the secret of success: to "make the seat of your pants adhere to the seat of your chair for long enough."
Chimamanda Ngozi Adichie
Are You an Investment Historian or a Futurist? | Morningstar
The article "Are You an Investment Historian or a Futurist?" by John Rekenthaler, published on Morningstar, explores the contrasting approaches of investment historians and futurists. Historians rely on past data, focusing on mean reversion to predict future returns, while futurists look ahead, attempting to foresee new trends and changes. Each method has its own strengths and weaknesses, and the article uses the example of the investment management company GMO to illustrate these points. GMO’s success around the 2000s and its more recent struggles highlight the challenges and potential pitfalls of relying solely on historical data.
Key Points
Investment Historians
Approach and Philosophy:
Investment historians base their strategies on past market performance and historical data. The core idea is that markets tend to revert to the mean over time. This means that if an asset is currently undervalued compared to its historical average, it is likely to eventually increase in value, and vice versa.
This approach provides a structured methodology for making investment decisions. By relying on historical norms, historians can create a "battle plan" for reinvesting during market downturns, helping them stay calm and avoid panicking.
Example: GMO's Success:
GMO's strategy in the late 1990s and early 2000s is a prime example. During this period, growth stocks were highly overvalued, while other asset classes were neglected. GMO's decision to avoid these overvalued assets and invest in undervalued ones paid off when the market corrected itself, validating their historical approach.
The success of GMO's December 1999 predictions, which accurately forecasted the performance of various asset classes, bolstered their reputation and attracted new clients.
Challenges:
The main challenge for historians is the potential delay in mean reversion. Market conditions may remain irrational longer than anticipated, leading to periods of underperformance.
Additionally, historical patterns may not always hold if there are fundamental changes in the market dynamics. For instance, technological advancements or regulatory changes can permanently alter market behavior.
Investment Futurists
Approach and Philosophy:
Futurists look forward, attempting to predict future trends and shifts in the market. They focus on identifying new growth opportunities and potential changes in economic conditions.
This approach requires a deep understanding of macroeconomic trends, technological advancements, and geopolitical developments. Futurists aim to position their investments to capitalize on these anticipated changes.
Example: The "New Normal" Theory:
After the 2008 financial crisis, many futurists believed that the economic landscape had fundamentally changed. They predicted that the era of US stock market dominance was over and that emerging markets, particularly China, would lead the next phase of growth.
This theory suggested pairing bonds from developed nations with stocks from emerging markets. Although some aspects of the prediction were correct (e.g., China's continued growth), the overall strategy failed because US companies continued to perform well, driven by strong earnings growth.
Challenges:
The primary difficulty for futurists is the inherent uncertainty of predicting the future. Accurately forecasting economic and market trends is extremely challenging, and even small errors in judgment can lead to significant losses.
Additionally, distinguishing between temporary market noise and genuine long-term trends is a significant obstacle. Futurists must be able to separate short-term market reactions from lasting changes, which is not always straightforward.
GMO's Experience: A Case Study
Historical Approach:
GMO's historical approach worked well in the early 2000s but faced challenges in the following decade. After the 2008 crisis, GMO correctly embraced US equities, but their subsequent predictions of prolonged US stock market losses from 2013 onward did not materialize.
Despite their success in predicting bond market returns, their incorrect equity forecasts had a more substantial impact on overall portfolio performance, highlighting the importance of getting stock market predictions right.
Lessons Learned:
This case study illustrates that even seasoned investment firms with a strong historical track record can struggle when market conditions change or when their predictions do not align with actual market performance.
It underscores the need for flexibility and adaptability in investment strategies, as well as the importance of continuous reassessment of market conditions and assumptions.
Psychological and Analytical Challenges
For Historians:
The psychological challenge for historians lies in maintaining discipline during market turbulence. It's easy to succumb to panic or overreaction when market conditions are unfavorable, but sticking to a historically grounded strategy requires conviction and resolve.
Jeremy Grantham's quote about buying stocks when "blood runs in the streets" encapsulates this challenge. Investors must be willing to act against prevailing sentiments based on their historical analysis.
For Futurists:
Futurists face the analytical challenge of correctly forecasting future trends amidst the noise of current events. The unpredictability of markets and the complexity of economic systems make accurate predictions difficult.
