LTCM Co-Founder on Risk, Leverage & Simplicity | Victor Haghani

Key Concepts

Alpha: Excess return of an investment relative to the return of a benchmark index.
Leverage: Using borrowed money to increase the potential return of an investment.
Idiosyncratic Risk: Risk specific to a particular company or asset, which can be diversified away.
Tax Efficiency: Structuring investments to minimize tax liabilities.
Broad Equity Market ETFs: Exchange-Traded Funds that track a broad index of stocks, offering diversification.
Real Estate (REITs): Real Estate Investment Trusts, which own, operate, or finance income-generating real estate.
Skin in the Game: Having a personal financial stake in an investment.
Expected Utility Theory: A framework for decision-making under uncertainty, aiming to maximize expected utility (satisfaction).
Risk-Adjusted Wealth/Return: Measuring investment performance relative to the risk taken.
Hedge Fund: An investment fund that pools capital from accredited investors or institutional investors and invests in a variety of assets, often with complex portfolio-construction and risk-management techniques.
Long-Term Capital Management (LTCM): A prominent hedge fund that collapsed in 1998 due to excessive leverage and concentrated risk.
Basis Trade: A strategy that seeks to profit from the difference in price between two related assets, often involving leverage.
Stop Losses: An order placed with a broker to buy or sell a security when it reaches a certain price.
Fat Tails: Refers to the tails of a probability distribution being heavier than those of a normal distribution, indicating a higher probability of extreme events.
Options Markets: Markets where options contracts are traded, giving the buyer the right, but not the obligation, to buy or sell an underlying asset at a specific price on or before a certain date.
Smile/Skew (in options pricing): Patterns in implied volatility that reflect market expectations of future price movements, often indicating a pricing of fat tails.
Extrapolative Return Estimation: Projecting future returns based on past performance.
Return Chasing: Investing in assets that have recently performed well, often leading to poor outcomes.
Indexing: Investing in a passive portfolio that tracks a market index.
Dynamic Index Investing: An investment approach that uses index funds as building blocks but dynamically adjusts asset allocations based on expected returns and risk.
Systematic Risk (Undiversifiable Risk): Risk that affects the entire market or a large segment of it, such as economic downturns or interest rate changes.
Risk Premium: The excess return an investor expects to receive for taking on a risky asset compared to a risk-free asset.
Contango/Backwardation (in commodity markets): The relationship between the price of a commodity for immediate delivery and its price for future delivery.
Capital Market Assumptions (CMAs): Forecasts of future investment returns, risks, and correlations for various asset classes.
Markowitz Efficient Frontier: A set of optimal portfolios that offer the highest expected return for a defined level of risk or the lowest risk for a given level of expected return.
Human Capital: The economic value of an individual's skills and abilities.
Correlation: A statistical measure that describes the extent to which two variables move in relation to each other.
Diversification: Spreading investments across different asset classes, industries, and geographies to reduce risk.
Trend Following: A trading strategy that aims to profit from established trends in financial markets.
Turtles Experiment: A famous experiment in the 1980s that demonstrated the effectiveness of a simple trend-following strategy taught to novice traders.
Long Volatility: Strategies that profit from increases in market volatility.
Debasement Hedge: An investment strategy aimed at protecting against the erosion of currency value due to inflation or government policies.
John Bogle: Founder of Vanguard and a pioneer of index investing.

Career Trajectory and Lessons Learned from LTCM

Victor Hagani's career began at Solomon Brothers in research before transitioning to the trading floor from 1984 to 1993. He was a co-founder of Long-Term Capital Management (LTCM), where he managed the London office until its collapse in 1998. After LTCM's failure, he stayed for over a year to help liquidate the portfolio and return capital to consortium banks.

The most significant lesson Hagani derived from the LTCM experience was the critical importance of "skin in the game" and a robust risk management framework. He admitted that at the time of LTCM's founding, he lacked a satisfactory framework for deciding how much of his and his family's savings to invest. His reasoning was primarily based on investing as much as possible in what he believed was the best investment available, while ensuring he wouldn't be financially ruined if it all went wrong. This approach, he now recognizes, was a "woefully partial analysis."

