Agency Theory

When human interaction is viewed through the lens of the economist, it is presupposed that all individuals act in accordance with their self-interest. Moreover, individuals are assumed to be cognizant of the self-interest motivations of others and can form unbiased expectations about how these motivations will guide their behavior. Conflicts of interest naturally arise.

These conflicts are apparent when two individuals form an agency relationship, i.e. one individual (principal) engages another individual (agent) to perform some service on his/her behalf. A fundamental feature of this contract is the delegation of some decision-making authority to the agent. Agency theory is an economic framework employed to analyze these contracting relationships. Jensen and Meckling (1976) present the first unified treatment of agency theory.

Unless incentives are provided to do otherwise or unless they are constrained in some other manner, agents will take actions that are in their self-interest. These actions are not necessarily consistent with the principal’s interests. Accordingly, a principal will expend resources in two ways to limit the agent’s diverging behavior: (1) structure the contract so as to give the agent appropriate incentives to take actions that are consistent with the principal’s interests and (2) monitor the agent’s behavior over the contract’s life. Conversely, agents may also find it optimal to expend resources to guarantee they will not take actions detrimental to the principal’s interests (i.e. bonding costs). These expenditures by principal and/or agent may be pecuniary/non-pecuniary and are the costs of the agency relationship.

Given costly contracting, it is infeasible to structure a contract so that the interests of both the principal and agent are perfectly aligned. Both parties incur monitoring costs and bonding costs up to the point where the marginal benefits equal the marginal costs. Even so, there will be some divergence between the agent’s actions and the principal’s interests.

The reduction in the principal’s welfare arising from this divergence is an additional cost of an agency relationship (i.e. ”residual loss”). Therefore, Jensen and Meckling (1976) define agency costs as the sum of: (1) the principal’s monitoring expenditures; (2) the agent’s bonding expenditures; and (3) the residual loss.

Barnea et al. (1985) divide agency theory into two parts according to the type of contractual relationship examined – the economic theory of agency and the financial theory of agency.

The economic theory of agency examines the relationship between a single principal who provides capital and an agent (manager) whose efforts are required to produce some good or service. The principal receives a claim on the firm’s end-of-period value. Agents are compensated for their efforts by a dollar wage, a claim on the end-of-period firm value, or some combination of the two.

Two significant agency problems arise from this relationship. First, agents will not put forward their best efforts unless provided the proper incentives to do so (i.e. the incentive problem). Second, both the principal and agent share in the end-of-period firm value and since this value is unknown at the time the contract is negotiated, there is a risk sharing between the two parties (i.e. the risk-sharing problem). For example, a contract that provides a constant dollar compensation for the agent (principal) implies that all the risk is borne by the principal (agent).

Contracts that simultaneously solve the incentive problem and the risk-sharing problem are referred to as “first-best.” First-best contracts provide agents with incentives to expend an optimal amount of effort while producing an optimal distribution of risk between principal and agent. A vast literature examines these issues (see e.g. Ross, 1973; Shavell, 1979; Holmstrom, 1979).

The financial theory of agency examines contractual relationships that arise in financial markets. Three classic agency problems are examined in the finance literature: (1) partial ownership of the firm by an owner-manager; (2) debt financing with limited liability; and (3) information asymmetry. A corporation is considered to be a nexus for a set of contracting relationships (Jensen and Meckling, 1976). Not surprisingly, conflicts arise among the various contracting parties (manager, shareholder, bondholders, etc.).
When the firm manager does not own 100 percent of the equity, conflicts may develop between managers and shareholders. Managers make decisions that maximize their own utility. Consequently, a partial owner-manager’s decisions may differ from those of a manager who owns 100 percent of the equity. For example, Jensen (1986) argues that there are agency costs associated with free cash flow. Free cash flow is discretionary cash available to managers in excess of funds required to invest in all positive net present value projects. If there are funds remaining after investing in all positive net present value projects, managers have incentives to misuse free cash flow by investing in projects that will increase their own utility at the expense of shareholders (see Mann and Sicherman, 1991).

