Understanding Long Term Capital Management Failure Using Minsky Model

Long Term Capital Management (LTCM), a hedge fund, was an investment structure for managing a private, loosely regulated investment pool that invested in both cash (physical securities) and derivative markets on a leveraged basis. My essay analyzes the failure of LTCM in September 1998 using the Hyman Minsky model and provides recommendations for policy makers to avert similar future crisis.

According to a Wikipedia entry “Minsky proposed theories linking financial market fragility, in the normal life cycle of an economy, with speculative investment bubbles endogenous to financial markets. Minsky stated that in prosperous times, when corporate cash flow rises beyond what is needed to pay off debt, a speculative euphoria develops, and soon thereafter debts exceed what borrowers can pay off from their incoming revenues, which in turn produces a financial crisis. As a result of such speculative borrowing bubbles, banks and lenders tighten credit availability, even to companies that can afford loans, and the economy subsequently contracts.”

LTCM is a case study in exceptions — extreme, undiversified portfolio exposures in extraordinary market conditions. It did not, and does not, represent the hedge fund industry in general. The main reason for LTCM’s debacle was a lack of control of liquidity risk. Hedge funds can fail either because they are insolvent, or because an aggregate shortage of liquidity can render them insolvent. LTCM’s failure could itself have caused liquidity shortages leading to a cascade of failures and a possible total meltdown of the system. This risk of contagion could lead to a contraction in the common pool of liquidity. Therefore, there is a possible role for government intervention. Unfortunately, liquidity problems and solvency problems interact, and can each cause the other. It is therefore hard to determine the root cause of a crisis from observable factors. 

LTCM was founded in 1994 by John Meriwether, a former Salomon Brothers trading star, along with a small group of associates including Nobel Laureates Robert Merton and Myron Scholes. It became an immediate success. By the end of 1997 it had achieved annual rates of return of around 40 percent and had nearly tripled its investors’ money. 

The main strategy implemented was fixed income arbitrage. It is a market neutral hedging strategy that seeks to profit by exploiting pricing inefficiencies between related fixed income securities while neutralizing exposure to interest rate risk. Managers attempt to exploit relative mispricing between related sets of fixed income securities. One example is to long relatively cheap off-the-run treasury bonds and short relatively expensive on-the-run treasury bonds of the same maturity at the same time. Supposedly both bonds will have the same future cash flows and their price will converge and the arbitrager can make a profit when that happens. Normally the spread between on-the-run and off-the-run treasury bonds are small, so LTCM used leverage to amplify the returns. By early 1998, the leverage ratio (asset-to-equity ratio) reached 16:1, which is extremely high.

First, in terms of the Minsky model, there was an exogenous shock when Russia defaulted and there was a flight to quality – to mostly on the run US Treasury bonds. In the summer of 1998, the market deteriorated and LTCM began to suffer losses in July. One month later, Russian government devalued the ruble and declared a moratorium on future debt repayments. Those events led to a major deterioration in the creditworthiness of many emerging-market bonds and corresponding large increases in the spreads between the prices of Western government (especially US treasury bonds) and emerging market bonds. And immediately a massive “flight to quality” by general investors ensued, with investors flooding out of any remotely risky market and into the most secure instruments. Trades that were expected to converge did not do so. 

In terms of Hyman Minsky’s model, the boom created by the profit opportunities after the shock is fed by increasing money supply. LTCM’s fund presented new opportunities for even higher returns. Actually, it meant an opportunity for LTCM if it did not have the high leverage they were using. Given panicking investors and “flight to quality”, the most secure instruments’ prices were driven artificially high and the spread became larger as the models would suggest. If the market prices finally converged to the underlying value, the wider initial spread would indicate higher profit. So even though LTCM’s portfolio was down 44 percent for the month of August, there was potential opportunity for it to recover. It led to speculation that initially had positive feedback; speculators earned money and invested more, which encouraged more people to invest. In a letter CEO Meriwether wrote “On the other hand, we see great opportunities in a number of our best strategies, and these are being held by the Fund”. He also reminded that investors to keep in mind that the Fund’s relative-value strategies may require a long convergence horizon.

Minsky emphasized the inherent instability of the credit system and attaches importance to the role of debt structures in causing financial difficulties. This happened in the LTCM case for it was highly leveraged. After having had returned capital to its limited partners its leverage ratio went even higher to fund the acquisition of speculative assets for subsequent resale. LTCM’s deterioration was dramatically exacerbated by its high leverage ratio. By early September, the leverage ratio had climbed to 45:1. Considering high volatility due to the market instability, that’s an extremely high number by all standards.

Minsky stresses the role of pure speculation and excessive gearing. LTCM often took the opposite ends of the trade and was a provider of liquidity to the market. Minsky’s model is limited to single country, but in this case speculators were overtrading in London and other overseas markets too. The panic fed it until the prices became very low and trades were cut off. The actions of distressed parties attempting to reduce the size of their balance sheets had an impact on the value of others’ assets. Weakened balance sheets generated further forced sales, feeding the vicious circle. The liquidity squeeze generated by such forced sales exacerbated the crisis.

