forecasting foreign exchange rates: an expert judgment approach

Socio-Econ. Plann. Sci. Vol. 21, No. 6, pp. 363-369, Pnnted in Great Britain. All rights reserved

1987 0038-0121/87 $3.00 + 0.00 Copyright 0 1987 Pergamon Journals Ltd

FORECASTING FOREIGN EXCHANGE RATES: AN EXPERT JUDGMENT APPROACH

ANDREW R. BLAIR, ROBERT NACHTMANN, JOSEPHINE E. OLSON and THOMAS L. SAATY

Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA 15260, U.S.A.

(Received 2 May 1987)

Abstract-Studies have indicated that forecasts by market experts can be more accurate than time series forecasts. This article describes a process for structuring an expert foreign exchange forecast using Saaty’s Analytic Hierarchy Process (AHP). The specific example developed is a forecast of the yen/dollar spot exchange rate from the standpoint of a company considering the desirability of arranging for forward exchange cover.

INTRODUCTION

A number of studies suggest that forecasts of financial market phemomena by market analysts and other experts can be more accurate than time series forecasts [l]. We are aware of few studies, however, that describe the process by which experts pool and integrate their knowledge in order to produce a forecast. A widely employed approach for structuring complex decision making is the Analytic Hierarchy Process (AHP) developed by Saaty [2]. This paper demonstrates how the AHP framework can be employed to produce a foreign exchange rate forecast incorporating expert judgment. This method should be of use in a variety of contexts including that of a firm whose managers have access to knowledge perti- nent to foreign exchange market outcomes. The specific example concerns the future value of the Japanese yen/U.S. dollar exchange rate from the standpoint of a company considering the desirability of arranging for forward exchange cover.

The first section of the paper describes the decision support system we have employed. An outline of the, factors included in the forecasting framework is presented in the second section. The third describes the actual exercise employing this approach, and the final section provides concluding perspectives.

DECISION SUPPORT SYSTEM: THE ANALYTIC HIERARCHY PROCESS

The decision support system used to structure our framework, the AHP, organizes clusters of factors related to a problem into a hierarchy, as depicted in Fig. 1. The method has the following components:

(1) The problem is decomposed into a hierarchy. A cluster in a given level consists of a few manageable factors. Each factor in a cluster is further decomposed into a simpler set of factors. Each of the simpler sets appears in the next lower level of the hierarchy where it is decomposed into even more primitive factors. This decomposition provides an efficient way of structuring a complex problem.

(2) A measurement methodology is used to establish priorities within the cluster which comprises the second level as well as within the clusters on each succeeding level of the hierarchy. An evaluation is made of the relative importance of pairs of factors in a cluster located on a given level with respect to the influence they exert upon the related factor in the next higher cluster. A 9-point scale is used to make nu- merical judgments in effecting pairwise comparisons. Each pairwise comparison becomes an element of a square matrix associated with a given cluster.

(3) In order to establish the relative importance of the factors in a cluster, the procedure solves for the principal eigenvalue in each pairwise comparison matrix. The associated principal eigenvector yields the relative importance or priority of each factor’s influence on the related factor in the next higher cluster. Since this method is based on pairwise comparisons, the procedure also includes an analysis of the consistency (transitivity) of judgments, thus al- lowing for possible revisions.

(4) A weighting procedure is used to synthesize the priorities of the factors in a given cluster by multi- plying them by the priority of their parent factor. These weighted priorities are then added for each factor in that cluster. The process is repeated for every cluster on a given level. In general one is interested in the composite weights of the alternatives at the bottom level of the hierarchy (e.g. level 4 in Fig. 1).

(5) The method is amenable to group participation. Consensus judgments, however, are not necessarily required, although such a procedure was employed in the exercise described in this paper.

THE FOREIGN EXCHANGE FORECASTING FRAMEWORK

This section outlines a set of foreign exchange rate forecasting factors. The treatment is eclectic and draws upon existing theories of foreign exchange rate determination. It should be emphasized, however, that the identification and conceptualization of the factors may well vary depending on the composition

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364 ANDREW R. BLAIR et al.

Level 1

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of the expert group. Accordingly, application of this method requires that careful consideration be given to the range of competencies and experiences of the experts participating in the process. However, small perturbations in choices have been shown to have little effect on the judgments [3]. In this instance, the expert group consisted of the first three authors, all of whom teach and research in the areas of international economics or international finance.

