In corporate finance, we examine how a business decides what investments to take (the investment decision), how much to borrow to fund these investments (the financing principle) and how much to return to stockholders (the dividend principle), if it wants to maximize its value. Traditional corporate financial prescriptions on each of these dimensions assume that both capital and asset markets are liquid. Introducing illiquidity into the process changes the game in significant ways.
a. Investment Principle: In most corporate finance books, the capital budgeting chapters wend their way through familiar territory. The best decision rule for investment analysis for a value-maximizing firm is the NPV rule and firms should accept all positive net present value investments. Within the NPV rule, you estimate expected cash flows on each investment and discount these cash flows back at a risk-adjusted rate. The expected cash flows are assumed to be available to the firm (to reinvest elsewhere or to pay dividends) and the risk adjusted for in the discount rate is macroeconomic or market risk.
How would introducing illiquidity alter this process? First, illiquid capital markets limit access to external funds (from both equity and debt) and may act as an impediment to taking every positive NPV investment. Second, if not all positive net present value investments can be accepted, the firm is better off getting the most bang for the buck. Thus, using a percentage return such as IRR to judge investments, instead of NPV, may allow a firm to generate the most value. Third, not all cash flows are equally liquid. Firms can be restricted in their use of cash flows, if they face regulatory or lender-imposed constraints or invest in countries with remittance restrictions. Finally, the discount rates (costs of equity and capital) will be higher for firms, if there is illiquidity, since there will be transactions costs associated with raising money. Firms facing more liquidity constraints are therefore less likely to take longer term infrastructure investments, with large negative cash flows up front and positive cash flows on the back end.
b. Financing Principle: The optimal mix of debt and equity for a firm is the one that maximizes its value. If we hold operating cash flows fixed, this is usually achieved when the cost of capital is minimized. In conventional corporate finance, then, the optimal financing mix is the one that minimizes the overall cost of capital, with neither the cost of equity and debt reflecting liquidity concerns. In the APV approach, it is the dollar debt level that maximizes value, after taking into consideration the tax benefits of debt and expected bankruptcy costs.
Using both the cost of capital and APV approaches, bringing in illiquidity into the equation will alter the dynamics. Illiquidity will push up both the costs of debt and equity and the effects on the optimal debt ratio will then depend on whether the equity or the debt market is more illiquid. If the equity market is illiquid and the debt market (bonds or bank loans) is liquid, the optimal debt ratio will rise in the face of illiquidity. In contrast, if the equity market is liquid and the debt market is not, the optimal debt ratio will fall as liquidity concerns rise. In the APV approach, illiquidity will raise both the probability of bankruptcy (since distressed firms will be unable to raise new financing to keep going) and the cost of bankruptcy (since assets will have to be sold at much bigger discount in an illiquid asset market). The net effect should be a decline in the use of debt by illiquid firms.
c. Dividend policy: There are two big questions that animate the dividend principle: How much cash should you return to stockholders (and how much should you hold back)? What form should you return the cash in, dividends or stock buybacks? In conventional corporate finance, firms are advised to return any cash that they have no use for in the current period back to stockholders, since it is assumed that they can return to liquid capital markets and raise new funding. It follows that cash balances should be minimal. And the form in which cash gets returned will be a function of investor taxes. If dividends and price appreciation are taxed at the same rate, investors should be indifferent between the two. If dividends are taxed more highly, firms should use stock buybacks.
Introducing illiquidity into this decision changes the answers to both questions. Firms that are more concerned about illiquidity should return less cash to stockholders and hold back more cash. Thus, you would expect cash balances to be higher at small and emerging market companies or during liquidity crises. The evidence seems to back this proposition. Investors faced with illliquid markets will value dividends more, simply because they represent cash in hand, whereas price appreciation is more risky (since you have to sell your stock to get it). Consequently, you should expect dividends to rise in the face of higher illiquidity, while stock buybacks to fall off.
In summary, you should expect firms in illiquid market to invest less in long term projects, to use less debt to fund these investments and to accumulate more cash, while paying out more in dividends.
I have a paper on the effects of illiquidity on financial theory, where I look at the implications for corporate finance in more detail:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1729408
Wednesday, December 29, 2010
Monday, December 27, 2010
Making money off illiquidity: Two Strategies
At first sight, illiquidity is bad news for investors, since it gives rise to transactions costs, which, in turn, can lay waste to investment strategies. In a post from a few months ago, I examined how transactions costs can explain why so many strategies that look good on paper don't deliver their promised upside.