Conflicts also arise between stockholders and bondholders when debt financing is combined with limited liability. For example, using an analogy between a call option and equity in a levered firm (Black and Scholes, 1973; Galai and Masulis, 1976), one can argue that increasing the variance of the return on the firm’s assets will increase equity value (due to the call option feature) and reduce debt value (by increasing the default probability). Simply put, high variance capital investment projects increase shareholder wealth through expropriation from the bondholders. Obviously, bondholders are cognizant of these incentives and place restrictions on shareholder behavior (e.g. debt covenants).

The asymmetric information problem manifests itself when a firm’s management seeks to finance an investment project by selling securities (Myers and Majluf, 1984). Managers may possess some private information about the firm’s investment project that cannot be credibly conveyed (without cost) to the market due to a moral hazard problem. A firm’s securities will 20 commands a lower price than if all participants possessed the same information. The information asymmetry can be resolved in principle with various signaling mechanisms. Ross (1977) demonstrates how a manager compensated by a known incentive schedule can use the firm’s financial structure to convey private information to the market.

Agency Theory Resources:

  1. Barnea, A., Haugen, R. & Senbet, L. (1985). Agency problems and financial contracting.
    Englewood Cliffs, NJ: Prentice Hall.
  2. Galai, D. & Masulis, R. (1976). The option pricing model and the risk factor of stock. Journal of Financial Economics, 3, 53–82.
  3. Holmstrom, B. (1979). Moral hazard and observability. Bell Journal of Economics, 10, 74–91.
  4. Jensen, M. (1986). Agency costs of free cash flow. American Economic Review, 76, 323–29.
  5. Mann, S. & Sicherman, N. (1991). The agency costs of free cash flow: acquisition activity and equity issues. Journal of Business, 64, 213–27.
  6. Myers, S. & Majluf, M. (1984). Corporate financing and investment decisions when firms have information that investors do not have. Journal of Financial Economics, 13, 187–221.
  7. Shavell, S. (1979). Risk-sharing and incentives in the principal–agent relationship. Bell
    Journal of Economics, 10, 55–73.


Asset Allocation

In the analysis of portfolio management, the initial work of Markowitz (1959) was directed towards finding the optimal weights in a portfolio. It was quickly realized that the decisions involved in building up a portfolio were less frequent than the decisions to modify existing portfolios. This is especially important when analyzing how profitable portfolio managers have been over time. If, for example, a portfolio consists of equities and bonds, some investment managers might be particularly skilled in choosing specific companies in which the portfolio should invest, while others might be able to forecast at which times the portfolio should be more heavily invested in shares. The first type of skill would be classified as being more concerned with portfolio selection while the latter would be described as connected with timing or asset allocation.

Asset allocation decisions can be further divided. Investors can decide on an ad hoc basis to alter their portfolio by changing the weights of the constituent assets as a result of some specific model. For example, forecasting models are used to predict the performance of equities relative to bonds or real estate relative to equities. Dependent on the outcome of these forecasts, the investor will switch into or out of the asset being forecasted. Models are used to derive frequent forecasts of one asset against another and to move the portfolio day by day depending on the outcome of the forecasting model.

This type of model is sometimes referred to as tactical asset allocation (TAA) and in practice is used in conjunction with some sophisticated trading in derivatives such as options or futures. Instead of buying more shares, this system buys options or futures in an index representing equities. If equities rise in value, so will the options and futures position and the portfolio thereby will increase in value to a greater extent than underlying equities. TAA is used to adjust portfolio exposure to various factors such as interest rates and currency movements as well as overseas investments (see Arnott et al., 1989).

An alternative category of asset allocation is the technique of dynamic asset allocation, where there is less emphasis on forecasting which component assets will perform well in the next period and more on setting up a policy by which the portfolio reacts automatically to market movements. This can be organized with the help of options and futures but can also be carried out by adjusting the weights of the component assets in the light of predetermined rules. For example, the policy of buying an asset when that asset has performed well in the current period and selling when it has done badly can be carried out in such a way as to provide portfolio insurance, i.e. it protects the portfolio by reducing the exposure to successive falls in the value of one of its constituent assets. An alternative dynamic asset allocation policy is that carried out by rebalancing so as to maintain a reasonably constant proportion in each asset. This involves selling those assets which have just risen in value and selling those assets which have just fallen in value.

The two strategies are profitable in different phases of the market. When the market is moving strongly, the insurance policy is most successful. If, however, the market is tending to oscillate without a strong trend, the 25 rebalancing policy works best. These principles are well illustrated in Perold and Sharpe (1988).

