A rigorously scientific approach to identifying patterns in data, grounded in economic theory, provides the best means of reducing uncertainty and achieving superior long-term outcomes. We've been a leader in artificial intelligence for investing since our founding.
STRIVING FOR PURE SCIENCE
At the core of Arialytics® approach to investment research are carefully structured methods for constructing, testing, and validating portfolio strategies in as scientific a manner as possible. We seek to understand how the systematic application of different investment ideas will perform in the real world of the future, not a backtest of the past. We employ artificial intelligence, unique datasets and proprietary algorithms to fully exploit available information and increase the scientific validity of our insights.
REDUCING RISK and UNCERTAINTY
We research a wide range of strategies to discover the most appropriate investment solutions. Potential strategies include: value, momentum, carry, trend-following, liquidity, quality, and timing strategies, to name just a very few. The diversity of our research reduces return distribution uncertainty and portfolio risk.
Our approach to building systematic investment solutions draws on econometric, statistical, artificial intelligence, and high-speed data processing technologies to identify explanatory variables and risk factors which could provide the foundation for profitable investment strategies. Our research systems evaluate massive amounts of data across asset classes, regions, countries, industries and thousands of individual securities for this purpose.
UNDERSTANDING LIKELY OUTCOMES
The objective of our strategy discovery approach is not to find the best performing strategy over past years, but rather to identify an investment strategy or strategies that are most likely to perform as desired over the coming years. We use proprietary, forward-looking evaluation technologies to assist in the development of investment strategies and understand likely outcomes. This capability is especially valuable for the management and mitigation of risks.
MAXIMIZING NET RETURNS
While it is common practice to optimize investment strategies for transaction costs after strategy selection – because it is computationally simple – we seek to eliminate the substantial performance slippage inherent to this approach. Our investment in understanding and quantifying the interplay among transaction costs and strategy choice allows us to identify better performing strategies under a range of transaction cost considerations.
AN END-TO-END APPROACH
We understand that the linkage between the discovery and implementation of an investment strategy must be seamless. Any difference among testing and real-time implementation methodologies has the potential to introduce unwanted deviations from expected performance. Accordingly, our development and operations systems are tightly integrated at the algorithmic level, ensuring that strategy operations are as systematic as strategy development.