However, in this post, I want to take the "glass half full", optimistic view of the phenomenon. Illiquidity or potential illiquidity is not all bad news for investors. After all, to beat the market, you have to have an edge over other investors and here are two competitive advantages that can be created out of illiquidity.
a. Illiquidity arbitrage: Not all investors value liquidity equally. To the extent that you need or care about liquidity less than the typical investor in the market, you should be able to exploit this difference to make money. How? Wait for a period of illiquidity (either on the entire market or on an individual stock), where asset prices are marked down by typical investors, who observe the illiquidity and price it in. Then, step in and offer to buy assets. You will get these assets at a bargain price (from your perspective) but at a fair price (from the perspective of the median market participant). Wait for the illiquidity to ease and then sell the asset. This may very well be the biggest weapon that an old-time value investor brings to the market. In fact, Warren Buffet did exactly this type of bargain hunting during the banking crisis of 2008, taking large positions in Goldman Sachs and GE, during their most illiquid days. (I know... I know.. technically, this strategy is not arbitrage, since it is not riskless.. )
"This is easy. I too can be a liquidity arbitrageur", you may say, but it is easier said than done. There are two factors that are at least partially under your control. The first is the use of financial leverage in your investment strategy. Borrowing money to fund investments may increase your expected upside, if things go well, but it also increases your need for liquidity. The second is a combination of patience and a strong stomach. Buying during periods of illiquidity will expose you to down side risk, at least in the short term, and you have to be able to ride it out. But the desire for liquidity is also a function of factors that are not in your control. First, if your income stream is stable, predictable and in excess of your spending needs (Do you have tenure?) and you have have less need for liquidity. Second, it is subject to what I will loosely term "acts of God". A sudden illness, accident or unforeseen event (Did you invest with Bernie Madoff?) may quickly eliminate whatever buffer you thought you had. Third, if you manage other people's money, it is their need for liquidity that will drive your decisions, not your own. It is one significant advantage that you and I have on the most skilled portfolio manager.
b. Illiquidity timing: Both the level of illiquidity and the price demanded for it change over time in the market. An investor who can forecast changes in illiquidity well can profit off these changes. But how does forecasting liquidity translate into a payoff? You have to be able to shift into liquid assets, before the market becomes illiquid, and into illiquid assets, ahead of periods of liquidity. With the former action, you cut your losses and with the latter, you gain as the illiquid asset regain their value. This is particularly true, if you use financial leverage and invest in illiquid assets, as many hedge funds do. In fact, one study argues that liquidity timing may be one of the biggest competitive advantages in the hedge fund business.
How do you get to be a good liquidity timer? First, you have to track not just the standard investment measures - multiples and fundamentals - but also liquidity measures - trading volume, short selling and bid-ask spreads. In fact, those technical indicators that fundamentalists view with such contempt, such as trading volume and short sales, may be useful in detecting shifts in liquidity. Second, you have to be clear about how exposed an individual asset is to market shifts in liquidity - a liquidity beta, so to speak. In my extended paper on liquidity (linked below), I describe ways in which you may be able to estimate this beta.
For more on liquidity betas and the potential for making money off illiquidity, you may want to look at this paper that I just posted on liquidity and its effects on financial theory and practice:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1729408
However, in this post, I want to take the "glass half full", optimistic view of the phenomenon. Illiquidity or potential illiquidity is not all bad news for investors. After all, to beat the market, you have to have an edge over other investors and here are two competitive advantages that can be created out of illiquidity.
a. Illiquidity arbitrage: Not all investors value liquidity equally. To the extent that you need or care about liquidity less than the typical investor in the market, you should be able to exploit this difference to make money. How? Wait for a period of illiquidity (either on the entire market or on an individual stock), where asset prices are marked down by typical investors, who observe the illiquidity and price it in. Then, step in and offer to buy assets. You will get these assets at a bargain price (from your perspective) but at a fair price (from the perspective of the median market participant). Wait for the illiquidity to ease and then sell the asset. This may very well be the biggest weapon that an old-time value investor brings to the market. In fact, Warren Buffet did exactly this type of bargain hunting during the banking crisis of 2008, taking large positions in Goldman Sachs and GE, during their most illiquid days. (I know... I know.. technically, this strategy is not arbitrage, since it is not riskless.. )
"This is easy. I too can be a liquidity arbitrageur", you may say, but it is easier said than done. There are two factors that are at least partially under your control. The first is the use of financial leverage in your investment strategy. Borrowing money to fund investments may increase your expected upside, if things go well, but it also increases your need for liquidity. The second is a combination of patience and a strong stomach. Buying during periods of illiquidity will expose you to down side risk, at least in the short term, and you have to be able to ride it out. But the desire for liquidity is also a function of factors that are not in your control. First, if your income stream is stable, predictable and in excess of your spending needs (Do you have tenure?) and you have have less need for liquidity. Second, it is subject to what I will loosely term "acts of God". A sudden illness, accident or unforeseen event (Did you invest with Bernie Madoff?) may quickly eliminate whatever buffer you thought you had. Third, if you manage other people's money, it is their need for liquidity that will drive your decisions, not your own. It is one significant advantage that you and I have on the most skilled portfolio manager.
b. Illiquidity timing: Both the level of illiquidity and the price demanded for it change over time in the market. An investor who can forecast changes in illiquidity well can profit off these changes. But how does forecasting liquidity translate into a payoff? You have to be able to shift into liquid assets, before the market becomes illiquid, and into illiquid assets, ahead of periods of liquidity. With the former action, you cut your losses and with the latter, you gain as the illiquid asset regain their value. This is particularly true, if you use financial leverage and invest in illiquid assets, as many hedge funds do. In fact, one study argues that liquidity timing may be one of the biggest competitive advantages in the hedge fund business.
