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Throughout history humans have found it desirable to construct cities along streams. Streams are sources of water for consumption, agriculture, and industry. Streams provide transportation routes, energy, and a means of disposal of wastes. Stream valleys offer a relatively flat area for construction. But, human populations that live along streams also have the disadvantage that the flow of water in streams is never constant. High amounts of water flowing in streams often leads to flooding, and flooding is one of the more common and costly types of natural disasters.
A flood results when a stream runs out of its confines and submerges surrounding areas.
In less developed countries, humans are particularly sensitive to flood casualties because of high population density, absence of zoning regulations, lack of flood control, and lack of emergency response infrastructure and early warning systems.
Bangladesh is one of the most susceptible countries to flood disasters. About one half of the land area in Bangladesh is at an elevation of less than 8 meters above sea level. Up to 30% of the country has been covered with flood waters. In 1991 more 200,000 deaths resulted from flooding and associated tropical cyclones.
In industrialized countries the loss of life is usually lower because of flood control structures, zoning regulations that prevent the habitation of seriously vulnerable lands, and emergency preparedness. Still, property damage and disruption of life takes a great toll, and despite flood control structures and land use planning, floods still do occur.
Causes of Flooding
From a geological perspective, floods are a natural consequence of stream flow in a continually changing environment. Floods have been occurring throughout Earth history, and are expected so long as the water cycle continues to run. Streams receive most of their water input from precipitation, and the amount of precipitation falling in any given drainage basin varies from day to day, year to year, and century to century.
The Role of Precipitation
Weather patterns determine the amount and location of rain and snowfall. Unfortunately the amount and time over which precipitation occurs is not constant for any given area. Overall, the water cycle is a balanced system. Water flowing into one part of the cycle (like streams) is balanced by water flowing back to the ocean. But sometimes the amount flowing in to one area is greater than the capacity of the system to hold it within natural confines. The result is a flood. Combinations of factors along with exceptional precipitation can also lead to flooding. For example, heavy snow melts, water saturated ground, unusually high tides, and drainage modifications when combined with heavy rain can lead to flooding.
Areas along coastlines become subject to flooding as a result of tsunamis, hurricanes (cyclonic storms), and unusually high tides. In addition, long term processes like subsidence and rising sea level as a result of global warming can lead to the encroachment of the sea on to the land.
Dam & Levee Failures
Dams occur as both natural and human constructed features. Natural dams are created by volcanic events (lava flows and pyroclastic flows), landslides, or blockage by ice. Human constructed dams are built for water storage, generation of electrical power, and flood control. All types of dams may fail with the sudden release of water into the downstream drainage. Spectacular and devastating examples of dam failures include that resulting in flooding downstream include:
The St. Francis Dam, near Saugus, California, failed in 1929 killing 450 people.
The Johnstown, Pennsylvania dam, built of earthen material (soil and rock) collapsed after a period of heavy rainfall in 1889. 2,200 people were killed by the flood.
The Vaiont Dam in Italy (discussed in a previous lecture on mass-wasting) did not fail in 1963, but the landslides that moved into the reservoir behind the dam caused water to overtop the dam killing over 3,000 people.
As we have seen during Hurricane Katrina in New Orleans, levee systems designed to prevent flooding can also fail and lead to catastrophic flooding and loss of life.
A stream is a body of water that carries rock particles and dissolved ions and flows down slope along a clearly defined path, called a channel. Thus streams may vary in width from a few centimeters to several kilometers. Streams are important for several reasons
Streams carry most of the water that goes from the land to the sea, and thus are an important part of the water cycle.
Streams carry billions of tons of sediment to lower elevations, and thus are one of the main transporting mediums in the production of sedimentary rocks.
Streams carry dissolved ions, the products of chemical weathering, into the oceans and thus make the sea salty.
Streams are a major part of the erosional process, working in conjunction with weathering and mass wasting. Much of the surface landscape is controlled by stream erosion, evident to anyone looking out of an airplane window.
Streams are a major source of water and transportation for the world's human population. Most population centers are located next to streams.
Ultimo aggiornamento 2014-10-26
Predicting Australian Takeover Targets:
A Logit Analysis
* Discipline of Finance,
School of Finance,
The University of Sydney
Positive announcement-day adjusted returns to target shareholders in the event of a
takeover are well documented. Investors who are able to accurately predict firms that
will be the subject of a takeover attempt should be able to earn these excess returns. In
this paper a series of probabilistic regression models were developed that use financial
statement variables suggested by prior research as explanatory variables. The models,
applied to in-sample and out-of-sample data, led to predictions of takeover targets that
were better than chance in all cases. The economic outcome resulting from holding a
portfolio of the predicted targets over the prediction period are also analysed.
