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November 9, 2023

Revolutionizing Finance: How AI Will Transform the Financial Services Market

The world of finance has become a fertile hunting ground for exploring the potential of Artificial Intelligence (AI) because information processing is the backbone of the global financial markets. In the past as well, all kinds of financial institutions have invested heavily in technology and data in order to develop some kind of strategic or tactical advantage. However, the changing competitive dynamics over the last few years provide some hints about what could happen as AI becomes a real, scalable solution.

In recent years, it has become abundantly clear that AI can disrupt industry dynamics very quickly and decisively. With technology playing an increasingly important role in critical decision making, the rise of automated, passive investing solutions are already very common. In the hedge fund space, quantitative methodologies have become the norm for stock picks and entry/exit decisions. The finance industry is often an early adopter for any new technology that could give managers and investors an advantage such a competitive landscape.

Importance of AI InvestmentManagement chart

Source: EnY’s Report: AI and the Investment Management Industry

The financial services industry stands on the cusp of a profound transformation, driven by the steady march of AI. As AI technologies continue to evolve and mature, their impact on the financial sector is becoming increasingly significant. Even though change is always slow (and the financial services sector would be the first to testify to that), this ace of disruption in technological advancements is almost unprecedented and AI could be the tipping point for a quicker change in the industry than ever before.

Robot hand and Human hand touching fingertips

AI: A possible game changer?

Information is power, it has been since the dawn of time, and there will always be a race for it. Venetian merchants used Galileo’s telescopes not to study the stars, but rather to study the cargoes of approaching ships a few hours before their competitors. Unsurprisingly, the most vigorous application of AI for investment managers is attempting to generate the holy grail of new insights for the investment process to improve the investment outcomes for clients.

In today’s information age, AI is playing an increasingly important role in shaping economic and financial sector developments and is seen as an engine of productivity and economic growth through efficiency, improved decision-making processes, and the creation of new products and industries. Right now, the world is still in the early stages of that progression but there are already different applications of AI through the value chain, all generating business. If computing power and data generation keep growing at the current rate, then ML (machine learning) could be involved in almost all of investment management in the near future, affecting other industries and having a real long-term effect on investment returns.

Source: EnY’s Report: AI and the Investment Management Industry

AI also is rapidly changing the financial sector landscape by reshaping the nature of financial intermediation, risk management, compliance, and prudential oversight. Generative AI (GenAI) especially holds certain key implications for service-based industries. At the heart of GenAI are large language models, which are neural network–based models trained on massive amounts of data, including text and documents, and capable of producing understandable and meaningful text or human languages. LLMs (large language models) enable a wide range of applications across various domains with significant implications for the global economy and financial sector.

GenAI will most definitely accelerate AI adoption in the financial sector and investment management. Competitive pressures have fueled rapid adoption. This could further lead to gains in efficiency and cost savings, reshaping client interfaces, enhancing forecasting accuracy, and improving risk management and compliance. GenAI could also deliver to cybersecurity benefits ranging from implementing predictive models for faster threat detection to improved incident response. However, the deployment of GenAI in the financial sector has its own risks that need to be fully understood and mitigated by the industry and prudential oversight authorities.

Robot with a brain and stockmarket charts in the background

A.I.’s impact on financial services and investment management

The ways in which AI is set to revolutionize the financial services market are as follows:

Data-Driven Investment Strategies: AI’s ability to process enormous amounts of data in real-time gives investment managers a powerful tool for decision-making. Whether it’s analyzing financial reports, social media sentiment, or economic indicators, AI can quickly identify trends and correlations that may not be apparent to human analysts. This data-driven approach enables more informed investment strategies and better risk management.
Predictive Analytics: One of AI’s key strengths lies in its predictive capabilities. Machine learning algorithms can forecast market movements, asset price fluctuations, and even macroeconomic trends with a high degree of accuracy. Investment managers can leverage these insights to adjust portfolios, optimize asset allocation, and make timely decisions to capture investment opportunities.
Behavioral Insights: Understanding investor behavior is critical in investment management. AI can analyze investor sentiment, track behavioral patterns, and provide insights into market sentiment. This information can be used to fine-tune investment strategies and provide personalized recommendations to clients.
Customer-Centric Personalization: AI-powered algorithms are redefining customer interactions in the financial services sector. This hyper-personalization enables the creation of financial products and services tailored to individual needs. Chatbots and virtual assistants powered by natural language processing are also becoming integral parts of customer service, offering real-time assistance and information, enhancing customer satisfaction, and saving costs.
Algorithmic Trading: AI-powered algorithms are reshaping the landscape of trading and investment. Automated trading systems use AI to analyze market data, identify trends, and execute trades at lightning speeds. These algorithms can process vast amounts of information in real-time, enabling more informed and timely trading decisions. The result is increased efficiency, reduced risk, and the potential for higher returns.
Enhanced Fraud Detection: Machine learning models can detect unusual patterns in transactions, flagging potentially fraudulent activities in real-time. By continuously learning and adapting, AI systems become more adept at identifying new and sophisticated fraud schemes, thereby reducing financial losses and bolstering security.
Credit Risk Assessment: Traditional credit risk assessment models have limitations, often relying on historical data and fixed parameters. AI, on the other hand, can analyze a broader array of data sources to assess an individual’s creditworthiness. This allows financial institutions to make more accurate lending decisions and expand access to credit for individuals and businesses with limited credit histories.
Regulatory Compliance: The financial services sector is heavily regulated and adherence to these regulations is critical. AI can assist in compliance efforts by automating the monitoring and reporting of transactions, ensuring that all activities are in line with regulatory requirements. Machine learning models can also help identify and mitigate potential compliance risks.
Cost Reduction and Efficiency: AI can significantly reduce operational costs in the overall financial industry, but especially in investment management. Automation of routine tasks, such as data entry, document processing, and customer support, can free up human employees to focus on more complex and value-added activities. Additionally, AI-driven analytics can optimize resource allocation, leading to better cost management.

