Journal of Productivity Analysis, 9, 35–51 (1998)
c 1998 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands. °
The Relationship Between Stock Market Returns and Technical Efficiency Innovations: Evidence from the US Airline Industry ILA M. SEMENICK ALAM Tulane University, Dept. of Economics, 206 Tilton Hall, New Orleans, LA 70118
[email protected]
ROBIN C. SICKLES
[email protected] Rice University, Dept. of Economics, 6100 South Main Street-MS 22, Houston, TX 77005-1892
Abstract This paper analyzes the association between two firm performance measures: stock market returns and relative technical efficiency. Using linear programming techniques (Data Envelopment Analysis and Free Disposal Hull), technical efficiencies are calculated for a panel of eleven US airlines observed quarterly from 1970–1990. A relationship, between efficiency news in a quarter and stock market performance in the following two months, is found. A risky arbitrage portfolio strategy, of buying firms with the most positive efficiency news and short-selling those with the worst news during this time frame, results in zero beta risk yet yields annual returns of 17% and 18% for the two methodologies. Keywords: panel data, productivity, technical efficiency, stock market performance, airline industry
1.
Introduction
This paper examines the link between two measures of a firm’s performance: its relative technical efficiency scores and its stock market price. The first measure evaluates a firm’s competence at combining inputs and outputs in its production process while the second measure reflects a firm’s fundamental value. The primary objective of corporate management is generally assumed to be the maximization of stockholder wealth which requires the maximization of the price of a firm’s common stock. Since the price of a firm’s common stock is determined by the discounted present value of the cash flows of the firm, the management must pick projects with cash flows yielding the highest net present value. Thus a firm’s profitability and stock price are intrinsically linked to a certain degree. Profitability, in turn, is partly determined by how efficiently a firm utilizes available technology in the chosen projects. Banker and Johnston (1995), using the airline industry as their empirical application, show that high correlations exist between profitability and productivity. Similarly, Kumbhakar (1993), in his study of Utah dairy farmers, finds that smaller farms tend to be less technically efficient as well as less profitable. Since the production process relates to profitability which in turn relates to firm valuation, a change in a firm’s technical efficiency is relevant news and, under the semi-strong form of the efficient market hypothesis,1 should
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be reflected in the market price of the firm. If news about a firm’s efficiency is viewed as publicly available, then no opportunity should exist for industry analysts to extract abnormal returns since this information would already be captured by the stock price. Despite the wealth of technical efficiency applications and the understanding that efficiency and profits are associated, we are unaware of any study which has empirically explored the above qualitative theoretical statement.2 By establishing that the stock market values technical efficiency news, this analysis goes beyond just proving a connection between the two performance measures. It is also a market based test of the technical efficiency methodologies themselves and of the concept underlying technical efficiency measurement: namely that these techniques capture some firms’ inability to operate at their most efficient level. This assumption is at odds with conventional economic theory which states that producers operate on their production possibility frontier. Caves and Barton (1990) and Caves (1992) discuss how this “touchstone of professional belief” (1990, p. 1) is contradicted by the multitude of studies documenting technical inefficiency. The traditional view is somewhat constrained and these authors highlight, in detail, the reasonable hypotheses which have developed in the literature to explain how actual performance sometimes deviates from the theoretical objective. By documenting a correlation between changes in firm valuation and technical efficiency innovations, our paper contributes to this view of the production process. It also verifies that these measurement techniques are able to capture these efficiency shortfalls accurately. The strength of these techniques is a critical issue because of their widespread usage. The multitude of applications covers both public and private institutions. Lovell (1993) provides an overview, as well as an extensive reference section, illustrating the diverse utilization of these techniques. These methodologies have broad appeal because both government policy makers and industry managers are concerned about measuring productive performance. More importantly, once efficiency differentials are identified, these studies are useful policy making tools since they indicate areas of deficiency and directions for change. Recent developments in the literature have overcome the methodological shortcomings which limited early applications to cross-sectional studies.3 The ability to exploit panel data sets, in order to capture the dynamic nature of a firm’s performance relative to its competitors, has stimulated even greater interest in the topic. For our application we turn to the US airlines over the period 1970 to 1990. We find this industry empirically appealing for two main reasons. First, domestic carriers have a history of substantial upheavals including economic deregulation, mergers, failures, bankruptcy filings, re-organizations and operating loss reports which continue into the present. There exist concerns that the future is bleak in terms of the number of carriers which will survive and prosper. Relatively recent events have prompted rumblings that deregulation failed—witness reports in both the televised and the printed news media and the commission established in 1993 by President Clinton to evaluate the impact deregulation has had on the airline “crisis”. Identification of a positive relationship between increased firm valuation and efficient resource utilization would be timely, interesting and relevant for future policy direction. A second empirically attractive aspect of this sector, a consequence of the strict filing requirements imposed by the federal government, is the wealth of accessible and accurate data available at a level not found in most other industries. The panel data
STOCK MARKET RETURNS AND TECHNICAL EFFICIENCY INNOVATIONS
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set we have compiled includes several additional years of data not found in other airline studies and provides the largest, cleanest data available on the production of US scheduled air transport. The remainder of this paper is organized as follows. Section 2 describes the production data set used for calculating the technical efficiency scores; the compilation of the stock returns data set is discussed in Appendix A. Section 3 presents the technical efficiency methodologies and explains how the relationship to stock market performance is identified and quantified. Section 4 contains the empirical findings and an analysis of the results. Section 5 concludes. 2.