The models used by LTCM, although complicated, did not give liquidity risk the importance it deserved. Two bonds might have the same underlying cash flows, yet they can be priced differently exactly because they have different liquidity risk. So in the short run, the market value doesn’t simply reply on fundamental values. Most of the assets LTCM longed were illiquid and the assets they shorted were liquid ones. As “flight to quality” happened, more liquid assets were priced even higher and the spread widened instead of narrowed as the long-run expectation. Because of consecutive losses, the Fund had increasing difficulty meeting margin calls and needed more collateral to ensure that it could meet its obligations to counterparties.

The Fund was running short of high-quality assets for collateral to maintain its positions, and it also had great difficulty liquidating its positions: many of its positions were relatively illiquid (i.e., difficult to sell) even in normal times and hence still more difficult to sell—especially in a hurry—in nervous and declining markets. To preserve and raise the cash needed for operation, LTCM limited investors’ withdrawals and encouraged them to put more cash into the Fund. But it did not get the positive response that it much needed from the investors. The situation continued on deteriorating in September, and the Fund’s management spent another three weeks looking for assistance in an increasingly desperate effort to keep the fund afloat. However, no immediate help was forthcoming, and by September 19 the fund’s capital was down to only $600 million. 

Hedge funds should continue to have the flexibility they have for they bring liquidity, take speculative positions, serve as diversification classes and bring efficiency to the markets. The case for regulating hedge funds and possibly prevent another LTCM debacle should be seen from a perspective of a) protecting consumers b) protecting market integrity c) preventing systemic risk.

Since hedge funds are allowed to solicit investments only from high net worth individuals, protecting retail consumers is not a very important issue. However, the regulatory framework should ensure that hedge funds do not abandon the risk profile investors have been led to expect. While it is unjustified to ask for full disclosure as mutual funds do, more transparency, especially in explaining the risk involved, should be required. Hedge fund managers should disclose the underlying risks involved for each specific fund and explain the driving forces of the risks so that investors are better prepared to understand and manage their portfolio risk. In this case had LTCM provided more disclosure to its investors on the nature of its trades, its leverage ratios etc. it might have led to greater confidence in its operations

LTCM was able to borrow such large sums of money and leverage itself up by using collateral to borrow. It bought more assets, and these were used as collateral to borrow more money perpetuating a vicious cycle. This could have been prevented if there was one coordinated agency keeping track of its leverage. Many prestigious banks and influential individuals had lent to LTCM. Government policy can be enacted to monitor the interconnection between banks, hedge funds and other participants.

In the LTCM case we see that potential losses need not have arisen from direct credit exposures to the LTCM, but from the proprietary trading positions of banks, similar to those of the LTCM. This made the creditor banks similarly exposed to market movements that would have followed a forced liquidation of the LTCM. Government regulation were enacted after the global financial crisis to set limits on proprietary trading positions. 

With the wisdom of hindsight subsequent waves of crisis could have been prevented from happening was by acute awareness that there are limits to how much risk can be hedged away. Aggregate risk is there in the financial system even though each individual hedge fund may have hedged its own risk away. The risk as we see in LTCM’s case is increased co movement of prices, increased correlation between credit risk and counterparty risk.

Lastly liquidity risk can have a devastating effect on a highly leveraged financial system. As LTCM attempted to dispose its own assets, the negative price impact of this action impacted on the balance sheets of all others. First of all, LTCM’s counterparties would incur direct losses as their contracts remained unfulfilled. Secondly, and this was a much more serious threat, a disorderly unwinding of LTCM’s positions would have led to an even stronger downward movement of asset prices, which would affect even those banks who did not maintain direct relationships with LTCM. These dangers were amplified by the fact that many other firms had followed very similar strategies as LTCM and were, thus, subject to the same risks. And as per Minsky’s Model the market stabilizes after intervention from a lender of last resort intervenes (the Fed here) or orchestrates a resolution. When the New York Fed organized LTCM’s rescue by its creditors, it was for fears of systemic repercussions, rather than bailout perse.

Do Illiquidity Provisions Boost Hedge Fund Performance?

Retail investors who are ineligible to invest in quality hedge funds, in hard to access investment vehicles, either because they do not meet minimum levels of wealth or income standard, or the high subscription amounts often needed to participate in such funds gravitate to liquid alternative investments. The rise of liquid alternative investment funds, packaged in mutual fund formats over the past years, as the fastest growing category of “alternative investments” is now well documented. McKinsey & Company suggest “retail alternatives will be one of the most significant drivers of U.S. retail asset management growth over the next five years, accounting for up to 50 percent of net new retail revenues”.

