It is important to note the effect of dividends in equity forward contracts. Any equity portfolio nearly always has at least a few stocks that pay dividends, and it is inconceivable that any well-known equity index would not have some component stocks that pay dividends. Equity forward contracts typically have payoffs based only on the price of the equity, value of the portfolio, or level of the index. They do not ordinarily pay off any dividends paid by the component stocks. An exception, however, is that some equity forwards on stock indices are based on total return indices. For example, there are two versions of the well-known S&P 500 Index. One represents only the market value of the stocks. The other, called the S&P 500 Total Return Index, is structured so that daily dividends paid by the stocks are reinvested in additional units of the index, as though it were a portfolio. In this manner, the rate of return on the index, and the payoff of any forward contract based on it, reflects the payment and reinvestment of dividends into the underlying index. Although this feature might appear attractive, it is not necessarily of much importance in risk management problems. The variability of prices is so much greater than the variability of dividends that managing price risk is considered much more important than worrying about the uncertainty of dividends. In summary, equity forwards can be based on individual stocks, specific stock portfolios, or stock indices. Moreover, these underlying equities often pay dividends, which can affect forward contracts on equities. Let us now look at bond and interest rate forward contracts.

Many equity forward contracts are based on a stock index. For example, consider a U.K. asset manager who wants to protect the value of her portfolio that is a Financial Times Stock Exchange 100 index fund, or who wants to eliminate a risk for which the FTSE 100 Index is a sufficiently accurate representation of the risk she wishes to eliminate. For example, the manager may be anticipating the sale of a number of U.K. blue chip shares at a future date. The manager could, as in our stock portfolio example, take a specific portfolio of stocks to a forward contract dealer and obtain a forward contract on that portfolio. She realizes, however, that a forward contract on a widely accepted benchmark would result in a better price quote, because the dealer can more easily hedge the risk with other transactions. Moreover, the manager is not even sure which stocks she will still be holding at the later date. She simply knows that she will sell a certain amount of stock at a later date and believes that the FTSE 100 is representative of the stock that she will sell. The manager is concerned with the systematic risk associated with the U.K. stock market, and accordingly, she decides that selling a forward contract on the FTSE 100 would be a good way to manage the risk. Assume that the portfolio manager decides to protect £15,000,000 of stock. The dealer quotes a price of £6,000 on a forward contract covering £15,000,000. We assume that the contract will be cash settled because such index contracts are nearly always done that way. When the contract expiration date arrives, let us say that the index is at £5,925- a decrease of 1.25 percent from the forward price. Because the manager is short the contract and its price went down, the transaction makes money. But how much did it make on a notional principal of £15,000,000?
The index declined by 1.25 percent. Thus, the transaction should make 0.0125 X £15,000,000 = £187,500. In other words, the dealer would have to pay £187,500 in cash. If the portfolio were a FTSE 100 index fund, then it would be viewed as a portfolio initially worth £15,000,000 that declined by 1.25 percent, a loss of £187,500. The forward contract offsets this loss. Of course, in reality, the portfolio is not an index fund and such a hedge is not perfect, but as noted above, there are sometimes reasons for preferring that the forward contract be based on an index.

One of the most significant developments in financial markets in recent years has been the growth of futures, options, and related derivatives markets. These instruments provide payoffs that depend on the values of other assets such as commodity prices, bond and stock Options
A call option gives its holder the right to purchase an asset for a specified price, called the exercise or strike price, on or before a specified expiration date. For example, a February call option on EMC stock with an exercise price of $70 entitles its owner to purchase EMC stock for a price of $70 at any time up to and including the expiration date in February. Each option contract is for the purchase of 100 shares. However, quotations are made on a per-share basis. The holder of the call need not exercise the option; it will be profitable to exercise only if the market value of the asset that may be purchased exceeds the exercise price. When the market price exceeds the exercise price, the optionholder may “call away” the asset for the exercise price and reap a payoff equal to the difference between the stock price and the exercise price. Otherwise, the option will be left unexercised. If not exercised before the expiration date of the contract, the option simply expires and no longer has value. Calls therefore provide greater profits when stock prices increase and thus represent bullish investment vehicles.
In contrast, a put option gives its holder the right to sell an asset for a specified exercise price on or before a specified expiration date. A February put on EMC with an exercise price of $70 thus entitles its owner to sell EMC stock to the put writer at a price of $70 at any time before expiration in February, even if the market price of EMC is lower than $70. Whereas profits on call options increase when the asset increases in value, profits on put options increase when the asset value falls. The put is exercised only if its holder can deliver an asset worth less than the exercise price in return for the exercise price.

