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Financial analysts' forecasts and unprecedented events: the case of German reunification.

Abstract We use the chain of events from the fall of the Berlin Wall to the reunification of Germany to examine how capital market participants respond to momentous and unprecedented events. Our examination measures the impact of these events on analysts' forecasts for the earnings of West German firms. Our results show a significant decrease in analysts' ability to accurately forecast earnings. Contrary to the public's euphoria, the sense of the market was generally negative about the implications of unification for West German firms. This negative sentiment was spread across most of the broad sectors, but within those sectors the results were significantly positive for select groups of industries. It appears that, in the face of this extraordinary event, financial analysts were detached from the emotions it engendered and were discriminating in their assessment of its impact.

Keywords: German reunification * financial analysts * forecast revisions * forecast errors * East Germany, West Germany, German economy, Berlin Wall * analysts' expectations * sectoral valuation * event studies * forecast earnings * valuation effects

JEL Classification G14 * P51

Introduction

On October 3, 1990, the two German people (62 million West Germans and 16 million East Germans) reunified as one nation. It was a moment of euphoria and hopeful expectations that would soon be tempered by the realities of the challenges facing the new national entity. It would be an understatement to assert that they were two very different parts. As one observer noted, "The west was one of the richest, most highly industrialized and technologically advanced nations in the world; the east was near bankrupt, economically and psychologically shattered after nearly 60 years under two successive totalitarian regimes." (Economist, September 30, 2000).

But after pouring $1.5 trillion into eastern Germany since 1990, many believe the effort to rehabilitate the east has been a costly failure. The east's position may further erode as European investment finds greater returns in other former Soviet Bloc countries such as Poland and the Czech Republic. Klaus von Dohnanyi, chairman of the commission that produced the government's blueprint for the east, recently warned that, "if we do not address eastern Germany, the financial burden on Germany will become unbearable in the next 15 years." (Landler, 2004)

This paper investigates the value reassessment of West German firms triggered by the chain of events culminating in the reunification of Germany. The fall of the Berlin Wall and subsequent events leading to reunification were monumental events in history, without precedence and beyond the experience of market participants. Their political and economic ramifications are still being played out today. So it is inherently interesting to examine how capital markets and market participants evaluated and reacted to this profoundly significant event. More fundamentally, this study addresses the question of whether the market can be trusted (i.e., are market expectations and prices rational) during a time of sweepingly monumental and unprecedented circumstances.

Our approach examines forecast errors and forecast revisions to financial analysts' forecasts for the earnings of West German firms around 10 important events leading to reunification. We also focus on the forecasts for selected sectors and industries within the German economy. These forecasts incorporate the consensus of financial professionals who closely monitor the financing, investing, and operating activities of a firm and its competitors within an industry and are in a superior position to generate predictions of future earnings. Earnings estimates are a regular feature of financial reporting in the popular press. A number of studies collectively demonstrate a strong relationship between share price and both forecast revisions and forecasts errors (Ball & Brown, 1968; Beaver, Clarke, & Wright, 1979; Brown, Griffin, Hagerman, & Zmijewski, 1987; Chopra, 1998; Fried & Givoly, 1982).

A couple of prior studies have examined stock price behavior associated with the fall of the Berlin Wall and found reunification event showing mixed results. Sultan (1995) uses the 30-stock DAX index, the Germany Fund, the S & P Index and Financial Times World Stock Index for world sample to examine the stock market reaction to German reunification. Sultan found higher daily price volatility for the DAX during reunification period (August 1989-July 1990). For example, the DAX rose sharply as reunification euphoria intensified but it fell in August 1990 and later. For the Germany Fund, Sultan observed similar trend in prices around the initial reunification period. That is, the Fund's premium increased to as high as 100 percent on January 26, 1990. Later, when the earlier forecasts for benefits and costs of this event were revised, the Fund's share price fell as much as 90 percent from its previous high. For the World Stock Index, Sultan found both positive and negative information effects based on the volatility of the stock returns. Schnusenberg (2000) finds significant negative abnormal returns for a sample of US multinational corporations and significant positive abnormal returns for German firms in the week following the fall of the Berlin Wall which he attributes to a potential for increased acquisition activity and expanded market share.

Examination of analysts' forecasts can provide a more direct assessment of the impact of an event on expectations for the future prospects of firms. Moreover, the informed judgments of financial analysts may be especially important inputs determining the capital markets' response to unprecedented events. As specialists steeped in the idiosyncrasies of individual industries and companies and their products, analysts can be particularly attuned to the nuance and possibilities created by an unprecedented event for the companies encompassed by their expertise. Financial analysts may also employ heuristic rules they find useful when confronted with situations of high complexity and uncertainty, whose solutions are intractable using standard economic models and techniques. Prior research accepts the notion that financial analysts' forecasts are reasonable surrogates for market expectations. For example, Givoly and Lakonishok (1979) find that financial analysts' forecasts of earnings per share have information content. Fried and Givoly (1982) extend their work further and provide evidence that financial analysts' forecasts provide a better proxy for market expectations than forecasts generated by time-series models. We assume that a rational capital market response to the events leading to German reunification will reflect the reasoned assessments of financial analysts.