Risks of AI adoption

The AI life cycle can be broadly divided into three stages – proof of value, enablement, and ramp-up or go live. The proof of value stage solely aims at validating whether an idea is worth the additional investment and resources. The focus of the enablement stage is to quickly turnaround management support and resources to support technology development. An organization is ready to ramp-up or go live when a full product development plan and scope is in place, including the necessary technology assets and personnel. Throughout the entire process, it is important to keep in mind the principles of responsible and ethical AI to ensure that outputs are always fair and representative.

However, while following this process, there are many risks that an organization can face, especially in the financial services sector. With the algorithms making many decisions, it could be possible that the slightest oversight could have far reaching effects. It is important that companies remain cognizant of these and act accordingly. Some of the major risks of AI implementation are:

Data Privacy and Security: AI systems require access to vast amounts of data to function effectively, and any data breaches or misuse of this information could result in significant financial and reputational damage.
Bias and Fairness: AI algorithms can inherit biases present in the data they are trained on. In financial services, biased algorithms can lead to discriminatory lending practices or investment decisions, potentially violating regulations and harming customers.
Regulatory Compliance: The financial sector is heavily regulated, and the use of AI introduces complexities in terms of compliance with existing regulations. Institutions need to ensure that their AI systems adhere to regulatory requirements related to data protection, transparency, and fairness.
Ethical Concerns: AI raises ethical questions, especially in areas like robo-advisors. Decisions made solely by AI may lack human empathy or ethical judgment, and could potentially lead to undesirable outcomes for clients and breaking fiduciary responsibilities.
Model Explainability and Systematic Risks: Many AI algorithms, such as deep learning neural networks, are often viewed as “black boxes” because it can be challenging to explain how they arrive at their decisions. Lack of model transparency can hinder trust and regulatory compliance. In addition, as the widespread adoption of AI in financial markets occurs, it could lead to correlated trading decisions based on similar algorithms, potentially increasing the risk of market crashes or sudden volatility. This is a major risk that could culminate due to the combined efforts of all industry players and their over-reliance on AI.
Cybersecurity Threats: As AI evolves, so will the divergent uses of the technology. AI can be used by malicious actors to enhance cyberattacks. AI-driven attacks can be more sophisticated and difficult to detect, posing a significant cybersecurity threat to financial institutions and the information of those they serve.

To mitigate these risks, organizations must adopt a robust governance framework for AI, focusing on data governance, model transparency, and ethical considerations. Regular audits, ongoing monitoring, and collaboration between data scientists, compliance officers, and legal experts are essential for managing AI-related risks effectively. Additionally, staying informed about evolving regulations and industry best practices is crucial for navigating the growing AI landscape.

AI in Finance: A cautious revolution in the making

The impact of AI on financial services and investment management cannot be overstated. It is transforming the industry by providing investment professionals with advanced tools to make decision making easier and more comprehensive. However, it’s important to acknowledge that AI is not a silver bullet. Investment managers must combine AI’s capabilities with human expertise to create a winning formula.

However, the intrinsic risks could pose material risks for financial sector reputation and soundness—and, ultimately, could undermine public trust, if not handled with maturity. Enterprise-level applications could help mitigate some of the risks inherent in public-level application, but this option may not be cost efficient for smaller financial institutions. AI use needs close human supervision commensurate with the risks that could materialize from employing the technology in financial institutions’ operations. Prudential oversight authorities will strengthen their institutional capacity and intensify their monitoring and surveillance of the evolution of the technology, paying close attention to how it is applied in the financial sector. They will improve communication with public and private sector stakeholders as well as collaborations with jurisdictions at the regional and international levels.

As AI continues to evolve and mature, it will become an indispensable part of the investment management toolkit. To harness the full potential of AI in finance, institutions must invest in talent with AI expertise, build robust data infrastructure, and prioritize ethics and transparency. As AI continues to evolve, it will undoubtedly reshape the financial services landscape. The firms that embrace it, invest in the right talent, build robust data infrastructure, incorporate it into their strategies, and prioritize ethics and transparency will likely gain a competitive advantage in the dynamic and ever-evolving world of investments.

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Disclosure

Howard Capital Management, Inc. (“HCM”) is an SEC-registered investment advisor with its principal place of business in the State of Georgia. SEC registration does not constitute an endorsement of HCM by the SEC, nor does it indicate that HCM has arraigned a particular level of skill or ability. HCM is not an accountant, or a tax accountant and this document is for information purposes only. Please consult a tax accountant for information pertaining to your account. HCM only transacts business where it is properly registered or is otherwise exempt from registration.
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March 30, 2023

Deep Impact: What really happens when banks fail?

