In today’s Information Age, easy access to information has made active fund management a more challenging yet complicated task. This scenario demands a transformation in the investing ecosystem. That means we must rely on the combination of traditional investment strategies and quantitative factors like Big Data, Artificial Intelligence (AI), Machine Learning (ML), and mathematical models. This investment strategy that combines fundamental stock-picking methods and a quantitative approach is known as quantamental investing.

The term quantamental is a blend of ‘quantitative’ and ‘fundamental.’ A quantamental investment strategy also promises to deliver superior investment returns. By merging these two investing approaches, we combine human insight with computing power – a melding of man and machine. In simpler words, an investor harnesses the scalability and power of data, blending it with human insight to create winning investment strategies.

Fundamental Investing

Fundamental investing is the conventional bottom-up method of stock picking based on the quality of their fundamentals, such as market share and earnings. First, fundamental investors identify the stocks mispriced by the market relative to the company’s actual market value. Then they exploit this mispricing to generate alpha or returns that could beat the market’s or a benchmark index’s return. Legendary investors like Warren Buffett and Charlie Munger are the best proponents of this investing approach. They use detailed balance sheet analysis, sound judgment, and investing experience to generate alpha consistently.

Quantitative Investing

In quantitative or quant investing, an investor or a fund manager uses mathematical models, algorithms, and invariably vast volumes of data to identify trends and patterns to predict future price movements of financial instruments. One of the common quantitative investing strategies includes statistical arbitrage, in which the quant model searches for arbitrage opportunities — identifying price differences that arise for a few seconds between identical securities in different markets.

Factor investing is another significant quantitative investing model. This model identifies certain factors that influence a stock price. Macroeconomic, microeconomic, and business elements such as market capitalization come under these factors.

Human errors like emotional and cognitive biases are eliminated when algorithms make trading or investing decisions. Unfortunately, these human errors crop up when an investment manager or fund manager makes investment decisions. So, removing this significant error is a crucial advantage of quantitative investment strategy. The quantamental investor also holds the benefit of having a substitute strategy — a backup strategy an investor can take up when the primary investment strategy fails to work according to the market conditions

. On the other hand, given that humans are behind the creation of algorithms, quantitative investment strategies work within the limitations of their developers.

The Alternative Data Boom

Quantitative investors can confidently make investment decisions by identifying chart patterns and market trends from data. Therefore, data plays a crucial role in quantamental investing. As a result, quant investors procure as much relevant data as possible to make them better informed. This higher demand for data is responsible for the current alternative data boom.

Alternative data is non-traditional data such as geolocation and satellite imagery that can provide deeper insight into a particular investment avenue. For example, satellite images showing the status of the occupancy of a retailer’s parking lot during a quarter clearly signal the retailer’s sales volume and business performance for that period.

Fund managers of top asset management companies are now paying exorbitant amounts to obtain alternative data to attain an edge over the competition. The purpose of alternative data is to make forecasts, but this process is in its infancy, with these datasets often yielding little to no valuable information. But the advancements in data-mining techniques and ML technology allow computer programs to analyze large volumes of data without human intervention.

Ultimately, all these technological advancements empower asset management firms to analyze more extensive datasets and identify patterns effectively. Along with computers gaining more processing power and their storage becoming cheaper, profitable opportunities in quantitative investing are also growing.

The Current Global Quantamental Investing Scenario

Quantamental fund managers have been entering the investing arena for the last decade and rapidly gaining the trust of big-league investors by generating the best returns in the industry. So, traditional fundamental investors have faced tight competition from this new breed of investors.

Similarly, active fund managers are losing ground to passive investment strategies that simply invest in a specific index at a lower cost. As a result, the last decade has witnessed passive investment tools such as Exchange Traded Funds (ETFs) explode in popularity by easily outperforming active investment strategies, which charge hefty management fees.

Most quantamental investors began as fundamental investors who later started to absorb ideas from quantitative investment strategies. This transition also involves hiring data scientists inclined towards investing or trading who possess the skill to develop ML algorithms to derive investment insights from big datasets, which enhances the conventional stock-picking process.

A Challenging Road Ahead

A giant shadow of doubt hovers above the ability of conventional fundamental investors to successfully adapt to such a challenging investing discipline. Since there are huge differences between these two investing strategies, switching to quantamental investing is likely to involve sizeable organizational structural changes within an asset management firm. First, the hiring policy must be changed to employ more people with quantitative research and analysis skills. Then, they should significantly upgrade their computing systems to handle the load on resources put forward by the Big Data analytics process. Such changes can only be implemented by substantial investment.

An organization’s behavioral and cultural transformation may seem like a difficult challenge, but today’s data-driven world demands that change. With technology beginning to play an influential role in investment and asset management, fund managers must adapt to this new paradigm to survive.

Obstacles exist when taking the leap that could bridge the two investing worlds. But suppose traditional asset managers can successfully transition towards a quantamental strategy. In that case, they get a better chance of not only surviving but also evolving and ultimately thriving in this new world of investing.

Justin Raj

Alpha Research Team