How do you get to be a good liquidity timer? First, you have to track not just the standard investment measures - multiples and fundamentals - but also liquidity measures - trading volume, short selling and bid-ask spreads. In fact, those technical indicators that fundamentalists view with such contempt, such as trading volume and short sales, may be useful in detecting shifts in liquidity. Second, you have to be clear about how exposed an individual asset is to market shifts in liquidity - a liquidity beta, so to speak. In my extended paper on liquidity (linked below), I describe ways in which you may be able to estimate this beta.
For more on liquidity betas and the potential for making money off illiquidity, you may want to look at this paper that I just posted on liquidity and its effects on financial theory and practice:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1729408
Sunday, December 26, 2010
Asset selection & Valuation in Illiquid Markets
In my last post, I looked at how the asset allocation decision can be altered by differences in liquidity across asset classes, with the unsurprising conclusion that investors who desire liquidity should tilt their portfolios towards more liquid asset classes. Assuming that you have made the right asset allocation judgment, how does illiquidity affect your choices of assets within each class? In other words, if you have decided to invest 40% of your portfolio in stocks, how does illiquidity affect which stocks you buy?
To select assets within each asset class, you can either value each one on its fundamentals (intrinsic valuation), compare its pricing to how similar assets are priced (relative valuation) or price it as an option (contingent claim valuation). In each case, illiquidity can affect value.
a. Intrinsic Valuation: There are many different intrinsic valuation approaches but they all share a common theme. The value of an asset is a function of its expected cash flows, growth and risk. In discounted cash flow valuation, for instance, the expected cash flows discounted back at a risk adjusted discount rate yields a risk-adjusted value. In conventional valuation, the expected cash flows are unbiased estimates of what the asset will generate each period and the risk adjustment is for non-diversifiable market risk (with equity) and for default risk (with debt). Nowhere in this process is illiquidity considered explicitly. Not surprisingly, we tend to over value illiquid assets.
So, how do you bring illiquidity into intrinsic valuation? There are two choices. The first is to estimate the risk adjusted value, using the conventional approach, and to then reduce this value by an illiquidity discount. That discount can be estimated by looking at on how the market prices illiquid assets. For instance, studies have looked at restricted stock (stock issued by publicly traded companies that cannot be traded by investors for one year after the issue), pre-IPO transactions (where co-owners sell their stake in the months prior to an announced IPO) and companies with multiple classes of shares traded on different venues (with different liquidity characteristics). These studies generally yield large discounts (25-50%) for illiquid assets and private company appraisers have generally used these studies to back up the use of similar discounts when valuing non-traded businesses. Perhaps, this approach can be extended to publicly traded companies.
The second is to adjust the discount rate for illiquidity, pushing it up for illiquid companies. The illiquidity premium added to the discount rate is usually estimated by looking at the past. In its crudest form, you can assume that the premium that small cap companies or venture capitalists have earned over the market (about 3-4% on an annual basis over the last few decades) is due to illiquidity and add that number on to the cost of equity of any "illiquid" company. In its more sophisticated version, the adjustment to the discount rate can be linked to a measure of illiquidity on the company - its turnover ratio, trading volume or the bid-ask spread. One study concludes that every 1% increase in the bid-ask spread pushes up the discount rate by 0.25%. Thus, the cost of equity for a stock with a beta of 1.20 and a bid-ask spread of $0.50 (on a stock price of $ 10), with a riskfree rate of 4% and an equity risk premium of 6% is:
Cost of equity = 4% + 1.20 (6%) + 0.25% (.5/10) = 12.45%
With both approaches, the value will decrease with illiquidity.
b. Relative Valuation: In its most common form, relative valuation involves screening the market for cheap companies, with one screen for pricing (low PE, low price to book, , low EV/EBITDA) and one or more for desirable fundamentals (high growth, low risk, high ROE). If you ignore illiquidity, your cheap stock portfolio will end up with a lot of illiquid stocks. The simplest way to incorporate illiquidity is to add it as a screen. Thus, in addition to screening for high growth and low risk, you could also screen for high liquidity (high float, high turnover ratios, low bid-ask spreads, high trading volume etc.). The tightness of the liquidity screen can then be varied to fit your liquidity needs as an investor.
c. Contingent Claim Valuation: All option pricing models are built on two principles: replication (where a portfolio of the underlying asset and a riskfree investment is created to have the same cash flows as the option) and preventing arbitrage (the replicating portfolio and the option have to trade at the same price) . Both principles require liquidity: you be able to trade the option, the underlying asset and the riskfree asset in any quantity and at no cost. Illiquidity in any one of these markets will throw a wrench into the process and cause the option pricing models to yield incorrect values, with the imprecision increasing with illiquidity. So, what are your choices for bringing illiquidity into the process? You can try to modify the models to incorporate illiquidity explicitly but option pricing models are complicated enough already and this adds an additional layer of complication. Alternatively, you can adjust the inputs into the option pricing model. My choice would be the underlying asset value (S): using a lower value for illiquid underlying assets will reduce the value of call options on those assets.