They must also contend with the potential for cognitive biases and overconfidence in their ability to foresee future developments. The risk of misinterpreting short-term fluctuations as long-term trends is always present.
Key Quotes
"Buying stocks when blood runs in the streets is problematic because 'formerly reasonable people start to predict the end of the world, armed with terrifying and accurate data.'"
"Reversion to the mean can take so long that when it finally happens it is indistinguishable from being wrong."
"Understanding what will be is hard. Further complicating that task is the noise of the here and now."
Conclusion
Understanding the distinctions between investment historians and futurists is crucial for investors as it highlights different approaches to managing risk and identifying opportunities. Both strategies offer valuable insights, but they also come with inherent risks and challenges.
Investors can benefit from a balanced approach that incorporates elements of both historical analysis and forward-looking predictions. By doing so, they can build more resilient portfolios that are better equipped to navigate the complexities of financial markets.
US Equities: 9% Forever? - The Big Picture (ritholtz.com)
Barry Ritholtz reflects on his extensive research for his upcoming book, noting the mix of poor advice and predictions that have emerged from Wall Street over the years. He highlights a notable exception: an article by Justin Fox from the December 26, 2005 edition of Fortune, titled "9% Forever?" The article discusses a remarkable market prediction made in 1974 by Rex Sinquefield and Roger Ibbotson, who forecasted that the Dow Jones Industrial Average would reach 9,218 by the end of 1998 and 10,000 by November 1999. Their prediction turned out to be remarkably accurate, underscoring the validity of Ibbotson's insights into equity returns, which are driven by dividends, earnings growth, and inflation. The article reinforces the importance of a well-considered, academic-based approach to understanding market returns.
Key Points
Historical Market Prediction:
In May 1974, Rex Sinquefield and Roger Ibbotson predicted that the Dow Jones Industrial Average would hit 9,218 by the end of 1998 and 10,000 by November 1999.
The actual Dow at the end of 1998 was 9,181, and it reached 10,000 in March 1999.
Contributors:
Rex Sinquefield is the co-founder of Dimensional Funds (DFA).
Roger Ibbotson is a Yale professor known for his contributions to investing theory.
Ibbotson's Insight:
Equity returns are driven by dividends, earnings growth, and inflation.
From May 1974, the S&P 500 Index annualized gains of 8.44%, which increased to 11.43% when dividends were reinvested.
Academic Approach:
Ibbotson and Sinquefield's work is based on CRSP historical data.
Their predictions and insights have stood the test of time, providing a robust framework for understanding long-term market returns.
Validation:
Justin Fox's 2005 article highlighted the accuracy and importance of Ibbotson and Sinquefield's market call.
The work of these two academics remains influential in the field of investing.
Key Quotes
On the Prediction:
"Those two young men in Chicago in 1974 had made one of the most spectacular market calls in history."
On Ibbotson's Insight:
"Simply put, if you believe that stocks are fated to return 10% on average over the long haul, Ibbotson is probably the reason why."
On Equity Returns:
"Equity returns are driven by the combination of dividends, earnings growth, and inflation."
On Historical Performance:
"Calculating returns back to May 1974 gives us numbers that look like this: S&P 500 Index annualized generated gains of 8.44%; if you reinvested the dividends, the annualized gains were 11.43%."
Why It Matters
Historical Accuracy:
The accurate prediction by Sinquefield and Ibbotson underscores the value of a rigorous, data-driven approach to market forecasting, which contrasts sharply with the often-speculative nature of many Wall Street predictions.
Framework for Returns:
Understanding that equity returns are driven by dividends, earnings growth, and inflation provides investors with a clear, actionable framework for evaluating potential market performance over the long term.
Validation of Academic Research:
The success of Ibbotson and Sinquefield’s forecast validates the importance of academic contributions to investing theory, encouraging reliance on well-researched, empirical data rather than speculative or anecdotal advice.
Investment Strategy:
The insights from the article can help investors develop more informed, long-term investment strategies that take into account the fundamental drivers of market returns.
By highlighting the prescient work of Ibbotson and Sinquefield, Barry Ritholtz underscores the lasting value of robust, data-driven investment analysis over more speculative approaches.
