Operations in Financial Services—An Overview (6 of 6)

9. PRICING AND REVENUE MANAGEMENT

9.1. Pricing of Financial Services: Background and Academic Research

Financial services organizations expend serious efforts and resources on pricing and revenue management. Applications are diverse; they include the setting of:

(i) interest rates (APR) on deposits and credit products,

(ii) trading commissions,

(iii) custody fees,

(iv) investment advisory fees,

(v) fund fees (which for hedge funds can be a function of assets and performance), and

(vi) insurance policy premia.

Pricing and revenue management are intertwined with many operations management functions in large financial services firms, because pricing strongly affects consumer demand for products and services, and customer attrition. Complicated pricing mechanisms can increase the volume of billing questions to call centers. All of these can have significant implications on how these products and services are best delivered (e.g., capacity issues, quality issues) as well as on cash (inventory) management.

In addition to market mechanisms, pricing in financial services may be driven by other factors or constraints that may complicate or simplify it. For example, usury laws specify maximum interest rates to be charged for lending to consumers or businesses by banks, credit cards, or pawn shops. Insurance regulations vary by jurisdiction. Fees in open-ended mutual funds offered to US investors are governed by the Investment Company Act of 1940. The Employee Retirement Income Security Act of 1974 (ERISA), which regulates US pension plans, specifies that plan trustees and investment advisors are considered “fiduciaries” who should act in the best interests of plan participants. Among the duties of fiduciaries is to ensure that the plan pays reasonable investment expenses, including fund, advisory, and custody fees, and trading commissions. Preferred pricing provisions, known as Most Favored Nation (MFN) clauses, are typically included in investment management agreements of pension plans governed by ERISA in the United States and by similar laws elsewhere. By the fiduciary standard of ERISA, transactions in accounts that hold plan assets must reflect the best value for the services received. Such services include execution of trades, research, investment advice, and anything else that may be paid through these transaction costs.

A body of literature exists in economics and finance on some aspects of pricing in financial services, and vendors offer pricing services and software, but little academic research with an operations management tion, we discuss the economic foundations of price formation and attempt to link them to research in finance on pricing practices specific to insurance, credit, and hedge funds. We also review the meager operations management literature on pricing in financial services, examine the challenges faced by researchers, and propose links to other research that might help remedy some of the issues.

9.2. Theory of Incentives and Informational Issues in Pricing of Financial Services

By the neoclassical economic assumption of rational individual behavior in perfect market competition, prices for financial products and services should be formed by:

(i) firms’ efforts to maximize profit, i.e., revenue minus cost, and

(ii) consumers’ desire to maximize their utility when faced with exogenous prices.

In this elegant model, information is perfectly known and shared by all economic agents. In a setting that more closely approximates reality, information is incomplete and asymmetrically shared, making price formation a more complex process. The theory of incentives addresses the informational issues that arise in the principal–agent economic relationship. In such a relationship, a “principal” delegates a set of tasks to an “agent” who possesses special competencies to perform the tasks and the two may have conflicting interests. Informational issues present in a principal–agent relationship include:

(i) moral hazard, whereby the agent has private information that can be used to take actions to serve its interests; such actions may work against the interests of the principal, who has to assume some of these actions’ adverse consequences, and

(ii) adverse selection, where an agent uses private information about its own characteristics to gain advantage in selecting a contract offered by the principal.

Non-verifiability is a third issue that may arise when the principal and the agent share information that cannot be verified by a third party, for example, a court of law. The related free-rider problem refers to asymmetric sharing of benefits and costs in resource usage among economic agents. The principal–agent model addresses the design of contracts with appropriate incentives to best align the interests of principal and agent in the presence of the informational issues of moral hazard, adverse selection, and non-verifiability. Laffont and Martimort (2002) focus on the situation of a principal dealing with a single agent, to whom the principal offers a take-it-or-leave-it contract without negotiation. Their book begins with a review of the literature on incentives in economic thought since Smith (1776) examined incentives in agriculture, and Hume (1740) explicitly defined the free-rider problem. Contract negotiations can be addressed by game theory; Camerer (2003) wrote a very accessible text on behavioral game theory in which he cites all relevant literature. The text by Tirole (1988) contains thorough discussions on price formation.


