Human Insight, Computer Power: What is Quantamental Investing?
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Human Insight, Computer Power: What is Quantamental Investing?

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Quantamental Investing

What is Quantamental Investing?

The world is awash in data like never before. From a person’s morning Uber ride and favorite coffee spot, to the emails sent from their office—all these activities create massive amounts of data, but also behavioral and investment insights.

Warren Buffett’s investment style exemplifies the fundamental approach: “Which companies offer the best returns?”

On the other hand, hedge fund manager James Simons of Renaissance Technologies is a notable example of the quantitative approach: “What is the best way to predict returns?”

Both techniques have one thing in common—they seek excess return from the marketplace, or what is known as “Alpha”.

Quantamental: Combining Quantitative & Fundamental

Today’s infographic from GoldSpot Discoveries outlines quantamental investing as the blending of these two styles, human insight with computer power.

Despite both methods seeking excess returns in the market, there are some key differences:

Quantitative Analysis Fundamental Analysis
  • Seeks to understand behavior by using mathematical and statistical modeling, measurement, and research
  • Aims to represent reality in terms of a numerical value
  • Can measure or value a financial instrument, and/or predict real-world events
  • Trading focuses on broad market factors (data)
  • Attempts to measure a company’s intrinsic value based on its earnings outlined in its financial statements
  • Can identify securities that are not correctly priced by the market
  • If the fair market value is higher than the market price, then the stock is undervalued and a buy recommendation is given
  • If the fair market value is lower than the market price, then the stock is considered to be overvalued and a sell recommendation is issued
Cons Cons
  1. Takes financial data at face value to assume an economic reality
  2. Lacks in offering unique insight
  1. Analyzes a small subset of the investment universe
  2. Chasing glamor stocks or holding on to losing stocks which reflect behavioral biases
Pro Pro
  • Analyzes the investment universe, quickly
  • Offers deep, proprietary insights

The arrival of advanced sensor technology and computer processing power is creating huge opportunities for capturing the complexity of human activity on a larger scale.

Could these two distinct methods be fused together?

A New Frontier for Data: Combining Man and Machine

On a larger scale, tracking and storing data can reveal economic patterns over long periods of time. For example, satellite images of a mall’s parking lot can determine the mall’s sales volume. In the finance world, software can track sentiment in earnings call transcripts, and detect word patterns of executives.

The applications of sensor technology stretch across various cases, and could improve overall performance in different industries.

Case #1: Sabermetrics

Picking a winning baseball team is a lot like investing: with limited capital, one needs to optimize player selection and performance to beat the competition. That is why the Major League Baseball Association installed StatScan in 30 ballparks for 3 seasons (2015-2017).

These radar and camera systems captured the raw skills of players in ways that were previously available to or only understood by the baseball scouts.

Scouts are the stock pickers of the baseball. They know the ins and outs of a potential major league player, and consider health, family history, body mechanics and even personalities.

Team managers can use a scout’s insight, against the vast amounts of data collected during a baseball season, to uncover the exact metrics to predict the success of the next great home run or strike-out king.

Case #2: Mineral Exploration

Resource companies spend huge amounts of money on exploration to collect data. However, the volume of data generated is too much for one geologist, or even a team to sift through in a reasonable time.

Machine learning in mineral exploration can take in training data to help identify prospective land for a mineral deposit.

Computer Power with a Human Touch

Quantamental investing seeks to understand the depth and the breadth of the investment world. The goal is to produce superior returns in the marketplace by answering two questions.

  1. What are the best metrics for predicting success?
  2. Which are the companies performing the best on these metrics?

Quantamental investing harnesses the raw power and scale of data, coupled with human insight — increasing market returns by finding the next great investment.

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Thematic Investing: 3 Key Trends in Cybersecurity

Cyberattacks are becoming more frequent and sophisticated. Here’s what investors need to know about the future of cybersecurity.

