Portfolio Optimisation with Three Sigma
A deep learning-based quantitative model is developed to handle large data sets from the global financial markets. This model utilizes training and prediction algorithms to identify patterns and correlations in the data. The main goal of these quantitative methods is to aim for consistent returns. This approach is designed to be adaptable, aiming to perform in various investment climates and despite changing market sentiments. The model’s intent is to provide a reliable tool for financial analysis under different economic conditions.
How does Three Sigma's algorithm compare?
Medallion Fund vs Nested Fund
20-Year Growth Simulation
From the year 2000, starting with an initial investment of $100,000, the cumulative annual gross returns grew, reaching $15 million by the end of 2019. Over this 20-year period, the returns reflect the growth trajectory. It’s important to note that these results were simulated using Three Sigma’s machine learning model. This model, developed with deep learning algorithms, offers insights into potential market behaviors. The data emphasizes the capability of Three Sigma’s model in understanding financial market nuances.
Black Swan Event
The algorithm has been meticulously designed with flexibility at its core, allowing it to swiftly adjust to various macroeconomic shifts and challenges. Notably, its resilience was evident during the tumultuous times brought about by the COVID-19 pandemic, wherein it demonstrated the ability to navigate through negative market sentiments with astuteness.
Furthermore, the underlying quantitative methods have a clear objective: to consistently produce absolute returns, irrespective of the prevailing investment climate or overarching market sentiments. To achieve this, the models do not rely solely on recent data. Instead, they delve deep into a vast universe of historical data, spanning decades, to discern patterns and insights that might offer an advantage. The ultimate goal of these models is to generate alphas that not only outperform but do so with a high Sharpe ratio, indicating better risk-adjusted returns. At the same time, they strive to maintain minimal drawdowns, ensuring that the potential downside is limited and well-managed.
Simulated Monthly Returns for the Past 20 Years
Monthly net profit in thousands of dollar after 2% management fee and 20% incentive fee. The returns assume a portfolio starting with $100,000 in year 2000 with the investment held until 2019.
Collaboration and Engagement
We offer extensive expertise in our research domain and actively seek partnerships for collaborative projects. Additionally, for organizations requiring specialized solutions, our team is available to provide tailored services to address your challenges. To discuss collaboration or engage our services, contact us at hello@nested.ai or reach out to us below. We’re eager to explore how our skills can benefit your needs.
