IndustriesFinancial Services › Regime Forecasting

Improving Regime Forecasting

Price trends for publicly traded securities, as well as their relationship to macroeconomic indicators, often demonstrate stability over selected periods of time. However, these trends and relationships, referred to as regimes, can also shift quickly to form new patterns as the market enters new phases. Investors with a deep understanding of the characteristics of each regime, as well as the ability to recognize early indicators of the onset of new ones, can capitalize on the accompanying opportunities by employing sophisticated market regime forecasting software.

A precise understanding of market regimes aids the creation of better asset allocation strategies and more accurate liquidity forecasts. However, this requires the ability to analyze highly complex market and economic data to uncover and capture the key characteristics of each regime. Conventional statistical tools and machine learning techniques limit analyses to small sets of explanatory variables, and require analysts to hypothesize relevant partitions and analytical forms prior to analysis. As a result, uncovering regimes, their explanatory variables, and the implications for the future can be difficult and time-consuming.

Ayasdi’s regime forecasting software application helps portfolio managers uncover subtle, valid combinations of features that characterize different market regimes. It then rapidly pinpoints similarities to past regimes to help them more accurately assess the performance of various asset classes. The regime forecasting software can also surface the complex relationships between market regimes and liquidity proxies to aid the creation of more precise liquidity forecasting models.

As opposed to making global assumptions regarding all the underlying data, TDA effectively constructs an ensemble of models, each representing different market regimes and responsible for a different segment of the data. An ensemble of asset allocation or liquidity forecasting models can be much more accurate as they are each optimized for different segments of the data, thus reducing the possibility of systematic errors in the model output.

The insights derived from Ayasdi’s market regime forecasting software application can supplement portfolio managers’ professional experiences, helping them create effective regime-based asset allocation strategies and more precise liquidity forecasting models.

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