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Moral hazard and adverse selection are responsible for anomalies in insurance and credit product pricing. Akerlof (1970) noted that aggregate risk in segments of the insured population will be an increasing function of insurance premium paid. This is the case because private knowledge of individuals’ state of health, drinking or smoking habits, driving behavior, stability of employment, etc., leads them to accept or reject higher premia offered for health, life, automobile, or mortgage insurance. The same holds true for credit, as Stiglitz and Weiss (1981) noted. Higher interest rates offered would be a lot more likely to be accepted by borrowers whose private self-assessment of their default probability is high. These borrowers would then become even more likely to default because of the high credit cost burden. Insurance and credit are therefore rationed, because there is no price high enough to be profitable with certain customers. That means, as Rothschild and Stiglitz (1976) found, that equilibria in competitive insurance markets under imperfect and asymmetric information needed to be specified by both price and quantity of contracts offered. Stiglitz (1977) examined differences in the role of imperfect information in insurance pricing under a monopoly regime compared with perfect competition.

9.3. Pricing in Asset Management, Securities Trading and Brokerage, and Credit Cards

Pricing in the asset management industry consists primarily of fees charged on a client’s assets under management (AUM) in investment vehicles such as mutual funds, hedge funds, or separately managed accounts (SMAs). Fees can be

(i) fixed, regardless of AUM,

(ii) asset-based, i.e., a percentage of AUM, or

(iii) performance-based, i.e., dependent on AUM’s return.

Pricing structures reflect costs of different vehicles, are formed in the process of bringing investor demand into equilibrium with each vehicle’s capacity, and attempt to address the principal–agent issues between investor (principal) and investment manager (agent). Capacity of an investment vehicle refers to:

(i) operational infrastructure, which tends to be more sophisticated and expensive for hedge funds than for mutual funds, and has additional complexities for SMAs, and

(ii) implementation, whereby profitable opportunities become scarcer as investment vehicles become larger; and the effectiveness of execution of a vehicle’s investment strategies depends to a large degree on the market liquidity of the securities traded by the investment manager in the vehicle’s portfolio.

Mutual funds and similarly managed SMAs typically charge asset-based fees on a calendar basis. As assets grow due to good performance and inflows of new funds resulting from this good performance, the manager gets rewarded for skill and effort. As securities traded by mutual funds are typically liquid, implementation capacity is rarely an issue. In contrast, hedge funds and like-managed SMAs often face capacity constraints that limit their size due to diminishing returns to scale, which makes the asset-based fee an inadequate incentive for hedge fund managers. Hedge fund pricing, therefore, constitutes a more representative application of the principal–agent model because it must use more complete incentive mechanisms to minimize informational issues between fund manager (agent) and investor (principal). A typical pricing structure for hedge funds may consist of

(i) a base (or management) fee, which can be a percentage of AUM paid on a calendar schedule, and covers operating costs of the fund, and

(ii) an incentive (or performance) fee, which allows the manager to keep a share of the value created for the investor during agreed-on time intervals, to ensure that the interests of the two parties are aligned.

The manager earns an incentive fee if value is created for the investor according to an agreed-on metric usually based on either monetary units or rates of return (see Bailey 1990). Typically, incentive fee arrangements have asymmetric payoffs, i.e., they reward gains and do not penalize losses. However, they often require that an investment’s value be at or above a historical maximum, called a high water mark (HWM), before an incentive fee becomes payable to the manager. This implies that prior losses, if any, must have been recovered. Earning high incentive fees depends on fund manager skill; hedge funds with highly skilled managers have higher revenues. Such hedge funds can afford the higher expense of setting up and maintaining a robust operational infrastructure and control framework, as discussed by Alptuna et al. (2010). Incentive fees are also common in private equity, where they are interest and paid by investors on the liquidation of a partnership and distribution of proceeds.

A new rule adopted by the Securities and Exchange Commission under Section 205 of the Investment Adviser’s Act of 1940 permitted the use of performance fees by registered investment advisers. Several published articles then examined performance fee schemes, with emphasis on inherent moral hazard and its mitigation. Record and Tynan (1987) described incentive fee arrangements, and examined the basic issues involved. Davanzo and Nesbitt (1987) analyzed incentive fee structures and their impact to the business of investment management. Kritzman (1987) proposed ways to deal with moral hazard issues, and to reward the manager’s skill rather than investment style or chance; these issues were also addressed by Grinblatt and Titman (1987). Grinold and Rudd (1987) added another perspective by examining fee structures that would be appropriate for the investor and for the manager according to the latter’s investment skills. Bailey (1990) demonstrated that incentive fee metrics based on value added in monetary units serve investors’ interests better than metrics based on rates of return. Lynch and Musto (1997) examined how incentive fees compare with asset-based fees, especially in extracting value-creating effort by the manager. Anson (2001) valued the call option implicit in incentive fees by Black–Scholes analysis. The nonlinear optionality in incentive fees was found by Li and Tiwari (2008) to be optimal even for mutual funds. Golec and Starks (2004) examined the reduction in risk levels of mutual funds after the option-like incentive fee option was prohibited by an act of Congress in 1971.

