A Quick Guide to Data-Driven Investing

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 public web data to gain accurate investment insights.

Data-driven investment strategies

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

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 public web 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.

Wrapping up

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

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