How Often is Too Often? - by Conor Mac - Investment Talk
In "How Often is Too Often?", Conor Mac addresses the issue of investor distraction and the counterproductive nature of frequently checking stock prices and engaging in unnecessary market-related activities. He differentiates between productive and unproductive anxiety, advocating for a more disciplined and selective approach to managing investments.
Mac emphasizes that the overwhelming majority of information consumed by investors is irrelevant or even harmful, leading to irrational decisions. He suggests that long-term investors should focus on productive worrying—actively thinking about potential risks and decision-making processes rather than being swayed by market noise and short-term fluctuations.
Key Points
Inertia in Investing:
Over checking stock prices and market fluctuations consumes mental capacity and attention that could be better spent elsewhere.
Investors often engage in busy work and get distracted by irrelevant debates, leading to wasted time and resources.
Selective Information Consumption:
95% of the information investors consume is likely a waste of time.
A selective information diet and some inertia can improve investment outcomes by reducing distractions.
Productive vs. Unproductive Anxiety:
Productive anxiety involves worrying about things within your control and improving decision-making processes.
Unproductive anxiety, on the other hand, involves stressing over uncontrollable factors and market noise.
Long-term Focus:
Long-term investors should avoid the temptation to check their portfolios frequently and instead focus on fundamental research and periodic reviews.
Emotional responses to daily market movements can lead to irrational decisions.
Rationality and Irrationality:
Investors are not perpetually rational or irrational but are susceptible to irrationality during times of market stress or excitement.
Recognizing and mitigating these triggers can lead to better investment decisions.
Media and Market Noise:
Financial media often glamorizes certain investors and market trends, which can distract and mislead retail investors.
Avoiding the constant stream of market noise can help investors stay focused on their long-term goals.
Key Quotes
On Distractions:
"The distractions I referenced earlier would then be an added layer of distraction upon an already tempting body of distraction."
On Productive Worrying:
"Successful investing goes hand in hand with productive worrying. Worry enough during the day and you can, in fact, sleep justifiably well at night."
On Information Selectivity:
"A little selectivity in your information diet and some inertia can go a long way."
On Market Volatility:
"Significant gains are situated in proximity to significant losses. A good reminder that you are only ever a short distance away from a humbling."
On Emotional Responses:
"Is checking your portfolio value for the 6th time in a day going to make you more informed?"
Why It Matters
Improves Decision-Making:
By focusing on productive anxiety and selective information consumption, investors can make more rational and informed decisions, leading to potentially better investment outcomes.
Reduces Emotional Stress:
Reducing the frequency of checking portfolios and engaging in market noise helps mitigate emotional stress and anxiety, promoting a healthier investment mindset.
Encourages Long-Term Perspective:
Emphasizing long-term fundamentals over short-term fluctuations aligns with the principles of successful investing, encouraging a disciplined and patient approach.
Combats Overtrading:
Frequent portfolio checks and reactions to market movements can lead to overtrading, which incurs higher transaction costs and taxes, ultimately reducing returns.
Promotes Efficient Use of Time:
By avoiding unnecessary distractions and focusing on critical tasks, investors can allocate their time more efficiently, improving both personal and professional productivity.
Conor Mac's insights in "How Often is Too Often?" serve as a valuable reminder for investors to maintain discipline, focus on what truly matters, and avoid the pitfalls of constant market monitoring and distractions.
Three Things – Is a Stock Bubble Forming? – Discipline Funds
In the article "Three Things – Is a Stock Bubble Forming?" published by Discipline Funds, Cullen Roche discusses the potential risks and opportunities in the current stock market landscape, particularly focusing on the concentration of the "Magnificent 7" (Mag 7) tech stocks, the possibility of a new tech bubble driven by AI narratives, and the importance of investing in experiences.
Key Points
Concentration of the Mag 7:
The Mag 7, a small group of high-performing tech stocks, are significantly influencing market cap indices like the S&P 500.
Pros: Market cap indexing benefits from this concentration as top-performing stocks naturally rise to the top.
Cons: Increased concentration leads to higher single entity risk, which contradicts the diversification principle of indexing.
Advice: Investors might consider diversifying outside of the index to mitigate this risk, especially if they have a shorter investment horizon.
