Want to know about investment intelligence? This tutorial will give you complete details about investment intelligence and its essentials.
Computers and the internet are indefinitely changing our world and lifestyle. This is not only applicable on an individual level but is also firm-specific. For investment companies, artificial intelligence (AI) opens the door to a whole unknown universe of opportunities that range from back-office processes to front-office day-to-day decision-making.
Many investment firms consider AI the most innovative tool that will consolidate their competitive advantages – in other words, firms that fail to grasp the essentials of investment intelligence are likely to fall behind their competitors. The following sections introduce you to this core concept.
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Investment intelligence refers to using alternative data sources to identify and evaluate a firm’s investments. Alternative data is defined as data that do not come from traditional sources, such as a firm’s financial statements. Rather, it encompasses a broad range of factors, while investment intelligence may include, among others, financial ratios, such as ROI, debt-to-equity, and price-to-earnings. Learn more about investing intelligence.
Artificial intelligence refers to machines that collect and understand data generated by users from a variety of devices, ranging from mobile phones to smartwatches and even sensors. AI identifies trends in these data that humans can use for decision-making – in short, investors can employ this information to become more profitable. Advances in digital technology lead to absurd volumes of data that can now be analyzed using AI.
Nowadays, there is a plethora of alternative data that investment firms can use to make investment decisions. These can be structured or unstructured, such as information collected from media, social media, speeches, web traffic, websites, sensors, and even government data sets.
Alternative data is not commonly collected and analyzed manually due to the impressive amounts of information it contains, underpinning the basics of investment intelligence. Instead, investment firms use advanced algorithms and machine-learning methods to capitalize on it. These technologies enable businesses to collect and analyze large datasets, often unstructured and impressively heterogenous, in record time and with unprecedented accuracy.
The emergence of investment intelligence was generated by the explosion of information posted by users on social media platforms, blogs, websites, and many other sources. Numerous sophisticated algorithms were developed to analyze all of this information in a time-efficient manner. Initially, this was mostly used by hedge funds.
Artificial intelligence is quickly gaining territory in nearly every industry, ranging from transportation to manufacturing and investment firms. AI technologies affect not only cost and competition but also re-evaluate the meaning of value.
Regarding financial institutions, AI disrupts them on both the buy- and sell-side. The investment world has been based on largely the same assumptions throughout time, using traditional investment strategies and collecting publicly available data from traditional sources. With booming opportunities all over the world, investment intelligence breaks these assumptions and creates new opportunities.
In terms of purpose, many investment firms employ artificial intelligence in order to collect and curate long lists of securities. All types of relevant data are collected and analyzed by advanced algorithms in order to make better investment decisions and strengthen portfolios, while others use AI to improve operational efficiency. In other words, investment intelligence refers to minimizing risks and errors, enhanced accuracy of predictions, and overall better performance for this industry.
Information advantage is the key to implementing investment intelligence. Investment firms have one goal: to outperform their peers or a set benchmark. This is where alternative data disrupts the industry by providing information that has never been used before in investment strategies.
In short, investment intelligence can evaluate and track a company’s performance. Alternative data allows investment firms to screen start-ups or even entirely new industries and identify their growth potential. This may also search for companies looking for funding on your behalf and, using various factors, help you make an objective investment decision.
Other benefits are identifying business life-cycle, predicting a start-up’s potential with enhanced accuracy, and many more. For example, you can collect and analyze alternative data from social media, so you can find new products or companies that are very well-received and praised by their customers. This indicates that their products are highly likely to appeal to a much larger customer base, so you can invest in the company.
As a result, it cannot be denied that investment intelligence will be the new standard in this competitive industry. Investing firms failing to implement alternative data in their decision-making processes will be likely to fall behind their competitors, experiencing lower returns on their portfolios.
Apart from boosting investment opportunities, investment intelligence can automate numerous tasks related to data collection and analysis, providing investors with easy-to-understand trends and patterns. These advances allow investment firms to spend less time on time-consuming tasks, minimize errors, and enjoy better portfolios at the expense of their competitors.