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Products
Product Overview
We offer a suite of advanced portfolio management products to meet the needs of clients from the investment management industry, together with highly effective on-going customer support and consulting services.
Our core financial technology consists of global “hybrid” multi-factor models for stocks and Government bond returns. The hybrid model combines the accuracy of an implicit factor specification -- where common risk factors that cause stocks to move together are inferred from those stock co-movements -- with the explanatory power of an explicit model in which risk exposures are attributed to cross-sectional characteristics. Quantal's tried and tested risk forecasts allow users to solve a wide range of portfolio and basket trading objectives. Unlike explicit factor models or two-step hybrid models, the implicit factor based risk and returns models are especially responsive to structural shifts in the marketplace, and we are able to provide accurate forecasts of risk exposure to investment managers over their portfolio rebalance horizons.
Quantal PRO (Portfolio Risk and Optimization)
Combines our proprietary global risk estimates with a flexible portfolio optimizer and a specialized report generator, to provide a complete portfolio management solution for financial institutions, high net worth money managers, fund of funds managers, and long-short equity hedge fund managers. Already in its third generation, Quantal PRO has a proven track record in the portfolio analytics industry and is known as the leading offering for up-to-date reliable factor risk estimates. Our professional services organization complements the Quantal PRO solution by providing on-going support and consulting services to clients.
Quantal PRO’s strengths lie in up-to-date multifactor risk models that quickly respond to shifts in factor structure, thus providing investors with accurate forecasts over their portfolio rebalance horizons. The globally integrated model for conditional equity portfolio risk enables users to reliably predict, analyze, and control risk as the market evolves over time. The model can be used to provide estimates of tracking error relative to benchmark indexes, to reliably attribute portfolio risk to security characteristics (industry/sector, value/growth, style, size, etc.), to correctly incorporate baskets like ETFs into portfolios, and to take into account other objectives and constraints (trading costs, turnover, daily volume constraints, etc.) in rebalancing portfolios. The model works equally well in long-short and long-only portfolio applications.
The built in optimizer can easily be configured to incorporate user-defined objectives, allowing investment managers to evaluate and implement unique portfolio strategies.
An integrated suite of tools for viewing, analysis, risk attribution, charting, audit trailing, and report generation allows investment managers to efficiently perform their portfolio management tasks and generate up-to-date reports for their clients.
The implicit factor based model is capable of adapting to structural shifts as they occur, or even to market-level behavioral swings in the market, so ensuring that forecasts capture the dynamics of the underlying factor structure. By contrast, explicit factor models require a revision in the model specification to capture these changes. The ability to adapt to structural shifts in the factor structure allows the implicit factor-based model to protect investors from being surprised by new sources of risk.
Structural models, where all the emphasis is put on explicit factors that may have been incorrectly specified to begin with, or become out-of-date due to changes in companies’ competitive products, positions and strategies, changes in management, etc. cannot protect clients from such shifts in these factors. Given an accurate estimation of the implicit factor structure, the investment manager is then able to accurately relate factor risk to meaningful explicit characteristics, in essence a hybrid model approach. The approach is more reliable than so-called two-step hybrid models, where an explicit factor approach is applied first, and then an implicit factor approach is applied to capture the remaining “residual” factors. This two-step approach is fundamentally misconceived: if the explicit factor structure in the first step of the two-step model is properly specified, the second implicit factor step is redundant; alternatively, if the explicit factor structure in the first step of the two-step model is incorrectly specified, the exposures fitted to the explicit factors are just as incorrect as they are sans the second step, nor are they corrected in the second step.
Portfolio performance measurement and attribution
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