Why Investors are Irrational, According to Behavioral Finance

Key Highlights

Overconfidence
  • Investor overconfidence can lead to excessive or active trading, which can cause underperformance. In a 1999 study, the least active traders had annual portfolio return of 18.5%, versus the 11.4% return that the most active traders experienced.
Loss Aversion
  • Fear of loss. When asked to choose between receiving $900 or taking a 90% chance of winning $1000, most people avoid the risk and take the $900. This is despite the fact that the expected outcome is the same in both cases. However, if choosing between losing $900 and take a 90% chance of losing $1000, most people would prefer the second option (with the 90% chance of losing $1000).
  • The “disposition effect” is the tendency of investors to sell winning positions and hold onto losing positions. This effect directly contradicts the famous investing rule, “Cut your losses short and let your winners run.”
Portfolio Construction and Diversification
  • Familiarity Bias. Investors prefer to invest in “familiar” investments of their own country, region, state, or company. Although the best practice is for portfolios to hold at least 300 stocks, the average investor only holds three or four.
Misuse of Information
  • Gambler’s Fallacy. When asked to choose which is more likely to occur when a coin is tossed—HHHTTT or HTHTTH—most people erroneously believe that the second sequence is more likely. The human mind seeks patterns and is quick to perceive causality in events.
  • Attention Bias. A 2006 study posits that individual investors are more likely to buy rather than sell those stocks that catch their attention. For example, when Maria Bartiromo mentions a stock during the Midday Call on CNBC, volume in the stock increases nearly fivefold minutes after the mention.
Cultural Differences in Investing
  • International differences in loss aversion. After controlling for factors such as national wealth and growth, a study found that Anglo-Saxon countries are the most tolerant of loss, while investors in eastern Europe have the greatest loss aversion.
  • International differences in investor patience. The same study found that investors from Germanic/Nordic countries (85%), Anglo/American countries, Asia (66-68%), and Middle East cultures are more willing to wait.

“The investor’s chief problem—and even his worst enemy—is likely to be himself.”
– Benjamin Graham

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A Deep Dive into Elon Musk’s Investments: The Makings of a Billionaire

Key Highlights

Career History
  • After dropping out of Stanford in 1995, Musk started Zip2 with his brother Kimbal using $28,000 borrowed from their father. In 1999 it sold to Compaq for $307 million, with Musk earning $22 million.
  • Musk invested $10 million of his Zip2 proceeds into founding X.com, one of the first attempts at online banking. It later merged with Peter Thiel’s Confinity and became PayPal in 2000. After IPOing in 2002 it was sold to eBay the same year, Musk made $180 million from the sale.
  • Post-PayPal, Musk invested all of his proceeds into his new projects: SpaceX ($100 million), Tesla ($70 million) and SolarCity ($10 million). By 2008, he was almost penniless and living on $200 thousand monthly loans from his friends after a $20 million divorce.
  • By 2017, his fortunes had changed and his net worth had risen to $16 billion; just six years earlier, it was only $68 million.
Goes All-In with His Businesses
  • After being outmaneuvered in the boardroom at Zip2 and PayPal, Musk began to take more of an iron grip with managing his companies. At Tesla in 2007, he converted $8 million of preference stock to weaker common stock just to oust its CEO.
  • Aside from small angel investments, he avoided cashing out of his businesses at opportunistic or exit stage moments and maintained large ownership percentages. His proceeds from Tesla’s IPO were only $15 million.
  • Musk has personally borrowed over $620 million that he has used to purchase more stock in his companies. In 2013, he drew down a personal loan to buy stock and help Tesla pay off one of its own loans.
Creates Ecosystems around Himself
  • Investing within his network has provided Musk with a successful angel investing record. He made a $90 million return from DeepMind and only one of his investments has been a total loss: Halcyon Molecular in 2012.
  • Over his career, he has been involved in four businesses with his brother Kimbal and two with his cousin, Lyndon Rive.
  • His businesses regularly cross paths and transact with each other. SpaceX has purchased over $250 million of SolarCity’s bonds and Musk has personally bought $65 million. These kinds of links were a worry for investors during the Tesla and SolarCity merger.
Creative Financing Methods
  • By 2015, Musk’s businesses and their customers had benefited from $4.9 billion in savings from government subsidies. The benefit split between the two was 70% and 30% respectively.
  • Within the aforementioned benefits were $517 million in Zero Emission Vehicle credit allowances that Tesla sold to rival automotive producers. This allowed Tesla to boost revenue at opportune times.
  • A $1.6 billion contract from NASA in 2008 helped stave off bankruptcy at SpaceX.
Polarizing Opinions
  • Musk’s hands-on and all-encompassing roles have led to cries of poor corporate governance within his businesses. This issue was raised by a group of Tesla shareholders in a 2017 letter to him.
  • Capitol Hill lawmakers have also raised concerns about federal money paid to SpaceX being used to inadvertently prop up SolarCity.
  • He will only float SpaceX once regular travel to Mars has commenced (its first trip is planned for 2020). He has discovered that the public markets carry more pressure and scrutiny.

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How Artificial Intelligence Is Disrupting Finance

Key Highlights

Artificial Intelligence (AI) Is Exploding
  • The widespread adoption of AI across industries is predicted to drive global revenues of $12.5 billion in 2017 and $47 billion in 2020 with a CAGR of 55.1% from 2016 to 2020.
  • The industries that will invest the most in these technologies are banking and retail, followed by healthcare and manufacturing.
  • Economists designate general purpose technologies (GPT) as those important enough to spur protracted economic growth and societal advancements. For example, electricity is a GPT. A recent Harvard Business Review article designates AI as the most important GPT of our era.
Risk Management
  • PayPal has been able to boost security by leveraging deep learning technology. PayPal’s fraud is relatively low at 0.32% of revenue, a figure far better than the 1.32% average that merchants see.
  • While a linear model can consume 20-30 variables, deep-learning technology can command thousands of data points.
AI Trading
  • For years, investment management companies have relied on computers to make trades. Around 9% of all funds, managing $197 billion, rely on large statistical models built by data scientists.
  • However, these models are often static, require human intervention, and don’t perform as well when the market changes. Therefore, funds are increasingly migrating towards true artificial intelligence models that analyze large volumes of data and continue to improve themselves.
  • In 2000, Goldman Sachs’ US cash equities trading desk in its New York headquarters employed600 traders. Today, it has two equity traders, with machines doing the rest.
Robo-advisory
  • For investors, robo-advice can offer up to 70% in cost savings in certain services.
  • Some established investment firms are buying existing robo-advisors, such as Invesco’s acquisition of Jemstep and Blackrock’s purchase of FutureAdvisor. Others are even creating their own robo-advisors, such as FidelityGo and Schwab’s Intelligent Advisory.
  • 77% of wealth management clients trust their financial advisors and 81% indicate that face-to-face interaction is important.
Insurance Underwriting and Claims
  • PWC report predicts that AI will have automated a considerable amount of underwriting by 2020, especially in mature markets where data is available.
  • In a 2013 Oxford study analyzing over 700 professions to determine which were most susceptible to computerization, insurance underwriters were included in the top five most susceptible.
  • Underwriting may leverage not only machine learning but also wearable technology and deep learning facial analysis technology.

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