African Journal of Business Management Vol. 6(16), pp. 5472-5478, 25 April, 2012 Available online at http://www.academicjournals.org/AJBM DOI: 10.5897/AJBM11.2754 ISSN 1993-8233 ©2012 Academic Journals

Review

Risk management of structured notes Zhengwei Ma1* and Michael Wang2 1

School of Business Administration, China University of Petroleum-Beijing, China. 2 Business School, Northwestern Polytechnic University, USA. Accepted 7 December, 2011

Structured notes have become a focus of controversy lately, due to the much-publicized losses sustained by investors who were caught off guard by the reversal in the trend of interest rates. Since structured notes are relatively new and not as well known as other, more traditional, investment options in the past few years, it is important that people should be educated about this instrument. This paper will introduce common structures of risk and explain the type of analysis in which investors should engage both prior to and subsequent to purchase. It is intended to (1) ascertain the nature and characteristics of structured notes, (2) to examine the risks inherent in structured notes, and (3) to recommend some risk-managing strategies when investing in structured notes. Key words: Structured notes, derivatives, investments, risk management.

INTRODUCTION Investment and management of monetary assets are essential to a mature economy and have drawn considerable attention from economists and financial professionals among others. Basically, there are two approaches to the investment of assets. One is based on empirical verification such as chart analyses, and the other is grounded in academic models theory such as the random walk theory. The empirical approach sometimes does not work efficiently, particularly when the investing environment has changed. In addition, investing is mostly a mental approach, and therefore, even when the same approach is used, the results may vary depending on the idiosyncrasy of investors. On the other hand, some argue that academic financial theories are useless from the perspective of practical investing. Investing practitioners especially feel that investing with a textbook approach does not work. The essence of the financial market is „uncertain‟, and no theories can assure profit generation. Thus, a good investment theory is one that demonstrates how to deal with the market uncertainties. The classical financial theory, the efficient-market hypothesis (EMS), states that since market prices reflect all available information,

including that about the future, the only difference between the stock prices at time t and time t+1 is something that cannot possibly be predicted. Hence, in an efficient market, prices cannot be statistically tested and investigated for the random-walk hypothesis. However, researchers are also cognizant of many stock market anomalies that seem to be inconsistent with the efficient market hypothesis. As such, this paper will explore the exact nature of structured notes as a riskmanagement tool for investments.

*Corresponding author. E-mail: [email protected].

Most structures contain embedded options, generally

Structured notes There is marked difference between structured notes and straight derivatives. The derivatives value is entirely dependent on a certain security or index while structured notes are hybrids with components from both straight debts instruments and derivatives. These securities are designed with various possible indices or rates (McCann and Cilia, 1994): The Federal Home Loan Bank (FHLB), one of the United States‟ largest issuers of such products, has more than 175 indices or index combinations against which cash flows are calculated.

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sold by the investor to the issuer. These options are primarily in the form of caps, floors, or call features. The identification, pricing and analysis of these options give structured notes their complexity. Thus, the complexity of structured notes added to its attractiveness to investors. A main reason for investing the notes lies with the low interest rates experienced in the United States for the five years preceding 1994. For this reason, receiving sufficient returns on investments was difficult during this period. Structured notes offered the potential for more returns since its cash flows and market values were linked to various benchmarks. The benefit of higher returns via structured notes led many investors to acquire this instrument. Moreover, as structured notes are backed by the credit and full faith of the U.S. government it became a highly sought investment package (McCann and Cilia, 1994). Structured notes can be used effectively to hedge against unique risks concerning the investor. Still another reason for buying structured notes is in availability of market information that allows investors to anticipate the ups and downs of stock price. Purpose of the study The goals of this study are: 1) To determine the nature and characteristics of structured notes; 2) To examine the risk inherent in structured notes and 3) To recommend risk-management strategies when investing in structured notes. CAPITAL MARKET THEORY AND STRUCTURED NOTES Market efficiency hypothesis A number of studies of finance have premised that the market is efficient. The efficient-market hypothesis indicates that since market prices reflect all available information about the future, the only difference between the stock prices at times t and t +1 is a phenomenon that cannot possibly be predicted. Hence, in an efficient market; prices can be statistically tested and investigated for the random-walk hypothesis. That is, there are no biases with regard to future cash flows, and therefore, security prices can be reliable. As a result, if the market is efficient, investors cannot earn returns which exceeds risks. Sharpe (1964), Lintner (1965) and Black et al. (1972) introduced the capital asset pricing model (CAPM), which predicts appropriately required rate of returns. This theory assumes that rational investors do not take on any diversifiable risk. Fama and French (1992) describes efficient market as “A market in which firms can make production-investment