In summary, no matter which approach you use, illiquidity is not a neutral factor. The investments you make within each asset class will reflect both the illiquidity of the investment and your own liquidity needs (and preferences) as an investor.
I have a paper on the effects of illiquidity on financial theory, where I examine the effects of liquidity on valuation in more detail:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1729408
To select assets within each asset class, you can either value each one on its fundamentals (intrinsic valuation), compare its pricing to how similar assets are priced (relative valuation) or price it as an option (contingent claim valuation). In each case, illiquidity can affect value.
a. Intrinsic Valuation: There are many different intrinsic valuation approaches but they all share a common theme. The value of an asset is a function of its expected cash flows, growth and risk. In discounted cash flow valuation, for instance, the expected cash flows discounted back at a risk adjusted discount rate yields a risk-adjusted value. In conventional valuation, the expected cash flows are unbiased estimates of what the asset will generate each period and the risk adjustment is for non-diversifiable market risk (with equity) and for default risk (with debt). Nowhere in this process is illiquidity considered explicitly. Not surprisingly, we tend to over value illiquid assets.
So, how do you bring illiquidity into intrinsic valuation? There are two choices. The first is to estimate the risk adjusted value, using the conventional approach, and to then reduce this value by an illiquidity discount. That discount can be estimated by looking at on how the market prices illiquid assets. For instance, studies have looked at restricted stock (stock issued by publicly traded companies that cannot be traded by investors for one year after the issue), pre-IPO transactions (where co-owners sell their stake in the months prior to an announced IPO) and companies with multiple classes of shares traded on different venues (with different liquidity characteristics). These studies generally yield large discounts (25-50%) for illiquid assets and private company appraisers have generally used these studies to back up the use of similar discounts when valuing non-traded businesses. Perhaps, this approach can be extended to publicly traded companies.
The second is to adjust the discount rate for illiquidity, pushing it up for illiquid companies. The illiquidity premium added to the discount rate is usually estimated by looking at the past. In its crudest form, you can assume that the premium that small cap companies or venture capitalists have earned over the market (about 3-4% on an annual basis over the last few decades) is due to illiquidity and add that number on to the cost of equity of any "illiquid" company. In its more sophisticated version, the adjustment to the discount rate can be linked to a measure of illiquidity on the company - its turnover ratio, trading volume or the bid-ask spread. One study concludes that every 1% increase in the bid-ask spread pushes up the discount rate by 0.25%. Thus, the cost of equity for a stock with a beta of 1.20 and a bid-ask spread of $0.50 (on a stock price of $ 10), with a riskfree rate of 4% and an equity risk premium of 6% is:
Cost of equity = 4% + 1.20 (6%) + 0.25% (.5/10) = 12.45%
With both approaches, the value will decrease with illiquidity.
b. Relative Valuation: In its most common form, relative valuation involves screening the market for cheap companies, with one screen for pricing (low PE, low price to book, , low EV/EBITDA) and one or more for desirable fundamentals (high growth, low risk, high ROE). If you ignore illiquidity, your cheap stock portfolio will end up with a lot of illiquid stocks. The simplest way to incorporate illiquidity is to add it as a screen. Thus, in addition to screening for high growth and low risk, you could also screen for high liquidity (high float, high turnover ratios, low bid-ask spreads, high trading volume etc.). The tightness of the liquidity screen can then be varied to fit your liquidity needs as an investor.
c. Contingent Claim Valuation: All option pricing models are built on two principles: replication (where a portfolio of the underlying asset and a riskfree investment is created to have the same cash flows as the option) and preventing arbitrage (the replicating portfolio and the option have to trade at the same price) . Both principles require liquidity: you be able to trade the option, the underlying asset and the riskfree asset in any quantity and at no cost. Illiquidity in any one of these markets will throw a wrench into the process and cause the option pricing models to yield incorrect values, with the imprecision increasing with illiquidity. So, what are your choices for bringing illiquidity into the process? You can try to modify the models to incorporate illiquidity explicitly but option pricing models are complicated enough already and this adds an additional layer of complication. Alternatively, you can adjust the inputs into the option pricing model. My choice would be the underlying asset value (S): using a lower value for illiquid underlying assets will reduce the value of call options on those assets.
In summary, no matter which approach you use, illiquidity is not a neutral factor. The investments you make within each asset class will reflect both the illiquidity of the investment and your own liquidity needs (and preferences) as an investor.
I have a paper on the effects of illiquidity on financial theory, where I examine the effects of liquidity on valuation in more detail:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1729408
Friday, December 24, 2010
Asset allocation for illiquid markets
In my last post, I argued that illiquidity is not a minor problem restricted to a few stocks. In fact, it can affect all stocks, at least during some time periods, with its effect varying across stocks. I also noted that much of financial theory is built around the presumption that markets are liquid.