With liquid alternatives beginning to find increasing traction in institutional portfolios too, the question is – are they an effective substitute for hedge funds and other illiquid structures? Little empirical fact based exists and opinions abound.

Our research at ActiveAllocator.com attempts to answer this question and concludes that they are not.

Retail investors in considering hedge funds immediately encounter a wide variety of strategies, organizations and structures. Indeed, hedge funds, rather than being an asset class, are broadly a collection of governance structures and investing techniques with many common structural features. Unfortunately, from an investor’s perspective, it is often difficult to decipher which structural features are useful, which imply tradeoffs and which are simply undesirable. The dearth of empirical research leaves investors to make intuitive judgments about what features they should favor.

We re-examined a sub-set of provisions governing fund investing- liquidity terms. In contrast to alternative mutual funds, which allow investors to redeem their holdings on a daily basis with little or no advance notice, hedge fund investors are subject to a variety of terms that may restrict their ability to access their capital. All things equal, investors prefer more liquid investments to less liquid investments. Liquidity provides investors with a valuable option – specifically the opportunity to trade in and out of investments in order to rebalance a portfolio, respond to unforeseen cash flow requirements or redeploy capital towards other opportunities.

While the benefits that liquidity offers to investors are clear, the costs associated with greater liquidity are less apparent. We explored two related questions:

•Do investors pay a price – in terms of lower investment returns – for better liquidity? In other words, do funds with more favorable liquidity terms underperform less liquid funds?

•If the answer to the question is yes, then what drives this cost? In other words how can one explain the under-performance of more “liquid” funds?

Our research finds that there has been a substantial performance cost from offering increased liquidity. The under-performance cannot be attributed to fee levels or the strategy pursued by a particular fund. We are unable to attribute the liquidity cost to differences in skill across managers; we do not find strong evidence that less skillful managers (whose performance is weaker) offer more attractive liquidity terms.

Instead, our results indicate that managers who offer more restrictive liquidity terms are able to outperform more liquid managers because they are able to pursue a broader range of attractive trading opportunities.

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Do Illiquidity Provisions Boost Performance

ActiveAllocator Lets You Specify Turnover

Depending on portfolio turnover, execution costs often have significant negative impact on a portfolio’s or a trading strategy’s profitability. There is often a trade-off between paper profits from a trading strategy and portfolio turnover: lower-frequency strategies with lower turnover tend to have lower paper profits (without considering costs) and higher-frequency strategies with higher turnover tend to have higher paper profits. For example, traditional value signals such as book to price ratio have very low turnover—often less than 25% annually—and short-term mean reversion strategies have much higher turnover—often as high as 25% daily. For strategies with high turnover, reducing trading costs is crucial. A strategy with a daily turnover of 25% and an assumed trading cost of 10 basis points requires 12.6% annualized before-cost return to break even. High trading costs are the reason why some apparent anomalies are hard to monetize and continue to exist.

Our overall approach has been to choose factors that have good performance and consistency in predicting portfolio returns balanced against turnover.

By design, some factors change slowly from one period to the next, whereas others change rapidly. For example, the size factor stays stable over months or even years. Therefore, the single-factor portfolio constructed using size factor has low turnover. In contrast, values of a short-term reversal factor change rapidly from day to day (if the signal works, recent losers become winners and recent winners become losers). Therefore, a single-factor portfolio constructed using a short-term reversal factor has high turnover. Since the turnover depends on how we use a factor to construct a portfolio, there is no standard approach to estimate turnover. One approach is to directly look at a zero-investment portfolio, e.g., the top quintile minus the bottom quintile, to calculate portfolio turnover. A different approach, independent of portfolio construction, measures the serial correlation of factor scores: the correlation between factor scores of the stocks at t and the factor scores of the same stocks at t + 1. Higher correlation means that factor scores are more stable over time and indicates lower factor turnover.

Factors with high turnover need to be, and sometimes are, compensated with higher predictive power. If we have a reasonable estimation of trading costs, they can be directly incorporated to estimate after-cost returns and information ratios. Mediocre predictable power and high turnover, however, do not automatically make the factor a poor signal. Investors usually use multiple factors in the portfolio construction and the final turnover depends on the interaction of the selected factors.

The hysteresis approach is a valuable tool for reducing turnover as well. After we enter a long position when the stock moves into the top quintile, we do not necessarily exit the position when it moves out of the top quintile. Instead, we only exit the position if the stock falls below the 60th percentile. Although the expected alpha is lower when the stock falls from the top quintile to the fourth quintile, holding the existing long position does not require extra trading and does not incur trading costs. Similarly, after we enter a short position when the stock moves into the bottom quintile, we only exit the position if the stock moves above the 40th percentile. By using different thresholds for entry and exit, we reduce the turnover of fast-moving signals and achieve a better trade-off between raw returns and trading costs.

This is just one of hundreds of active management approaches we use at ActiveAllocator.com to create better portfolios.