We report a significant decrease in analysts' ability to accurately forecast earnings, which is consistent with the unprecedented nature of the events and a concomitant increase in risk. The results also show a significant downward revision in earnings forecasts for three events out of the ten information events identified in our paper. The three events are the fall of the Berlin Wall, the signing of the unification treaty, and the official unification of the two Germanys. For five events, the forecast revisions are negative but statistically insignificant. Only one event records a significant positive forecast revision. Taken collectively, these revisions suggest a negative sense of the market with respect to the overall economic impact of reunification. Clearly, market participants were not carried by the euphoria accompanying the toppling of the Berlin Wall. The reaction arguably reflects a tempered assessment of the difficulties and financial commitment needed to rehabilitate the east.

For further evidence of a rational response by market participants to the events leading to reunification, we examine analyst's forecasts for important sectors of the German economy and for specific industries within those sectors. The overall negative assessment of reunification might reflect irrational contagion stemming from an inordinate fear of uncertainty, a possibility for momentous events with no historical precedence. Alternatively, a rational response would display discrimination and recognition that while some industries will be hurt by the diversion of resources to the east and the high potential for their mismanagement, other industries will benefit from the opening of the eastern market and from the enormous public spending contemplated by the government.

Our analysis has some interesting results for selected industries. The fall of the Berlin Wall was a negative event for Basic Industries as a whole; however, within that sector, it was a very positive event for the Construction Industry (but neutral for Building Materials). This seems reasonable, as there would be many new construction projects in the east. The fall was particularly negative for Chemicals, Steel, and Automobiles. Within the Consumer Non-Durable Sector, the fall of the Berlin Wall was highly positive for Beverages and Tobacco. Results are also positive for Food and Household, but neutral for Textile and Apparel (West German items that might be beyond the budget of East Germans). The evidence strongly points to a discriminating rational response by capital market participants to the unprecedented events that tore down a dividing wall and reunited a people.

Background

Historical Context

Following World War II, Germany was divided into two geographic sections. The "Iron Curtain," symbolized by the Berlin Wall, divided the country into East and West. East Germany comprised 30 percent of the country's total area and 20 percent of its total population. The democratic, capitalistic, and political institutions in the Federal Republic of Germany (FRG) progressed with astounding stability and growth. In contrast, the political and economic conditions in the German Democratic Republic (GDR) became increasingly oppressive as viewed by a major segment of the local populace, in turn, triggering a constant fleeing of people to FRG. "On August 13, 1961, the GDR began building a Wall through the center of the Berlin to divide the city and slow the flood of refugees to a trickle. The Berlin Wall became the symbol of the east's political debility and the division of Europe." (1)

Economic Outlook

German unification posed unique problems for policy-makers and planners since it involved the combination of two radically different economic systems. The merits of the various methods of unification were debated, particularly the efficiency of an immediate transformation versus that of a gradual transition. The imbalance of the two economies, in addition to the lack of historical precedents, made prediction of unification's economic impact extremely problematic.

The imbalance of the two economies was evidenced in three main areas. First, productivity in West Germany far exceeded that of its East German counterparts. In 1990, estimates placed East German productivity at one-half to one-third that of West Germany, with manufacturing lagging the farthest behind at around one-sixth the productivity of West German manufacturing (G. Sinn & H.-W. Sinn, 1992). Second, most of the East German economy had been tightly regulated by the Soviet Union. Gosplan, the Soviet planning commission, controlled production and investment decisions. It was not until the movement toward unification began that private ownership of the means of production, freedom of occupational choice, and joint ventures became permissible. Third, the Soviet Union also crippled the economic development of East Germany by taking "machinery, equipment, and large quotas from farms and factories" during the Cold War (Lloyd, 1999). East Germany's development was also stunted as a result of her trading within the Soviet sphere; 70 percent of its trade was with other Soviet bloc countries, including 37.5 percent directly to the Soviet Union (Heitger & Waverman, 1993).

Despite its economic inferiority with respect to West Germany, East Germany had positive attributes. According to Heitger and Waverman (1993), "East Germany had achieved a remarkable standard of living ... It ranked about seventeenth in the world in terms of per capita income." East Germany had a surprisingly well-educated and skilled workforce that was underutilized by the dilapidated pre-unification economy. East Germany also presented West German companies with vast, basically untapped consumer demand.

Hypotheses Development

The Pros and Cons of German Reunification

The West German firms were in a unique position to benefit from the reunification. Some of the significant potential advantages that could accrue are: First, according to one author (Economist, July 1990), "the combination of Western capital and expertise with a cheap and skilled East German workforce should result in a thriving industrial base and extensive consumer markets." Unlike other European, Japanese and US firms, the West German firms have an inherent competitive advantage and could move swiftly in filling this resource and entrepreneurial gap. Second, the west part had to spend a major portion of its GDP on building, revamping and transforming the industrial, transportation, communication, education, legal, and financial infrastructure of the east and for that it needed strong partnership from the private sector. This partnership role, subsidized by the government, could translate into huge bonanza for the business growth of the West German firms. Finally, the reunified entity would have nine more European countries around its borders to benefit from. This significant increase in the number of markets would not only give the unified country the most neighbors in all of Europe but would also translate this new reality into tremendous growth opportunities for the West German firms (Economist 319, June 1991).

