The sheer amount of data available online has been a topic of discussion for years. As every sector of the global economy marches towards digital transformation, the volume of data generated, stored, and available for analysis will only rise.
Worldwide, across sectors, there has been a 7-year increase on average, in the rate at which businesses have developed new digital offerings. It, therefore, comes as no surprise that in 2020, 1.7MB of data was created every second by every individual, resulting in approximately 40 trillion gigabytes (or 40 zettabytes) of data. The sector-wide digital transformation efforts hold strategic importance for the finance sector.
Structuring Data for Success in the Financial Markets
Technology now enables us to ingest, process, and analyse these huge volumes of data in both unstructured and structured forms from diverse sources. Such data is gathered from news releases, articles, websites, television, social media posts, IoT sensors, presentations, consumer transaction data, speeches, proprietary databases and government datasets.
Firms can now harness these sources of alternative data to make informed investment decisions, construct near-accurate portfolios, and take proactive risk management decisions. Alternative data is set to transform the face of active investment management, from hedge funds and mutual funds to pension funds and active trading, over the next few years. The global market size for alternative data is set to expand to $11.1 billion by 2026, accelerating at a CAGR of 44% between 2020 and 2026.
Why Alternative Data
In the investment world, information advantage has been the primary reason for the adoption of alternative data. Any information that has the power to impact market behaviour or asset price movement is immediately shared with all market participants and analysed by multiple analysts, traders, and investors worldwide. So, access to information that provides a competitive edge, even outperformance versus peers or against benchmarks, is highly desired.
Data that has been used traditionally, such as company-issued earnings reports and financial statements, or economic data published by governments and central banks, are released at specific times. The data is published monthly, quarterly or yearly, and has a laggard characteristic. In contrast, alternative data can offer a quasi-real-time view of consumer sentiment in different areas, highlighting the level of economic activities beyond the range of the reporting of official data.
What Kind of Alternative Data is Considered for Investment Decisions?
Here are some examples of how firms and individuals are using alternative data sets to make informed decisions in the financial markets.
Consumer Transaction Data
Credit and debit card transaction volumes can give investors insights into a company’s sales. They can make timely decisions rather than waiting for the quarterly company reports or public company disclosures.
Satellite Imagery/IoT Sensor Data/Geolocation Data
A well-known application of satellite imagery is the analysis of parking lots, to see the volume of buyer foot traffic near malls and shops. This is replacing the traditional approach of counting physical foot traffic with clickers. This again can be a way for investors to predict company sales. Data providers track GPS signals from smartphones of buyers visiting various stores to provide similar data.
In a similar way, commodities traders can use automatic identification system (AIS) tracking on all commercial ships to track movements of oil. Tanker patterns have an impact on oil prices.
Sensor data of warehouse inventory movements in the supply-chain sector offers insights into consumer demand for products.
Social Media Posts and Comments
From predicting asset prices and demand to general consumer behaviour, social media commentaries offer insight into overall market sentiment. A 2010 research indicated a relationship between Twitter market sentiment and the Dow Jones Industrial Average (DJIA), which mentioned an 87.6% accuracy rate in the prediction of the DJIA movement in a few days. This was again confirmed in 2016, with a study showing a correlation between sentiments expressed in tweets and stock price movements of companies on the S&P 500. In 2021, we have seen Elon Musk’s tweets move the cryptocurrency markets and send Dogecoin soaring. On the other hand, his Twitter poll fuelled a massive sell-off and led Tesla to lose $199 billion in valuation in just two days, on November 10, 2021.
Media reports on stock price fluctuations often impact investor sentiment. Today, thousands of articles, blog posts, and news items are published, which make it difficult for investors to gauge overall market sentiment for an asset. Sentiment analysis done through powerful algorithms can make sense of these multiple news sources, to provide an average position (bullish or bearish) of a particular asset.
Environmental, Social, and Governance (ESG)
The focus on environmental and social impacts of companies has never been stronger. Investors today are concerned whether listed companies are at risk of reputational damage, due to poor ESG decisions. Data providers thus have to develop metrics to assess companies on everything from diversity in their boards, charity initiatives to their carbon footprints. They are empowering clients to see how ESG factors can impact their portfolios. This again needs technologies to track company news releases, press releases, and sensor data tracking activities like mining and deforestation, end-user activities, and even indications of huge employee turnover.
Use of Alternative Data by Different Types of Investors
Public investors can use alternative data to forecast earnings and analyse a company’s quarterly performance before they are released. Many investors use such data before investing in IPOs, which often involves limited public information.
Investors in listed companies (buy-side), hedge funds and asset managers (fundamental and quant funds), and equity research companies (sell-side) also use alternative data for investment decisions. Fundamental asset managers use the information to validate their investment theses, while quant funds integrate alternative data sets in existing workflows to find out the competitive positioning of companies in their ecosystem.
Investors in non-listed companies have much less information to rely on and, therefore, tend to use alternative data sets to review portfolio performance. They can use the insights to form strategic business decisions.
There are many advantages of alternative data in the financial sector, right from greater transparency into company performance and competitive advantage to unforeseen insights.
However, the accepted practices for regulating the use of such data are still in their nascent stages. There are open questions about what construes as acceptable practices for gathering information from the web. There is regulatory risk, and choosing to adopt this route calls for review of the process, regulatory oversights, copyright and IP issues, potential missteps in information gathering, and confidentiality terms with consumers. Vendors providing compliance software and advice on procedures to support investment managers should be chosen to ensure that the data is legal.