Bayesian probability underpins some of the principles of artificial intelligence (AI) to discover relationships or patterns. These form part of the trend for knowledge management to dig the wealth that lies at the bottom of the bank’s and fund’s databases. Some research shows that AI has benefit for the way in which we study stock-market movements.38 Customer relationship management reflects this drive towards profitable data-mining.
ATM machines use expert systems derived from AI principles to link the transactions with possible stolen bank cards. For example ($250 withdrawn from Site A)
+ ($250 withdrawn from Site B)
+ ($300 spent at Store C) using Card X = 75 % sure that this card has been stolen. American Express has been working with such a system based on AI for nearly 20 years. Citibank developed a computer-based credit-scoring system for profiling customer credit worthiness 15 years ago. Now, we have progressed and there are more compliance issues to handle in the 21st century.

A futures contract calls for delivery of an asset (or in some cases, its cash value) at a specified delivery or maturity date for an agreed-upon price, called the futures price, to be paid
at contract maturity. The long positionis held by the trader who commits to purchasing the asset on the delivery date. The trader who takes the short positioncommits to delivering the asset at contract maturity.
The trader holding the long position profits from price increases. Suppose that at expiration the S&P500 index is at 1450.50. Because each contract calls for delivery of $250 times the index, ignoring brokerage fees, the profit to the long position who entered the contract at a futures price of 1447.50 would equal $250 %?(1450.50 %?1447.50) %?$750. Conversely, the short position must deliver $250 times the value of the index for the previously agreed-upon futures price. The short position’s loss equals the long position’s profit.
The right to purchase the asset at an agreed-upon price, as opposed to the obligation, distinguishes call options from long positions in futures contracts. Afutures contract obliges the long position to purchase the asset at the futures price; the call option, in contrast, conveys the right to purchase the asset at the exercise price. The purchase will be made only if it yields a profit.
Clearly, a holder of a call has a better position than does the holder of a long position on a futures contract with a futures price equal to the option’s exercise price. This advantage, of course, comes only at a price. Call options must be purchased; futures contracts may be entered into without cost. The purchase price of an option is called the premium. It represents the compensation the holder of the call must pay for the ability to exercise the option only when it is profitable to do so. Similarly, the difference between a put option and a short futures position is the right, as opposed to the obligation, to sell an asset at an agreed-upon price.