Keywords: takeovers, targets, prediction, classification, logit analysis
JEL Codes: G11, G17, G23, G34
This is a draft copy and not to be quoted.
In this paper our aim is to accurately predict companies that will become takeover
targets. Theoretically, if it is possible to predict takeovers with accuracy greater than
chance, it should be possible to generate abnormal returns from holding a portfolio of
the predicted targets. Evidence of abnormal returns of 20% to 30% made by
shareholders of firms on announcement of a takeover bid is why prediction of these
events is of interest to academics and practitioners alike.
The modelling approach adopted in this study was based on the discrete choice
approach used by Palepu (1986) and Barnes (1999). The models were based on
financial statement information, using variables suggested by the numerous theories
that have been put forward to explain takeover activity. The performance of the
models was evaluated using statistical criteria. Further, the predictions from the
models were rated against chance and economic criteria through the formation and
tracking of a portfolio of predicted targets. Positive results were found under both
Takeover prediction studies are a logical extension of the work of Altman (1968)
who used financial statement information to explain corporate events. Early studies by
Simkowitz and Monroe (1971) and Stevens (1973) were based on the Multiple
Discriminant Analysis (MDA) technique. Stevens (1973) coupled MDA with factor
analysis to eliminate potential multicollinearity problems and reported a predictive
accuracy of 67.5%, suggesting that takeover prediction was viable. Belkaoui (1978)
and Rege (1984) conducted similar analyses in Canada with Belkaoui (1978)
confirming the results of these earlier researchers and reporting a predictive accuracy
of 85% . Concerns were raised by Rege (1984) who was unable to predict with similar
accuracy. These concerns were also raised in research by others such as Singh (1971)
and Fogelberg, Laurent, and McCorkindale (1975).
Reacting to the wide criticism of the MDA method, researchers began to use
discrete choice models as the basis of their research. Harris et al. (1984) used probit
analysis to develop a model and found that it had extremely high explanatory power,
but were unable to discriminate between target and non-target firms with any degree
of accuracy. Dietrich and Sorensen (1984) continued this work using a logit model
and achieved a classification accuracy rate of 90%. Palepu (1986) addressed a number
of methodological problems in takeover prediction. He suggested the use of statebased
prediction samples where a number of targets were matched with non-targets
for the same sample period. While this approach was appropriate for the estimation
sample, it exaggerated accuracies within the predictive samples because the estimated
error rates in these samples were not indicative of error rates within the population of
firms. He also proposed the use of an optimal cut-off point derivation which
considered the decision problem at hand. On the basis of this rectified methodology,
along with the application of a logit model to a large sample of US firms, Palepu
(1986) provided evidence that the ability of the model was no better than a chance
selection of target and non-target firms. Barnes (1999) also used the logit model and a
modified version of the optimal cut-off rule on UK data. His results indicated that a
portfolio of predicted targets may have been consistent with Palepu’s finding, but he
was unable to document this in the UK context due to model inaccuracy.
In the following section the economic explanations underlying takeover activity
are discussed. Section 3 outlines our takeover hypotheses and describes the
explanatory variables that are used in the modelling procedure. The modelling
framework and data used in the study is contained in Section 4, while the results of
our model estimation, predictions, classification accuracy and portfolio economic
outcomes are found in Section 5. We conclude in Section 6.
2. Economic explanations of takeover activity
Economic explanations of takeover activity have suggested the explanatory
variables that were included in this discrete choice model development study. Jensen
and Meckling (1976) posited that agency problems occurred when decision making
and risk bearing were separated between management and stakeholders1, leading to
management inefficiencies. Manne (1965) and Fama (1980) theorised that a
mechanism existed that ensured management acted in the interests of the vast number
of small non-controlling shareholders2. They suggested that a market for corporate
control existed in which alternative management teams competed for the rights to
control corporate assets. The threat of acquisition aligned management objectives
with those of stakeholders as managers are terminated in the event of an acquisition in
order to rectify inefficient management of the firm’s assets. Jensen and Ruback
(1983) suggested that both capital gains and increased dividends are available to an
1 Stakeholders are generally considered to be both stock and bond holders of a corporation.
2 We take the interests of shareholders to be in the maximization of the present value of the firm.
acquirer who could eliminate the inefficiencies created by target management, with
the attractiveness of the firm for takeover increasing with the level of inefficiency.