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decisions, and investors can choose among the securities that represents ownership of firms‟ activities under the assumption that security prices at any time fully reflect all available information”. This author takes efficiency at three levels: 1) The weak-form hypothesis asserts that stock prices already reflect all information that can be derived by examining market-trading data such as trading volumes, charts and long / short statistics. This examination is known as technical analysis, which could not be used to earn and/or select stocks to make profits above the average. 2) The semi-strong-form hypothesis states that all publicly available information regarding the prospects of a firm such as earnings, stock splits, dividends, must be reflected immediately in the stock price. Also, investors selecting stocks using public or open-source information would not be able to earn more profits than those who just use a simple “buy and hold” strategy in this scenario; and, 3) The strong-form hypothesis posits that stock prices reflect all information relevant to the firm, including the information available only to company insiders. Inside information could lead to “extra” profits, albeit illegal. Ideally, in efficient securities markets, the price can be expected and in the long run such price will approximate the average price within that particular class of securities. But in reality, markets are either weak-form or semistrong-form efficient but probably, not strong-form efficient. This means that the prices and returns of securities could not be predicted. This unpredictability or uncertainty may cause risks. However, even the pioneer studies by Cowles (1933) made it clear that investment professionals do not and cannot beat the market. It has already been stated in this article that an efficient market is one where the prices of securities fully reflect all available information, but there are differences between efficient-market and capitalmarket efficiency. The criterions for ideal capital-market efficiency are: 1) Free transaction for trading securities, 2) Free access to all available information, and 3) All agree on the implications of current information on both current and future distribution of individual security prices. However, there is no ideal capital-market efficiency in the real world. In any market there is a combination of transactional costs, costly information and disagreements concerning commonly held information. A large part of EMH is about measuring the effect that these three factors have on efficient allocation of free-market prices. The debate about market efficiency has resulted in an abundance of empirical studies attempting to determine

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whether particular markets are in fact „efficient‟ and, if so, to what degree. In fact, a majority of studies of technical theories have come to the result that it is difficult to predict prices. In overall, this literature suggests that markets are in fact efficient, although some researchers have uncovered many stock-market anomalies that seem to be inconsistent with the efficient-market hypothesis. Trading strategies seem to be widely used among fund managers, but there is little evidence that in practice they would generate excess returns in practice (Malkiel, 2003). Empirical evidence proves that the use of trading strategies might be closely related to behavioral anomalies. When the market is efficient, it is impossible to consistently obtain abnormal returns using a trading strategy based on a given set of information. This postulate, of course, is based on the premise that (1) all investors have cost-free access to currently available information about the future (2) they are good analysts and (3) they pay close attention to the market processes and adjust their holdings accordingly. Up to the late 1970‟s, empirical studies supported the view that the capital markets are information-efficient. Many models related to security valuation have been based on this concept of “informational efficiency of capital markets.” However, the late 1970‟s and the 1980‟s brought in evidences that laid bare various anomalies related to the capital-market efficiency and cast its validity in doubt. Using historical data and publicly available information ruling out the efficiency of markets, many studies have demonstrated how trading strategies may yield abnormal rates of returns.

exhibit any trend of investing, their activities would affect the stock prices randomly. Thus, the random-walk theory cannot be refuted. Moreover, the objection that all related information is not spread immediately can be refuted either. This is because if investors who have important information relevant to making security prices take the initiative to invest, the stock prices will move the way the randomwalk theory predicts. Finally, there is the objection that the efficient market cannot be created because the existence of transaction costs exist in the actual market will restrict rational investing. However, the objection does not hold either because, at least in the markets of developed countries, transaction costs are low. The random-walk theory assumes that fundamental and technical analyses are useless because all new information affects stock prices so quickly that the next price movements are determined solely by uncertainties. Thus, the traditional styles of investing are time and costineffective. In addition, with the random-walk theory, active investment, which tries to gain a return by choosing a brand name or by considering the timing of buying and selling, is just a gamble because future prices cannot be predicted and thus the expected return is always zero. Strictly speaking, because there are transaction costs in the actual market, the expected return is „minus‟, that is, equal to the transaction costs. The expected return of active investing is lower than that of passive investing, which attempts at index return automatically. The reason for a mostly efficient market