Mental biases are not our only bias. Another kind of bias arises when one person is acting on behalf of another. This is called an agency problem—a situation in which the owner of a project has to rely on someone else for information, and this someone else has divergent interests. An example may be shareholders who rely on corporate management to undertake projects on their behalves, or a division manager who has to rely on department managers for information about how profitable their proposed projects really are. A cynical synopsis of agency biases would be “all people act and lie in their own self-interest.” Now, although everyone does have incentives to lie—or at least color the truth—corporations are especially rife with such agency distortions. Of course, few people sit down and contemplate how to best and intentionally lie. Instead, they convince themselves that what is in their best interest is indeed the best route to take. Thus, mental biases often reinforce incentive problems: “wishful thinking” is a disease from which we all suffer.
You can take the fact that we have already had to mention agency issues repeatedly in this blog as an indication of how important and pervasive these are. But, again, lack of space forces us to highlight just a few issues with some examples:
1. Competition for Capital Managers often compete for scarce resources. For example, division managers may want to obtain capital for their projects. A less optimistic but more accurate estimate of the project cash flows may induce headquarters to allocate capital to another division instead. Thus, division managers often end up in a race to make their potential projects appear in the most favorable and profitable light.
2. Employment Concerns Managers and employees do not want to lose their jobs. For example, scientists tend to highlight the potential and downplay the drawbacks of their areas of research. After all, not doing so may cut the project and thereby cost them their jobs.
3. Perks Managers do not like to give up perks. For example, division managers may like to have their own secretaries or even request private airplanes. Thus, they are likely to overstate the usefulness of the project “administrative assistance” or “private plane transportation.”
4. Power Managers typically love to build their own little “empires.” For example, they may want to grow and control their department because bigger departments convey more prestige and because they are a stepping stone to further promotion, either internally or externally. For the same reason, managers often prefer not to maximize profits, but sales.
5. Hidden Slack Managers like the ability to be able to cover up problems that may arise in the future. For example, division managers may want to hide the profitability of their divisions, fearing that headquarters may siphon off “their” profits into other divisions. They may prefer to hide the generated value, feeling that the cash they produced in good times “belongs” to them—and that they are entitled to use it in bad times.
6. Reluctance to Take Risk Managers may hesitate to take on risk. For example, they may not want to take a profitable NPV project, because they can only get fired if it fails—and may not be rewarded enough if it succeeds. A popular saying used to be “no one was ever fired for buying IBM,” although these days Microsoft has taken over IBM’s role.
7. Direct Theft Managers and employees have even been known to steal outright from the company. For example, a night club manager may not ring sales into the cash register. Or a sales agent may “forget” to charge her relatives. In some marginal cases, this can be a fine line. For example, is taking a paper clip from the company or answering a personal e-mail from the company account really theft? In other cases, theft is blatantly obvious. In September 2002, Dennis Kozlowski, former CEO, was charged with looting $600 million from Tyco shareholders. His primary defense was that he did so in broad daylight—with approval from the corporate board that he had helped put in place.

Most cash flow and cost-of-capital estimates rely on judgments. Unfortunately, it is often difficult to obtain accurate judgments. Our brains tend to commit systematic decision errors.
Managers who do not recognize these biases will systematically make poor decisions.
There are literally dozens of well-known behavioral errors, but limited space allows us to highlight just three: overconfidence, relativism, and compartmentalization.
1. Overconfidence is the tendency of people to believe that their own assessments are more accurate than they really are. In lab experiments, ordinary people are found to be dramatically overconfident. When asked to provide a 90% confidence interval—which is just a range within which they are confident that their true value will lie in nine out of ten tries—most people end up being correct only five out of ten times.
It is difficult to empirically document overconfidence—after all, if it were easy, managers would recognize it themselves and avoid it. However, we do have evidence that many managers who are already heavily invested in their own company tend to throw caution overboard and voluntarily invest much of their own money into the corporation—and even in companies going bankrupt later on. There is also good empirical evidence that those of us who are most optimistic in overestimating our own life-expectancy disproportionately become entrepreneurs. Even if optimism is a disease, it seems to be a necessary one for entrepreneurs!
To understand this better and to test your own susceptibility to these problems, you can take a self-test at the website, http://www.cashtrailer.com/. Doing so will likely make you remember this problem far more than reading long paragraphs of text in this blog. Incidentally, the only population segments who are known not to be systematically overconfident are weather forecasters and clinically depressed patients.
2. Relativism is the tendency of people to consider issues of relative scale when they should not. For example, most people are willing to drive 15 minutes to a store farther away to save $20 on the purchase of $30 worth of groceries, but they would not be willing to drive the 15 minutes to a car dealer farther away to save $100 on the purchase of a new $20,000 car. The savings appears to be less important in the context of the car purchase than in the context of a grocery purchase. This is flawed logic, similar to comparing IRR’s while ignoring project scale. The marginal cost is driving 15 minutes extra, and the marginal benefit is a higher $100 in the context of the car than the $20 in the context of the groceries. Put differently, the problem is that we tend to think in percentages, and the $20 is a higher percentage of your grocery bill than it is of your car purchase. The smaller the amount of money at stake, the more severe this problem often becomes. When a gas station advertises a price of $2 per gallon rather than $2.10, customers often drive for miles and wait in long lines—all to fill a 20 gallon gas tank at a total savings that amounts to a mere $2.
3. Compartmentalization Compartmentalization is the tendency of people to categorize decisions. Most people are more inclined to spend more when the same category has produced an unexpected windfall earlier. For example, winning a lottery prize while attending a baseball game often makes winners more likely to purchase more baseball tickets, even though the project “baseball game” has not changed in profitability. Similarly, an unexpected loss may stop people from an otherwise profitable investment that they should make. For example, say an individual likes to attend a particular baseball game. If she loses her baseball game ticket, she is less likely to purchase a replacement, even though the cost and benefit of purchasing the ticket are the same as they were when the original ticket was purchased.