So, how would financial theory and practice change, if illiquidity is explicitly incorporated into the process? Let's start with the first step in investments, asset allocation, where you decide how much of your overall wealth you will invest in different asset classes - treasuries, corporate bonds, stocks, real estate, collectibles. In fact, defining asset classes loosely, private equity, hedge funds and mortgage backed securities can be considered new entrants in the game, vying for portfolio dollars.
In the classic mean variance framework, the optimum asset allocation mix is the one that maximizes expected returns, given a risk constraint. Thus, you feed in the expected returns and standard deviations of different asset classes, in conjunction with their covariances with each other, and let optimization work its magic. Here is the catch. The average returns, standard deviations and correlations all come from historical data. With illiquid asset classes, standard deviations tend to be under estimated (for a completely illiquid asset, there will be no trading and the standard deviation will be zero) and the covariances consequently will be misestimated. In fact, the least liquid asset classes often look like they offer the best risk/return tradeoffs, if you don't control for illiquidity. Plugging these values into the optimization framework will generate weights that are too high for the illiquid asset classes, for the typical investor. In the last decade, especially, this has led many endowment funds to over invest in real estate, private equity and hedge funds, categories notoriously over exposed to the vagaries of illiquidity.
So, how would you bring illiquidity into the mix and what are the consequences? There are two routes you can follow. In the first, you adjust the expected returns of illiquid asset classes downwards to reflect the expected cost of illiquidity. That would make these asset classes less desirable and counter act the underestimation of standard deviations. The other is to restate the optimization problem thus: Maximize expected return subject to the constraints that risk be below a "stated" level and that liquidity be greater than a specified constraint.
With both approaches, the "right" asset allocation mix will vary across investors. Investors who desire or need more liquidity will tilt their portfolios towards more liquid asset classes (large market cap stocks, highly rated corporate bonds) . Investors who value liquidity less may actually gain by tilting their portfolios away from more liquid asset classes towards less liquid ones (real assets, small cap and low priced stocks, low rated corporate bonds).
I have a paper on the effects of illiquidity on financial theory, where I look at asset allocation in more detail:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1729408
So, how would financial theory and practice change, if illiquidity is explicitly incorporated into the process? Let's start with the first step in investments, asset allocation, where you decide how much of your overall wealth you will invest in different asset classes - treasuries, corporate bonds, stocks, real estate, collectibles. In fact, defining asset classes loosely, private equity, hedge funds and mortgage backed securities can be considered new entrants in the game, vying for portfolio dollars.
In the classic mean variance framework, the optimum asset allocation mix is the one that maximizes expected returns, given a risk constraint. Thus, you feed in the expected returns and standard deviations of different asset classes, in conjunction with their covariances with each other, and let optimization work its magic. Here is the catch. The average returns, standard deviations and correlations all come from historical data. With illiquid asset classes, standard deviations tend to be under estimated (for a completely illiquid asset, there will be no trading and the standard deviation will be zero) and the covariances consequently will be misestimated. In fact, the least liquid asset classes often look like they offer the best risk/return tradeoffs, if you don't control for illiquidity. Plugging these values into the optimization framework will generate weights that are too high for the illiquid asset classes, for the typical investor. In the last decade, especially, this has led many endowment funds to over invest in real estate, private equity and hedge funds, categories notoriously over exposed to the vagaries of illiquidity.
So, how would you bring illiquidity into the mix and what are the consequences? There are two routes you can follow. In the first, you adjust the expected returns of illiquid asset classes downwards to reflect the expected cost of illiquidity. That would make these asset classes less desirable and counter act the underestimation of standard deviations. The other is to restate the optimization problem thus: Maximize expected return subject to the constraints that risk be below a "stated" level and that liquidity be greater than a specified constraint.
With both approaches, the "right" asset allocation mix will vary across investors. Investors who desire or need more liquidity will tilt their portfolios towards more liquid asset classes (large market cap stocks, highly rated corporate bonds) . Investors who value liquidity less may actually gain by tilting their portfolios away from more liquid asset classes towards less liquid ones (real assets, small cap and low priced stocks, low rated corporate bonds).
I have a paper on the effects of illiquidity on financial theory, where I look at asset allocation in more detail:
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1729408
Tuesday, December 21, 2010
All assets are illiquid
Much of financial theory is built on the premise that markets are liquid for the most part and that illiquidity, if it exists, occurs in pockets: it shows up only with very small, lightly traded companies, emerging markets and privately owned businesses. In fact, almost the prescriptions we provide to both investors and corporate finance reflect this trust that both security and asset markets are liquid.
To see how unrealistic the assumption of liquidity is, consider what a liquid market would require: you should be instantaneously be able to sell any quantity of an asset at the prevailing market price with no transactions costs. Using that definition, no asset is liquid and the only question then becomes one of degree, with some assets being more liquid than others.