The recent misfortunes of the Silicon Valley and Signature Banks are being identified as echoes from the financial crisis of 2008. Fears of a lasting economic downturn, impending stock market crashes, a debt crisis and government bailouts have investors worried whether the worst is still to come. Shareholders and depositors in the two banks in question have raised pertinent questions on the regulatory bodies like the FDIC and the auditory capabilities of the Federal Reserve. Not only has this sudden collapse affected a range of asset classes, but market leaders are suggesting that this could be the first of many more collapses in the months to come.

To fully understand how a collapse like this affects the economy and individual investors one must revisit the crisis of 2008 and draw lessons from that dark period.

The Great Financial Crisis of 2008

The crisis of 2008, which had far-reaching effects around the globe, was triggered by a combination of factors, including the housing bubble, the proliferation of subprime mortgages and the use of complex financial instruments, such as mortgage-backed securities, that were not well understood or regulated.

The overextension of credit by banks would be the first step in this crisis. Banks offered subprime mortgages to borrowers with poor credit histories, often with adjustable interest rates that could lead to payment shock when rates rose. As the housing market began to decline, many borrowers defaulted on their mortgages, leading to significant losses for banks and investors. The collapse of the subprime mortgage market triggered a broader financial crisis, as it undermined confidence in the banking sector and led to a credit crunch. The interconnectedness of the banking sector played a major role in the crisis, as the failure of one bank had a ripple effect throughout the system. The failure of Lehman Brothers was the significant event that sparked a global financial panic.

The fallout from that event led to an economic downturn that lasted for several years. The crisis caused a sharp decline in housing prices, a rise in foreclosures and bankruptcies, and a contraction in credit markets. Many businesses failed and unemployment rates increased in many countries. The years after 2008 were replete with stock market declines, banking system failures (including Lehman Brothers, Washington Mutual and Bear Stearns) and supremely controversial government bailouts. Finally, there were far-reaching sovereign debt crises in several countries, including Greece, Ireland and Spain. These crises had significant impacts on these countries’ economies, leading to major austerity measures and political unrest.

The banking sector’s excessive risk-taking and reliance on complex financial instruments played a pivotal role in the financial crisis of 2008. Opinion makers then stated that this crisis highlighted the need for better regulation of the banking sector to prevent similar events in the future.

Silicon Valley Bank: A brief history of prosperity and the sudden collapse

Silicon Valley Bank (SVB) is a commercial bank founded in 1986 and is based in Santa Clara, California. It primarily serves the technology, life science and venture capital industries. SVB operates across the United States, China, India, the United Kingdom and other countries. In addition, SVB has been recognized as one of the top banks in the United States and has received numerous awards for its banking services and commitment to innovation.

SVB’s collapse came all too suddenly as depositors sought to withdraw their deposits from the bank during a frantic 48 hours after learning the bank was in trouble after interest hikes by the Federal Reserve. Like many other banks, SVB had plowed billions into US government bonds during the era of near-zero interest rates. As the Federal Reserve hiked interest rates aggressively to tame inflation during 2022, the lagged impact of these hikes hit the startup-friendly bank really hard. When interest rates rise, bond prices fall. SVB’s portfolio yielded an average 1.79% return last week, far below the 10-year Treasury yield of around 3.9%.

With the Federal Reserve’s hiking spree seeing no end in the near term, tech startups had to channel more cash toward repaying debt. They were also struggling to raise new venture capital funding. This forced these companies to draw down on deposits held by SVB to fund their operations and growth. With this flurry of withdrawal requests, the bank quickly ran out of funds to service these requests. SVB’s stock plummeted 60% in one day and dragged other bank shares down with it, causing investors to panic.

SVB’s collapse could create systemic risks to the broader financial system, as the interconnectedness of the banking sector means that the failure of one bank can have ripple effects throughout the system. Some of these effects may include:

Lack of Access to Capital: SVB provides funding to early-stage and emerging technology and life sciences companies. The collapse of the bank would reduce the available capital for these companies and may limit their growth potential.
Slowdown in Innovation: The loss of funding and support from SVB could lead to a decrease in innovation and technological advancement.
Job Losses: The technology and life sciences sectors are significant employers in the United States. Job losses in these sectors could follow, which would have a ripple effect throughout the economy.
Economic Slowdown: The sectors that the bank supports are key drivers of economic growth. A decline in these sectors could lead to an economic slowdown or worse, a recession.
Disruption in the Banking Industry: The collapse could lead to a loss of confidence in the banking system. This could have negative impacts on other banks and financial institutions, leading to a broader economic impact.
Forced Bailout: The US government has previously bailed out large financial institutions, such as Citigroup and Bank of America, during the 2008 financial crisis. However, these bailouts were controversial, faced widespread criticism and decelerated overall growth. A bailout would only put more pressure on the tax system and invite the general public’s ire.

Note:SVB is not classified as a Systemically Important Financial Institution (SIFI) by the Financial Stability Oversight Council (FSOC), which means it is not subject to the same level of regulatory oversight and government support as SIFIs.