Potential Tech Bubble:
There is growing talk about a tech bubble, particularly in AI.
Scenario: AI is seen as a transformative technology that could boost corporate margins, but current market pricing may be overly optimistic.
Risk: If AI investments don’t yield expected results, it could lead to reduced spending, higher unemployment, and significant stock price corrections.
Advice: Understand your investment time horizon. Investing in tech stocks requires a long-term perspective to weather potential near-term volatility.
Investing in Experiences:
Roche shares a personal anecdote about attending a concert at the Sphere in Las Vegas, highlighting the value of investing in unique experiences.
Takeaway: While technology offers exciting opportunities, real-life experiences and relationships are invaluable.
Key Quotes
On the Mag 7: "The Concentration of the Mag 7 is Both Good and Bad."
On indexing: "When you own a market cap weighted index like the S&P 500 you don’t have to worry about picking the best stocks in the index because you just own them all."
On diversification: "Increased concentration means increased single entity risk which is the main risk you’re trying to eliminate when indexing."
On tech bubble: "I always hesitate to use the word ‘bubble’ because that word implies that the market has to pop."
On time horizon: "Investing is really all about time. If you don’t understand the time horizon within which your investment strategy operates then you are creating unnecessary behavioral risks."
On experiences: "It reminded me of how important it is to invest in experiences."
Why It Matters
Investment Strategy: Understanding the dynamics of market concentration and the potential risks of a tech bubble can help investors make more informed decisions.
Diversification: Highlighting the risks of concentration in index funds underscores the importance of maintaining a diversified portfolio.
Long-Term Perspective: Emphasizing the need for a long-term investment horizon helps investors align their strategies with their financial goals and risk tolerance.
Personal Well-being: The reminder to invest in experiences highlights the balance between financial investments and personal fulfillment, encouraging a holistic approach to well-being.
One To The Negative 230 - A Teachable Moment (tonyisola.com)
In the article "One To The Negative 230 - A Teachable Moment," Anthony Isola reflects on the improbability of the Universe's existence and draws parallels to the unpredictable nature of investing. He uses Sebastian Junger's near-death experience as a metaphor to emphasize the inherent uncertainty of life and markets. Isola advocates for humility and realism in investing, warning against the false certainty often presented by market pundits.
Key Points
Improbability of the Universe:
The Universe exists despite astronomical odds against it, calculated as one chance in a number with 229 zeros after it.
Numerous precise parameters had to align perfectly to allow life to exist, such as the exact amount of gravity and the forces between atomic particles.
Sebastian Junger's Near-Death Experience:
Junger survived a ruptured abdominal aneurysm, an event he described in his book "In My Time Of Dying."
During his ordeal, Junger, an avowed atheist, experienced a vision of his deceased father, which he later rationalized using physics, suggesting that if the Universe could exist against such odds, so could his vision.
Lessons for Investors:
The improbability of the Universe and Junger's experience highlight the uncertainty of predicting future events.
Isola advises investors to remain humble and realistic, warning against the confident predictions of market experts.
Key Quotes
On the Universe’s improbability: "The odds are one to the negative 230. There is one chance in a number with 229 zeros after it."
On humility in investing: "Choose humility and accept the fact the only certainty is uncertainty."
On expert predictions: "Take pause the next time you see someone on TV predicting with certainty the direction of the market, a presidential election, or the economy."
Why It Matters
Understanding Uncertainty: The article underscores the importance of acknowledging the inherent uncertainty in both life and investing. Recognizing this can prevent overconfidence and impulsive decisions based on seemingly certain predictions.
Investment Strategy: By emphasizing humility and realism, Isola encourages a more cautious and thoughtful approach to investing, which can lead to better long-term outcomes.
Skepticism of Market Predictions: Highlighting the improbability of accurate predictions, the article advises investors to be skeptical of market pundits who claim to know the future. This skepticism can help investors avoid being misled by false certainties.
Holistic Perspective: The connection between the improbability of the Universe and investing provides a broader perspective, reminding investors of the bigger picture and the need for a balanced, well-thought-out strategy.
