On March 9, for the first time since 2008, the S&P 500 sell-off triggered a “circuit breaker” after the index dropped more than 7% shortly after the day’s opening. That measure has since been repeated several times. On March 12, the U.S. stock market had its worst day in 33 years, with the S&P 500 closing 9.51% down, before closing 9.29% up the following day. On March 16, the Nasdaq Composite had a one-day loss of 12.3%, which was its worst day ever.
Since the market’s peak February 19, $11.5 trillion has been wiped from the U.S. stock market. The bond market has lost liquidity this month, and even the price of gold crashed as more people turn to cash. The world’s 20 wealthiest people lost $293 billion between mid-February and mid-March during the sell-off driven by the economic impact of COVID-19 and the oil price war. Fear is ruling the crowds.
A number of technical indicators show the level of fear on the market, and the most well-known one is the CBOE Volatility Index, which on March 16 surpassed its previous peak from the 2008 financial crisis. The Fear & Greed Index, developed by CNNMoney, also shows just how much fear is driving the current market.
According to behavioral finance, the majority of trading mistakes are made due to emotions. This makes fear and greed the main risk factors in any trading strategy. In that sense, robots are much better emotionless traders. However, the shortcoming of robotrading strategies is their inability to factor in and capture all market trends, emotions, fears, beliefs, macro understanding and risk management. This is why it’s important to combine human decision-making and automated tools, which boost and yield-enhance by adding magnitude.
Consider this: The market value of a company is based on financial modeling extrapolating its growth trajectory. At the same time, some companies (like Tesla) are just market favorites, and these financial models can be easily altered by changing one input variable. Companies are valuated by investors, who are people with emotions, biases and perceptions. Investors vote with their money, and sometimes these decisions are irrational. For example, an investor might put their money into a company simply because they believe and share the CEO’s values or they enjoy using the company’s service.
Fear and greed can have a tremendous impact on trading patterns from a behavioral finance point of view. Such emotions can lead to panic buying, confirmation bias, memory bias, framing, crowding, overconfidence, FOMO and fire sale. But all these biases make us human beings, who are emotional and irrational and who invest in stocks based on our beliefs.
For high net worth individuals, one investment approach offered by private banks can help mitigate fear-based investment decisions. This approach combines four main trading strategies: volatility-linked, high-yield, fixed-income, actively managed certificates; forex arbitrage; leveraged credit-linked notes; and long-leveraged upside participation notes with downside protection.
The advantage of these strategies from a behavioral finance risk point of view is they do not require active management, and they can cushion market swings. Since the majority of trading mistakes are made by investors when they are forced to buy or sell, the note products above restrict investors from active management for two to three years or until they are auto-called. This by default provides a longer investment horizon and can be structured depending on the cash-flow needs of the investor.
The information provided here is not investment, tax or financial advice. You should consult with a licensed professional for advice concerning your specific situation.