Leveraging technology to drive returns is not new in the investment world. Initially used by hedge funds, data-driven investing has become increasingly popular among other institutional and private investors. Different from model-driven investing methods, data-driven investing aims to examine information in its entirety and find connections or patterns that cannot be revealed through traditional formulae or models. This article explores the top trends in data-driven investing that drive portfolio returns.
What is data-driven investing?
Data-driven investing refers to combining traditional information with alternative data to provide a clearer and more accurate investment insight.
Although there are many strategies available, all of them have one main similarly: the use of alternative data.
These data are used to make better investment decisions, identify risks, and others. Read more about data-driven investing.
Perhaps the most important emerging trend in data-driven investing is AI (artificial intelligence) investing.
This strategy uses big data to make automated investment decisions to enhance alpha, which is a measurement of your active return on investment.
Essentially, artificial intelligence is defined as algorithms and programs specially designed to be similar to human intelligence.
Since the term AI was coined back in 1956, it cannot be considered a new term.
Regardless, AI investing is rapidly gaining popularity due to its numerous benefits.
For instance, programs can collect and analyse data quicker and more accurately compared to humans, especially nowadays when zettabytes of data are available.
Using Unconventional Data
Traditionally, investment strategies mostly used publicly available data, such as financial statements, along with market data (prices, volumes, returns, and others).
Data-driven investing allows both institutional and private investors to incorporate unconventional data in their analyses.
For instance, internet web traffic, satellite images, and social media create a more detailed picture so you can make a better investment decision.
These data-driven investment models are on the rise, given the fact that they provide investors with informational advantages.
Using New Data
Computers have always been able to analyse data, but this mostly had to be structured or quantifiable.
The rise of data-driven investing creates the opportunity to use unstructured information, including language, speech, images, and others.
These are used for business valuations; for instance, new data types allow you to identify positive trends on the market and incorporate them into business valuation or value creation abilities.
Social Media Sentiment Analysis
Another strong trend in data-driven investing is sentiment analysis.
In other words, investors can check how a brand’s customers feel about it by analysing its posts on social media.
This strategy does not offer a simple count of comments or mentions online; rather, data-driven methods can depict the general emotions or opinions related to a certain business.
Conducting social media sentiment analysis is important from several perspectives. For instance, knowing that customers have complaints or negative opinions about a clothing company is essential if you were about to invest in it.
It may indicate future issues, even though the historical performance is positive.
This could possibly be due to inefficient management, change in leadership, or other issues at an organizational level.
Leveraging Data for Competitive Advantages
Most investors do not use data-driven investing to find new stocks to invest in – rather, to identify new investment factors that could increase the portfolio returns.
In other words, data-driven investing is increasingly being used to select stocks and improve the selection process by making data-based decisions, free of emotions and human bias or irrationality.
This decreases the chance of errors, mainly when investment managers use machine learning methods.
Machine Learning Methods
Data-driven investment is not only about having access to non-traditional data but also having the right means of analysing it according to a strategy or model.
Machine-learning methods are quickly gaining popularity and are a new trend among many institutional investors.
In other words, this technology is based on algorithms that can learn and then adapt the results according to new, real-time data.
This is more accurate than historical information that can become quickly outdated, especially in today’s fast-changing environment.
Data-driven investing may create new opportunities when it comes to emerging markets.
Traditionally, uncertainty, volatility, and lack of transparency were the main causes of mispricing and risks when investing in emerging markets.
Data-driven investing uses alternative, centralized data that do not require local specialists for business environment analysis.
Sophisticated technology can collect and analyse data from various internet sources, possibly revealing numerous opportunities for investment in emerging markets.
In conclusion, data-driven investing focuses on collecting and analysing non-traditional information in order to make better investment decisions.
This centralization of data from a wide variety of sources leads to new investment trends, creating informational advantages for early adopters willing to invest in alternative data sources.
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