Finding a way back to prosperity

The need of the hour currently is to avoid a recession induced by this collapse. The Federal Reserve and Washington have a range of tools and policies at their disposal to help prevent another financial crisis like the one in 2008. Some tested ways to reduce the likelihood of another crisis include:

Transparent government intervention: The government took significant steps to stabilize the financial system by providing bailouts to major financial institutions such as AIG, Fannie Mae and Freddie Mac in 2008. The Troubled Asset Relief Program (TARP) provided financial support to banks, and the Federal Reserve implemented quantitative easing policies to increase liquidity in the markets. If things were to get worse and the banking sector reaches the brink of collapse, such intervention could help avoid the worst outcome just about in time.
Flexible fiscal policy: The government could take a flexible approach to fiscal policies to stimulate economic growth. The American Recovery and Reinvestment Act of 2009 provided funds for infrastructure projects, job creation and tax incentives for businesses that eventually helped get the economy back on track after a torrid year. However, in the current economic scenario, the large-scale spending bills (like the Inflation Reduction Act) already in place and inflation wreaking havoc on the economy, having a flexible approach might be more difficult.
Investing in building consumer confidence: Winning over consumers is the most assured way to create a sustained recovery path for any economy. If consumer confidence improves, so will their spending and investing habits. As consumers power the economic machinery, it could be the platform needed for further government spending to support the ailing sectors.
Seeking international support: The American financial and banking system does not operate in isolation from the rest of the world. Even now, the impact of SVB’s collapse has had far-reaching effects across geographic boundaries. Seeking international cooperation and support could help to stabilize the affected sectors and in turn, the economy. In 2008, the G20 countries worked together to implement policies that stimulated growth on a global level. Investing efforts in this route during dire circumstances wouldn’t be a bad idea.

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June 21, 2022

The Genesis and Failings of Market Predictions

In the year 1982, a leading global publication published a special dossier about what the world would be like in the year 2000. The publication invited a range of professional ‘futurists’ make their predictions. From a robotic revolution to the extinction of inflation to family experimentation practices, these predictions were published for all to read and gauge the future themselves. It’s not surprising that the most of the predictions remain unfulfilled till today. However, these futurists have not been completely discredited, nor have they become extinct. They live among us even today, with different roles and identities.

The practice of making predictions based on factual observations can be traced back to a time when markets didn’t even exist. From an instinctual perspective, making predictions is as much a part of human nature as is any other cognitive practice like rationalizing and reasoning. Over centuries, the art of making a prediction has undergone many-a transformation. This has been facilitated through philosophical, mathematical and even theological sciences. Even then, there still remains a strong element of uncertainty about the validity of making predictions. This is so because making predictions is essentially choosing one of two (or numerous) possible outcomes. There are some who will make the right choices and others who won’t. Those who make the right choice won’t be right all the time, and neither will the ones who made the wrong choice. However, the frequency or consistency in such a game of chance is anybody’s guess a.k.a. prediction.

For anyone who is looking to follow or disregard the plethora of predictions that are made every day, it is advisable to understand what governs such predictions and why they are so popular.

Five Famous Predictions That Never Happened

In the 80s, Nobel Prize-winning economist Paul Krugman predicted that the internet was just another fad and would have no discernible impact on investing.
Nobel Prize-winning economist Paul Samuelson was certain that the Soviet economy would surpass the American Economy by 1990.
Just before the 2008 economic meltdown, AIG had endorsed and promoted the Credit Default Swaps (CDS), which was a prominent cause of the market crash and led to the $85 billion bailout for them.
Economist Irving Fisher predicted the stock market was on the verge of a boom, 9 days before the great market crash of 1929 and lost most of his wealth in the process.
In 2007, former Fed Chair Alan Greenspan claimed that interest rates would be in double digits to control inflation, but a year later Fed fund rates were at historical lows.

Making a Prediction: An erroneous human tendency

The start of this practice is probably anyone’s guess. In recorded history though, the end goal guiding the making of predictions has generally been to attain a strategic advantage over the competition. Whether it is in financial markets, war or sports, predictions are a guesstimate that have a tendency of giving confidence to those who are hopeful of a positive outcome. On the contrary, people also have a general tendency to ignore the possible outcomes that are not so positive, even though it might be most probable.

When Nostradamus, the French polymath, wrote his predictions in the form of prose, he did not write any specific details. Yet, today, people attribute many historical events (including 9/11 attacks, the atomic bomb and the rise of Adolf Hitler) to the predictions made by him. It is also important to note that there are many predictions of his that have not manifested in any way or form. That doesn’t make his supporters and followers discredit his work though. They continue to look for patterns where none might exist. Similarly, there are many who came before and after Nostradamus, making claims about future events and the people involved who are revered in different walks of life, including business, politics and economics.

The truth is human beings are geared to widely accept certain visions of the future that suit their view of things, even if those visions improbable or impossible. It confirms how one analyzes the world one is familiar with and wants to believe that their view is correct. When that hypothesis fails, it is put down to chance or an anomalistic inconsistency in one’s analysis. In light of this, in today’s information age, it is not unusual to find a range of predictions on a daily basis about almost anything and everything happening in the world. Even though these predictions may border on being totally ridiculous, it can find its own stream of subscribers who believe that a particular outcome would suit their worldview.

The questionable foundation of general predictions

“Predictions fail because the world is too complicated to be predicted with accuracy.”

Dan Gardner, Author of Future Babble

Experts have been trying to forecast the price of oil since the inception of the industry in the 19th century and they continue their hot streak of being wrong till today. Modern futurists use retrospective quantitative methods to predict socio-political scenarios decades into the future (known as cliodynamics) yet all they have achieved is the creation of a broad, over-arching theme that captures only part of any future. For decades, there have been innumerable predictions of the Dow Jones Industrial Average index either soaring through the proverbial ceiling or crashing with devastating effects. Just like in the case of cliodynamics, only a miniscule part of the prediction has actually occurred, enough to be branded as an inaccurate prediction.