He learned that the conventional approach of maximizing expected utility, or risk-adjusted wealth/return, was largely absent from his decision-making process. This realization, gained through reflection and study years after LTCM's collapse, became the cornerstone of his later investment philosophy. He emphasizes that the most valuable, yet least discussed, lesson from LTCM was the need to make sound risk decisions, which led him and his partner James White to write a book focused on how much to invest in chosen assets, rather than just what to invest in.

Modern Industry Landscape: Leverage and Capital Deployment

Hagani contrasts the LTCM era with the current financial industry, noting that while LTCM was considered large with approximately $5 billion in capital, today's strategies deploy capital that is "two orders of magnitude bigger" (100 times larger). He observes that the amount of leverage in many current trades, particularly basis trades, remains similar to what was employed in the past to achieve good returns on capital after expenses and frictions.

While acknowledging positive adaptations like tighter stop-loss policies and greater liquidity management by successful alternative managers, Hagani expresses surprise at the scale of capital and similar leverage levels seen today. He recalls LTCM's balance sheet leverage being in the "low teens" (around 14%), which was not higher than that of investment banks at the time. He notes that the perceived high leverage after LTCM's collapse was a result of losing 90% of its capital, significantly increasing the leverage ratio on the remaining balance sheet. He speculates that balance sheet leverage in today's "pod shops" might be similar, with off-balance sheet activities also being a factor, though he believes the latter might be exaggerated due to offsetting positions common in past market conventions.

Quantifying Risk and the Problem of Extrapolation

Hagani believes that while the realization of fat tails in market movements is present (evidenced by the pricing of volatility skew in options markets since 1987), the more significant issue impacting markets is extrapolative return estimation. He argues that many investors base their expectations on recent history, leading to a powerful impact on markets. This "return chasing" is identified as a primary driver of poor investor returns and market dysfunction, more so than underestimating fat tails. He contrasts this with indexing, which he considers relatively benign, suggesting that extrapolators and return chasers are the source of market inefficiencies, not index funds themselves.

Elm Wealth's Approach: Dynamic Index Investing

Hagani's current investment philosophy, implemented at Elm Wealth, centers on dynamic index investing. This approach, while rooted in "evergreen kinds of ideas," is considered modern because of its unique implementation.

The "Aha Moment" for Index Funds

Around 2006-2007, Hagani decided to invest his family's savings primarily in index funds. This decision was driven by several factors:

Dissatisfaction with Alternative Investments: After attempting to emulate the Yale endowment model (investing in hedge funds, private equity, distressed debt) from 2000 to 2006, he felt financially burdened by the complexity, paperwork, and constant monitoring.
Tax Inefficiency of Alternatives: A conversation with his accountant revealed that many alternative investments were highly tax-inefficient for individual investors. This was due to non-deductible expenses and the generation of ordinary income and short-term capital gains, leading to significantly higher tax rates than expected.
Desire for Alpha without Idiosyncratic Risk or High Fees: Hagani concluded that the only ways to generate alpha were through leverage or concentrated risk. However, he believed that idiosyncratic risk, particularly in stocks, should not be expected to earn a risk premium. He also wanted to avoid high fees.

These factors led him to the conclusion that broad equity market ETFs and some real estate (REITs) were the most suitable building blocks for his portfolio.

Dynamic Asset Allocation

The "dynamic" aspect of Elm Wealth's approach involves determining how much to allocate to different asset classes. This is driven by:

Expected Returns: Estimating future returns for various asset classes.
Risk: Assessing the risk associated with each asset class.
Risk Aversion: Considering the investor's tolerance for risk.

This framework guides overallocations and underallocations to broad equity markets and other asset classes available in low-cost, tax-efficient formats.

Discretionary Macro Trading vs. Systematic Approaches

Hagani expresses skepticism about short-term discretionary macro trading, stating he was never good at it and never believed he would be successful. He conducted an experiment called the "Crystal Ball Challenge," where participants could see future news (from the Wall Street Journal) and trade stocks and bonds with leverage based on past prices. On average, participants, including successful macro traders, struggled to make money.

