AI RESEARCH PHILOSOPHY

Arialytics develops artificial intelligence research and portfolios. We employ a research process that is grounded in science and driven by technology. We've been a leader in artificial intelligence for investing since our founding in 2010.

AI Investment Research

Arialytics is an artificial intelligence investment research firm, founded on the idea that a rigorously scientific approach to investing provides the best means of reducing uncertainty and achieving superior outcomes. Guided by artificial intelligence, quantitative finance, and structured scientific processes, we apply large amounts of computation and data to discover unique insights and portfolio solutions.

 

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 exploit available information and increase the scientific validity of our insights.

 

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RESEARCH and DISCOVERY

A systematic research approach emphasizes discovery and encourages the rapid formulation and testing of many investment ideas. It allows us to quickly find robust, unbiased answers to research questions, and to develop further ideas. Through rapid ideation and testing, we make new connections and discoveries that contribute to the investment process.

Our approach to building systematic investment solutions draws on AI, economic, financial, statistical, and high-speed data processing technologies to identify explanatory variables and risk factors which could provide the foundation for profitable investing. Our research systems evaluate massive amounts of data across asset classes, regions, countries, industries and thousands of individual securities for this purpose.

 

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UNDERSTANDING LIKELY OUTCOMES

The objective of our discovery approach is not to find the best performing strategy over past years, but rather to identify profitable investment strategies going forward. We use proprietary inference technologies to robustly understand likely performance. This capability is especially valuable for the management and mitigation of risks.

 

NOVEL TRANSPARENT PORTFOLIOS

We develop novel portfolios for managers and allocators. This includes portfolios optimized for complex risk preference functions and constraints. We understand it is essential to maximize the unbiasedness of our research through carefully constructed analytic procedures. In doing so, we maximize transparency and minimize return distribution uncertainty of each investment strategy. 

 

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AI and MULTI-FACTOR INVESTING

Artificial intelligence (AI) is one tool in an expansive analytic toolbox. Multi-factor investing – basing investment decisions on risk factors and systematically harvesting risk premiums from many securities – offers another way to achieve return objectives and manage risks deliberately, transparently and understandably. We combine nearly ten years of experience with AI with economically motivated investing tools in a best-of-both-words approach to risk management and return uncertainty reduction

 

SYSTEMATIC RISK MANAGEMENT and REDUCING RISK

We understand that trading and asset management are actually systematic risk management and that returns are a result of timed exposure to risks selected by a strategy. Risk management is an essential building block in our process, not an add-on or a constraint to be considered or optimized ex-post. We strive to develop strategies with inherent protection characteristics.

We research a wide range of strategies to discover the most appropriate investment solutions. The diversity of our research reduces return distribution uncertainty and portfolio risk.

 

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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

The linkage between the discovery and implementation of an investment solution 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 operations are as systematic as development.