This further begs the question, how does one qualify to be an expert? What is the criteria that separates the ones who make consistently successful predictions from those who are much less accurate in doing so? As it turns out, there is no such criteria and those who are considered to be ‘investing gurus’ are barely ever absolutely right with their predictions. According to a study by CXO Advisory Group which collecting predictions from 68 different forecasters, after study 6,500 different predictions, it was found that, on average, a forecaster will be correct 46.9% of the time. In other words, if the example of a coin toss were considered where the chances are 50-50, an average person would likely beat the average ‘market guru.’

Dr. Tetlock, The Hedgehog and The Fox

From 1984 till 2003, Dr. Phillip E. Tetlock, a political science writer and revered author, conducted one of the most comprehensive studies of analyzing expert predictions. He assembled a group of 284 experts from a myriad of fields, including government officials, professors, and journalists. They had a diverse range of opinions, from Marxists to free-marketers. In this 20 year period, Dr. Tetlock collected over 28,000 forecasts about the future, including questions about inflation, economic growth, the price of oil, the stock market, elections and the global order of power.

As he validated and analyzed the data sets that he had collected over 2 decades, the final result that he arrived at was that ‘the average expert was as accurate as the famous symbol of random guessing – a dart throwing chimpanzee.’ Of course, some did better than others but they were all far from perfect when it came to making predictions. However, there was a critical pattern he identified while studying these predictions. The way these experts were making predictions had a strong correlation with their individual style of thinking.

In order to simplify this observation, Dr. Tetlock drew inspiration from an essay written by a political theorist and writer, Sir Isaiah Berlin. The essay was titled ‘The Hedgehog and The Fox’ which divided writers and thinkers into hedgehogs (who view the world through the lens of a single defining idea) and foxes (who draw on a wide variety of experiences and for whom the world cannot be boiled down to a single idea).

In Dr. Tetlock’s analysis, the hedgehogs were those who use one big idea (usually an analytical tool) to make forecasts. They preferred simplicity and clarity, so they preferred to keep information pared down to what they thought was the bare essentials. They like using words like ‘certain’ and ‘impossible’ and are usually the ones on television. The foxes, on the other hand, gathered information from many sources, looked at issues with more than one analytical lens, readily admitted mistakes, and were comfortable with complexity and uncertainty. They often said ‘maybe’ and they were humble about their ability to see into the future.

The study also showed an inverse relationship between fame and accuracy. The more famous the person, the less accurate their prediction would be. One would assume that this finding would keep the inaccurate hedgehogs out of the limelight and the focus should remain on the humble foxes. However, hedgehogs are brimming with confidence and make absolute declarations, something that dispels uncertainty. The fox talks about probabilities and possibilities. For the general viewing audience, it’s always more attractive to have their concerns and uncertainties absolutely dispelled, even if it is through a hedgehog analysis which is less accurate than a dart throwing chimpanzee.

The role of bias in analyzing predictions

“Everything makes sense in hindsight. We cannot suppress the powerful intuition that what makes sense in hindsight today was predictable yesterday.”

Daniel Kahneman, Behavioral Scientist, Nobel Prize winner

In the path breaking study called Prospect Theory by Daniel Kahneman and Amos Twersky, the study of human decision making under conditions of risk was thoroughly analyzed. The study’s findings run contrary to the normative implications inherent within classical subjective expected utility theories. This theory gave validation to the idea that risk taking and risk averse behavior occurs not in isolation, but in conjunction with individual behavioral dispositions. These dispositions are driven by the biases individuals may have.

When it comes to making market predictions, such biases play an important role in influencing the final decision of an individual. Some of the prominent cognitive biases that affect predictions are as follows:

Overconfidence bias: Overconfidence is one of the cognitive biases that underlie the illusion of validity. Overconfidence bias can be defined as an unwarranted and often times illogical faith that an investor has in their ability to predict the market. This bias is remarkably prevalent in the investment community. James Montier, former co-head of global strategy at Société Générale, once conducted a study with over 300 professional fund managers to measure the scale of behavioral biases. He asked if they believe themselves to be above average in their ability. 74% of fund managers responded in the affirmative, believing they were above average at investing. And of the remaining 26%, most thought they were average. In short, virtually no one thought they were below average.
Confirmation bias: The illusion of validity persists because people fall prey to the confirmation bias; they focus on information that is consistent with their beliefs while neglecting inconsistent information. As physicist Robert Park once said, “People are awfully good at fooling themselves. They’re so sure they know the answer that they don’t want to confuse people with ugly-looking data.” This bias is more prevalent in market predictors with higher perceived knowledge about the market and higher strength of belief (i.e., sentiment). They will also have a negative interaction effect between the perceived knowledge and the strength of prior belief on confirmation bias.
Representative bias: Representative bias occurs when the similarity of objects or events confuses someone while thinking about the probability of an outcome. According to Kahneman and Tversky, representative bias badly affects people’s decisions during their opinions building and reasoning. This may mislead investors and forecasters to believe they have already processed the information correctly right before they make a decision. While making a judgment, they are likely to ignore other related factors which may affect the value of the investment. Because of the representative bias, forecasters may ignore the law of small numbers where they incorrectly consider a small sample size as being representative of the population. Even though these numbers may reflect the current trend, they cannot possibly describe the properties of a whole population.
Hindsight bias: Hindsight bias leads people to exaggerate the quality of their foresight. It is a behavioral trait where one becomes convinced they accurately predicted an event before it occurred. Based on this, they make predictions about the future. Once an event is part of history, there is a tendency to see the sequence that led to it as inevitable, as if uncertainty and chance were banished. Armed with such a bias, one can become overconfident of one’s foresight and make exaggerated predictions. Those who have hindsight bias, it is common to hear the phrase, “I knew it all along.”