Asset Allocation Resources:

  1. Arnott, R. D., Kelso, C. M., Kiscadden, S. & Macedo, R. (1989). Forecasting factor returns: an intriguing possibility. Journal of Portfolio Management, 16, 28–35.
  2. Markowitz, H. (1959). Portfolio selection: Efficient diversification of investments. New York: John Wiley and Sons.
  3. Perold, A. & Sharpe, W. F. (1988). Dynamic strategies for asset allocation. Financial
    Analysts Journal, 44, 16–27.
  4. Sharpe, W. F. (1992). Asset allocation: management style and performance measurement.
    Journal of Portfolio Management, 18, 7–19.


Asset Pricing

The modern theory of asset prices has its foundations in the portfolio selection theory initiated by Markowitz (1952). In a one-period framework Markowitz assumed that agents’ utilities, and hence the price they will pay, depend only on the means and variances of returns.

This mean-variance model can be justified either on the grounds of quadratic utility (for arbitrary distributions of the asset returns) or on the grounds of multivariate normal (or, more generally, elliptic) distribution of asset returns (for arbitrary preferences). Although quadratic utility has the unappealing properties of satiation and increasing absolute risk aversion in the sense of Arrow–Pratt and multivariate normality violates the limited liability properties of assets, the mean-variance model has had a pervasive influence on financial economics.

The portfolio frontier obtained within the mean-variance framework can be generated by any two frontier portfolios, a property called two-fund separation. Lintner (1965), Sharpe (1964), and Mossin (1966) combined the two-fund separation with the assumptions that agents have homogeneous beliefs, that markets clear in equilibrium, and that there is unlimited lending and borrowing at the riskless rate. The resulting model, the capital asset pricing model (CAPM), has been the major framework of thinking about the trade-off between risk and return. The (unconditional) CAPM states that the excess return on each asset (return less the risk-free rate) is proportional to the asset’s market beta: where Rm is the return on the market portfolio and ?im = cov(Ri, Rm)/var(Rm), the asset’s market beta, measures the covariance of the asset’s return with the market return.

Black (1972) derived the CAPM for an economy without a riskless asset (the zero-beta CAPM). The CAPM has been extensively tested. Black et al. (1972) and Fama and MacBeth (1973) originated the two frameworks in which most of the tests were done. However, the unsatisfactory empirical performance of the CAPM, as well as the problems identified by Roll (1977) related to the unobserved nature of the market portfolio, are the reasons why the single-period, single-beta relation had to be relaxed.

Historically, the first direction was to place the individual decision making in an inter temporal set-up in which agents maximized utility, thus leading to the inter temporal CAPM (ICAPM) of Merton (1973). The other is the arbitrage pricing theory (APT) of Ross (1976). Merton, working in continuous time under the assumptions of many identical agents with homogeneous expectations and market clearing, derived the ICAPM. The asset prices in Merton’s model follow a diffusion process. If the investment opportunity set, namely the drift and diffusion parameters, and the instantaneous correlations between the returns of the different assets, do not change over time, then a continuous time version of the static CAPM holds: one obtains a single-beta security–market-line relationship. If the investment opportunity set is stochastic, however, a multi-beta relationship emerges: where ?is measures the covariance of the return of the ith asset with the sth state variable. Thus, with a stochastically changing investment opportunities set agents need to hedge the future changes in their consumption for a given level of wealth. Given the interpretation of the S state variables as portfolios, the S portfolios are often referred to as hedge portfolios.

To derive the arbitrage pricing theory (APT) Ross (1976) assumes that asset returns are generated by a linear factor model: where each factor fk (without loss of generality) and the error ?i are zero-mean. In well-diversified portfolios the excess expected return on each asset will be given by a linear combination of the ?’s above: where the ?’s are referred to as factor loadings while the ?’s are the risk premiums.

Huberman (1982) offers an alternative derivation of the APT. Connor (1984) was the first to use equilibrium arguments in relation to the APT. Connor and Korajczyk (1986, 1988) extend his arguments. Dybvig and Ross (1983) and Ingersoll (1984) contain useful extensions and refinements of the APT.