Some noteworthy potential disadvantages of reunification are: First, the dilapidated political, economic and institutional infrastructure in the east would require huge resource commitment and years of hard work and patience on the part of the west. This in turn would translate into significant uncertainty about the economic future of the reunified entity (Bryson & Clarke, 1990). Second, a prolonged resource commitment would place tremendous strain on the fiscal and monetary infrastructure of the west resulting in burdensome deficit, inflationary pressure, waste, and adverse impact on future economic growth. Third, the rehabilitation of the east would be directed by government central planning that emphasized public work projects and an equal distribution of benefits rather than the encouragement of business investment and enterprise that would create productive new jobs. The potential for a massive misdirection and mismanagement of resources was high.

Empirical Analysis

Sample Selection and Description

Earnings forecast data are obtained from the Institutional Brokerage Estimation Service (IBES) Global database. IBES reports earnings forecasts made by analysts on a monthly basis for the listed firms. Out of the total number of listed firms only 179 firms have the earnings forecast data available around the analysis period. Thus, these 179 firms form the final sample that is employed to examine the earnings forecast behavior around the event dates as shown in Table 1.

Table 1 identifies 10 potentially value relevant events related to German reunification starting with the fall of the Berlin Wall.

A. Method for Calculating Forecast Error

To test whether there are changes in analysts' ability to predict firms' performance around the events surrounding the fall of the Berlin Wall, we examined analysts' earnings forecast by identifying the mean analyst earning forecasts and actual earnings reported on the IBES tape in the final month for fiscal years -1, +1, and +2 relative to the November 1989. Accuracy of analyst earnings forecasts is then calculated as a mean forecast error at time t (FE) as follows:

[FE.sub.t] = 1/n[n.summation over (i=1)][( [AEPS.sub.i,t] - [F.sub.i,t])/[AEPS.sub.i,t]]

where n is the number of firms in fiscal year t relative to the fall of the Berlin Wall on November 1989. [AEPS.sub.i,t] is the actual ESP in fiscal year t for firm i. [F.sub.i,t] is the mean of analysts' forecasts for firm i at month t. This variable is measured in the final month of the fiscal year.

B. Method for Estimating Revisions of Earnings Forecasts

The monthly forecast revision (FR) of the current year's earnings per share for firm i in month t are calculated as:

[FR.sub.i,t] = ([F.sub.i,t] - [F.sub.i,t-1])/[P.sub.i,t-1]

where, [F.sub.it] is the mean of analysts' forecasts for firm I at month t, [F.sub.i,t-1] is the mean of analysts' forecasts for firm i at month t - 1, and [P.sub.i,t-1] is the market price of firm i's common stock at month t - 1 as reported by IBES. The monthly forecast revisions of the current year's earnings per share are normalized by the stock price following Christie (1987).

Forecast revisions are also cumulated and evaluated across months. The Cumulative Forecast Revision for firm i from month 1 to month T is calculated as:

[CFR.sub.i] = [T.summation over (t=1)][FR.sub.i,t]

Results and Discussion

Test of 2Analysts' Average Forecast Error

Table 2 reports the accuracy of analysts' earnings per share forecasts of 159 West German firms (20 firms dropped out because of missing data) around the fall of the Berlin Wall. As described in "Results and Discussion," the accuracy of analysts' earnings forecasts is measured using analysts' earnings forecast errors (FE). To evaluate changes in forecast accuracy following the fall of the Berlin Wall in November 1989, we compare the forecast errors for pre-fall year 1988 (December 1988) with the forecast errors for post-fall years 1990 (December 1990) and 1991 (December 1991).

As reported in Table 2, the mean forecast error for the sample of German firms increases significantly following the fall of the Berlin Wall. The mean forecast error for December 1988 is 0.210 and for December 1989, its 0.366. The increase in mean FE is statistically significant at the 0.025 levels and implies that in the year following the fall of the Berlin Wall the mean error of analysts' earnings forecasts increased by 74 percent. This decline in forecast accuracy persists for at least 2 years after 1989. The mean forecast error for December 1991 is 0.448. The difference with the forecast error for December 1988 is statistically significant at the 0.01 level and represents a 113 percent increase in mean error. These results are consistent with our discussion in the previous sections and suggest that the imbalance of the two economies, in addition to the lack of historical precedents, made the job of analysts' to predict unification's economic impact rather problematic.

Test of Analysts' Average Forecast Revisions

Table 3 presents the monthly average earnings forecast revisions and the percent of positive and negative revisions around different event dates. These revisions are based on current-year earnings per share for a sample of 179 German firms. These multiple earnings forecast dates are tested based on events previously identified in Table 1. The period of analysis extends over 15 months starting October 1989 through February 1991.

On November 9, 1989, the Berlin Wall fell triggering a massive immigration from East to West Germany. In that month the average forecast revision has a negative sign and is significant at the 0.05 levels (t-statistic=-2.02). Interestingly, roughly 70 percent of the earnings per share forecast are unchanged during the same event month reflecting a heightened sense of uncertainty on the part of the financial community regarding future direction of the German economy.

The next statistically significant event in terms of equity valuations is the date when the terms for the newly formed German Economic, Monetary & Social Union (GEMU) becomes effective. In that month, the average forecast revisions have a positive sign and are significant at the 0.05 levels (t-statistic=2.10). In addition, roughly 47 percent of the analysts revise their forecasts upward showing a Wilcoxon t-statistic of 2.51 at 0.01 levels of significance. On August 31, 1990, the representatives of the GDR and the FRG sign a treaty for unification. Around that period, the average forecast revisions are significantly negative (t-statistic=-4.53) at the 0.01 levels.