Six possible primary indicators of future spot exchange rates vis-a-vis the dollar were identified by the group: (1) relative interest rates (designated as INTRAT in Fig. 2); (2) forward exchange rate biases (FDBIAS); (3) official exchange market intervention (EXCINT); (4) relative degree of confidence in the U.S. economy (CONFUS); (5) the size and recent direction of the U.S. current account balance (CURBAL); (6) the past behavior of exchange rates (PASREC). Weightings for these factors can be expected to vary not only with the make-up of the expert group but also with the time frame for the forecast and the currency in question.

Relative interest rates (INTRAT)

This factor is intended to capture the impact on the exchange rate of changes in interest rate differentials between foreign and domestic financial centers. Other things being equal, changes in interest rates force

Changes in exchange rates as multicurrency portfolios are adjusted. For example, if interest rates in the U.S. rise relative to world rates, the demand for dollar- denominated assets will rise, resulting in an increased demand for dollars in the foreign exchange market.

Forward exchange biases (FDBZAS)

A class of models holds that the forward exchange rate (as determined by the existing interest rate differential between foreign and domestic financial centers) provides an unbiased estimate of the future spot rate [4]. In general, such models assume floating exchange rates and both an absence of capital controls and transactions costs, as well as perfect substitutability of domestic and foreign assests in terms of their risk characteristics and maturity. Forecasting future spot exchange rates under such conditions is quite straightforward: one simply uses the relevant forward rate. (Alternatively, if a forward market for the currency does not exist, one can directly apply the interest rate differential.)

In point of fact, there are transactions costs and exchange/capital controls. Where the latter exist, a disparity may emerge between the observed forward rate premium (discount) and that which would be calculated from the interest rate differential; this may indicate that arbitrage opportunities are being re- stricted. In the case of currencies that are at least partially fixed (e.g. currencies in the European Mon- etary System), this disparity may be signalling a misalignment and a forthcoming exchange rate ad- justment. However, since asset substitutability is not likely to be perfect, it would also be necessary to adjust the forward rate to capture a risk premium before using it as an indicator of the future spot rate [5].

Oficial exchange market intervention (EXCZNT)

Another factor is central bank intervention in the

Forecasting foreign exchange rates 365

foreign exchange markets. One view suggests that the size of any reasonable intervention will be so small relative to today’s huge market volume that the impact on exchange rates will be small and transitory. Others believe that intervention can be effective if it alters market expectations by signalling a change in exchange rate policy [6]. The coordinated intervention resulting from the September 1985 “G5” agreement designed to depress the exchange value of the dollar has been cited in the financial press as an example of this phenomenon [A.

Still others argue that “sterilized” interventions (that is, those in which offsetting domestic actions are taken to avoid any impact on the monetary base) can only have an impact when differently denominated assets are not perfect substitutes [8]. Finally, if central bank intervention is not sterilized, it is likely to affect interest rate differentials and, therefore, exchange rates whether or not domestic and foreign currency assets are perfect substitutes.

Relative confidence in the U.S. (CONFUS)

Investor confidence in the relative economic and political stability of the U.S. and its medium- and longer-term prospects for continued economic growth and prosperity were regularly invoked by various commentators seeking to explain the un- precedented appreciation of the dollar between early 1980 and early 1985. Such conditions presumably enhanced projected rates of return to be earned on investments in the U.S. relative to those available elsewhere, thus inducing an increased demand for dollars in the foreign exchange markets.

An increase or decrease in world confidence in the relative economic or political stability of two or more countries could thus result in significant revisions in expenditure and investment strategies. To the extent that a summary judgment can be made for the forecast period regarding such shifts in confidence, this factor is an element in the overall framework.

Current account balance (CURBAL)

The countries’ current account balances (or their trade balances as a more regularly reported proxy) may also be a relevant factor. In the traditional view, foreign currency is demanded primarily to purchase a desired flow of foreign goods and services, and a current account deficit provides an indication that the domestic currency will depreciate in order to elimi- nate the country’s excess demand for foreign output. A more contemporary view holds that exchange rates are the prices that equilibrate the world demand and supply of financial assets. Although the requirements for maintaining asset market equilibrium may domi- nate the foreign exchange markets, at least in the short run, the status of a country’s current account may nonetheless portend a change in foreign exchange rates [9]. For example, an unanticipated change in the current account may provide new information about shifts in the terms of trade, which is likely to lead to a change in the exchange rate. To the extent that forecasters believe they have more accurate information about the current account than is now being reflected in the market, such information can be used to forecast forthcoming changes in the exchange rate.

