Investment research is basically the art and process of studying and evaluating the performance
of stocks, shares, markets, and industries. The investment research and planning market faced seismic shifts every now and then. This calls for improving the process with the transforming technologies and incorporating the same in the regular operations. Due to the abundance of information and data available, market players wish to facilitate investment decisions using the same. This would not only help them make more informed decisions but also stay ahead of the average industry performance.
Evolving trends in the Investment Research industry
A recent study called ‘Seismic Shifts: The Future of Investment Research’ produced by Greenwich Associates and commissioned by Thomson Reuters talks about the various regulatory, technological and competitive dynamics that create opportunities and pose threats for investment professionals. In a nutshell, the report spoke about the various factors like information explosion, new regulations, new technologies, and evolving commercial models that revolutionize the financial research landscape. Most industry experts have identified three major themes driving the future of investment research – artificial intelligence, alternative data and transforming relationships. When questioned on the channels of investment research, proprietary internal research and conferences took the lead as the primary source of research. However, alternative data, investment bank study, and academic research were significant candidates as well.
When investigated for the scope of growth, alternative data sources and internal research turned to be forerunners. Around 55% of the users source their alternative data themselves. However, third-party aggregators, large market data platforms, banks and individual vendors are prominent sources too. The most common forms of alternative data are: web-scraped data (with ~35% of users relying on it), search trends and expert networks data (29% each), web traffic along with credit card systems, consumer transaction and demographic data and data from wearables, IoT sensors and drones (with ~11% of users using these data points).
Furthermore, advancement in technology and data science has pushed for the adoption of artificial intelligence (AI) techniques, like machine learning (ML), deep learning and natural language processing (NLP). Commercialization of AI will put it to use in analysis of data, news and research content from various channels. Around 56% of the market experts believe, more investment process will use AI to make more informed decisions.
Use of Alternative Data in Investment Research
It involves analyzing past return trends to predict future returns and creating a guide to the type of investment modes that best suit an investor’s needs. To be able to do so and make informed decisions, a lot of information from multiple viewpoints and aspects is needed. These data points might or might not come from conventional data sources, and are called alternative data. Crowdsourcing and collective intelligence investing might sound like buzzwords to a lot of people out there. However, they are a good source of reliable information about the latent market trends and opportunities that might not be visible otherwise. Information provided by online communities can tell a person about the potential alpha advantage. Data scraped from open source web platforms and other forums can be one major source of reliable information for investors.
One can use alternative data in the investment research process in the following ways:
As a supplemental or primary input to the fundamental investment approach: Reinforcing investment decisions using insights from alternative data sources is a good way to use these data points.
As a research tool to explore and find new investment opportunities: Alternative data sources can unveil a lot of hidden trends and behaviors in the market which can thus, open avenues to new investment options.
To decide on the timing of trade after the investment decision has been made: Alternative data sources can help a user make informed decisions about when to make a trade so as to maximize returns.
To supplement the quantitative investment approach: One can decide the amount to invest or the distribution of assets across various investment channels using alternative data.
As an input to an algorithm that automates trades based on predefined data points and signals: Using Artificial Intelligence algorithms, one can make intelligent investment engines that make automatic trading decisions for good returns.
Using Artificial Intelligence in Investment Research
Advancement in technology empowers a computer to handle everything from administrative
back-office tasks to compliance, marketing, and investment management. This allows financial advisers to spend their time expanding their businesses and serving their clients personally
more efficiently. As of today, approximately 17 percent of the financial firms interviewed are incorporating AI into their investment process. However, 56 percent of these players expect a growth in the trend in the coming years.
AI can help firms automate mundane tasks like reporting thus making processes faster and more efficient and error-free. Advanced ML and NLP algorithms can crunch huge datasets, analyze
data-points and provide intelligent insights from them. These algorithms can then be fed into
code pipelines and decision boards for real-time assessment of a particular investment option.
An investor can feed in his/her financial profile and needs along with the investment option concerned and predict the performance using AI. This will not only allow him/her to conduct research independently but also to gain transparency in the entire process.
The study, The Future of Investment Research concludes that the future of investment research will change radically from the current scenario. Asset managers will have to adapt to the technological changes. Additionally, AI and advanced analytics will be coupled with new research data. This will lead to faster and better investment processes.
Summary
Data is the new oil when it comes to most industries. Data-driven reforms will empower people to conduct better investment research. Increased reliance on and demand for data, analytics and technology skills will directly cause an increase of budget allocation for dedicated analytical departments. Firms need to strike a perfect balance of human insight and sentiment with technological intelligence. This will create a more informed market with a better understanding of all investment options across the board.
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