The random-walk theory The random-walk theory assumes that because price movements will not follow any trend, knowing the past price movements will not help predict future price movements. The random-walk theory posits that price movements will follow the geometric Brownian motion if the market is efficient. The geometric Brownian motion is such that if pollen grains are put in a bin, they will move randomly (Brown, 1827). In order for security prices to be affected by Brownian motion, the following three premises must be present: (1) all information which can affect stock prices is spread in the market immediately (2) there are no transaction costs and taxes on buying or selling securities or stocks and (3) all investors work to maximize monetary profits. A general rebuttal of the random-walk theory is that all investors do not invest in the market rationally and thus, the stock prices do not follow the Brownian movement. However, if certain investors do not invest in the market rationally, the security prices in which other, more rational, investors invest will be largely affected, and eventually the security prices will move close to the geometric Brownian motion. In addition, if irrational investors do not

When the markets are mostly efficient, the price movement will follow the pattern predicted by the random-walk theory since a huge amount of money is invested in the market. Since many professionals participate in this investment and organise the system for analysis, all available information is then reflected in the stock prices (Behrensb et al., 2004). The market, however, is not perfectly efficient because investors would put most of their money in passive investing after arbitrage opportunities are eliminated. In the mostly efficient markets, lots of rational investors invest huge amounts. When the arbitrage decreases, most rational investors cannot earn enough returns to maintaining the costly system of analysis because many of them are competing for the now low arbitrage opportunities. As a result, most of the money will flow into low-cost passive management making it difficult for available information to be reflected in the stock prices, since passive investing is done systematically. Moreover, when market inefficiency starts to increase, rational investors will start to invest actively forcing the markets back to efficiency. Consequently, the structure of markets is characterized by the proportion of markets inefficiency circulating in the mostly efficient markets.

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On the contrary, as the psychological bias of investors affects the stock prices, the markets will not be perfectly efficient. Investors are not always rational and if such a psychological bias has exhibits a pattern, markets are not efficient. The premise of market efficiency is that the market is systematically managed at low costs and that the security prices are not distorted by huge irrational investments. For example, governments and central banks are irrational investors because they do not follow economic rationale as much as they do political agendas. If the activities of these investors greatly affect the making price, the rational public investors would stop investing. If these phenomena would occur frequently, or if the market system lack credibility, rational investors would hesitate to come in. As a result, rational investors would not affect the price setting and, hence, the nature of the efficient markets would fade out. The markets in developed countries are efficient. Those in developing countries, on the other hand, are inefficient because there is considerable government intervention in the market and because the markets are not so big in the first place. For instance, even though Japan is a developed market, the efficiency of the Japanese market is slightly low compared to that of the United States and the United Kingdom because the government intervention is frequent, the diversity of investors is small and the level of system maturity is low. In Japan, the shape of the logarithm of the empirical price movement tends to be slightly smaller than that of the United States and United Kingdom. The efficient-market hypothesis has been challenged by numerous studies on the grounds that, in stock market, there are often under-reactions or over-reactions to information (Hong and Stein, 1999). Accordingly, in a variety of markets, sophisticated investors can earn superior or risk-free profits by taking advantage of market imperfections that is under-reactions or over-reactions. Fama (1998), however, is against the empirical findings generated from these studies. He outlined a number of studies and identified a series of flaws in these studies and proposed two reasons why the efficient market hypothesis can never be rejected. First, Fama (1998) stated that an efficient market generates a category of events that individually suggests that prices may overreact to information. He suggested that in an efficient market apparent under-reactions will be about as frequent as over-reactions. Consequently, if anomalies split randomly between over-reactions and underreactions, they are consistent with the efficient-market hypothesis (Fama, 1998). Second if unusual behaviors of long-term returns are so regular that they cannot be attributed to chance, then an even split between over and under-reactions is a pyrrhic victory for the market (Fama, 1998). Fama asserted that the long-term return anomalies are sensitive to methodology turning marginal or even disappear when exposed to different models for

