Our investment principles
Integrating modern finance with mass psychology
The homo economicus (the perfectly rational economic being) has long been a fundamental element of economic theory. However, insights from crowd psychology and related interdisciplinary research reject this idealistic model. Instead they propose to account for the complexity of social relations and emergent collective patterns in the real world which often give rise to bubbles and panics. At SIMAG®, we “mine” and exploit network structures caused by such collective behavior in financial markets, which reveal investment opportunities created by positive and negative hierarchical feedback dynamics
Embracing regime changes - markets are not stationary
By aggregating signals from stock to sector and country level, we are able to assess the degree of over and under reaction inherent in the markets. Our portfolio construction engine quantitatively selects stocks and opportunistically allocates cash depending on the prevailing market regime at any given point in time. Our approach is agnostic to consensus about stock fundamentals or macro outlooks
Tapping into computational intelligence
Only with massive computational power and our proprietary programming are we able to analyze, structure and filter such huge quantities of data. LPPLS fittings are done on each individual stock (thousands of stocks monitored globally in our database) as well as on market level, for time scales from days to weeks, months and years. The core software is enhanced by deep machine learning algorithms. Artificial intelligence is used as an overlay for robust optimization
Addressing pitfalls of market cap weighting and factor investing
Due to the fat-tailed distribution of firm capitalizations, market cap weighted portfolios are not efficient. It is very important to be aware of estimation errors when constructing efficient portfolios. We use a hierarchical weighted portfolio construction method to take full advantage of these two facts. At the same time, we are style agnostic. We do not follow the crowd – and we are not chasing the same excess returns as typical smart beta or factor investors do
Delivering on transparency and consistency
Our models are as simple as possible in constructing portfolios while being as complex as necessary for pattern recognition. Artificial Intelligence is used as an overlay after having put lots of human intelligence into analyzing the data. We do not ask AI to find the patterns for us, we use it for robust optimization. We strive for “white box” transparency on rules and methods – the result is not a black box as the algorithm is interpretable and is accepted only after it has passed the “smell test”. By systematically applying our approach, we can deliver consistent results
Emphasizing risk control
Risk and transaction costs are the only things an investment manager can control. Portfolio management is the management of individual risks. SIMAG® does not add a portfolio overlay to manage risks. Rather, the portfolio construction and its risk control mechanisms are intrinsically embedded in the specification of our investment strategies. In other words, the investment strategy and the portfolio construction occur together in symbiosis
Lessons learned?
A short history of the S&P500 (1975-2017)
Lessons learned? Financial history is there for all to read, yet market participants have short memories. Additionally, each generation of investors seems to believe it is different this time. This inevitably leads to each investor generation repeating the mistakes of former generations. The collective behavior of investors and human nature does not change over time as it goes through cycles of fear, greed and exuberance, which predictably leads to asset price booms and busts. Financial crises, as shown in the video, are interrelated and interdependent phenomena; they share the same root cause(s) and thus constitute an integral part of the system and its dynamics.
Read Whitepaper
Read more of our Whitepapers about financial markets and collective investor behavior
Technology
Our proprietary software
Our proprietary analytics engine is based on sophisticated LPPLS based algorithms analyzing daily market prices of thousands of stocks globally. These algorithms look for specific patterns over multiple time horizons (74 per stock) that are caused by hierarchical feedback of market participants. Next generation behavioral finance insights are at the core of our unique approach.
Our engine detects price patterns (LPPLS signatures) and exploits forming bubbles. It has the ability to anticipate endogenous directional price movements days or weeks in advance. Contrary to many other traditional quantitative and machine learning approaches which try to predict all the time, our approach inherently distinguishes random periods from periods where limited visibility can be achieved. Detecting such pockets of predictability is key.
The technology underlying our analytics engine is based on decades of academic research and years of hands-on experience developing a practical LPPLS approach which is both proprietary and – while still requiring enormous computational power – is orders of magnitude faster than other approaches and is also implementable in portfolios. Our analytics engine has been extensively backtested in different economic cycles over the past 40 years and has shown consistent outperformance. The core software is enhanced by deep machine learning algorithms.
Technology we use
We are standing on the shoulders of giants as we use decades of academic research and open-source software tools. All of our production systems run Linux and PostgreSQL. For data analysis, machine learning and back tests, we are heavy users of Python and its full scientific stack. For our core LPPLS engine we use Cython/C/C++ and Intel MKL to push the performance boundaries of Python. We implement the data collecting and preprocessing systems that need the highest data throughput in Java / Scala.
At SIMAG® we have benefited hugely from open-source technologies. We contribute back to the community whenever we can.