He notes that successful macro traders often limit their trades to specific days with high expected Sharpe ratios and predominantly trade bonds. He suggests that a key factor for successful macro traders has been aligning with trend following principles ("cut your losses early, let your profits run"), which has historically been a successful strategy. He contrasts this with the natural human tendency to let losses run and cut profits early.

Portfolio Construction: Building Blocks and Diversification

Hagani outlines a systematic approach to portfolio construction:

Screening for Investable Assets

Safe Assets: Prioritizes low-fee, liquid, and as safe as possible assets, primarily Treasuries.
Risky Assets:
- Must Pay a Risk Premium: The risk must be systematic (undiversifiable) and inherently painful when it occurs (e.g., broad stock market downturns). Idiosyncratic risk, which can be diversified away, is avoided. Zero-sum bets against others are also avoided.
- Estimable Expected Return: The expected return must be prospectively verifiable, not just theoretically present. This eliminates assets like oil or long-term government bonds where estimation is difficult. Broad equity markets, with their link to earnings, dividends, and buybacks, are amenable to estimation.
- Low Fees and Tax Efficiency: A strong preference for low fees and tax-efficient investments.

This screening process leads to broad equity market ETFs and some real estate (REITs) as the primary building blocks for risky assets.

Portfolio Sizing and Asset Allocation

Hagani dismisses static asset allocation models like the 60/40 portfolio or target-date funds as potentially "malpractice." He argues that asset allocation should be a function of:

Expected Return: The prospective return of an asset class.
Risk: The volatility or potential for loss.
Risk Aversion: The investor's tolerance for risk, which can change with wealth levels.

He believes that the relationship between expected return and variance is not constant, and therefore, asset allocation should dynamically adjust. He emphasizes that the optimal portfolio for an individual today might be different next month, though practical considerations like taxes and transaction costs influence the frequency of changes.

Diversification Strategy

Hagani defines good diversification as the maximum amount of diversification achievable at a low fee. He advocates for owning as many assets as possible at the lowest possible cost.

Broad Market ETFs: Owning ETFs that cover large-cap, mid-cap, and small-cap stocks (e.g., VTI, VXUS) for a few basis points is considered excellent diversification.
Avoiding High-Fee Diversifiers: He would avoid assets with high fees (e.g., 30 basis points for preferred stock ETFs, or significantly higher for private credit/equity) if the diversification benefit is marginal.
Systematic Risks Beyond Beta: While acknowledging other systematic risks like inflation risk, he emphasizes the need to verify compensation for these risks. He is hesitant to invest based solely on belief without quantifiable evidence of a risk premium, citing concerns about how inflation risk or oil risk premiums are priced. He prefers to see and verify these premiums rather than relying on models like risk parity without empirical validation.

Exotic Diversification and Other Strategies

Trend Following: Hagani likes trend following for its potential negative correlation with beta and performance in bad markets. However, he finds it difficult to access at low cost and tax-efficiently, and he cannot easily verify its expected return.
Gold: He views gold as similar to oil – a large asset that likely has a risk premium but is difficult to analyze. While acknowledging its potential as a safe haven, he believes its impact on a portfolio would be minimal due to its limited allocation.
Long Volatility (e.g., VIX): Hagani expresses caution regarding long volatility strategies. He fears that if VIX becomes widely recognized as a hedge, its expected return could become negative. He finds it difficult to estimate the prospective return of VIX positions and prefers to own less equity rather than engage in VIX trading. He prioritizes simplicity in his investment approach.

Conclusion: The Best Strategy is the One You Stick With

Hagani concludes by referencing John Bogle's sentiment: "The best investment strategy is the one you're going to stick with." He believes that an investment approach resonates deeply and makes fundamental sense to an individual, they are more likely to adhere to it, especially during challenging market conditions. He warns against investing solely based on past performance, as this can lead to "return chasing" and underperformance.

For those interested in Elm Wealth's approach, Hagani directs them to elmwealth.com for research and games, and elmfunds.com for information on their low-cost, diversified, dynamic index investing ETF (ticker: ELM, the L Market Navigator Fund). He is also active on LinkedIn.