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expected (normal) returns or when different statistical approaches are used to measure them. Fama (1998) further stated that even if these models are viewed oneby-one, most long term return anomalies can be attributed to chance. Building on Fama‟s (1998) criticisms, Hong and Stein (1999) have opened up new grounds for testing momentum and reversal. They have started developing behavioral models intended to unify a range of previously documented anomalies in asset returns (Hong and Stein, 1999). They also adopted a parsimonious model of investor sentiment, or investors form beliefs, consistent with empirical findings of under-reactions and overreactions to stock markets. Hong and Stein (1999) tested the Hong-Stein version of the under-reactions hypothesis. Their aim was to provide evidence that momentum reflects the gradual diffusion of firm-specific information. Hong and Stein (1999) began their work by sorting stocks into different classes wherein information is a priori more or less likely to spread gradually. Their hypothesis was based on the assumption that stocks with slower information diffusion should exhibit more pronounced momentum. Hong and Stein (1999) considered firm size a basis for the theory‟s first set of tests. This is because information about small firms gets out more slowly (Hong and Stein, 1999). Given the fact that traditional asset-pricing models, such as the capital asset pricing model (CAPM), the arbitrage pricing theory (APT), or the inter-temporal capital asset pricing model (ICAPM), have a hard time explaining the growing set of stylized facts, most modern models today are turning to behavioral theories, where “behavioral” may be thought of as involving some departures from the classical assumptions of strict rationality and unlimited computational capacity on the part of investors (Hong and Stein, 1999).

Anomalies The word “anomaly” has been used in financial markets as the norm of an irregularity which has deviated from neo-classical financial theories such as EMS and the CAPM. While a number of researchers have shown strong evidence that stock markets are highly efficient, many systematic deviations from theoretical expectations have been reported. Since the 1980‟s, many researchers have questioned the efficiency of the markets. Anomalies that yield more returns than risks indicate the limits to arbitrage. Benchmarks such as the sub-indexes model of the S and P 500; however, pose a problem as we examine anomalies because it is possible to conclude that the model as a benchmark is not suitable for value, and therefore, anomalies are observed. The judgement whether the model used as a benchmark is wrong or whether the market is really inefficient is very difficult to make. However, not all anomalies need a benchmark and

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Percent deviation

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Figure 1. Log Deviations from Royal Dutch 1. The deviations are on a percentages basis from the theoretical parity of Royal Dutch and Shell shares and American Depository Receipt (ADRs), traded on the New York Stock Exchange (NYSE). Data are from the Centre for Research in Security Prices (CRSP).

such cases can help us to judge whether the model is appropriate. Hence, subsequently two examples of anomalies that do not need benchmarks are introduced.

Royal Dutch and Shell Transport Froot and Dabora (1999) examined stock prices of Royal Dutch and Shell Transport, whose stocks are traded around the world while having different trading and ownership habitats. The authors pooled these firm‟s cash flows with integrated markets, so that the stocks would move together. Royal Dutch and Shell Transport are located in the Netherlands and England, respectively. They agreed to merge their interests on a sixty-to-forty (60:40) basis while remaining separate and distinct entities (Royal Dutch 20 F, 1994). Figure 1 shows log deviations from the Royal Dutch and Shell Transport parity. If stock prices reflect the fundamentals, the value of Royal Dutch should always be one-and-half times that of Shell Transport. However, it can be seen that the stock prices of Royal Dutch are not one-and-half times that of Shell Transport. Furthermore, the period of mispricing is considerably long and shows that arbitrage do not work and that the markets are not efficient. This is an anomaly. Froot and Dabora (1999) argue that the stock prices of three of the world‟s largest and most liquid multinational companies are strongly influenced by location factors. In this case, they stated that market-wide noise shocks from irrational traders, which affected locally traded stocks more than foreign traded stocks, could explain the comovements (Froot and Dabora, 1999).

Standard and poor’s 500 Index and Nikkei’s 225 Index Shleifer (1999) demonstrated that stocks newly included into the Standard and Poor‟s 500 Index had earned a significant and positive abnormal return, with an average of 3.5%, at the announcement of the inclusion and that this abnormal return lasted for at least ten days after the inclusion (Shleifer, 1999). Anomalies based on documented fundamentals and values are low-Price-tobook, low-price-to-sales, low-price-to-earnings, highdividend-yield, neglected-stocks, international-valuestudies and after-transactions-costs. The following headings will present a discussion of structured-notes markets and their risks. The analysis and framework are basically the same as in stocks, foreign exchange and other investments based on capital market theories. Structured notes, however, are much more complex and the use of these theories will depend upon the kinds of contracts the investors will purchase. Structured-notes market and risks Structured notes gained much attention from regulators and investors in 1994 because the series of increments of interest rates by the Federal Reserve had resulted in radical changes causing large and unforeseen losses for many investors of structured notes (McCann and Cilia, 1994). These securities fell below par and they became less attractive as investments compared to other instruments. At any rate, even though there had been several losses from structured notes, these securities had generally received triple-A rating since they were issued by Federal Home Loan Banks (GSEs) such as Federal Home Loan