The past behavior of exchange rates (PASREC)

The framework also addresses the possibility that historical exchange rate information is predictive of future spot rates. Again, viewpoints differ markedly on the relevance of this factor, and forecasters will need to assign it an appropriate weight.

Theories of pricing in financial markets can be divided into three distinct categories: “technical”, “fundamental” and “efficient market” analyses. Both technical and fundamental analysts assert that there is predictive power in historical information, whereas the efficient market concept argues against such a capability [lo].

APPLICATION OF THE FORECASTING FRAMEWORK

Employing the categories developed in the previous section, an exercise was conducted to identify factor clusters and establish a set of judgmental factor loadings. The exercise addressed the problem of constructing a distribution of priorities, which may be interpreted as a probability distribution, for the yen/dollar spot rate in 90 days.

The specific market conditions were those in effect as of 3:30 p.m. (EDT) on Tuesday, April 21, 1987. Figure 2 displays the clusters and their associated loadings as outlined below. The second and third levels of the hierarchy present our judgments of relative factor importance, while the fourth level is a mixture of likely occurrences and relative importance. The fifth level represents our judgment of the impact of each fourth level factor, other things being equal, on the range of future exchange rate values. The following describes some of the salient features of this particular hierarchy.

Relative interest rates (ZNTRAT). Under this factor cluster at the third level of the hierarchy, the authors judged that the primary factors affecting yen/dollar interest rate differentials are the monetary policy of the Federal Reserve, the monetary policy of the Bank of Japan, and the size of the U.S. federal budget deficit. The fourth level provides evaluations of the likely directions of these third level factors. For example, Federal Reserve and Bank of Japan monetary policy directions were assigned three indicators-tighter, steady, or easier-and judgments were made concerning their likelihood.

At the fifth level, the probable impact of each of these indicators on the future value of the yen/dollar spot rate was evaluated, using a set of arbitrary ranges which are discussed later in this section. We used the designations sharp increase, moderate increase, no change, moderate decline, and sharp decline to characterize these ranges. The critical values associated with these ranges must be determined in advance of the exercise and should reflect the economic significance for a given firm of swings in foreign exchange rates. (Since these fifth level ranges are the same for all fourth level factors, further reference to them is omitted in the ensuing discussion.)

Forward exchange rate biases (FDBZAS). The third level of this cluster consists of two factors, each of which may serve as a proxy for addressing this

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dimension of the forecasting framework. One concerns the presence in the actual forward rate of a premium or a discount relative to the “calculated” rate (based on observed interest rate differential). The other factor embodies the size of this discount or premium. On the fourth level, high, medium, or low significance was assigned to the presence of the discount or premium factor [ll].

Oficial intervention (EXCZNT). Regarding official intervention, two factors were identified for the third level: consistent and erratic. Each of these indicators was assigned three possible intensities on the fourth level in terms of the size of the intervention-strong, moderate and weak-and judgments were made as to their likelihood.

Relative confidence in the U.S. (CONFUS). The third level of this cluster utilizes two indicators of relative economic stability-relative rates of inflation and relative real growth-as well as a variable for relative political stability. On the fourth level, the model provides three assessments of the likelihood of relative inflation and growth: higher (in the U.S.), equal, or lower. For the political variable, the fourth level assessments are: more, equally, or less stable.

Current account balance (CURBAL). Two third- level factors summarize the impact of the current account balance: the size of the deficit or surplus and anticipated changes in the balance. On the fourth level, two indicators relating to the size of the current account balance are presented-large and small- and three for anticipated changes: decrease, no change, and increase.

Past behavior of exchange rates (PASREC). The third level categorizes factors according to the relevance or irrelevance of historical data. On the fourth level each category is further designated as having high, medium or low explanatory power [12].

Summary of the judgments

The composite priorities of all the factors affecting the closing yen/dollar exchange rate in New York 90 days forward from Tuesday, April 21, 1987 (i.e. Monday, July 20, 1987) are presented in Fig. 2. In all, there were 61 matrices of paired comparisons, each of which (depending on its order, n) involved n(n - 1)/2 judgments, for a total of 473 possible paired comparisons.