More recent works have studied the role of HWMs in incentive fee contracts. In a seminal paper, Goetzmann et al. (2003) valued incentive fee contracts with HWMs using models with closed-form solutions. An-son (2001) and Lee et al. (2004) examine the free-rider problem in hedge funds that offer all investors the same HWM: late investors can avoid paying incentive fees if they enter after losses suffered by earlier investors. This issue was addressed in practice by offering investors a different HWM based on their time of entry.

Pricing for security transactions is determined in a deregulated and competitive market. It has been experiencing a downward trend driven by the dramatic cost reductions brought by technological innovations such as electronic trading (see Bortoli et al. 2004, Levecq and Weber 2002, Stoll 2006, 2008, Weber 1999, 2006). Transaction commissions typically pay for the costs of brokerage, clearing and execution, trading, research, and investment advice before the brokerage firm can make a profit. Economies of scale are very important, because a brokerage’s operational infrastructure requires a very high capital investment and is costly to operate. Generally, institutions have access to lower pricing than individual investors. A pricing application for brokerage commissions and investment advisory fees was studied by Altschuler et al. (2002). They developed models to determine appropriate commission rates for a new discount brokerage channel, and asset-based fees for advice and unlimited trading in full-service accounts introduced by Merrill Lynch. Among the issues to contend with were adverse selection, which might prompt full-service clients without a strong relationship with their financial advisors to select the low-cost discount channel. Another known issue is moral hazard, when an advisor might not be committing much effort to a client’s portfolio after the account was converted to the asset-based annual fee.

Credit card pricing and line management were studied together by Trench et al. (2003). They built a Markovian model to select the optimal APR and credit line for each individual cardholder based on historical behavior with the goal to maximize Bank One’s profitability. Offering an attractive APR and a large credit line to entice a cardholder to transfer a balance presented the moral hazard issue of the client taking advantage of the offer and then defaulting, which the model successfully addressed. In his book, Phillips (2005) discusses consumer loan and insurance pricing. He expands on it in a more recent book (Phillips 2010) that also includes a chapter by Caufield (2010), who examines pricing for consumer credit, including credit cards, mortgages, and auto loans. In his book, Thomas (2009) discusses the use of risk-based pricing in consumer credit. Wuebker et al. (2008) also wrote a book on pricing in financial services.

9.4. Revenue Management in Financial Services: Challenges and Opportunities

Revenue management principles can be applied to financial services pricing with some adaptation. The framework can address key considerations, such as rationing in credit and insurance (see, e.g., Akerlof 1970, Phillips 2010, Stiglitz and Weiss 1981). Evidence of rationing is reflected in protecting capacity for airline fare classes (Talluri and Van Ryzin 2005). Another key consideration is consumer behavior (Talluri and Van Ryzin 2004). Some revenue management concepts, for example, capacity, can be quite different in financial services than in the transportation and hospitality industries. It can also be idiosyncratic to each financial product, for example, a CD vs. a mutual fund, or a hedge fund. Boyd (2008) discussed the challenges faced by pricing researchers. Identification problems exist (see Koopmans 1949, Manski 1995) that make it hard to build demand curves from data and therefore figure out demand elasticities, which are key to pricing. These problems are especially acute in industries where sales and client relationship persons have a lot of information on customers, including demand and price elasticities. Such information remains private and not centrally shared, as is often the case in some areas of financial services. For example, firms have databases with prices of closed sales but no information on prices of refused offers; the latter remains in the field. Remedies have been proposed and can be considered, such as Gonik’s (1978), which Chen (2005) analyzed and compared with a set of linear contracts. Tools used by Athey and Haile (2002) may also be considered.

