October 2024 Newsletter

“Ideology is the antithesis of reason.”
McCartney summarizing Charlie Munger

“You see, one thing is, I can live with doubt and uncertainty and not knowing. I have approximate answers and possible beliefs and different degrees of certainty about different things… It doesn’t frighten me.”
Richard Feynman

The efficient market hypothesis is just “a model”, Fama stresses. “It’s got to be wrong to some extent.” “The question is whether it is efficient for your purpose. And for almost every investor I know, the answer to that is yes. They’re not going to be able to beat the market so they might as well behave as if the prices are right.”
Gene Fama Interview, Financial Times, 8/30/2024

The third quarter was another nice quarter for the markets. Below are the indices we track.

Data Series	YTD	3 Months	1 Year	5 Years	10 Years	1/1999 to 9/2024
Russell 3000	20.63%	6.23%	35.19%	15.26%	12.83%	8.29%
S&P 500	22.08%	5.89%	36.35%	15.98%	13.38%	8.17%
Russell 2000	11.17%	9.27%	26.76%	9.39%	8.78%	8.12%
Russell 2000 Value	9.22%	10.15%	25.88%	9.29%	8.22%	8.69%
MSCI World ex USA	13.10%	7.76%	24.98%	8.36%	5.68%	4.98%
MSCI World ex USA Small Cap	11.53%	10.45%	23.36%	6.85%	5.99%	7.44%
MSCI Emerging Markets	16.86%	8.72%	26.05%	5.75%	4.02%	7.96%
Bloomberg U.S. T Bond 1-5 Years	4.17%	3.43%	7.51%	1.24%	1.46%	2.90%
ICE BofA 1-Year US T Note	4.01%	2.03%	5.87%	1.99%	1.55%	2.32%

Our friends at Dimensional summarized the 3d quarter as follows:

”US stocks built on a strong first half, with many market indices at or close to record levels as the third quarter neared an end. But those gains came amid a spike in volatility unseen since the COVID pandemic. Fulfilling expectations that had been building for months, the US Federal Reserve in September cut interest rates—another thing investors hadn’t seen since 2020—as core inflation eased. Developed equity markets outside the US rose, and emerging markets were slightly higher for the quarter. In the bond market, US Treasuries posted price gains, sending the benchmark 10-year yield below 4%.”

Playing with Artificial Intelligence

I decided to do something different for this newsletter. I took 11 very influential academic papers and articles (which I will list at the end of this newsletter with appropriate links, in case anyone wants to dig further), and I uploaded those into the artificial intelligence “AI” engine ChatGPT. ChatGPT allows you to upload research. I then posed the following query to ChatGPT.

“Can you summarize the lessons from the uploaded papers for investors, including how to structure a portfolio for the long-term? Please write the memorandum combining the knowledge contained in the papers uploaded, and give a good argument why artificial intelligence will not lead to improvements in investing based upon the knowledge contained in the papers.”

I hope you enjoy it. It was illuminating how quickly the memorandum was prepared, and I do believe ChatGPT did a good job of summarizing the papers. We are always happy to discuss further. I included the most cited paper ever in the history of finance, Portfolio Selection, by Harry Markowitz, as well as several papers by Eugene Fama, who is considered the possibly the most cited Finance professor ever because of multiple papers, a list of which you can find here. Eugene Fama vitae One may quibble with the papers uploaded, but it is hard to argue against Markowitz, Fama, Sharpe, Black (of Black-Scholes fame who would have won a Nobel prize if he was alive) and the other authors uploaded, as well as their contribution to finance and investing. I am always willing to listen to counterpoints.

I did slant my question against AI. Next quarter, I will ask the same tool what it envisions as the benefits of AI for investing now and in the future. Remember, this is ChatGPTs output regarding the papers uploaded at the end of this newsletter, and not mine or Emily’s. However, I have to admit that the advice is sound.