As a result of this exercise, the most important second level factors thought to affect the spot rate in 90 days were relative interest rate differentials and the current account balance. The third most important factor was judged to be official exchange market intervention, and the fourth was the relative degree of confidence in the U.S. vis-a-vis Japan. The other two factors (the relevance of historical exchange rate data and the forward exchange rate premium or discount) were held to be of rather minor significance for this particular go-day forecasting problem. All of these factors, of course, could change in relative importance for different currencies, time horizons, and exchange rate institutional arrangements.

The judgments for the subsidiary factors in the lower levels of the hierarchy are also presented in Fig. 2. Relative interest rates, for example, were held to respond chiefly to the future course of Federal Re- serve monetary policy, which was judged further to

be tending toward greater tightness in the next 90 day period. Other things being equal, it was assessed that this would either produce a moderate improvement in the yen value of the dollar or a stabilization at the current exchange rate. As another example, official exchange market intervention was held to be effective if the market perceives the intervention policy to be consistent. Further judgments were made that for the forecast period such intervention would be consistent and moderate. However, other things again being equal, this moderate intervention was likely either not to stave off a further moderate decline in the yen value of the dollar or, at best, to result in no change from the current level. Judgments were made for most of the factors in a similar manner. Equal weightings were assigned, however, to factors deemed either to be of minor overall significance or incapable of further discrimination. Factors treated in this manner can be readily identified by inspecting Fig. 2.

Forecast results

The foregoing exercise was conducted in the context of a firm contemplating the desirability of arranging for forward exchange cover. The firm in question is assumed to be committed to making certain payments in yen in 90 days. Given its knowledge of the foreign and domestic mix of its product markets and sources of supply as well as its schedule of other foreign payments and receipts, the firm is able to form an estimate of the extent to which varying degrees of fluctuations in the yen/dollar rate would affect its profitability.

Lacking specific firm data, we assumed the following in our exercise: (1) a significant decrease or increase was a 15% or more change from the go-day yen/dollar forward rate [13]; (2) a moderate decrease or increase was a 5% to 15% change and (3) plus or minus 5% was judged to have no significant effect on the company’s performance. Obviously, an actual firm could have a much tighter range of tolerances. The go-day forward rate on this particular day (April 21, 1987) was *141.17/$. The critical value for the above ranges are displayed in Table 1.

In order to make a cover decision, the firm had to assign likelihoods to each of these ranges. One approach could be to take the “naive” view that the future spot rate is normally distributed around the current forward rate. Another approach could be to use the outcome weights derived from the exercise described in this paper.

Table 1. Critical yen/dollar values for outcome ranges

Direction and degree of change Ranges?

Sharp decline Y119.99 and below Moderate decline 119.9!K134.11 No change 134.11-148.23 Moderate increase 148.23-162.35 Sharp increase 162.35 and above

tUpper bounds are included in each range. Lower bounds are limiting levels but arc not included.

Sharp decreases: - 15% and below; moderate decrease: -5% to - 15%; no change: &5%; moderate increase: + 5% to + 15%; sharp increase: + 15% and above.

368 ANDREW R. BLAIR et al.

Table 2. Probabilitv distributions for yen/dollar outcome ranges?

Normal 180-dav Reagan era AHP

Sharp decline o.oOOO/o.ooOO 0.0674/0.0674 0.1330/0.1330 Moderate decline 0.0011/0.0011 0.2420/0.3094 0.2940/0.4270 No change 0.9978/0.9989 0.3812/0.6906 0.2640/0.6910 Moderate increase 0.0011/1.000 0.2420/0.9326 0.2280/0.9190 Sharp increase 0.0000/l .ooo 0.0674/l .OOO 0.0820/l ,000

TFigures to the left of the strokes (/) are the probabilities for each of the ranges; figures to the right are the cumulative probabilities.

Table 2 presents normally distributed probability estimates for these critical ranges. In the absence of knowledge of the decision-makers’ estimate of the parameters of the future probability distribution, we generated standard deviations for the future spot rate based on two arbitrary (and perhaps extreme) time periods: the previous 180 day period (2.30); and the period since the first trading day of January, 1981 (14.17 jthe month of the beginning of the Reagan administration and the onset of a more laissez-faire U.S. official exchange market intervention policy. Table 2 also presents the subjective set of weights for these ranges generated on the basis of our framework. The means of all three distributions were assumed to be the April 21, 1987 90-day rate (141.17).

Using the likelihoods derived from the 180-day historical distribution, this (presumably risk-averse) firm would not have been inclined to cover its future yen payments since the cumulative probability of a sharp or moderate decrease in the yen/dollar rate was virtually zero. Employing the longer historical time period, the cumulative probability was 31%-which clearly would have provided an incentive to cover [ 141.