The role of bias in analyzing predictions

It is easy to suffer the pitfalls of following the predictions of the numerous ‘hedgehogs’ that populate mass media nowadays. The markets, just like the future, will forever remain unpredictable. All the predictions that are made, whether scientific or instinctual, are essentially rooted in some form of cognitive bias and are probably driven by the possibility of making some personal gain. To base one’s investment decisions on ‘expert’ predictions is akin to sailing in a storm without a life jacket.

The advisable way to achieve one’s investment return goals is to have a clear objective and an innate ability to shut out the noise made by the so-called market gurus. Instead of listening and acting on the words of the ‘hedgehogs,’ it is more prudent to follow the analysis of the foxes who gear themselves for an uncertain future. These foxes will be advisors and managers who prefer diversification of risk and do not make claims about knowing what comes next. Instead of relying on instincts and emotions, an investor must trust those who take a calculated approach to mitigating possible losses and acting in time as the market moves.

While it might be considered a lot more amenable to do away with market gurus altogether, it is not possible in the current structure of information dissemination and market speculation. In certain markes, like Prediction markets where one trades the outcome of events and market prices indicate what all the investors think the probability of the event is, the role of the guru/expert is almost non-existent. There are many proponents for such markets but they remain a laggard to the popular markets where ‘crystal ball’ predictions still attract a majority of investors. These markets also have the largest market capitalizations and will always remain difficult to transform.

In order to facilitate diversification, there exists a broad range of assets and asset classes in the world now. It would be the rarest of rare occurrence for all of them to boom or go bust at the same time. By planning one’s investments wisely across these classes, based on a sound investment plan, would most likely achieve the long-term gains that any investor dreams of. While ignoring market predictions may not be easy, avoiding making knee-jerk investment decisions based on them can be easily avoided through discipline and consistency.

In Warren Buffet’s now famous article “The Superinvestors of Graham and Doddsville,” he presents indisputable data about how a specific number of asset managers who shared a common teacher have consistently managed to outperform the index over decades. None of them claim to have any supernatural prediction powers and neither do they boast of knowing something that others do not. They have followed their tested methods over a long time frame to achieve a goal they set for themselves. They dedicated their lives to achieving those goals and stayed true to the practices taught to them by Benjamin Graham. In the concluding paragraph of the article, Warren Buffett detailed an undeniable reality of the world which encapsulated the distracting and irrelevant nature of market predictions:

“There seems to be some perverse human characteristic that likes to make easy things difficult… It’s likely to continue that way. Ships will sail around the world but the Flat Earth Society will also continue to flourish.”

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June 17, 2022

Focus on the Data—Not the Drama. How Advisors Can Educate Clients on Today’s Turbulent Market

Stock market volatility has reached a fever pitch in recent months, as has investor anxiety. We don’t expect it to dissipate any time soon. The markets are not trading on fundamentals. Instead, they’re trading on investor sentiment and emotions fueled by outside noise.

How did we get here, and how should advisors work with their clients to focus on the data, not the drama?

Not Your Father’s Stock Market

There are a few things investors need to understand about this stock market that make it considerably different from 15 or 20 years ago. One thing we know is the markets don’t work and function the way they did before. The velocity with which stock markets move is much faster. The upward and downward swings are happening with much greater speed.

A significant part of that transformation over the last decade is the dominance of automated trading. As much as 85% of market trades today are executed by computerized trading or automated machine execution. That’s a far cry from just ten years ago when automated trading accounted for about 20% of trades. Automated trading not only contributes to the higher velocity of trading—but is also a primary contributor to the more significant market selloffs and runups we’ve been experiencing. Couple the increased velocity with the tens of millions of investors accessing hundreds of millions of pieces of data 24/7 at digital speed, and you have a market that can, and will, quickly react to new information or the instant investor sentiment changes.

The media has exacerbated the problem in its seemingly hyperbolic headlines and 24/7 new cycle. The thirst for advertising dollars often makes the media focus on bad news simply because it sells better than good news. Much of the information investors get from the media is centered on driving clicks or viewership, not necessarily on conveying optimal investment advice.

The Challenge for Advisors: Control Client Performance Expectations

Between the stock market volatility and the constant drumming from the media, investors have been pulled in many different directions, often causing them to change their expectations and become emotional about the market.

Emotions often lead to poor investment decisions, which then may lead to underperformance. Current investor losses may have little to do with asset allocation, diversification, or fund choices. Losses may have more to do with suboptimal buy and sell decisions driven by emotions.