And finally, on October 3, 1990, the two Germanys are officially unified. Around that period, we again observe that the average forecast revisions are significantly negative (t-statistic=-3.24) at the 0.01 levels. In addition, significantly higher proportion (59.8 percent) of analysts' forecasts is revised downward showing a Wilcoxon T of 2.91 at the 0.01 levels. The results indicate a negative reaction of the financial analysts' community to this event.

Industry Analysis Results and Discussion

To gain further insight into the valuation effects of German reunification on specific industries, we classified the full sample into broad sectors of the German economy and conducted analysts' forecast revision tests. Next, we examined a select set of industries within these sectors for valuation effects. We cumulated these average forecast revisions (hereafter, CFR) over various trading intervals and employ them for further analysis on two grounds: (1) to capture significant revisions in analysts' expectations due to the German-reunification-related information leakage around the event date, and (2) to determine the sensitivity of the results associated with any possible timing measurement error.

Average Cumulative Forecast Revisions (CFR) -- Basic Industries

Table 4 reports the average analysts' forecast revisions of current year earnings per share for firms from basic industries, such as chemicals, forest products, gold, mining metals, etc. The forecasts are cumulated over different monthly intervals ranging from -12 to -1, -6 to -1, -4 to -1, 1 to 12, and 1 to 15. Event month 0 is the month when the Berlin Wall fell. As reported in Table 4, the consolidated results show that the CFR are highly significant and positive for different forecast intervals leading up to the event month. In the post-event period, the CFR are significant but negative and significant when cumulated over the next 15 months. A further examination into the specific industries reveals some interesting results. For example, for the chemical sector, though the analysts are quite optimistic before the fall of the Berlin Wall, the sentiments for this industry turns significantly negative. The CFR for 1-15 months interval are negative (t-statistic=-3.18) at the 0.05 or better levels. In other words, the downward revisions in forecasts in the post-event period for this sector can be attributed to negative sentiments for the chemicals industry.

Average Cumulative Forecast Revisions (CFR) -- Capital Goods

Table 5, Panel A provides the CFR of current year earnings per share for capital goods industries. The consolidated results in Table 5 show that the CFR are significantly positive for all intervals leading up to month 0. For month 0 and subsequent intervals the CFR turn insignificant.

A further analysis of different industries within the broad sector shows that the CFR for building materials and for machinery and engineering industries are roughly similar to the consolidated results. In contrast, the construction industry shows significantly positive optimism even in the post-event period, that is, after the fall of the Berlin Wall. The CFR are positive and significant at the 0.05 levels for month 1-12 interval.

Under Communist rule, East Germans were guaranteed "artificially low prices for housing" (Lloyd, 1999). West Germany planned to systematically increase East German rent prices "with the express objective of attracting investment to (modernize) the run-down stock" (Flockton, 1999). In addition, tax breaks would be made available to the construction industry to launch the East German economy and to induce explosive growth in the housing stock. Such growth was possible (and necessary), as noted by G. Sinn and H.-W. Sinn (1992): "Seventy percent of the housing (in East Germany) had been constructed before (WWII), more than twice the proportion than in West Germany, and the condition of the vast majority of these buildings was deplorable." Real estate and construction industries were expected to profit substantially from the government-provided incentives, as well as from the demand for housing rehabilitation and development in East Germany. The positive sentiments of analysts observed above are consistent with the expectations about the construction industry, particularly, in the post-event period.

Average Cumulative Forecast Revisions (CFR) -- Consumer Non-Durables

Table 6 reports the consolidated results of the Consumer Non-Durables sector and the results of specific industries in that sector. The consolidated results show that the CFR are significant and positive for month -6 to -1 and for -4 to -1 interval, though it is negative but insignificant for the event month. Interestingly, the CFR are significantly positive (T-statistics=2.40 at the 0.05 or better levels) for 1-12 post-event month intervals. An industry analysis reveals that analysts significantly revised their forecast upward for the Food & Household industry both in the pre- and post-event periods. On the other hand, for the Beverages & Tobacco industry the CFR are negative and insignificant in the pre-event period but positive and significant in the post-event period. Similarly, the CFR for the Textiles & Apparel industry are marginally significant before the fall of the Berlin Wall but subsequent CFR (for 1-12 month interval) are insignificant.

The East German economy was subjected to continual shortages in raw materials, equipment, parts, and intermediate products. Bryson and Melzer (1991) point to the "complicated and bureaucratic central supply system" as the cause of many shortages. (Others, such as water, were caused by pollution of streams by industrial runoff.) This system was hugely inefficient, and was unable to deliver raw materials and intermediate goods in "sufficient volume, with satisfactory quality, or with acceptable punctuality" (Bryson & Melzer, 1991). Thus, poor allocation and delivery of unfinished goods was a significant inhibitor of East German economic growth, as it impeded their smooth flow through the production process. (2)

Conclusion

The reunification of Germany triggered profound political and economic changes, and to a large extent these developments are without precedence and beyond the experience of capital market participants. Therefore, the chain of events leading to German reunification provides an excellent case to examine how market participants respond to momentous and unprecedented events. The response can be contagious and irrational if the market is carried by the emotions of the event or manifests an inordinate wariness of the uncertainties created by the unique nature of the event. In such case, prices in the capital markets would be unreliable and misleading. Alternatively, market participants may deal with the complexities and uncertainties resulting from the event on an atomistic basis where detailed firm specific knowledge can be employed. If so, then the overall market reaction would be a rational response built from a firm-by-firm assessment of the implication of the event for each firm. In which case, capital market prices would reflect the best, unbiased estimate of the probable future.