Given the premise that all assets are illiquid, here is a follow up question: how do you measure illiquidity? One obvious measure, especially in securities markets, is the total transactions costs, including not only the brokerage costs, but the bid-ask spread and the price impact from trading. The other is waiting time. In real estate, for instance, illiquidity manifests itself in properties staying on the market for longer periods. Days on market (DOM) is a widely reported statistic in real estate and is used to measure the health (and liquidity) of different markets.
There is significant empirical evidence that illiquidity varies across asset classes, within assets in a given asset class and across time.
If illiquidity varies across asset classes, across assets and across time, and investors price in this illiquidity, it seems prudent that both portfolio and corporate financial theory be modified to reflect the potential for illiquidity. Alas, given the length of this post, that has to wait for the next one.
To see how unrealistic the assumption of liquidity is, consider what a liquid market would require: you should be instantaneously be able to sell any quantity of an asset at the prevailing market price with no transactions costs. Using that definition, no asset is liquid and the only question then becomes one of degree, with some assets being more liquid than others.
Given the premise that all assets are illiquid, here is a follow up question: how do you measure illiquidity? One obvious measure, especially in securities markets, is the total transactions costs, including not only the brokerage costs, but the bid-ask spread and the price impact from trading. The other is waiting time. In real estate, for instance, illiquidity manifests itself in properties staying on the market for longer periods. Days on market (DOM) is a widely reported statistic in real estate and is used to measure the health (and liquidity) of different markets.
There is significant empirical evidence that illiquidity varies across asset classes, within assets in a given asset class and across time.
- Across asset classes, illiquidity is more of a problem in real asset markets (real estate, collectibles) than in financial asset markets. Within financial asset markets, the US treasury market is the most liquid, followed by highly rated bonds and developed market stocks, with low-rated (junk or high yield) bonds and emerging market stocks bringing up the rear.
- Within each asset class, there are wide variations in liquidity. In the treasury market, the just-issued, standard maturity treasuries (3 month, 6 month, 10 year) are more liquid than the seasoned, non-standard maturity treasuries. Within the stock market, larger market cap and higher priced stocks are more liquid than smaller market cap, lower priced stocks.
- Liquidity also varies widely over time. While the long term trend in liquidity in equity markets has been towards more liquidity, liquidity moves in cycles, increasing in bull markets and decreasing in bear markets. Punctuating the long term trend are crises, like the 1987 sell-off in the US and the 2008 banking crisis, where liquidity dries up even for the largest market cap companies. During these crises, illiquidity manifests itself in many ways: trading halts, higher bid-ask spreads and bigger price impact when trading.
If illiquidity varies across asset classes, across assets and across time, and investors price in this illiquidity, it seems prudent that both portfolio and corporate financial theory be modified to reflect the potential for illiquidity. Alas, given the length of this post, that has to wait for the next one.
Tuesday, November 30, 2010
Are complex models the answer?
There seems to be consensus that conventional economic models did a poor job predicting the magnitude of the last crisis and that we need to do better. In today's Wall Street Journal, we see the beginnings of one response:
http://online.wsj.com/article/SB10001424052702303891804575576523458637864.html?mod=WSJ_economy_LeftTopHighlights
In short, a physicist, a psychoanalyst and an economist believe that they can build a bigger model that captures the complexities of the real world and does a better job of forecasting the future. Good luck with that! While I wish them well, my response is that this will go nowhere or worse, go somewhere bad.
To those who believe that complex models with more variables are the answer to uncertainty, my response is a paper by Ed Lorenz in 1972, entitled Predictability: Does the flap of a butterfly's wing in Brazil set off a tornado in Texas?, credited with creating an entire discipline: chaos theory. In the paper, Lorenz noted that very small changes in the initial conditions of a complex models created very large effects on the final forecasted values. Lorenz, a meteorologist, came to this recognition by accident. One day in 1961, Lorenz inputted a number into a weather prediction model; he entered 0.506 as the input instead of 0.506127, expecting little or no change in the output from the model. What he found instead was a dramatic shift in the output, giving rise to a Eureka moment and the butterfly effect. (One of my favorite books on the topic of Chaos is by James Gleick. It is an easy read and well worth the time.. for investors and economists)
Complex models work best with inputs that behave in thoroughly predictable ways: software and engineering models come to mind. They break down when the inputs are noisy and the relationships are unstable: macro economic models are perfect lab experiments for chaos. The subjects (human beings) belong in strange and unpredictable ways, the variables that matter keep shifting and the relationships between them change over time. In fact, I will wager that the models that worked worst during the last crisis were the most complex models with dozens of inputs and cross relationships.
So, what is the solution? My experience in valuation suggests that you should go in the other direction. When faced with more uncertainty, strip the model down to only the basic inputs, minimize the complexity and build the simplest model you can. Take out all but the key variables and reduce detail. I use this principle when valuing companies. The more uncertainty I face, the less detail I have in my valuation, recognizing that my capacity to forecast diminishes with uncertainty and that errors I make on these inputs will magnify as they percolate through the valuation. More good news: if I am going to screw up, at least I will do so with a lot less work!!
http://online.wsj.com/article/SB10001424052702303891804575576523458637864.html?mod=WSJ_economy_LeftTopHighlights
In short, a physicist, a psychoanalyst and an economist believe that they can build a bigger model that captures the complexities of the real world and does a better job of forecasting the future. Good luck with that! While I wish them well, my response is that this will go nowhere or worse, go somewhere bad.