Memorandum: Lessons for Long-Term Investors and AI in Investing

Key Insights on Long-Term Portfolio Structuring

Embrace Passive Management: Fama and French’s research repeatedly shows that, after accounting for costs, active management generally underperforms passive strategies. Active management is often a zero-sum game, where winners and losers offset each other, and once fees are included, most funds fail to outperform passive benchmarks. Sharpe’s “Arithmetic of Active Management” further reinforces this, showing that after costs, the average actively managed dollar performs worse than a passive dollar. Therefore, a long-term investor should focus on a low-cost, passively managed, diversified portfolio to maximize returns over time.
Diversification is Essential: Jim Davis emphasizes that effective diversification significantly reduces portfolio risk. His work shows that increasing the number of stocks in a portfolio to a very high level (up to 11,000 stocks) lowers portfolio volatility to market levels. Building a globally diversified portfolio, across asset classes and geographies, helps mitigate the risk of underperformance from any single asset class.
Incorporate Value and Momentum Factors: Asness et al.’s research on “Value and Momentum Everywhere” highlights the consistency of value and momentum return premia across asset classes. These factors can be complementary, offering diversification benefits even within factor investing. Combining value (buying undervalued stocks) and momentum (buying stocks with strong recent performance) can yield a balanced portfolio that performs well across various market conditions, reducing risk and improving long-term returns.
Consider Profitability as a Core Factor: Novy-Marx’s work introduces profitability, measured by gross profits-to-assets, as a factor that rivals book-to-market in predicting returns. Profitable firms tend to outperform less profitable ones, and controlling for profitability improves the performance of value strategies, especially for larger stocks. A long-term investor should look to incorporate this factor into their portfolio for an additional performance edge.
Market Efficiency and Random Walk Hypothesis: Fama’s work on market efficiency suggests that markets reflect all available information, making it hard for investors to consistently beat the market. Similarly, Fischer Black’s analysis of the Random Walk Hypothesis supports the idea that stock prices move randomly, making market timing ineffective. Therefore, attempting to time the market is generally futile for long-term investors.
Stick to a Buy-and-Hold Strategy: Studies show that active trading not only incurs costs but often leads to lower performance. Long-term investors should adopt a buy-and-hold strategy with periodic rebalancing to maintain their target asset allocation. This minimizes transaction costs and takes advantage of compounding over time.

Argument Against AI Improving Active Investing

While artificial intelligence (AI) holds promise in many fields, there are fundamental reasons why AI is unlikely to dramatically improve active investing returns in the long run:

Active Management’s Structural Limits: According to Sharpe’s arithmetic and the research by Fama and French, active management remains a zero-sum game. AI’s advanced data analysis may help some managers make better predictions, but it cannot change the underlying market dynamics where the outperformance of some investors is balanced by the underperformance of others. AI cannot alter the cost of active management, and after fees, active strategies will still likely underperform passive strategies over the long term.
Efficiency of Markets: AI may process vast amounts of data quickly, but markets are highly efficient, especially in developed economies. The Efficient Market Hypothesis (EMH) suggests that prices already reflect all available information. AI might detect short-term pricing inefficiencies, but these are likely small, fleeting, and eroded by competition. Furthermore, transaction costs and market impact from frequent trading can diminish any small advantage AI might generate.
Behavioral and Liquidity Risks: As shown by Asness et al., value and momentum strategies perform consistently across various asset classes because of fundamental economic drivers like liquidity risk. AI could exacerbate these risks, particularly in times of market stress, as many algorithms may trigger similar trades simultaneously, amplifying market volatility and reducing the benefits AI might offer in normal market conditions.
Complexity of Financial Markets: AI might improve short-term predictions, but financial markets are influenced by complex, unpredictable macroeconomic factors, geopolitical events, and human behavior. As demonstrated in the studies on market anomalies and behavioral biases, these factors are difficult to model, even with advanced AI. Human oversight and judgment will still be necessary to interpret these broader market influences.

In conclusion, while AI might provide some incremental improvements in certain aspects of portfolio management, the fundamental advantages of passive investing, diversification, and long-term buy-and-hold strategies—rooted in decades of academic research—suggest that AI will not significantly alter the structural limitations of active investing. Therefore, for most investors, a well-constructed, diversified, and passive portfolio remains the optimal strategy for long-term success.

Recommendations for Portfolio Construction:

Use Passive Index Funds: Invest primarily in low-cost index funds across various asset classes.
Diversify Globally: Include a mix of equities, bonds, and potentially alternative assets (e.g., REITs).
Incorporate Factors: Consider adding value, momentum, and profitability strategies within the passive framework.
Stick to Long-Term Investing: Rebalance periodically, avoid market timing, and stay invested through market cycles.

This approach maximizes your chances of achieving consistent, risk-adjusted returns while minimizing costs—key tenets of sound long-term investing.

Papers Referenced

Luck versus Skill in the Cross-Section of Mutual Fund Returns – Eugene Fama and Ken French

The Arithmetic of Active Management – William Sharpe

The Loser’s Game – Charles Ellis

Portfolio Selection – Harry Markowitz

Value and Momentum Everywhere – Asness, Moskowitz and Pedersen

Implications of the Random Walk Hypothesis for Portfolio Management – Fischer Black

The other side of value: The gross profitability premium – Robert Novy-Marx

Dissecting Anomalies with a Five-Factor Model – Eugene Fama and Ken French

Are Markets Efficient? | Chicago Booth Review

Embrace Passive Management Already | Chicago Booth Review – Eugene Fama