Alternative Data

What do we mean by alternative data? Data which originates outside the standard repertoire of market data, but which is useful for predicting stock prices, managing risk and anticipating market movements.

Relevant commodity-centric examples include satellite imagery, GPS, IoT / sensor data and shipping/freight movements. As commodity markets continue to remain unpredictable and ever-changing, alternative sources of data are growing in popularity even more so, despite being staples of the commodity trading industry for a while by this point.

The rise of alt data has given quantitative, algorithmic hedge funds and other data and technology-driven investment firms the possibility of exploring a plethora of new data sources in search of an edge in the markets. Firms such as Kpler, Veson Nautical, OilX, Signal Ocean and Vortexa are modern examples of firms that specialise in supplying niche data sets to trading/Quant desks.

The global alternative data market is expected to grow from $2.21 billion in 2021 to $3.23 billion in 2022. The increase in demand for alternative data sources is, in part, owing to the rising interest in stock market trading. The market is expected to reach $13.91 billion by 2026.

The need for trading firms to own physical assets is less essential than before, levelling the playing field slightly. Due to the provisions of these alternative data sets, we are seeing more ‘asset light’ trading firms establishing themselves. The rising demand from hedge funds is expected to propel the growth of the alt data market in the coming years.

While the availability and adoption of alternative data is broadening the possibilities for investment management and trading firms, it also requires significant efforts to organise disparate data sources so that they can be used systematically. Machine Learning is great for some activities but not for others, NLP may be a more practical adoption in this instance. Analytics systems such as Tableau, Spark, and Elastic Search to name a few leading products, are needed to make the data actionable. Those firms that have mature ecosystems; cloud native data platforms with APIs and web-based solutions that use AI/NLP will benefit the most.

Whilst its fair to say the most technically advanced and data-centric quantitative hedge funds have been sifting for signals in various types of data sources for several years, it is only recently that the scope of what is considered financially relevant data has risen significantly. One of the main reasons for this growing interest and willingness to invest money and time exploring alternative data is that it has become technologically possible and economically sound to leverage such data. The increase in computer power and server capacity combined with the significant lower cost of hardware, software and data storage has made it possible for investment management firms to embark on new data-processing endeavours. As the availability of data has greatly increased, so too has the need for firms to attract and hire data scientists possessing machine-learning techniques to parse these complex, heterogenous datasets.

As ever the hunt for talent in this space is highly competitive. The appetite for alternative data is growing and as a result, jobs in this space are cropping up exponentially. This refers to several different positions, from data engineers to quant researchers even to ESG-data scientists.

Traditional finance degrees are struggling to keep pace with the need for the specific skillsets needed in this arena. The number of job descriptions in finance that mention the phrase ‘alternative data’ has skyrocketed over the past year, reflecting the need for upskilling for those currently in this field and the need to establish alt-data-specific training modules for those entering commodity trading. Alternative data providers such as Thinknum, Similarweb, and Eagle Alpha, have begun to design an Academy to help fill this talent gap and promote access to research of alternative data.

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