To those who believe that complex models with more variables are the answer to uncertainty, my response is a paper by Ed Lorenz in 1972, entitled Predictability: Does the flap of a butterfly's wing in Brazil set off a tornado in Texas?, credited with creating an entire discipline: chaos theory. In the paper, Lorenz noted that very small changes in the initial conditions of a complex models created very large effects on the final forecasted values. Lorenz, a meteorologist, came to this recognition by accident. One day in 1961, Lorenz inputted a number into a weather prediction model; he entered 0.506 as the input instead of 0.506127, expecting little or no change in the output from the model. What he found instead was a dramatic shift in the output, giving rise to a Eureka moment and the butterfly effect. (One of my favorite books on the topic of Chaos is by James Gleick. It is an easy read and well worth the time.. for investors and economists)
Complex models work best with inputs that behave in thoroughly predictable ways: software and engineering models come to mind. They break down when the inputs are noisy and the relationships are unstable: macro economic models are perfect lab experiments for chaos. The subjects (human beings) belong in strange and unpredictable ways, the variables that matter keep shifting and the relationships between them change over time. In fact, I will wager that the models that worked worst during the last crisis were the most complex models with dozens of inputs and cross relationships.
So, what is the solution? My experience in valuation suggests that you should go in the other direction. When faced with more uncertainty, strip the model down to only the basic inputs, minimize the complexity and build the simplest model you can. Take out all but the key variables and reduce detail. I use this principle when valuing companies. The more uncertainty I face, the less detail I have in my valuation, recognizing that my capacity to forecast diminishes with uncertainty and that errors I make on these inputs will magnify as they percolate through the valuation. More good news: if I am going to screw up, at least I will do so with a lot less work!!
Wednesday, November 24, 2010
The insider trading scandal: Thoughts about the hedge fund business
As many of you are aware, the last week has been filled with news stories about imminent arrests in an insider trading scandal that supposedly entangles multiple hedge funds and bankers at a couple of large investment banks. I am being leery about naming names, even though they are now in the public domain, since these selective leaks can be devastating not only to the individuals named but also to the entities that they represent. While some may feel that "they" deserve this, I think we still live in a country where you are innocent until proven guilty.
So, I am going to use this story to talk about a bigger question. Not insider trading, because I have put my views on the topic down in a previous blog post, but about the hedge fund business. Note that many of the targets in this investigation are hedge fund managers. Since I have no reason to believe that hedge fund managers are any more immoral or unethical than any other random group of money managers, the question then becomes: What is it about the hedge fund business that seems to drive this constant search for an information edge? Or why do we not see more traditional mutual fund managers involved in these scandals?
At first sight, hedge funds and mutual funds share much in common. They both solicit money from investors, promising to deliver above-market returns to them and they get compensated for managing this money. But there are three significant differences:
1. Constraints on investing: Mutual funds are far more constrained in where and how they invest than most hedge funds are. Some of these constraints are imposed by regulators, some are self imposed and some are the created by clients.
a. Long versus long/short strategies: Most mutual funds can only buy stocks (The Investment Company Act of 1940 that governs mutual funds puts significant restrictions on short sales by funds), whereas hedge funds often can both sell short on some stocks and go long on others.
b. Investment choices: There are several mutual funds that are judged based on "tracking error", measured by how far their returns deviate from a specified index's return. This constraint, usually imposed by clients, is designed to prevent fund managers from straying too far from the companies in the index. Hedge funds generally can invest not only in whatever company they want but many invest across asset classes.
2. Clientele mix: Hedge funds attract investments from either the very wealthy or institutions (pension funds, for instance). In fact, most of them actively discourage small, individual investors by requiring a large, minimum investment. Mutual funds tend to attract individual investors. At the risk of a gross generalization, institutional and wealthy investors are more demanding than individual investors; they move their money out of loser funds and into hot funds far more quickly than other individuals do. Put another way, if the herding effect is a phenomenon that affects all investors, it seems to affect wealthier and institutional investors more. (Some hedge funds put withdrawal restrictions on investors to counter.)
3. Financial leverage: Hedge funds are far more likely to use borrowed money to lever their bets. Most mutual funds either do not borrow money or do so on a very restrictive basis.
4. Compensation systems: Mutual funds generally make their money in two ways. All mutual funds cover their expenses from the money under management; the management; these management expenses are public information and can be accessed at services like Morningstar. A subset of funds also assess a one-time up-front sales charge (load), when you invest, where a certain percent of what you invest is taken by the fund at the time of the investment. Hedge funds assess an annual load (1% or 2% of the invested amount each year) and a percentage of the profit on the investment (10 or 20% of the profits).