The cumulative weights generated by our method, on the other hand, even more unambiguously indicated a need for cover (43%). Assuming that the “expert” group had been carefully constituted to include an appropriate range of market intelligence, such an indication should not have been taken lightly. The example is a modest one, but it suggests the potential usefulness of our framework for making judgmental forecasts about exchange rates and deci- sions about foreign exchange cover.

The outcome of this exercise is not just the usual point estimate but includes, in addition, a subjective probability distribution which could be non-normal and asymmetric. The skewness of this expected distribution could argue for cover even if the expected value of the future spot rate falls within the “no change” range. As it happens, this is the case for the expected value of the spot rate in 90 days emanating from this exercise-which was *139.30/S-a figure well within the range which produces no appreciable impact on the company’s performance.

CONCLUSION

We have described a method of structuring an expert forecast of a foreign exchange rate, and we have suggested six primary factors potentially affecting future values of exchange rates. Employing the AHP framework, we then presented the results of our judgments regarding the variables likely to affect

1.

2.

See, for example: L. D. Brown and M. S. Rozeff. The superiority of analyst forecasts as measures of ex- p&tations: evidence-from earnings. J. Finance 33, 1-16 (March 1978): D. Fried and D. Givolv. Financial analysts forecasts of earnings. J. Accounta&y Econ. 4, 85-107 (1982); B. Cornell. Money supply announce- ments and interest rates: another view. J. Business 56, l-23 (January 1983). Three basic works are: T. L. Saaty. A scaling method for priorities in hierarchical structures. J. Math1 Psy- chol. 15,234-281 (June 1977); T. L. Saaty. The Analytic Hierarchy Process. McGraw-Hill, New York (1980); T. L. Saatv. Axiomatic foundation of the analvtic hierarchy p;ocess. Mgmt Sci. 32, 841-855 (July 1986). For a further review of AHP and other bibliographic references, see F. Zahedi. Interfaces 16, 96-108 (July-August 1986), and Special Issue: The Analytic Hierarchv Process. Socio-Econ. Plunn. Sci. 20(6),

I 3. 4.

(1986). - Saaty (1980).

5.

6.

7.

8.

9.

REFERENCES

these primary factors and their probable impacts on the yen/dollar exchange rate in 90 days. Finally, we derived the resulting weights for the possible ranges for the 90-day future spot rate as of April 21, 1987. On that date we were signalling to a particular firm needing to make yen payments 90 days hence that is was advisable to arrange for forward exchange cover as a hedge against an impending decline in the exchange value of the dollar vis-a-vis the yen.

The use of our framework allows us to incorporate current market knowledge and expertise to generate a subjective probability distribution of the future spot rate: something not directly accomplished when using more conventional forecasting approaches.

See. for example: J. A. Frankel. Monetary and portfolio-balance models of exchange rate determination. In Economic Interdependence and Flexible Exchange Rates, (Edited by J. S. Bhandari and B. H. Putnam), pp. 84-115. MIT Press, Cambridge, Mass. (1983). R. A. Korajczyk. The pricing of forward contracts for foreign exchange. J. Political Econ. 93, 346-368 (April 1985). R. Solomon. The International Monetary System, 1945-1981. Harper & Row, New York (1982). E. Boyer. The attack on the dollar is over. Fortune 5961, 63, 65 (March 1986). R. Solomon. Official intervention in foreign exchange markets: a survey. Brookings Discussion Papers in Inter- national Economics, No. 1 (June 1983). R. Dombusch. Exchange rate economics: where do we stand? Brookings Papers on Economic Activity (1980), pp. 143-185. Frankel (1983).


10. R. M. Levich. The International Monev Market: An Assessment of Forecasting Techniques- and Market E’ciency. JAI Press, Greenwich, Conn. (1979). 13.

11. For technical programming purposes, the fourth level also includes a further subdivision of the size of the discount/premium factor, which had no bearing on the results. 14.

12. With regard to the judgment that historical data is irrelevant, this trichotomy is included only for technical

purposes in executing the program and again had no impact on the outcome. The firm uses the forward rate rather than the spot rate since it believes that the forward rate is an unbiased, though not necessarily efficient, estimator of the future spot rate. The decision-makers’ perceived cumulative probability distribution could well be some composite of these extremes.

forecasting foreign exchange rates: an expert judgment approach

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