Evaluating Quantitative Metrics: Part 1

Introduction

In 2023, we live in a world that is almost entirely data-driven. In every corner, we see marketing analytics, mathematics, scientific research, and much more. Whether quantitative or qualitative data, this data gives us a limited but still useful insight into everything from humans and life to math and finance. In this series, I want to focus on quantitative metrics as a significant way to interpret financial data and why that’s important for anything we can do in finance.

In finance, there is quantitative and qualitative data. Qualitative data can be analysis of an industry, a firm’s products, and other non-numerical data. On the other hand, quantitative data involves numerical data about a firm’s financials, investment returns, and other numerical metrics which can usually be shown as a function of time.

These can be divided into different aspects, which measure different parts of a firm’s activity and help investors make decisions based on general trends in order to maximize returns. See below for some of these metrics.

Pros of Using Quantitative Metrics

In finance, quantitative metrics go hand-in-hand with qualitative metrics, but there are a few major reasons why these quantitative metrics should be used particularly.

1. Objective Decision Making

Quantitative metrics provide an objective basis for decision-making. Instead of relying on gut feelings or intuition, we can actually have computers analyze data and give us output without any human bias.

2. Performance over Time

Apart from looking at holistic performance, we can see the performance of the different aspects of a firm over time and how it will do seasonally as well as given certain economic events. This long-term analysis accounts for basically an infinite amount of data and trends to be summarized and given to us in only seconds.

3. Predictions

Not only do quantitative metrics help us make decisions in the present time, we can get extrapolations and predictions of our data based on the previous data and market trends. With AI, this is still an emerging and somewhat dangerous field, but it will definitely get better as time goes on.

Cons of Overly Relying on Quantitative Metrics

Although quantitative analysis is my personal preference, I would also caution that there are some things you need to consider when looking at it.

1. Context

Numbers can get close to, but will never perfectly describe larger trends affecting the market. Quantitative analysis (not on purpose) can sometimes hide these trends. For example, media and social media, investor sentiments, and other things can only partially be captured numerically.

2. Manipulation

Since numbers are very easy to manipulate, certain inputs can be tweaked slightly and have huge effects on what we would see as an output. We need to make sure that all human bias is eliminated, but some of this bias still remains, even after using quantitative models.

Types of Metrics

In future articles, I plan to elaborate on types of metrics and their function in helping determine what decisions can be made regarding investments. I will also explore what methods can be used in looking at these metrics. However, I will explore some surface metrics and their usage.

1. Profitability

This includes profit margins and return on equity, and can show how stable or “reliable” a company is.

2. Liquidity

These ratios show a firm’s assets and liabilities.

3. Valuation

These are probably the most common metrics since we always look at the P/E ratio and EPS.

Each metric provides unique insights into various aspects of business operations, enabling companies to focus on areas that require improvement and capitalize on strengths.

Future Importance and Conclusion

In today’s competitive landscape, it becomes even more important that we use these metrics and determine new ways to interpret them. With the AI boom and different reinforcement/deep learning methods, we can look at data and process it faster than ever before, with even better accuracy. Instead of qualitative data which is subject to more human bias and can take time to have clarity on, we can swiftly adapt to market shifts and capitalize on emerging opportunities for the maximum profit using computation. Many times, this can even be automated with little oversight from investors. As we move forward, the connection between decision-making and quantitative metrics becomes more apparent and necessary.

Disclaimer: The information offered by us may not be suitable for all investors. If you have any doubts as to the merits of an investment, you should seek advice from an independent financial advisor.