But should we really charge zippo for parking corporate cars if we suspect that the unused capacity will not be unused forever? What if a new division might come along that wants to rent the five currently unused garage space in the future? Do we then kick out all current parkers? Or, how should we charge this new division if it wanted to rent six spaces? Should we give it the five remaining unused parking spots for free? Presuming that garages can only be built in increments of ten parking spots each, should we build another ten-car garage, and charge it entirely to this new division that needs only one extra parking spot in the new garage? Should this new division get a refund if other divisions were to want to use the parking space— but, as otherwise unused parking space, should we not use the garage appropriately by not charging for the nine extra spaces that will then be a free resource?
When there are high fixed and low variable costs, then capacity is often either incredibly cheap (or even free) or it is incredibly expensive—at least in the short run. Still, the right way to think of capacity is in terms of the relevant marginal costs and marginal benefits. From an overall corporate perspective, it does not matter how or who you charge—just as long as you get the optimal capacity utilization. To the extent that cost allocation distorts optimal marginal decision-making, it should be avoided. In our case, if optimal capacity utilization requires zero parking cost for the old garage, then so be it. Of course, when it comes to the decision to build an entirely new garage, you simply weigh the cost of building the 10-spot garage against the reduced deterioration for 1 car.
Unfortunately, real life is not always so simple. We know that our division managers will not want to pay for it if they can enjoy it for free—so we cannot rely on them telling us the correct marginal benefit. So, would it solve our problem to charge only divisions that are voluntarily signing up for the Internet connection, and to forcibly exclude those that do not sign up? If we do, then we solve the problem of everyone claiming that they do not need the Internet connection. However, we are then stuck with the problem that we may have a lot of unused network capacity that sits around, has zero marginal cost, and could be handed to the non-requesters at a zero cost. It would not impose a cost on anyone else and create more profit for the firm. Of course, if we do this, or even if we are suspected to do this, then no division would claim that they need the Internet to begin with, so that they will get it for free. In sum, what makes these problems so difficult in the real world is that as the boss, you often do not know the right marginal benefits and marginal costs, and you end up having to “play games” with your division managers to try to make the right decision. Such is real life!

A closely related mistake is to forget that “overhead” is often a sunk cost. By definition, over-head is not a marginal cost, but something that has been incurred already and is allocated to departments. For example, assume your firm has spent $500,000 on a computer that is currently idle half the time. It serves only one division. Assume that another division can take an additional project that produces $60,000 in net present value, but that will consume twenty percent of the computer’s time. Should your firm take this project? If twenty percent of the cost of the computer is allocated to this new project (i.e., 20% · $500, 000 = $100, 000), the net present value of the new project would appear to be a negative −$40, 000. But the correct decision process is not to allocate the existing overhead as a cost to divisions. The $500,000 on overhead has already been spent. The computer is a sunk cost—assuming that it really would sit idle otherwise and find no better purpose. It may seem unfair to have charged only the original division for the computer and exempt the opportunistic other division. Yet taking this additional project will produce $60,000 in profits without cost—clearly, a good thing. I personally know of plenty of examples in which overhead allocation has killed very profitable projects.
“Capacity” is a subject that is closely related. For example, a garage may be currently only used for half its space. Adding the project “another car” that could also park in the garage would reduce this car’s depreciation. The garage would then have a positive externality on project “corporate cars.” The marginal cost of storing other cars in the garage should be zero.