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Banks (FHLB), Federal National Mortgage Association (FNMA), Sallie Mae Corporation (SLMA), and Federal Home Loan Mortgage Corporation (FHLMC). As such, their credit risk was minimal; however, investors did not realize that there are other risk factors which should have been considered. Most prominent of these risks are those related to interest rates, markets and liquidity (McCann and Cilia, 1994). Much of the innovation with structured notes in recent times has been introduced through a security coined as “Euro medium-term notes” (MTNs). These instruments had been distributed internationally, thus, estimating its market size globally has been difficult. This is because structured notes had various designs. Nevertheless, Euro MTNs were estimated to be about fifteen percent (15%) of total MTN value during 2003. These instruments were linked to various financial-asset prices, interest rates, equity prices, commodity prices, etc. (Rule et al., 2004). In structured-notes market includes investors, issuers, swap counterparties, arrangers and distributors. Most often, these issuers would enter into a swap in order to receive the cash-flows on the instruments. In return, issuers pay the floating interest rate such as a spread relative to London Interbank Offered Rate (LIBOR). Finally, the swap counterparty is the one that arranges the notes so that they could be sold further through distributors or aggregators such as retail banks (Rule et al., 2004). Many structured notes are issued in small denominations (for example, less than US $10 million) for sale to high net-worth Furthermore, the biggest investments in Euro MTNs made by high net-worth individuals were made in U.S. dollars, and in recent years, in euro-denominated notes. These investors were mostly from Asia, the Middle East and from among the customers of Swiss private banks. These investors bring notes which are linked to the US dollar or euro interest rates, with options to enhance the initial coupon (Rule et al., 2004). In the United States (US) domestic market, investments in callable notes have increased drastically, and there appears to be a search for yield due to low short-dated US interest rates. Banks and brokers in states such as Arkansas and Tennessee focused on the distribution of callable bonds to smaller US banks and financial institutions (McCann and Cilia, 1994; Rule et al., 2004). European and Asian life insurance companies have made large investments in structured notes in recent years. The motivation behind the risk-taking with these investments may underscore by one of two needs: either to risk their own asset portfolios in search of abovemarket returns or to hedge the options they have sold by buying similar options. Finally, life insurers would find it profitable to combine the purchase of derivatives with the purchase of a bond (Rule et al., 2004). Some issuers of structured notes retain the associated exposures to market risk as a means of hedging exposures for their portfolio. An example for this is the

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European Investment Bank‟s notes which were linked to UK retail price inflation could hedge partially the inflationlinked cash-flows in its loan records. At a larger scale, the Federal Home Loan Banks, Fannie Mae and Freddie Mac are colossal issuers of callable notes which are meant to purchase interest options, so that pre-payment risks on their holdings (for example, US home mortgages) can be hedged. This example illustrates how banks may use structured-notes to manage their liquidity risks (Rule et al., 2004).

A case study of Microsoft Corporation The daily historical equity performances of Microsoft Corporation from 2003 to 2008, as shown in Figure 2, are linked to structured notes. It can be seen in Figure 2 that the fluctuations from 2003 to 2008 are relatively small. The performance has its ups and downs, but utilizing sophisticated statistical tools, one can predict the trend line and evaluate the risks of investing in the stocks as linked to the structurednotes.

CONCLUSIONS The market for structured-notes is as worldwide as it is complex. It is intractable as all types of market risks may be embedded in a note. Wholesale financial markets such as equity, bond and foreign exchange are also tied up with structured notes. In general, fund flows and risk transfers are quite significant in the financial markets. Risk transfers are conducted among both investors and dealers. Structured notes are also means of borrowing, and as such, they pose certain problems for risk management, for example, controlling credit exposures to swap counterparties. Structured notes are used by many banks as part of a funding and liquidity strategy. Structured notes are instruments that can diversify the investment risk for a company by being linked to various indices. Such being the case, it is at the investors‟ discretion as to which indices they would choose to link up to. Unlike other investment instruments which are dependent upon one index, structured notes can be tied up with multiple indices that can protect one‟s investments. If the investors‟ main currency is the US dollar, they can hedge it against the yen or other currencies while protecting their investments. One method of risk management, particularly in structured notes linked to foreign currencies, is locking the investment to a ceiling wherein the exchange rate of the currency in use can be fixed into a certain position which is no lower than what had been specified in the contract. This lock can minimize losses in case the value of the currency in use goes down.

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Daily historical performance of the common stock of Microsoft Corporation

Figure 2. Equity of Microsoft Corporation as linked to structured notes.

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