The artificial segmentation of an investment management process into a workflow with silos of offline human operators can restrict silos from collectively and adaptively pursuing a unified optimal investment goal. To meet the investor's objectives, an online algorithm can provide an explicit incremental approach that makes sequential updates as data arrives at the process level. This is in stark contrast to offline (or batch) processes that are focused on making component level decisions prior to process level integration. Here we present and report results for an integrated, and online framework for algorithmic portfolio management. This article provides a workflow that can in-turn be embedded into a process level learning framework. The workflow can be enhanced to refine signal generation and asset-class evolution and definitions. Our results confirm that we can use our framework in conjunction with resampling methods to outperform naive market capitalisation benchmarks while making clear the extent of back-test over-fitting. We consider such an online update framework to be a crucial step towards developing intelligent portfolio selection algorithms that integrate financial theory, investor views, and data analysis with process-level learning.
We have witnessed a permanent shift in the role that data and technology are playing in investment decision-making. Idea generation techniques that had mainly been seen as emerging or experimental are now increasingly being adopted as mainstream. However, one of the biggest challenges for asset managers is how to incorporate, assimilate and integrate many of these techniques into the daily investment processes of the various investment teams. Regardless of the approach taken, data and how it is integrated and analyzed is going to play an increasingly pivotal role across all investment strategies. I will touch upon some key themes in this blog, but will go into more detail in a series to follow.
James Williams, managing editor at Hedgeweek, assesses how data analytics techniques can be used to personalise client experiences for investment managers. The amount of data is growing exponentially. According to IDC, there were 16.3 zettabytes of information generated in 2017 alone; one zettabyte is 1 billion terabytes. However you cut it, that's a huge number. One that is too large to comprehend. In simplistic terms, according to one industry professional "if every piece of data were a penny, it would cover the earth's surface five times over". Indeed, with Amazon and Apple both hitting the trillion dollar market cap mark, and Alphabet and Microsoft sitting at over USD900 billion, it is clear that the stock market values data as the most valuable resource, not oil or consumer products. Against this growing tsunami, investment managers and service providers alike are looking for ways to ingest and make sense of it all. To find information that they can translate into insights and turn into knowledge, that if done correctly, could lead to improved business performance and enriched customer relationships.
We are in the midst of a technological revolution. The increased use of artificial intelligence (AI), machine learning, blockchain, and big data technologies is rapidly changing the asset management industry. A recent survey on the future of the hedge fund industry by the Alternative Investment Management Association (AIMA) found that new "statistical and computational tools, including advanced quantitative techniques and artificial intelligence is forcing hedge fund firms to re-evaluate how they operate and invest." So how are manager selection and due diligence changing as a result of these new technologies? For insight on this question, we met with Michael Oliver Weinberg, CFA, chief investment officer of MOV37 and Protégé Partners, to discuss the evolution of the manager selection and due diligence processes. Protégé and MOV37 are specialized asset management firms that invest in smaller hedge funds and selected emerging managers. Founded in 2002, Protégé has been a key market player in supporting the growth of the hedge fund industry through seed deals and capital allocations to discretionary managers.
Alternative data will likely transform active investment management over the next five years, according to a white paper by Deloitte. Those firms that do not update their investment processes within that timeframe could, they argue, face strategic risks. Alternative data is a wide term that spans multiple categories. In brief, it refers to any non-traditional data (ie market price data, trade volume data) and includes online search data, trade data, satellite and weather data, consumer transaction data, geo-location data, etc. "The amount of data is growing exponentially. IDC said that there were 16.3 zettabytes of information generated in 2017 alone (one zettabyte is 1 billion terrabytes).