Quantitative Investment Strategies
Systematic rules-based strategies to meet clients’ needs for risk-adjusted performance, transparency, liquidity and efficiency
We believe that persistent inefficiencies in capital markets present opportunities for risk-adjusted return. At Aberdeen Standard Investments we look to harness this potential through our global quantitative investment capability.
Our team of over 30 quant specialists* operate from Edinburgh, London, and Shanghai. They have developed a comprehensive range of clear rules-based approaches to generate systematic performance for our clients from equities, fixed income and derivatives.
Combining innovative research, investment theory and an in-depth understanding of sources of investment return, we seek to use our quant expertise to achieve our clients’ risk-return goals reliably, efficiently and cost-effectively.
*Source: Aberdeen Standard Investments, June 30, 2018.
For a full understanding of Aberdeen Standard Investments' Quantitative Investments expertise and global reach, please click the link below:
Aberdeen Standard Investments has over a decade’s experience in quant-based strategies. Having launched our first indexation strategies in 2005, we have since developed a range of solutions to achieve excess return in a consistent and efficient way.
Environmental, social and governance (ESG) considerations are embedded throughout our investment process to help enhance returns, mitigate downside risk and support our role as responsible investors.
We focus on “factor premia” – stock characteristics shown to be persistent drivers of excess return, such as value, quality, momentum and low volatility. Our BETTER Beta range uses factor “tilts” to target above-benchmark returns without additional risk. Our SMARTER Beta strategies concentrate factor exposure to maximize risk-adjusted return.
Our DISCOVER Alpha strategy, developed in partnership with Japanese think-tank MTEC, uses artificial intelligence in its goal to beat market returns through dynamic factor timing.