You may be wondering at this stage what all of this has to do with insider trading. Consider why people seek out insider information. If they succeed, they can buy or sell a stock prior to that information becoming public; when it goes public, the stock will pop up or down, depending on whether the information is good or bad news.
All four differences highlighted play into why hedge fund managers see more gain from seeking out and exploiting private information:
In closing, though, I am not sure that all of this seeking out of information is generating a payoff for hedge funds. In the aggregate, they continue to either match or under perform actively managed mutual funds (which, in turn, under perform index funds), when fees and transactions costs are factored in. It is entirely possible that some of the "super" performers among hedge funds got there because they had access to private information that no one else had. I just don't think it is likely! As Shakespeare would put it, this seems like much ado about nothing!
So, I am going to use this story to talk about a bigger question. Not insider trading, because I have put my views on the topic down in a previous blog post, but about the hedge fund business. Note that many of the targets in this investigation are hedge fund managers. Since I have no reason to believe that hedge fund managers are any more immoral or unethical than any other random group of money managers, the question then becomes: What is it about the hedge fund business that seems to drive this constant search for an information edge? Or why do we not see more traditional mutual fund managers involved in these scandals?
At first sight, hedge funds and mutual funds share much in common. They both solicit money from investors, promising to deliver above-market returns to them and they get compensated for managing this money. But there are three significant differences:
1. Constraints on investing: Mutual funds are far more constrained in where and how they invest than most hedge funds are. Some of these constraints are imposed by regulators, some are self imposed and some are the created by clients.
a. Long versus long/short strategies: Most mutual funds can only buy stocks (The Investment Company Act of 1940 that governs mutual funds puts significant restrictions on short sales by funds), whereas hedge funds often can both sell short on some stocks and go long on others.
b. Investment choices: There are several mutual funds that are judged based on "tracking error", measured by how far their returns deviate from a specified index's return. This constraint, usually imposed by clients, is designed to prevent fund managers from straying too far from the companies in the index. Hedge funds generally can invest not only in whatever company they want but many invest across asset classes.
2. Clientele mix: Hedge funds attract investments from either the very wealthy or institutions (pension funds, for instance). In fact, most of them actively discourage small, individual investors by requiring a large, minimum investment. Mutual funds tend to attract individual investors. At the risk of a gross generalization, institutional and wealthy investors are more demanding than individual investors; they move their money out of loser funds and into hot funds far more quickly than other individuals do. Put another way, if the herding effect is a phenomenon that affects all investors, it seems to affect wealthier and institutional investors more. (Some hedge funds put withdrawal restrictions on investors to counter.)
3. Financial leverage: Hedge funds are far more likely to use borrowed money to lever their bets. Most mutual funds either do not borrow money or do so on a very restrictive basis.
4. Compensation systems: Mutual funds generally make their money in two ways. All mutual funds cover their expenses from the money under management; the management; these management expenses are public information and can be accessed at services like Morningstar. A subset of funds also assess a one-time up-front sales charge (load), when you invest, where a certain percent of what you invest is taken by the fund at the time of the investment. Hedge funds assess an annual load (1% or 2% of the invested amount each year) and a percentage of the profit on the investment (10 or 20% of the profits).
You may be wondering at this stage what all of this has to do with insider trading. Consider why people seek out insider information. If they succeed, they can buy or sell a stock prior to that information becoming public; when it goes public, the stock will pop up or down, depending on whether the information is good or bad news.
All four differences highlighted play into why hedge fund managers see more gain from seeking out and exploiting private information:
- Hedge funds can exploit both good news and bad news, by buying stocks in advance of the former and selling short ahead of the latter. Mutual funds can only buy on good news (though they can sell any existing holdings of companies on which bad news lies ahead).
- While money management at any level is a rat race, where funds try to keep their own clients and coax clients away from their competitors, the race becomes more frenetic with hedge funds. A hedge fund that lags its peer group can enter a death spiral, where losses spur withdrawals which feed into more losses.
- If the "inside information" is precise, you can use even more debt in your investment strategy, creating huge payoffs on your investment, when the information becomes public. Financial leverage acts as a multiplier on profits from insider trading.
- The compensation system at hedge funds essentially gives every hedge fund manager a call option: they make 10 or 20% of all profits, no matter how large, and do not share in losses. Trading on information ties in well with this system, since it delivers skewed returns: most of the time, information turns out to be worthless, but when it is relevant information, the payoff is very large. Thus, even if insider information is noisy and provides little benefits or even creates costs for investors over the long term, hedge fund managers may still benefit personally from its use because of the upside call built into their compensation systems...
In closing, though, I am not sure that all of this seeking out of information is generating a payoff for hedge funds. In the aggregate, they continue to either match or under perform actively managed mutual funds (which, in turn, under perform index funds), when fees and transactions costs are factored in. It is entirely possible that some of the "super" performers among hedge funds got there because they had access to private information that no one else had. I just don't think it is likely! As Shakespeare would put it, this seems like much ado about nothing!
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