Artificial Intelligence and Worker Power
The document titled "Artificial Intelligence and Worker Power" by Owen F. Davis, published in June 2024, explores the economic implications of artificial intelligence (AI) on worker power. While much research has focused on AI's role in replacing or augmenting human labor, the impact of AI on worker power remains underexplored. Davis's paper aims to fill this gap by surveying and reframing the economic literature on AI and labor, detailing how new technologies might alter worker power, and discussing the potential consequences for income distribution and job quality.
The paper distinguishes between the labor demand impacts and worker power impacts of AI. Labor demand impacts stem from AI substituting human tasks, while worker power impacts arise from AI enhancing managerial control, undermining workplace norms, or increasing information asymmetries. The study formalizes several avenues through which AI could shift power dynamics in the workplace, including monitoring and surveillance, predictive analytics in wage bargaining, and algorithmic management systems that reduce worker autonomy.
In-Depth Analysis
Theoretical Framework
Labor Demand vs. Worker Power Impacts
Davis introduces a critical distinction between labor demand impacts and worker power impacts:
Labor Demand Impacts: These are changes in the tasks that humans perform due to AI automation. When AI substitutes or augments human labor in specific job tasks, it directly affects the demand for labor.
Worker Power Impacts: These encompass changes in the management and organizational structures due to AI. AI's influence on worker power includes enhancing managerial control, undermining workplace norms, and creating information asymmetries that can disadvantage workers.
This distinction helps to understand not just how many jobs might be lost or transformed by AI, but also how the nature of work and the dynamics of worker-employer relationships might change.
Key Mechanisms of AI's Impact on Worker Power
Monitoring and Surveillance:
AI technologies enhance the ability of employers to monitor employees. This has potential efficiency gains but also shifts power towards management.
Enhanced surveillance can lead to a shift in rents from workers to firms, as workers may have less autonomy and more pressure to perform under constant observation.
Predictive Analytics in Wage Bargaining:
AI can use predictive analytics to personalize wage offers, potentially pushing wages closer to each worker's reservation wage.
This can lead to a more discriminatory monopsony, where employers have more power to exploit individual differences among workers.
Algorithmic Management Systems:
AI-driven management tools can automate decision-making processes traditionally handled by humans.
These systems can reduce the need for managerial oversight but also limit workers' ability to negotiate and exercise their voice in the workplace.
Recruitment and Hiring:
AI-based recruitment systems can select for specific traits, potentially favoring compliance and reducing diversity in thought and approach.
This can further shift power dynamics by creating a workforce that is more easily controlled.
Business Reorganizations and Fissuring:
AI can facilitate business reorganizations that disintermediate the workforce, such as through gig economy models or outsourcing.
This can erode internal wage fairness norms and weaken worker solidarity.
Targeted Automation and Deskilling:
AI can automate specific tasks within jobs, leading to deskilling and reducing the bargaining power of skilled workers.
This can fragment the workforce and erode collective bargaining structures.
Empirical Findings and Case Studies
Davis references several empirical studies and high-profile examples to support his analysis:
Warehouse Work: AI-driven systems in warehouses, like those used by Amazon, enhance productivity but also lead to intensive monitoring and reduced worker autonomy.
Taxi Services: Ride-sharing platforms like Uber use AI for dynamic pricing and driver management, which can undermine traditional labor norms and reduce driver independence.
Call Centers: AI tools in call centers can automate customer interactions and monitor employee performance in real-time, impacting job quality and worker satisfaction.
Broader Economic and Social Implications
Income Distribution and Job Quality
The paper discusses how AI's impact on worker power can influence broader economic trends:
Income Inequality: By shifting power towards employers, AI could contribute to widening income inequality. Workers may have less leverage to negotiate higher wages, leading to a larger share of economic gains accruing to capital owners.
Job Quality: AI-driven changes in the workplace can affect job quality, not just in terms of wages but also in terms of job satisfaction, autonomy, and working conditions.
Policy and Regulatory Considerations
Davis emphasizes the importance of policy responses to address the challenges posed by AI:
Labor Laws: There might be a need for new labor laws that protect workers from excessive surveillance and ensure fair wages.
Regulation of AI Tools: Ensuring transparency and fairness in AI algorithms used for hiring, management, and wage setting is crucial.
Support for Worker Voice: Strengthening collective bargaining rights and supporting worker organizations can help balance the power dynamics altered by AI.
