Technology Solutions

Factor investing is a widely popular investment strategy that seeks to construct portfolios based on factors that have historically driven returns in the market. While this strategy can potentially deliver higher returns or manage risks more effectively than traditional market-cap-weighted indices, its effectiveness depends heavily on the features used as signals and their predictive power. This is where quantumrock’s innovative machine learning approach comes in.

Our ML algorithms are specifically designed for feature extraction in huge datasets, enabling us to identify features that are not humanly understandable but have high predictive power. By leveraging advanced AI and finance expertise, our models can identify more complex and dynamic characteristics with a high impact on future asset performances. In addition, our models can capture nontrivial, nonlinear, multidimensional, and fuzzy dependencies between features, which are hard to capture but show more significant signals than the raw data. By incorporating these signals into our factor investing strategies, we are able to achieve more accurate and precise predictions of future asset performance.

We also have built an intensive testing environment to ensure the quality of our identified patterns and to maximise the probability of future successful exploitation of the identified alpha. This includes various robustness checks, such as cross-validation and regularisation techniques. Finally, based on the signals retrieved from our ML models, we introduce another layer of optimised portfolio construction to ensure tradability. Our factor investing strategies are fully tailored to the specific needs and constraints of our clients, maximising the effectiveness of our approach according to their preferences and goals.