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A Quick Guide to Data-Driven Investing

Susanne Morris

Susanne Morris

March 10, 2022

data-driven investing

Data-driven investing is defined as a broader term for investment methods that involve analyzing traditional and alternative data sources to provide investors with specific investment insights. As more and more data gets created every day, investors can leverage it for trend analysis, patterns, and risks. This article will provide a quick explanation of data-driven investing and highlight its differences from model-driven investing and outline the benefits of data-driven investing and strategies.

The rise of data-driven investing

Starting with their creation in the late 1960s and progressing significantly into the present, automated investing techniques have been favored by investors worldwide. From an automated investing tool such as the Telequote III utilized by stockbrokers in the 1960s to Bloomberg’s invention of the Bloomberg Terminal in 1982 to big data advancements today, the data-driven investing market has proven to bring substantial returns.

Additionally, due to their proven performance, data-driven investing methods have been adopted by companies across various industries, such as pharma, e-commerce, real estate, and more. Alternative data’s broad application and usability have been catalysts for investing in technology development and data processing software. The integration of alternative data with AI investing technologies has demonstrated that when investors utilize traditional financial data with alternative data, the opportunities are limitless.

Investment intelligence and data-driven investing applications

Beyond utilizing data to increase a company’s alpha, data-driven investing expands into multiple use cases that are not so obvious as capital gain or ROI. Data-driven investing has expanded to include use cases such as signal generation, decision enhancement, and market analysis.

These applications, combined with measuring ROI, ultimately illustrate one’s investment intelligence. Here is a closer look at the major use cases and practical applications of utilizing data to enhance investment intelligence.

Identify potential trends

Signal generation utilizes investment strategies seen in predictive analytics, particularly AI and ML techniques. Particularly, venture capitalists can use AI strategies that analyze social sentiment, providing VCs with market popularity insights. More specifically, processes such as web data integration are able to provide VCs with sentiment analysis insights for start-ups and early-stage products.

Enhanced decision-making

In addition to utilizing AI to find hidden investment insights, investors are also able to utilize alternative data such as financial data, firmographics, technographics, and job postings data to enhance their trading decisions. For example, hedge funds are able to track employee satisfaction of specific companies with job postings data, providing investors with internal health information on potential companies they are looking to invest in.

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Richer insights for analysis

Market analysis is a major component of investing. Not having a firm grasp on market trends can either prevent investors from missing out on opportunities or cause investment losses due to missing out on declining market indicators. For instance, financial institutions are able to utilize financial data and hiring data to extract operational insights. Investors are able to then take these insights and screen up-and-coming startups as well as expanding industries to determine if they should invest.

Data-driven versus model-driven investing

As previously mentioned, there are many different types of data-driven investing strategies. This is also true for model-driven investing. Model-driven investing is defined as an investing strategy that prioritizes insights explained by pre-existing algorithms and formulas. While these methods share some similarities, such as the ability for predictive analysis and evidence-based decision-making, they are also relatively distinct, especially when examining the two processes on a granular level.

For example, model-driven investing specifically involves data that works with formulas but may overlook hidden insights that a formula cannot explain. Relative to the investing sector, this is quite different from data-driven investing. Data-driven investing examines data in its entirety and looks to find connections and trends that are not easily explained by more traditional investment approaches. Furthermore, one can spot the differences between these two investing methods when examining their outcomes or conclusions. Let’s take a closer look at data-driven investing strategies.

Data-driven investing refers to analyzing traditional and alternative data to gain accurate investment insights.

data-driven investing models

Data-driven investment strategies

Investors utilize many data-driven investment strategies; however, here are some of the most common investment methods informed by alternative data.

Risk parity

Risk parity investing is a technique utilized in investment management, in which investors focus on distributing risks associated with assets. Recently, due to the advancements in the alternative data industry, investors can make data-driven decisions surrounding risk factors based on proven algorithms such as the Sharpe ratio. For example, insights and data surrounding employee satisfaction may indicate if a company might be declining internally, and therefore becomes an investment risk factor.

Managed futures

Managed futures refers to investing practiced by trend-following financial professionals such as commodity trading advisors and hedge funds. This investment strategy is highly systematic and relies on major market trends, or “commodities.” Additionally, because this strategy relies on tracking market trends, managed futures are considered alternative investments and consequently diversify portfolios while also managing risk.

AI investing

AI investing, also known as big data investing, is a relatively new strategy of investing that relies on making investment decisions based on a measure called alpha. Alpha refers to the measure of active return on investment, indicating a successful investment cycle. In addition to enhancing alpha, companies are also utilizing other AI techniques to track social sentiment, business operations, as well as security management.

Event-driven investing

Event-driven investing, also known as event-driven arbitrage, is a strategy that typically utilizes new and current alternative data sources and leverages insights that surround current major financial events. Hedge funds and private equity firms often employ this strategy when there are mergers, earning releases, acquisitions, bankruptcy, and even major world events such as natural disasters. This method is helpful for investors looking to balance a portfolio with short-term securities.

Below you will find a brief overview of data-driven investing strategies and primary investor types for each strategy.

data-driven investing strategies

Three benefits of data-driven investing

1. Broad coverage

Data-driven investing involves other data processing techniques, such as web scraping, data parsing, and data normalization. These processes are often automated and require additional software beyond just purchasing raw data sets. Data analysts and researchers from other departments are able to tackle large quantities of data due to the processing power of new software and AI technology. Analysts and researchers are then able to find insights from datasets from various sources and various databases in an efficient workflow. This process also permits a more cost-efficient workflow, as overhead costs can be reduced simply by the need for fewer analysts.

2. Decision objectivity

Due to the major stakes involved in investing, absolute objectivity is difficult to come by. Data-driven investing helps remedy any conscious or subconscious biases investors and analysts may have toward a particular investment. This removes any chance for poor or irrational investment practices and decision-making. One prominent example is the success companies have reported when implementing AI or utilizing data managing processes such as web data integration.

3. Predictive analytics

By far, new advancements in alternative data have had the most significant impact on predictive analytics. Predictive analytics is essentially the practice of utilizing real-time or current data to help AI models predict risk and return rates. This is different from earlier forms of model-driven investing, in that older model-driven investing relied on historical data and had limited customizability that led to restricted insights, and often missed hidden market gaps, and could not account for short-term risk and market trends.

statistics on a phone

Wrapping up

In all, the advancement of alternative data has created massive opportunities within data-driven investing. Like investors utilizing both traditional and alternative data sources, it is crucial that investors also understand the many different investment strategies' benefits and shortcomings.

Credits: featured image from QuoteInspector.com under the CC license.

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