PSARM at A glance

Modern Quantitative Asset Management with AAAccell’s PSARM: Heading towards Institutional Robo-Advisory and Robo-advisory 2.0

Background - Buy, sell or hold

Investment advice and guidance is often promised to be delivered through expert judgment and skillful opinions by professionals in asset- or wealth management. However, the three core decisions – buy, sell or hold – are regularly based on coincidence. Not using a more structured approach happens, despite a substantial body of literature and research proving that decisions will improve by applying quantitative methods. Such methods better measure the risks and performance of portfolios with a large amount of data which will result in portfolios with lower risk and higher returns.

Change – new methods and new computing power

The emergence of “Modern Finance” around 70/80s was driven by several ground-breaking inventions and Nobel prize winners. The innovations originated from academia, including, e.g., CAPM, Markowitz, or Black-Scholes. However, data analytics and computers were still inefficient at the time, which was the reason that many models remained rather simple. As a result, many financial institutions did not adapt to the advances in the models. Today, in the area of “New Finance,” were fast computers and extensive data are much more common, these advanced solutions showed promising results, undoubtedly much better results than portfolios with traditional methods.

Solution: Institutional Robo-Advisory and Robo-advisory 2.0:

PSARM combines enhanced quantitative portfolio management approaches with active forward-looking risk monitoring. The former selects the optimal portfolio weights. The latter serves as an early warning system against large market risk and excessive systemic risk. PSARM may be used on various types of portfolios. Any existing constraints are respected, while the resulting allocation decision leads to superior returns for a given level of risk. The main functionalities are:

  • Capturing the stylized facts of financial returns from the real world by incorporating multivariate fat-tailed distributions, tail dependences, time-varying volatilities, and correlations, as well as asset-specific asymmetries.
  • Performing fast and accurate portfolio optimization based on numerically sound algorithms/machine learning and methodologies.
  • Enabling a forward-looking approach to signal when the portfolio’s exposure to risky assets should be reduced.
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