Advisors have historically been challenged to meet client expectations. Past experiences or current circumstantial pressures can be driving forces behind shifting expectations. It’s much more of an issue when clients’ expectations whipsaw along with the market. You’re familiar with the concept: “Keeping up with the Joneses.” Applying this theme in a behavioral finance context, and you have what is referred to as “Anchoring Bias,” where investors errantly anchor portfolio return expectations to recent market highs, instead of taking a holistic view of their progress towards their long-term financial goals.

How are advisors supposed to manage that?

The key for advisors is to get control of client expectations through communication and education—and put current market events in perspective. Advisors can work to help their clients avoid common behavioral biases during periods of heightened market volatility by educating them on three critical points:

Volatility is not only normal, it also may be good for investment returns
Volatility and risk have separate meanings
Ignore the noise

Volatility Is Normal

Investors need to understand that market volatility is a normal part of markets. Market pullbacks between 5-10% are a common occurrence, and not unusual to see multiple market pullbacks within the same year. These recalibrations typically occur when the market is overbought or the release of new fundamental news. Investors should in fact consider welcoming market pullbacks because they often help the market reset proper valuation and present investors with a good buying opportunity.

Market Corrections (10-15% declines) are also normal, and while not as prominent as Market Pullbacks, can be viewed in a similar light, but with more caution. When clients are concerned about volatility, it helps to put it in perspective. Remind them that since 2000, the S&P 500 has gained, on average, more than 8% one month following a market correction bottom, going on to then gain more than 24% the following year. So, not only have corrections been short-lived, but they have also been a precursor to significant market gains in their aftermath.

Consider that, in each year since World War II, the market has experienced an average intra-year decline of roughly ~14%, with the market ending the year lower only every third year. As the graphic below shows, the market finished the year in the positive.

Source: Bloomberg

When looking at periods of volatility over a historical timeline of the stock market, it’s clear that stocks can be volatile for periods of time. But over the long-term they historically appear more stable, steadily producing positive returns over time.

The two charts below examine the relative volatility of stock market returns of 5-year rolling returns versus 30-year rolling returns.

A critical takeaway is to educate clients on the importance of following their financial plans and drive awareness of investor behaviors that lead to poor investment decisions during periods of normal market volatility. Reviewing and analyzing a client’s progress towards specific goals should be a primary benchmark for determining changes to an investors financial plan, not TV headlines.

Volatility And Risk Are Not the Same

Investors’ greatest fear is losing money; it’s natural to equate volatility with market risk. However, volatility and risk are not the same, and it’s critical to educate clients on the difference.

Market risk is the chance that by being in the market, you will lose money—though true losses are only realized when a security is sold for a loss. Investors suddenly spooked into bailing out of the market after it has already declined 10 or 15%, could be subjectively “sounding the horn” on a financial plan, still in the 2nd quarter.

Volatility on the other hand, is a range of expected returns or price changes for a particular security, relative to its average – also known as Standard Deviation. Securities with a larger range tend to be larger swings in price and returns, through changing market conditions.

Also, it’s important to remind your clients that volatility is a two-way street. There can be as much volatility to the upside as to the downside. However, investors tend to only focus on the latter, which is why their investment performance can suffer. According to DALBAR’s Investor Behavior Study, twenty years of investor data shows that those who abandoned the market to avoid its worst days almost invariably miss the market’s best days as well. That makes it difficult to overcome the losses incurred and hurts overall investment performance.

Consider this graphic, which shows how your returns would have been impacted on a $10,000 investment if you missed the market’s 5, 10, 30, and 50 best days. What makes it worse for investors is, typically, the market’s best days have occurred near some of its worst days, which is why it’s essential to stay the course during periods of extreme volatility.

Source: FactSet. Returns are based on the S&P 500 Total Return Index. Past performance is not indicative of future returns. An individual cannot invest directly in an index. Data is as of January 31, 2022.

Ignore The Noise

When it comes to investing, the media noise can be deafening. Helping clients understand, while all the significant news events of today may seem consequential, they often have little if any impact on reaching their long-term investment goals. A sudden 1,000-point drop in the market today can certainly be uncomfortable. But through full market cycles, they may barely register as an afterthought on a 5-, 10- or 20-year performance timeline.

It’s critical to have a component to your investment management process that keeps the negative news out of perspective. That will help replace the emotional buy and sell decisions that come with outside market noise, creating more explicit client expectations and potentially more consistent outcomes.

The Bottom Line

We have a phrase at Howard Capital Management, “Focus on the data, not the drama.” We want to make sure we’re making investment decisions on the information we know, not what we think is going to happen.

Right now, there are a lot of unknowns. We are looking for the trends; we concern ourselves with the data, the math, and probabilities.

We trade based on our proprietary HCM-BuyLine®, which is designed to identify clear market trends and seek optimal portfolio allocations through buy and sell decisions based on those trends. Emotions have no place in our process.

The HCM-BuyLine®

The HCM-BuyLine® has been identifying and confirming trends for three decades, enabling Howard Capital Management to sidestep catastrophic market declines in 2000, 2008, and 2020. To learn more about the HCM-BuyLine® and how it is used to tactically manage mutual funds and ETFs, please click here.

About Vance Howard

Vance Howard’s vision for HCM originated after seeing the devastating financial losses investors suffered during the stock market crash of 1987, an event precipitated by computer program trading and investor panic. In an effort to the help investors monitor changing market conditions, he developed the HCM-BuyLine®, a proprietary math-driven indicator, designed with the goal of reducing the impacts of emotional investment decisions.

