Fundamental vs data-driven: how to pick your approach to investing

When it comes to picking investments, there are a number of approaches to analyse what to buy. Fundamental and data-driven methods are two of the most common ways to do this. But how do they work?

When people first begin to invest, one of the biggest challenges is deciding how to choose what to buy. 

Some rely on company reports and market commentary, while others turn to algorithms and large datasets to guide their decisions. These two broad approaches are often described as fundamental investing and data-driven investing. 

Understanding how each works can help you decide which style fits your personality, skills and long-term goals. 

Importantly, these methods are not mutually exclusive. Many experienced investors use a blend of both to balance insight with efficiency.

Before we get into the detail of this guide it is important to note that these investment styles are not for everyone. For many people, investing in long-term low-cost index tracker funds are the way to go.

This guide is for anyone who wants to take a more proactive asset-level approach, such as through holding individual stocks and other assets including gold or bitcoin. 

What is fundamental investing?

Fundamental investing – often called ‘fundamental analysis’ – is built around the idea that every company or asset has an intrinsic value that can be estimated. Perhaps the most famous proponent of this style of investing is legendary investor Warren Buffet and his compatriot Charlie Munger.

If a share is trading below that value, it may be considered a bargain. If it is trading above, it may be overvalued. This style focuses on analysing the real-world performance of a business and its prospects, rather than short-term price movements.

To do this, investors study financial statements such as annual and quarterly reports. They look at revenue growth, profit margins, debt levels and cash flow. They assess the quality of the management team, the strength of the company’s competitive position and the trends shaping its industry. 

Broader economic conditions, such as interest rates or consumer spending, can also play a role in their analysis.

Valuation tools are central to this approach. Some investors use simple measures like the price-to-earnings ratio, while others build detailed discounted cash flow models to forecast future profits and work out what a business should be worth today. 

This kind of investing tends to attract those with a long-term mindset who are willing to research and hold positions for years, rather than trading frequently. 

What is data-driven investing?

Data-driven investing takes a different path. Instead of focusing on individual company stories, it uses numbers at scale to spot patterns and build rules for decision-making. 

Advances in computing power and access to information have made this approach more widely available, although it still requires a degree of technical ability.

At its simplest, a data-driven strategy might screen thousands of shares to find those with certain measurable traits, such as momentum in their price, low volatility or strong profitability scores. 

More advanced forms use statistical models, machine learning or alternative data sources such as satellite images of retail car parks, web traffic to e-commerce sites or social media sentiment. These signals are combined to generate buy or sell decisions, often automatically.

Data-driven investing can be purely quantitative, with little human judgement once the rules are set. It also covers styles such as factor investing, which tilts portfolios towards proven drivers of long-term returns like value or quality. This type of approach is also known as quantitative or ‘quant’ investing. 

The data-driven approach to investing is not widespread among individual investors, typically reserved for professionals and institutions that have access to the right tools and resources. But this is changing with a new generation of investing solutions. 

Apps such as Stratiphy, whose founder Daniel Gold has appeared on the Mouthy Money podcast, exemplify this approach. 

How the two styles compare

Fundamental investing tends to have a longer time horizon. Investors may hold positions for years as long as the underlying company continues to grow and generate cash. 

Data-driven strategies can also be long term, but many operate over shorter or medium time frames, adjusting portfolios as new information appears.

The decision-making process differs sharply. Fundamental investors rely on human judgement and analysis with an understanding of industries and behaviour. Data-driven investors depend on models and predefined rules, sometimes with very little discretion once a system is running.

Information sources also vary. Fundamental investors pore over reports, earnings calls and economic or political and regulatory news. Data-driven investors might still use those, but they also analyse huge historical price databases, real-time market feeds and data points.

Trading activity is often lower for fundamental investors. They tend to buy and hold, adjusting only when the underlying story changes. Data-driven systems can trade more frequently, especially when responding to market signals.

Access is an issue when considering the two approaches. Anyone can begin with a fundamental approach by reading company reports or using widely available analyst commentary. A wealth of resources to help you get started is out there. Warren Buffett-related books can be a good place to start. 

But building a robust data-driven strategy usually requires programming skills, access to large datasets and the ability to test models properly.

Pros and cons of each approach

The appeal of fundamental investing lies in its intuitive nature. It allows investors to understand what they own and why. It encourages a patient, long-term outlook that can smooth out the noise of market swings. It can also provide confidence during downturns if the investor believes the underlying business remains strong.

However, this approach has drawbacks. It can be slow to react when circumstances change. Even careful forecasts are based on assumptions that may prove wrong. Human judgement can be biased by overconfidence, emotion or selective attention to facts. For some investors, the depth of research required can also feel overwhelming.

Data-driven investing offers mathematical objectivity. Once a model is built and tested, it applies rules without emotion, potentially capturing patterns that human analysts might miss. It can also scale well, potentially handling thousands of assets or huge data streams simultaneously.

Yet there are risks. Models can overfit to past data, working well in backtests but failing in real markets. Sudden shifts in economic or political conditions can break patterns that algorithms expect. 

Building and maintaining a reliable system demands technical skill and access to high-quality information. For investors who do not fully understand the models they use, it can be hard to know when and why performance changes.

Combining the approaches?

Many professionals blend fundamental and data-driven methods. For example, a quantitative screen might sift through thousands of shares to find those with strong profitability and reasonable valuations. 

An investor could then study the short list in depth to understand each company’s prospects before buying. This approach uses data for efficiency but still applies human insight.

Conversely, some investors lead with fundamental but monitor price momentum or other signals to help time their trades. Large asset managers increasingly combine both to diversify their sources of return and reduce the risk of relying on a single way of seeing the market.

For private investors, a hybrid method can make sense. Data tools can help identify opportunities or manage risk, while fundamental research builds conviction and understanding.

Selecting an investing style depends on your skills, time and temperament. If you enjoy studying businesses, thinking long term and ignoring short-term noise, a fundamental approach may suit you. 

If you are comfortable with technology and prefer systematic decision-making, a data-driven strategy could be more appealing. Some investors prefer to keep a core portfolio based on fundamental analysis while using data-driven tools to add or adjust positions tactically.

Whatever path you choose, consistency matters more than chasing trends. Mixing styles without a clear plan can lead to confusion and poor decisions. It is also vital to stay diversified and to understand the risks each method carries.

There is no single best way to invest. Fundamental investing provides a clear link between company performance and long-term returns. Data-driven investing brings discipline and the power of modern analytics. Each has strengths and weaknesses, and both can fail if applied poorly. 

For many investors, a combination of the two may offer the best balance: using data to navigate a crowded market while relying on fundamental analysis to understand and stay confident in what they own.

DISCLAIMER

This article is produced for general informational purposes only.

It should not be construed as investment, legal, tax, mortgage or other forms of financial advice.

If in any doubt about the themes expressed, consider consulting with a regulated financial professional for your own personal situation.

Past performance is no guarantee of future results.

Investments can go down as well as up and you may get back less than you started with.

Investments are speculative and can be affected by volatility.

Never invest more than you can afford to lose.

For more information visit ⁠⁠⁠www.fca.org.uk/investsmart⁠