Production Data
The airline production data set used to calculate the technical efficiency scores consists of quarterly observations for the twenty- one year period from 1970 to 1990. This is a total of 84 time periods (events). The eleven carriers all have a Department of Transportation (DOT) Group III classification4 and include: American, Continental, Delta, Eastern, Frontier, Ozark, Piedmont, Trans World, United, USAir and Western. Not all airlines span the entire time period: Frontier ends the second quarter of 1986 because it merged into People Express in 1985 which merged into Continental in 1987; Ozark ends the third quarter of 1986 since it merged into TWA in 1986; Piedmont and Western end the fourth quarter of 1986 because the former was absorbed by USAir in 1987 while the latter was acquired by Delta in 1986. In some cases the data end prior to the actual mergers because, after merger announcements are made, data reporting accuracy sometimes declines and it was decided a more conservative approach to data collection should be adopted. The primary source for the data is the Civil Aeronautics Board (CAB)/DOT Form-41 schedules. The reporting requirements of these air carriers are quite extensive and, as of 1970, the data are rigorously audited to maintain a high degree of accuracy. The Form-41 is therefore a rich and definitive source of data for industry analysis. This data set builds upon that originally constructed by Sickles (1985) and Sickles, Good and Johnson (1986). The procedure used to produce the updated version of the data set has changed considerably over the last decade. In particular, changes in the reporting requirements for the DOT Form-41 have been significant. In order to maintain consistency over time, data from all versions of Form-41 must be mapped into a single version. The objective was to maintain as much detail as possible in all areas of air carrier production in order to increase the usefulness of the data set for various studies. In those cases where price and quantity pairs of a specific input (output) are generated, several subcomponents to that input (output) are first constructed. These are aggregated into a single input (output) using a multilateral Tornqvist-Theil index number procedure. The result of this procedure is a price index (much like the consumer price index) which aggregates price information for commodities with disparate physical units. When the total expenditure of the input (output) category is divided by this price index, an implicit quantity index is produced. The data set consists of nine variables. The inputs are flight capital (number of planes), labor (an aggregate of pilots, flight attendants, mechanics, passenger and aircraft handlers and other labor), energy (gallons of aircraft fuel) and materials (consisting of supplies,
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outside services and non-flight capital). The aggregate output variable is the quantity of revenue output (revenue ton miles) including passenger and nonscheduled revenue output. Nonscheduled output consists of cargo and charter operations. Passenger miles are converted into ton miles assuming the industry standard of an average weight of 200 pounds per passenger (including baggage). Four control variables are also calculated. The first two describe airline output characteristics: aircraft stage length and load factor. Stage length describes the average length of route segments found by dividing flights into aircraft miles. A small average stage length means a carrier’s aircraft spend only a short period of each flight at an efficient altitude. Load factor measures the fullness, on average, of a carrier’s aircraft. Since it provides a measure of service quality it is often used as a proxy for service competition. A low load factor, indicative of a large number of planes on a particular route, is a measure of high service quality. Deregulation has switched the focus from service quality, i.e. high number of flights, to price competition causing load factor to increase as service has declined. The second two control variables describe capital stock characteristics: average size of the carrier’s aircraft and the percentage of a carrier’s fleet which is jet. These variables provide measures of the potential productivity of capital. For example, as the average size of a carrier’s aircraft increases, more services can be provided without a proportionate increase in factors such as flight crews, passenger and aircraft handlers and landing slots. The percentage of jets provides a measure of aircraft speed. Since jets fly approximately three times as fast as turboprops, they require proportionately less flight crew resources. 3.
Methods
Efficiency Measurement To measure technical efficiency we use two approaches:5 Data Envelopment Analysis and Free Disposal Hull; hereafter, DEA and FDH, respectively. Generalizations of these linear programming techniques allow panel data studies. Assume a panel data set where, for each point in time t = 1, . . . , T there are n = 1, . . . , N firms or decision making units (DMUs) in the sample each consuming j = 1, . . . , J different inputs to produce k = 1, . . . , K different outputs. Thus, x jnt is the amount of input j used by DMU n in period t and yknt is the level of output k produced by DMU n in period t. Further, assume a sequential production set where input and output observations from the first time period up to period to are used to calculate the technical efficiency score for (xn o to , yn o to ).6 This definition assumes that any production technology available in previous time periods is currently viable. The production technology, S, is: S = {(x, y) | x ∈