We use analysts' forecasts of earnings to directly examine the impact of German reunification on the expectations for the future prospects of West German firms. Our results show a significant decrease in analysts' ability to forecast earnings suggesting an increase in sample firms' risk during the test period; an outcome expected given significant economic uncertainty associated with the German reunification. Our examination of revisions to forecasted earnings indicates a negative sense of the market regarding the overall economic impact of the consequences of reunification. This is at odds with the general public euphoria attending these events. But it is consistent with the enormous financial burden that the rehabilitation of the east has imposed on the nation.

We also examine broad sectors of the German economy and selected industries within those sectors. A rational reaction to reunification would reflect the recognition that some sectors/industries can expect to benefit from and the ramifications of reunification will harm others. Most of the sectors we examine display the following pattern: significant positive earnings expectations over the six months prior to the fall of the Berlin Wall; expectations turn significantly negative within a year after the fall. Basic Industries are particularly hit by negative expectations. The fall of the Berlin Wall is a neutral event for Consumer Services and Finance Sectors. The major exception is Consumer Non-Durable Sector, for which the fall of the Berlin Wall had an immediate positive effect on earnings expectations. This is reasonable as the reunification gave the sector access (in a short period of time) to a large group of consumers who would probably prefer their products and brands (beverages, tobacco) to those of the old Soviet Block.

Some interesting results were found for selected industries within those sectors. Although the fall of the Berlin Wall was generally a negative event for Basic Industries, within that sector, it was a very positive event for the Construction Industry, reasonable given the scale of public work projects planned for the east. The fall was particularly negative for Chemicals, Steel, and Automobiles. These industries would be particularly sensitive to any negative impact the financial burden of the east would have on the economy. Within the Consumer Non-Durable Sector, highly positive revisions are observed for the earnings of the Beverages and Tobacco industries, most likely reflecting an increased market for the products offered by these industries. Results are also positive for Food and Household, but neutral for Textile and Apparel (items that might be beyond the budget of East Germans).

Collectively, the evidence supports a rational assessment by market participants of the impact on firm values of the events associated with the reunification of Germany. Capital markets appear to be dispassionate in the face of momentous events, and the rationality of market efficiency still holds even in situations in which market participants have scant historical precedents to draw on for guidance. This conclusion is certainly reassuring; but the socialist tyrannies of the Soviet Union and its European Empire passed from the scene, for the most part, with a whimper rather than a bang. The conclusions of this study may not apply when the unprecedented event is occasioned by sudden violent upheaval. It remains to be tested whether dispassion and rationality in the capital markets will still hold or break down at such momentous events.

Acknowledgments The authors gratefully acknowledge the contribution of I/B/E/S International Inc. for providing earnings per share forecast data, available through the Institutional Brokers Estimate System. These data have been provided as part of a broad academic program to encourage earnings expectations research.

References

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Published online: 7 February 2007

[c] International Atlantic Economic Society 2007

W. I. Ghani ([mailing address])

Department of Accounting, Saint Joseph's University, Philadelphia, PA 19131, USA

e-mail: wghani@sju.edu

S. H. Szewczyk

Department of Finance, Drexel University, Philadelphia, PA 19104, USA

T. Shabbir

Department of Finance, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104, USA

(1) Source "Background Notes on Countries of the World: May 2000 Germany, pp. 1-10 (ISSN: 1049-5517)."

(2) In the interest of saving the space, we will not report results for the Consumer Services sector (including tourism-merchandising industry), Finance (banking, insurance), Health Care, Consumer Durables (appliances, automobiles), and Technology. All these results are available with the authors for review by the readers. Key observations about these results are included in the conclusion section of the paper.
Table 1 Important event dates associated with the German reunification

Date                Event

November 9, 1989    The Berlin Wall Falls, followed by massive
                      immigration from East to West Germany
December 1989       Poll taken in East Germany indicates that 71 percent
                      of the people favor sovereignty rather than
                      reunification
January 1990        Passage of regulation to allow privately owned
                      producers and foreign investment in GDR
February 1990       East German leader Modrow announces that he favors
                      unification; FRG proposes economic and monetary
                      union between the two German economies.
March 1990          On March 1 The Berlin Treuhandanstalt, responsible
                      for privatizing East German businesses, is
                      founded. In the East German elections, candidates
                      who favor unification are elected.
May 18, 1990        Staatsvertrag between the two Germanys that
                      specifies the reforms required in East Germany
                      and set out the terms for German economic,
                      monetary, and social union (GEMU).
July 1, 1990        GEMU becomes effective.
August 31, 1990     Representatives of the GDR and FRG sign a treaty for
                      unification.
September 12, 1990  Allies give up their occupation rights in all parts
                      of Germany, which allows unification to be
                      realized.
October 3, 1990     Two Germanys officially unite.