About Howard Capital Management

Howard Capital Management, Inc. (HCM) is a SEC-Registered Investment Advisory Firm founded by Vance Howard, which offers professional money management services to private clients, financial advisors, and registered investment advisors through a suite of separately managed accounts, retirement tools, self-directed brokerage accounts, proprietary mutual funds, and ETFs. As of December 31, 2021, Howard had assets under management of $5 billion.

For more information, financial advisors should contact their wholesaler by contacting Howard Capital Management at howardcm.com or 770-642-4902.

HCM-061622-63

March 2, 2022

How to Respond to the Impact of Inflation on Today’s Stock Market

Look for the Silver Lining

The bears have current control of the market. Inflation will be with us for some time, and preservation of capital is critical.

The HCM-BuyLine®, Howard Capital’s proprietary math-based indicator, recently turned negative for the first time since the start of the pandemic in February 2020, which recommended our portfolios move a portion to cash and/or short-term bonds.

The inflation bear, too, is most definitely real, and it’s not transitory. During 2021, this higher bout of inflation has been felt at the grocery stores and gas pumps. Now it’s being felt by the markets. It was only a matter of time before the markets buckled under the weight of rising inflation and the prospects of higher interest rates.

Are we in a longer-term bear market or is this just a nasty correction? Only time will tell, of course, but for now the trend is down and fighting the trend can be expensive and painful. We will let the market find its base, and when the HCM-BuyLine® turns positive, we will re-enter.

How Did The Markets Get Here?

The Federal Reserve has put quantitative easing on steroids since the beginning of the pandemic, ballooning the federal balance sheet to nearly $9 trillion. This was done to increase the money supply and stimulate economic growth during the COVID pandemic. As the growth of production of goods and services slowed, the money supply growth eventually overtook it, causing the price of goods and services to be bid up. When too many dollars are facing too few goods, the result is inflation.

Then add in supply chain issues and increasing wages. Payroll has gone up as employers have had to pay more to get people to come to work. That has contributed heavily to inflationary pressures on the market. There are still four to five million people not working right now.

Often ignored in the inflation equation is the velocity of money—the rate at which money is exchanged in the economy. Following the financial crisis in 2008 and the COVID pandemic in 2020, consumers were more inclined to save their additional dollars out of caution. During COVID, savings increased because many services were no longer available for purchase, such as travel and restaurants.

Now, as many COVID restrictions have been lifted, consumer activity has reached pre-pandemic levels, increasing the velocity of money, but setting the stage for a more prolonged inflation, which we think could be with us for the next 12 to 18 months.

How Should Advisors and Their Clients Respond?

Whether we’re in for a repeat of the 1970s will depend on how the Federal Reserve responds to inflationary pressures. The Fed was a little late to the game and missed a rate hike opportunity in January. The Fed now has to walk a tightrope with future rate hikes later this year. Rising rates too quickly could potentially hurt markets, but not acting at all is only fueling the inflation problem, which is now at a 40-year high. The Fed is in a no-win situation of having to make some difficult decisions that may sting a bit. The latest indications are that it will hit the brakes on printing money in March.

The basic arrow left in the Fed’s quiver is to raise interest rates, which we expect them to do three or four times in 2022.

Some equities could hold up better than others. High-dividend-paying and big-value stocks have been performing relatively well so far. Look for value stocks and cyclicals to potentially outpace growth stocks, while an increase in interest rates later this year could mean a strong headwind for tech stocks. Energy stocks may also do well. But in a deep and sustained selloff, most stocks will likely get caught up in the contagion.

Advisors and their clients need to avoid making decisions based on emotions as it invariably leads to bad outcomes. Some specifics:

Know that the market fluctuations are normal. Declines of 5% have happened on average three times a year; declines of 10% on average have historically occurred every 16 months; and declines of 15% have happened every three years. Stay the course.
Aim to maintain enough investment power to get back in the market at lower prices when the market turns positive.
Seek to avoid going to long-duration bonds, especially those relying on their investment capital in retirement. Once interest rates start rising, bond prices may become an instant loser.
Consider short-duration bonds or even cash. Cash isn’t ideal because, at a 7% inflation rate, people would be losing 7% of their money. But it may be the best of the options.
Avoid interest rate-sensitive stocks like utilities.

The world is not going to end. It’s just going to be a bit more volatile than usual. It’s like being in an airplane. It doesn’t mean we don’t land safely, but advisors and their clients might want to tighten their seat belt along the way.

Reasons To Be Positive

There are several reasons to be optimistic right now. COVID may finally be waning, the economy has remained strong, and virtually anyone who wants a job can get one. Investors have had three excellent years in a row, so a downturn is to be expected. These market cycles run about three years, and they never last long, maybe six to twelve months at worst. This is completely normal market action.

Indeed, market selloffs may be healthy because they may allow for valuations to normalize. Note that volatility and risk are not the same. Risk refers to the permanent loss of capital. Volatility is simply the price fluctuation of a security. Firms with non-volatile stock prices may not generate a profit. Embracing volatility may lead to great buying opportunities for high conviction picks and how positive returns are generated. Advisors and their clients may want to welcome volatility—because it’s how positive returns are generated. For those reasons, embrace volatility and avoid letting emotions drive their investing decisions.