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The following content is sponsored by Global X ETFs
Global X BUG ETF Global X BUG ETF Holdings

Thematic Investing: 3 Key Trends in Cybersecurity

In 2020, the global cost of cybercrime was estimated to be around $945 billion, according to McAfee.

It’s likely even higher today, as multiple sources have recorded an increase in the frequency and sophistication of cyberattacks during the pandemic.

In this infographic from Global X ETFs, we highlight three major trends that are shaping the future of the cybersecurity industry that investors need to know.

Trend 1: Increasing Costs

Research from IBM determined that the average data breach cost businesses $4.2 million in 2021, up from $3.6 million in 2017. The following table breaks this figure into four components:

Cost ComponentValue ($)
Cost of lost business$1.6M
Detection and escalation$1.2M
Post breach response$1.1M
Notification$0.3M
Total$4.2M

The greatest cost of a data breach is lost business, which results from system downtimes, reputational losses, and lost customers. Second is detection and escalation, including investigative activities, audit services, and communications to stakeholders.

Post breach response includes costs such as legal expenditures, issuing new accounts or credit cards (in the case of financial institutions), and other monitoring services. Lastly, notification refers to the cost of notifying regulators, stakeholders, and other third parties.

To stay ahead of these rising costs, businesses are placing more emphasis on cybersecurity. For example, Microsoft announced in September 2021 that it would quadruple its cybersecurity investments to $20 billion over the next five years.

Trend 2: Remote Work Opens New Vulnerabilities

According to IBM, companies that rely more on remote work experience greater losses from data breaches. For companies where 81 to 100% of employees were remote, the average cost of a data breach was $5.5 million (2021). This dropped to $3.7 million for companies that had under 10% of employees working from home.

A major reason for this gap is that work-from-home setups are typically less secure. Phishing attacks surged in 2021, taking advantage of the fact that many employees access corporate systems through their personal devices.

Type of AttackNumber of attacks in 2020Number of attacks in 2021Growth (%)
Spam phishing1.5M10.1M+573%
Credential phishing5.5M6.2M+13%

As detected by Trend Micro’s Cloud App Security.

Spam phishing refers to “fake” emails that trick users by impersonating company management. They can include malicious links that download ransomware onto the users device. Credential phishing is similar in concept, though the goal is to steal a person’s account credentials.

A tactic you may have seen before is the Amazon scam, where senders impersonate Amazon and convince users to update their payment methods. This strategy could also be used to gain access to a company’s internal systems.

Trend 3: AI Can Reduce the Cost of a Data Breach

AI-based cybersecurity can detect and respond to cyberattacks without any human intervention. When fully deployed, IBM measured a 20% reduction in the time it takes to identify and contain a breach. It also resulted in cost savings upwards of 60%.

A prominent user of AI-based cybersecurity is Google, which uses machine learning to detect phishing attacks within Gmail.

Machine learning helps Gmail block spam and phishing messages from showing up in your inbox with over 99.9% accuracy. This is huge, given that 50-70% of messages that Gmail receives are spam.
– Andy Wen, Google

As cybercrime escalates, Acumen Research and Consulting believes the market for AI-based security solutions will reach $134 billion by 2030, up from $15 billion in 2021.

Introducing the Global X Cybersecurity ETF

The Global X Cybersecurity ETF (Ticker: BUG) seeks to provide investment results that correspond generally to the price and yield performance, before fees and expenses, of the Indxx Cybersecurity Index. See below for industry and country-level breakdowns, as of June 2022.

Sector (By security type)Weight
Cloud28.0%
Network25.1%
Identity17.7%
Internet15.0%
Endpoint12.8%
CountryWeight
🇺🇸 U.S.71.6%
🇮🇱 Israel13.2%
🇬🇧 UK8.2%
🇯🇵 Japan5.5%
🇰🇷 South Korea0.9%
🇨🇦 Canada0.6%

Totals may not equal 100% due to rounding.

Investors can use this passively managed solution to gain exposure to the rising adoption of cybersecurity technologies.

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