There has been substantial empirical work on the APT. Roll and Ross (1980) use factor analysis to test the APT while Connor and Korajczyk (1988) use “asymptotic” principal components analysis to uncover the factors. On the other hand, Chen et al. (1986) explicitly specify five macroeconomic variables (unexpected change in the term structure, unexpected change in the risk premium, change in expected inflation, the unexpected inflation rate, and the unexpected change in industrial production) that proxy for the economy–wide factors.

Shanken (1982, 1985) questions the possibility of testing the APT; Dybvig and Ross (1983) present the counter-argument. The works of Ross (1976), Cox and Ross (1976), Harrison and Kreps (1979), and Ross (1978) contain what has come to be known as the fundamental theorem of asset pricing: the absence of arbitrage is equivalent to the existence of a positive linear pricing rule and still further equivalent to the existence of an optimal demand for some agent with increasing preferences.

The theoretical work on both the ICAPM and APT preceded much of the empirical work which found that price–earnings ratios (Basu, 1977), dividend yields (Fama and French, 1988), and size (Banz, 1981; Reiganum, 1981; Schwert, 1983; Chan et al., 1985) improve the fit of the single-beta CAPM. Some of these papers, together with the weekend effect (French, 1980) and the January effect (Keim, 1983) have been interpreted as evidence of market inefficiency. However, as Fama (1970) has noted, a test of market efficiency, being performed within the framework of a specific model, is a test of that particular model as well. Much research has been done on whether any of these statistics are proxies for others. Reiganum (1981) has shown that the price–earnings effect disappears when controlling for size. Recently, Fama and French (1992, 1993) have shown that size and book-to-market equity (portfolios proxying for factors related to size and book-to-market equity) have high explanatory power for the cross-section (variance) of expected returns. Considerable work has also been done on testing the conditional CAPM (Sharpe, 1964; Constantinides, 1982). Keim and Stambaugh (1986), among others, have shown that in a CAPM context the risk premium is time varying. Harvey (1989) and Ferson and Harvey (1991) show that the betas are also time varying. These authors have argued that failing to recognize the time variability may lead to a premature rejection of the model.

Recent work on the consumption-based asset pricing model has focused on the calibration of preferences and investment opportunities to include various market frictions (borrowing and short-sales constraints, transactions costs (Bewley, 1992; Heaton and Lucas, 1992)), time non-separable preferences (Constantinides, 1982), recursive preferences allowing for a partial separation between the coefficient of relative risk aversion and the coefficient of intertemporal substitution (Epstein and Zin, 1989, 1991) and various forms of agent heterogeneity (following the work of Constantinides, 1982; Mankiw, 1986; Heaton and Lucas, 1992; Telmer, 1993).

Asset Pricing Resources:

  1. Bewley, T. (1992). Some thoughts on the testing of the intertemporal asset pricing model. Manuscript, Cowles Foundation, Yale University.
  2. Black, F., Jensen, M. & Scholes, M. (1972). The capital asset pricing model: some empirical tests. Studies in the theory of capital markets. (Ed.) Jensen, Michael, New York: Praeger, 79–121.
  3. Connor, G. & Korajczyk, R. (1986). Performance measurement with the arbitrage pricing theory. Journal of Financial Economics, 15, 373–94.
  4. Grossman, S., Melino, A. & Shiller, R. (1987). Estimating the continuous time consumption-based asset pricing model. Journal of Business and Economic Statistics, 5, 315–28.
  5. Harvey, C. (1989). Time-varying conditional co-variances in tests of asset pricing models. Journal of Financial Economics, 24, 289–318.
  6. Heaton, J. & Lucas, D. (1992). The effect of incomplete insurance markets and trading costs in a consumption-based asset pricing model. Journal of Economic Dynamics and Control, 16,601–20.
  7. Lucas, R. (1978). Asset prices in an exchange economy. Econometrica, 46, 1426–45.
  8. Reiganum, M. (1981). Misspecification of capital asset pricing. Journal of Financial Economics, 9, 19–46.
  9. Ross, S. (1976). The arbitrage theory of capital asset pricing. Journal of Economic Theory, 13, 341–60.
  10. Shanken, J. (1985). Multi-beta CAPM or equilibrium-APT? A reply. Journal of Finance, 40, 1189–96.
  11. Telmer, C. (1993). Asset pricing puzzles and incomplete markets. Journal of Finance, 48, 1803–32.