10. CONCLUDING REMARKS AND FUTURE RESEARCH DIRECTIONS

In this paper, we have attempted to present an overview of operations management in the financial services industry, and tried to make the case that this industry has several unique characteristics that demand attention separate from research in services in general. We have identified a number of specific characteristics that make financial services unique as far as product design and service delivery are concerned, requiring an interdisciplinary approach. In Appendix A, we provide an overview table of the various operational processes in financial services and highlight the ones that have attracted attention in operations management literature. From the table in Appendix A, it becomes immediately clear that many processes in the financial services industries have received scant research attention from the operational point of view and that there are several areas that are worthy of research efforts in the future. These include each step in the financial product and service life cycle as well as in the customer relationship life cycle.

Much work remains to be done on the design of financial products so that they are

(i) easier to understand for the customer (resulting in fewer calls to call centers),

(ii) easier to use (better online and face-to-face interactions, with less waiting),

(iii) less prone to operational risks induced by human errors,

(iv) easier to forecast and arrange the necessary operational resources for, and

(v) able to take advantage of pricing and revenue management opportunities.

Service designs need to recognize the fact that financial services are relatively sticky, involve long-term relationships with customers, and are at the same time prone to attrition due to poor performance or frustrating service encounters. Anecdotal evidence suggests that it is six times more expensive to acquire a new client than to service an existing one, making operations a really important factor in financial services.

As described in this paper, there is an extensive literature on traditional service operations research topics such as waiting lines, forecasting, and personnel scheduling that are applicable to financial services as well. Inventory models have been successfully applied to cash and currency management. Operational risk management is an emerging area that is attracting quite a lot of attention lately. We expect researchers to branch out and address other non-traditional operational issues in financial services, some of which we have highlighted here. Many of these are likely to be cross-disciplinary, interfacing information technology, marketing, finance, and statistics.

Another area with a potentially significant payoff is the optimization of execution costs in securities trading. Amihud and Mendelson (2010) and Goldstein et al. (2009) examine transaction costs in recent studies, and Bertsimas and Lo (1998) develop trading strategies that optimize the execution of equity transactions. A significant body of work exists in algorithmic trading, market microstructure, and the search for liquidity in securities markets (see, e.g., O’Hara 1998). Operations management researchers can examine the problem and develop solutions by synthesizing elements from this diverse set of disciplines and points of view.

Another interesting area of research could be the integration of the various objectives for improving operations in financial services in which interactions among components can be viewed and modeled holistically. For example, most financial services are quite keen on improving the productivity of their processes. However, one has to keep in mind that there are strong relationships between the productivity of the processes, the operational risk encountered in these processes, and the quality of the services delivered to the clients. When one reduces headcount, productivity may indeed go up; however, the operational risk may increase and the quality of service may go down. It is of great interest to the financial services industry that these interdependencies are well understood.

Pricing and revenue management of financial services could be an area that is ripe for academic research with a potential short-term payoff that may be large. Operations management researchers could leverage related work in economics, finance, and may adapt revenue management principles to develop novel pricing methodologies for financial services.

Operations management research may also be key in enabling visionary ideas for reforming corporate governance. In the principal–agent relationship between corporate shareholders and management, proxy voting is the most important tool available to shareholders for ensuring that management acts according to their interests. This tool is seldom effective due to shareholder apathy and obstacles to voting through institutional ownership of shares. Holton (2006) proposed proxy aggregation mechanisms, such as a proxy exchange, that could make the process more effective in improving corporate governance. Such mechanisms would ensure that the voting of proxies is handled by informed entities acting in the best interests of shareholders, with thorough knowledge of the issues to be voted on. Implementation of proxy aggregation mechanisms is almost certainly expected to face complex logistical issues, which operations management can examine and address.

REFERENCES

APPENDIX

1 | 2 | 3 | 4 | 5 | 6


–Emmanuel D. (Manos) Hatzakis, Suresh K. Nair, and Michael Pinedo. Hatzakis is a Vice President in Goldman Sachs Asset Management. Nair is a professor with the Department of Operations and Information Management at the University of Connecticut. Pinedo is the Julius Schlesinger Professor of Operations Management at the Leonard N. Stern School of Business at New York University. School of Business,


Acknowledgments

 

The authors thank five anonymous referees, an editor, and other readers for constructive comments that helped revise an earlier version of the paper. The presentation, content, flow, and readability of the paper has benefited as a result. The views expressed in this paper are those of the authors and do not necessarily reflect those of Goldman Sachs.

 

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