Source: Schnusenberg (2000)

Table 2 Accuracy of analysts earnings per share forecasts of German
firms around the fall of the Berlin Wall

                       Fiscal Year +1 Data     Fiscal Year +2 Data
                       (161 Observations)      (159 Observations)
                       Median           Mean   Median           Mean

Fiscal Year -1          0.210           0.100   0.211           0.100
Fiscal Year +1          0.336           0.127
Fiscal Year +2          0.143                                   0.488
Pairwise
Difference Test
t-statistic (p value)  -2.330 (-0.021)         -2.947 (-0.004)
Wilcoxon
Signed Rank Test
t-statistic (p value)   1.704 (-0.088)          3.281 (-0.001)

Table 3 Analysts forecasts revisions for current year earnings per share
around important events related to German reunification period

Forecast                 Average Forecast
Month               %          No    %     T-
Revision  T-Stat.   Neg.       Chg.  Pos   Stat  Event

-1         0.00005   0.31      17.4  66.9  15.7  0.03
 0        -0.00046  -2.02 (a)  17.3  70.4  12.3  0.87
 1        -0.00004  -0.13      16.4  63.9  19.7  0.88
 2         0.00093   1.19      20.5  65.9  13.5  1.07
 3         0.00030  -1.62      18.4  71.9   9.7  1.57
 4        -0.00030  -1.84      20.5  64.7  14.7  0.95
 5         0.00054   1.68      25.7  50.3  24.1  1.16
 6        -0.00008  -0.17      33.0  39.5  27.6  0.78
 7         0.00115   2.10 (c)  30.8  22.7  46.5  2.51 (a)
 8         0.00114   2.29 (b)  27.7  39.1  33.2  0.92
 9        -0.00169  -4.53 (a)  52.2  34.8  13.0  6.10 (a)
10        -0.00028  -1.90      26.6  64.7   8.7  3.75 (a)
11        -0.00042  -3.24 (a)  28.8  59.8  11.4  2.91 (a)
12        -0.00187  -3.04 (a)  45.7  40.8  13.6  5.23 (a)
13        -0.00026  -1.18      26.6  59.2  14.1  2.93 (a)
14        -0.00077  -2.92 (a)  28.1  61.6  10.3  4.07 (a)
15         0.00034   0.83      25.4  55.7  18.9  1.25

Forecast
Month
Revision  Wilcoxon

-1
 0        The Berlin Wall falls, ensuing a massive immigration from East
            to West Germany.
 1        East German (EG) polls indicate 71% of the people favor
            sovereignty over reunification.
 2        Regulation passage allowing private ownership in GDR for
            producers & foreign investments
 3        EG leader, Modrow, announces favoring unification; FRG
            proposes economic & monetary.
 4        EG election; candidates who favor unification are elected.
 5
 6        Setting up the terms for German Economics, Monetary, &
            Social Union (GEMU)
 7        GEMU becomes effective.
 8
 9        Representatives of the GDR & FRG sign a treaty for
            unification.
10        Allies give up their occupation rights in all of Germany
            allowing unification to be realized.
11        Two Germanys officially unify.
12
13
14
15

Event month 0 is November 1989 (N = 179).
(a) Significant at the 0.01 levels.
(b) Significant at the 0.05 levels.
(c) Significant at the 0.10 levels.

Table 4 Basic industries: chemicals, forest products gold mining
metals--steel metals-misc. Materials multi-industry

Basic Industries-Consolidated Results

                   Average Forecast
Forecast Interval  Revision          T-Stat  (P Val)      %Neg  %0

-12 to -1           0.02063           3.78   (0.001) (a)   9.1   0.0
-6 to -1            0.01596           3.66   (0.001) (a)   9.1   3.0
-4 to -1            0.01238           2.93   (0.006) (a)   9.1   6.1
0 to 0             -0.00013          -0.57   (0.570)      21.2  54.5
1 to 12            -0.00299          -0.89   (0.382)      60.6   0.0
1 to 15            -0.00907          -2.30   (0.028) (b)  63.6   0.0

Industry: Chemicals
-12 to -1           0.01718           3.04   (0.023) (b)   0.0   0.0
-6 to -1            0.01422           2.82   (0.030) (b)   0.0   0.0
-4 to -1            0.01066           3.37   (0.015) (b)   0.0  14.3
0 to 0             -0.00010          -0.11   (0.916)      14.3  57.1
1 to 12            -0.01744           2.87   (0.028) (b)  85.7   0.0
1 to 15            -0.02378          -3.18   (0.019) (b)  85.7   0.0

Industry: Steel
-12 to -1           0.07693           3.82   (0.019) (b)   0.0   0.0
-6 to -1            0.05287           2.48   (0.068) (c)   0.0  20.0
-4 to -1            0.04623           2.04   (0.111)       0.0  20.0
0 to 0              0.00007           1.00   (0.374)       0.0  80.0
1 to 12             0.00537           0.41   (0.703)      40.0   0.0
1 to 5             -0.01130          -0.86   (0.440)      60.0   0.0

Industry: Forest Products
-12 to -1           0.01134           1.93   (0.125)      20.0   0.0
-6 to -1            0.01053           1.70   (0.165)      20.0   0.0
-4 to -1            0.00419           0.87   (0.433)      20.0   0.0
0 to 0             -0.00025          -1.38   (0.241)      40.0  60.0
1 to 12            -0.00873          -1.97   (0.120)      80.0   0.0
1 to 15            -0.01930          -1.92   (0.128)      80.0   0.0

