through the robust, scalable revenue streams generated from our management and performance fees as our Assets Under Management (AUM) grows rapidly through ETF productization. Given our low-frequency trading and high capacity, our operational costs are designed to scale at a slower rate than our revenue. We are prepared to discuss specific collateral options, which could include future fee receivables, or other corporate assets, depending on the agreed-upon loan structure and desired interest rate.

We approach debt financing with flexibility and a focus on mutually beneficial terms. We would aim for a tenor that provides sufficient time for significant AUM growth and operational scaling, avoiding undue short-term liquidity pressure. Repayment schedules can be structured to align with our projected revenue growth and cash flow generation. We anticipate standard financial and operational covenants that ensure prudent management and transparency, while still providing the necessary operational flexibility to support our growth trajectory.

Our core strategic focus remains on continuously enhancing our proprietary AI and quantitative strategy, for which we maintain dedicated, specialized talent. For critical operational, compliance, and distribution functions required to scale globally, a cornerstone of our strategy is to strategically leverage the existing expertise and established teams of our white-label partners and external, specialized service providers (such as fund administrators and legal counsel). This approach allows us to scale efficiently and rapidly without the need to build extensive, non-core in-house teams.

Our long-term vision is to become a preeminent global leader in AI-enhanced investment strategies, fundamentally transforming the landscape of passive investing. We intend to achieve this through widespread adoption of our ETF products via strategic partnerships. Our ultimate exit strategy could involve being acquired by a larger, established asset manager seeking to integrate cutting-edge AI capabilities, or, as we achieve significant Assets Under Management and establish a dominant market position, a public listing.