Absolute Returns with Machine Learning
Three Sigma
A machine learning quantitative model that applies training and prediction algorithms to astronomical sets of data in the global financial market. The quantitative methods intend to generate absolute returns regardless of the investment climate or market sentiment.
How does Three Sigma's algorithm compares?
“We search through historical data looking for anomalous patterns that we would not expect to occur at random.”
― Jim Simons
“Economists are criticized for not being able to predict the future, but, because the data are incomplete and subject to revision, we cannot even be sure what happened in the recent past. Noisy data make effective policymaking all the more difficult.”
― Ben Bernanke
Monthly portfolio updates to see how your nested egg is performing
The models run simulations from a universe of past data up to decades, and aim to generate alphas with high Sharpe ratio with minimal drawdown.
The quantitative methods intend to generate absolute returns regardless of the investment climate or market sentiment.
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.
Medallion Fund vs Nested Fund
A past 20-year performance comparison of the returns by Medallion Fund by Renaissance Technologies and the returns by Nested Fund using Three Sigma’s algorithm.
Performance in Year 2020
A monthly simulated cumulative return for year 2020 starting with $100,000 with Three Sigma’s model. The algorithm is adaptable to macroeconomic factors such as being resilient to negative market sentiments caused by the COVID‑19 pandemic.
The quantitative methods intend to generate absolute returns regardless of the investment climate or market sentiment. The models run simulations from a universe of past data up to decades, and aim to generate alphas with high Sharpe ratio with minimal drawdown.