How can machine learning (ML) be effectively applied in finance? This Special Report looks to answer this question and more from a practitioner’s perspective.
Industry experts consider how machine learning compares to traditional techniques, special considerations for ML in finance, and specific examples of its…
PGIM’s 2024 Best Ideas paper shines a light on the areas where we believe investors will find promising opportunities, buoyed by PGIM’s distinct depth of expertise.
Welcome to Invesco’s Global Systematic Investing Study 2023. This year, we’ve refreshed the title of the annual factor investing study, to the Global Systematic Investing Study, reflecting the progression of quantitative investing over the past 7 years. Since its inception in 2016, the report has provided insights into…
The relationship between value factor returns & interest rates is explained by time-varying duration of a portfolio long cheap stocks/short expensive stocks.
In this article, we go beyond the futures and forwards of the traditional quant toolkit and consider how we can trade a fully systematic multi-asset climate portfolio using lots of single name cash investments and a novel risk management overlay. We look at potential challenges and how harnessing human teams and perspectives…
In recent years, machine learning (ML) has been a popular technique in various domains, ranging from streaming video and online shopping recommendations to image detection and generation to autonomous driving. The attraction and desire to apply machine learning in finance are no different.
The credit markets are finally embracing quantitative investing and this paper provides an in-depth primer to quant credit. Readers will learn about the dynamics of systematic trading, the factors employed in quant models, and the future path this rapidly-evolving strategy is likely to take.
In the 1990s PGIM was among the earliest to explore quantitative investment techniques in response to our own clients’ shifting needs. Thirty years and many market cycles later, quants continue to leverage their unique strengths to help investors navigate their complex financial challenges.
Robeco’s approach to emerging markets investing was initially met with considerable skepticism, but the live performance of our model has exceeded expectations. David Blitz, Robeco’s Chief Researcher, shares his thoughts on quant investing in emerging markets.
What are '8 things you should know about next-gen quant' - but might have been afraid to ask? Navigating the frontier of quant investing can be confusing, but we’re here to demystify some of the complex concepts that are suddenly becoming household terms.
In Russell Korgaonkar’s first ‘Diary of a Quant’, he discusses the potential AI has to disrupt traditional asset management, as well as why he is excited about the current wave of progress and innovation.
We show how warping time renders stock-price bubbles comparable, revealing common patterns that investors can use to detect new bubbles and time exposure to their rise and fall.
How many factors does it take to compress the factor zoo? Quant researchers Alexander Swade, Matthias Hanauer, Harald Lohre and David Blitz set out to find the answer.
Has the short-term reversal effect truly vanished? In a new paper, Robeco's Quant Investing research team—Chief Researcher David Blitz, and Senior Researchers Bart van der Grient and Iman Honarvar—continue to explore the complex landscape of short-term reversal strategies. Serving as a sequel to the 2022 publication, "Beyond…
Artificial intelligence is being hailed as the greatest advancement of the 21st century – but can it help sustainability? Yes, says quant expert Mike Chen, who believes it may turn into a ‘moon landing moment’ for solving the world’s greatest problems.