Basic Industries-Consolidated Results

                          Wilcoxon
Forecast Interval  %Pos   P-Val      N

-12 to -1           90.9  0.000 (a)  33
-6 to -1            87.9  0.000 (a)  33
-4 to -1            84.8  0.000 (a)  33
0 to 0              24.2  0.357      33
1 to 12             39.4  0.107      33
1 to 15             36.4  0.014 (b)  33

Industry: Chemicals
-12 to -1          100.0  0.009 (a)   7
-6 to -1           100.0  0.009 (a)   7
-4 to -1            85.7  0.009 (a)   7
0 to 0              28.6  0.306       7
1 to 12             14.3  0.031 (b)   7
1 to 15             14.3  0.014 (b)   7

Industry: Steel
-12 to -1          100.0  0.022 (b)   5
-6 to -1            80.0  0.022 (b)   5
-4 to -1            80.0  0.022 (b)   5
0 to 0              20.0  0.022 (b)   5
1 to 12             60.0  0.343       5
1 to 5              40.0  0.250       5

Industry: Forest Products
-12 to -1           80.0  0.060 (c)   5
-6 to -1            80.0  0.112       5
-4 to -1            80.0  0.173       5
0 to 0               0.0  0.022 (b)   5
1 to 12             20.0  0.069 (c)   5
1 to 15             20.0  0.040 (b)   5

Average Cumulative Forecast Revisions (CFR) for the Current-year
Earnings Per Share.
The forecast month is from the third Thursday in one month to the third
Thursday in the following month relative to the event month 0. Forecast
revisions for the current-year earnings are normalized by the price per
share listed by the IBES the month prior to the announcement. Forecast
revisions are cumulated over forecast months to calculate cumulative
forecast revisions. The null hypothesis tested by the t-statistics is
that the average CFR equals 0.
(a) Significant at the 0.01 levels.
(b) Significant at the 0.05 levels
(c) Significant at the 0.10 levels

Table 5 Capital goods: building materials construction electrical &
electr industrial comp machinery & eng cap multi-industry

Capital Goods--Consolidated Results

                   Average Forecast  T-                  %
Forecast Interval  Revision          Stat   (P Val)      Neg   %0

-12 to -1          0.00896            3.00  (0.005) (a)   9.5   4.8
-6 to -1           0.00755            2.29  (0.027) (b)  14.6   7.3
-4 to -1           0.00705            2.26  (0.029) (b)  17.1   4.9
0 to 0             0.00007            0.33  (0.742)      17.1  63.4
1 to 12            0.00511            1.20  (0.237)      32.6   4.3
1 to 15            0.00367            0.83  (0.410)      37.0   2.2

Industry: Building Materials
-12 to -1          0.00704            3.95  (0.008) (a)   0.0  14.3
-6 to -1           0.00664            3.65  (0.011) (b)   0.0  14.3
-4 to -1           0.00571            2.49  (0.047) (b)  14.3   0.0
0 to 0             0.00049            0.43  (0.681)      14.3  71.4
1 to 12            0.00328            1.13  (0.300)      28.6   0.0
1 to 15            0.00326            1.24  (0.263)      28.6   0.0

Industry: Construction
-12 to -1          0.00856            7.08  (0.002) (a)   0.0   0.0
-6 to -1           0.00657            4.48  (0.011) (b)   0.0   0.0
-4 to -1           0.00613            4.46  (0.011) (b)   0.0   0.0
0 to 0             0.00000            0.00  (0.997)      20.0  60.0
1 to 12            0.00495            2.94  (0.043) (b)   0.0  20.0
1 to 15            0.00495            1.61  (0.183)      20.0   0.0

Industry: Machinery & Engineering
-12 to -1          0.00999            2.26  (0.032) (b)  10.7   3.6
-6 to -1           0.00884            1.78  (0.086) (c)  18.5   7.4
-4 to -1           0.00834            1.78  (0.087) (c)  18.5   7.4
0 to 0             0.00005           -0.42  (0.677)      14.8  66.7
1 to 12            0.00766            1.25  (0.222)      35.5   3.2
1 to 15            0.00578            0.91  (0.368)      38.7   3.2

Capital Goods--Consolidated Results

                   %      Wilcoxon
Forecast Interval  Pos    P-Val      N

-12 to -1           85.7  0.000 (a)  42
-6 to -1            78.0  0.000 (a)  41
-4 to -1            78.0  0.000 (a)  41
0 to 0              19.5  0.327      41
1 to 12             63.0  0.031 (b)  46
1 to 15             60.9  0.205      46

Industry: Building Materials
-12 to -1           85.7  0.014 (b)   7
-6 to -1            85.7  0.014 (b)   7
-4 to -1            85.7  0.031 (b)   7
0 to 0              14.3  0.249       7
1 to 12             71.4  0.199       7
1 to 15             71.4  0.155       7

Industry: Construction
-12 to -1          100.0  0.022 (a)   5
-6 to -1           100.0  0.022 (a)   5
-4 to -1           100.0  0.022 (b)   5
0 to 0              20.0  0.250       5
1 to 12             80.0  0.022 (b)   5
1 to 15             80.0  0.069       5