Future Research Directions
Davis outlines several areas for future research to build on his findings:
Empirical Studies on Worker Power: More empirical research is needed to quantify AI's impact on worker power across different industries and job types.
Intersection with Other Trends: Investigating how AI interacts with other labor market trends, such as globalization and demographic changes.
Long-term Impacts: Studying the long-term implications of AI on labor markets, including potential shifts in employment patterns and career trajectories.
Key Quotes
"This paper surveys and reframes the economic literature on AI and labor, detailing how AI might alter worker power and outlining potential consequences for income distribution and job quality."
"To help clarify the theoretical effects of AI on work and workers, I draw a distinction between the labor demand impacts and the worker power impacts of new technologies."
"AI tools that alter the management and organization of work, or job context, affect workers through their potential to shift the division of rents or surplus."
"AI may affect worker power, for example, by disrupting norms or creating information asymmetries."
"AI-driven predictive analytics in wage offer and bargaining provide more scope for discriminating monopsony, or paying each worker as close as possible to their reservation wage."
"New technologies may affect the bargaining positions of firms and workers by raising the threat of automation or limiting workers’ outside options."
Translating Wall Street Jargon - A Wealth of Common Sense
In the article "Translating Wall Street Jargon," Ben Carlson humorously decodes various common phrases used in financial media and Wall Street, revealing the often hidden or ironic meanings behind them. He starts by pointing out the predictable nature of financial headlines, especially those involving "veteran forecasters," who are often confidently wrong. Carlson's main thesis is that financial jargon is frequently designed to sound sophisticated and authoritative, yet it often masks uncertainty, hedges bets, or disguises the speaker's lack of concrete knowledge. The article lists several phrases with their satirical translations, aiming to provide readers with a more realistic understanding of what these terms actually imply.
Key Points
Veteran Forecaster:
Translation: Someone who is supremely confident in their predictions but almost always wrong.
Explanation: Financial media favors certainty, even though forecasters are often unreliable.
Common Wall Street Phrases:
"I’m cautiously optimistic."
Translation: I have no idea what’s going to happen.
"We’re constructive on the stock market."
Translation: I wanted to say bullish but this is a way to both sound smart and hedge at the same time just in case I’m wrong.
"It’s trading at fair value."
Translation: I have no idea what this thing is worth so hopefully the market does.
"We’re a boutique investment firm."
Translation: We’re small and don’t manage very much money but we’d like to be bigger. Please give us money.
"It’s a proprietary trading system."
Translation: Everyone else on Wall Street uses this same model but calls it something different.
"This is a bubble."
Translation: I’m not invested in that asset that went up a lot.
"We’re the smart money."
Translation: We pay ridiculously high fees for “sophisticated” investment products.
"You’re being paid to wait in this stock."
Translation: The dividend yield is high for a reason. The stock stinks.
"This asset has an asymmetric risk payoff."
Translation: I’ve read The Big Short two-and-a-half times…okay I watched the movie once.
"The easy money has been made."
Translation: I didn’t make any of it.
"We’ll give you all of the upside without any of the downside."
Translation: This strategy is either going to blow up in spectacular fashion or get smoked during the next bull market.
"Sell in May and go away."
Translation: My research process relies exclusively on rhyming. I also buy when prices are high.
"Wall Street guru."
Translation: This guy wears a bow tie.
"We prefer to gauge performance over a full market cycle."
Translation: We are massively underperforming.
"I’m not wrong, just early."
Translation: I’m wrong but don’t think I won’t move the goalposts if I stay wrong.
"It’s a Ponzi scheme."
Translation: I disagree with that thing but don’t actually know what a Ponzi scheme really is.
"They predicted the 2008 financial crisis. Here’s why they say the next one will be even bigger!"
Translation: They also predicted a crisis in 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, etc. They got lucky, were “right” once, and have lived off that call ever since.
"I’m a contrarian."
Translation: Just like everyone else in finance.
"We’re waiting for the dust to settle."
Translation: We get fearful when others are fearful.
Discussion on Financial Media:
Observation: Financial media prefers certainty to nuance because it sells better.
Implication: This preference for certainty leads to the frequent appearance of veteran forecasters who are often wrong but confident.