Industry: Machinery & Engineering
-12 to -1           85.7  0.000 (a)  28
-6 to -1            74.1  0.001 (a)  27
-4 to -1            74.1  0.001 (a)  27
0 to 0              18.5  0.258      27
1 to 12             61.3  0.037 (b)  31
1 to 15             58.1  0.211      31

Average Cumulative Forecast Revisions (CFR) for the Current-year
Earnings Per Share.
The forecast month is from the third Thursday in one month to the third
Thursday in the following month relative to the event month 0. Forecast
revisions for the current-year earnings are normalized by the price per
share listed by the IBES the month prior to the announcement. Forecast
revisions are cumulated over forecast months to calculate cumulative
forecast revisions. The null hypothesis tested by the t-statistics is
that the average CFR equals 0.
(a) Significant at the 0.01 levels.
(b) Significant at the 0.05 levels.
(c) Significant at the 0.10 levels.

Table 6 Consumer non-durables: beverages & tobacco; food & household
recreation; textiles & apparel

Consumer Non-durables--Consolidated Results

                   Average Forecast
Forecast Interval  Revision          T-Stat  (P val)      %Neg  %0

-12 to -1           0.00105           0.43   (0.670)      50.0   6.3
-6 to -1            0.00416           2.72   (0.016) (b)  25.0   0.0
-4 to -1            0.00470           2.80   (0.013) (b)  18.8  12.5
0 to 0             -0.00005          -0.16   (0.875)      18.8  62.5
1 to 12             0.00616           2.40   (0.028) (b)  16.7   0.0
1 to 15            -0.00014          -0.02   (0.982)      16.7   5.6

Industry: Beverages & Tobacco
-12 to -1          -0.00245          -0.93   (0.405)      60.0  20.0
-6 to -1           -0.00009          -0.05   (0.965)      40.0   0.0
-4 to -1           -0.00026          -0.14   (0.899)      40.0   0.0
0 to 0              0.00010           0.63   (0.563)      20.0  60.0
1 to 12             0.00258           3.27   (0.031) (b)   0.0   0.0
1 to 15             0.00281           2.98   (0.041) (b)   0.0   0.0

Industry: Food & Household
-12 to -1           0.00825           1.01   (0.418)      33.3   0.0
-6 to -1            0.00999           4.33   (0.049) (b)   0.0   0.0
-4 to -1            0.00960           5.00   (0.038) (b)   0.0   0.0
0 to 0             -0.00099          -0.52   (0.652)      66.7   0.0
1 to 12             0.01339           3.96   (0.029) (b)   0.0   0.0
1 to 15             0.01139           2.65   (0.077) (c)   0.0  25.0

Industry: Textiles & Apparel
-12 to -1           0.00054           0.16   (0.878)      50.0   0.0
-6 to -1            0.00464           2.14   (0.070) (c)  25.0   0.0
-4 to -1            0.00596           2.31   (0.054) (c)  12.5  25.0
0 to 0              0.00020           1.00   (0.351)       0.0  87.5
1 to 12             0.00493           1.05   (0.323)      33.3   0.0
1 to 15            -0.00690          -0.57   (0.587)      33.3   0.0

Consumer Non-durables--Consolidated Results

                          Wilcoxon
Forecast Interval  %Pos   P Val      N

-12 to -1           43.8  0.418      16
-6 to -1            75.0  0.011 (b)  16
-4 to -1            68.8  0.013 (b)  16
0 to 0              18.8  0.378      16
1 to 12             83.3  0,009 (a)  18
1 to 15             77.8  0.032 (a)  18

Industry: Beverages & Tobacco
-12 to -1           20.0  0.112       5
-6 to -1            60.0  0.446       5
-4 to -1            60.0  0.446       5
0 to 0              20.0  0.446       5
1 to 12            100.0  0.022 (a)   5
1 to 15            100.0  0.022 (b)   5

Industry: Food & Household
-12 to -1           66.7  0.143       3
-6 to -1           100,0  0.054 (b)   3
-4 to -1           100.0  0.054 (b)   3
0 to 0              33.3  0.296       3
1 to 12            100.0  0.034 (b)   4
1 to 15             75.0  0.034 (b)   4

Industry: Textiles & Apparel
-12 to -1           50.0  0.444       8
-6 to -1            75.0  0.046 (b)   8
-4 to -1            62.5  0.046 (b)   8
0 to 0              12.5  0.062 (c)   8
1 to 12             66.7  0.157       9
1 to 15             66.7  0.429       9

Average Cumulative Forecast Revisions (CFR) for the Current-year
Earnings per Share.
The forecast month is from the third Thursday in one month to the third
Thursday in the following month relative to the event month 0. Forecast
revisions for the current-year earnings are normalized by the price per
share listed by the IBES the month prior to the announcement. Forecast
revisions are cumulated over forecast months to calculate cumulative
forecast revisions. The null hypothesis tested by the t-statistics is
that the average CFR equals 0.
(a) Significant at the 0.01 levels.
(b) Significant at the 0.05 levels
(c) Significant at the 0.10 levels
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Author:Ghani, WaQar I.; Szewczyk, Samuel H.; Shabbir, Tayyeb
Publication:International Advances in Economic Research
Geographic Code:4EUGE
Date:May 1, 2007
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