Jensen (1986) looked at the agency costs of free cash flow, another form of
management inefficiency. In this case, free cash flow referred to cash flows in excess
of positive net present value (NPV) investment opportunities and normal levels of
financial slack (retained earnings). The agency cost of free cash flow is the negative
NPV value that arises from investing in negative NPV projects rather than returning
funds to investors. Jensen (1986) suggested that the market value of the firm should
be discounted by the expected agency costs of free cash flow. These, he argued, were
the costs that could be eliminated either by issuing debt to fund an acquisition of
stock, or through merger with, or acquisition of a growing firm that had positive NPV
investments and required the use of these excess funds. Smith and Kim (1994)
combined the financial pecking order argument of Myers and Majluf (1984) with the
free cash flow argument of Jensen (1986) to create another motivational hypothesis
that postulated inefficient firms forgo profitable investment opportunities because of
informational asymmetries. Further, Jensen (1986) argued that, due to information
asymmetries that left shareholders less informed, management was more likely to
undertake negative NPV projects rather than returning funds to investors. Smith and
Kim (1994) suggested that some combination of these firms, like an inefficient firm
and an efficient acquirer, would be the optimal solution to the two respective resource
allocation problems. This, they hypothesised, would result in a market value for the
combined entity that exceeded the sum of the individual values of the firms. This is
one form of financial synergy that can arise in merger situations.
Another form of financial synergy is that which results from a combination of
characteristics of the target and bidding firms. Jensen (1986) suggested that an
optimal capital structure exists, whereby the marginal benefits and marginal costs of
debt are equal. At this point, the cost of capital for a firm is minimised. This
suggested that increases in leverage will only be viable for those firms who have free
cash flow excesses, and not for those which have an already high level of debt.
Lewellen (1971) proposed that in certain situations, financial efficiencies may be
realized without the realization of operational efficiencies. These efficiencies relied
on a simple Miller and Modigliani (1964) model. It proposed that, in the absence of
corporate taxes, an increase in a firm’s leverage to reasonable levels would increase
the value of the equity share of the company due to a lower cost of capital. By a
merger of two firms, where either one or both had not utilised their borrowing
capacity, would result in a financial gain. This financial gain would represent a
valuation gain above that of the sum of the equity values of the individual firms.
However, this result is predicated on the assumption that the firms need to either
merge or be acquired in order to achieve this result.
Merger waves are well documented in the literature. Gort (1969) suggested that
industry disturbances are the source of these merger waves, his argument being that
they occurred in response to discrepancies between the valuation of a firm by
shareholders and potential acquirers. As a consequence of economic shocks (such as
deregulation, changes in input or output prices, etc.), expectations concerning future
cash flow became more variable. This results in an increased probability that the value
the acquirer places on a potential target is greater than its current owner’s valuation.
The result is a possible offer and subsequent takeover. Mitchell and Mulherin (1996),
in their analysis of mergers and acquisitions in the US during the 1980s, provided
evidence that mergers and acquisitions cluster by industries and time. Their analysis
confirmed the theoretical and empirical evidence provided by Gort (1969) and
provided a different view suggesting that mergers, acquisitions, and leveraged
buyouts were the least cost method of adjusting to the economic shocks borne by an
These theories suggested a clear theoretical base on which to build takeover
prediction models. As a result, eight main hypotheses for the motivation of a merger
or acquisition have been formulated, along with twenty three possible explanatory
variables to be incorporated predictive models.
3. Takeover hypotheses and explanatory variables
The most commonly accepted motivation for takeovers is the inefficient
management hypothesis.3 The hypothesis states that inefficiently managed firms will
be acquired by more efficiently managed firms. Accordingly,
H1: Inefficient management will lead to an increased likelihood of acquisition.
Explanatory variables suggested by this hypothesis as candidates to be included in the
specifications of predictive models included:
1. ROA (EBIT/Total Assets – Outside Equity Interests)
3 It is also known as the disciplinary motivation for takeovers.
2. ROE (Net Profit After Tax / Shareholders Equity – Outside Equity Interests)
3. Earnings Before Interest and Tax Margin (EBIT/Operating Revenue)
4. EBIT/Shareholders Equity
5. Free Cash Flow (FCF)/Total Assets
6. Dividend/Shareholders Equity
7. Growth in EBIT over past year, along with an activity ratio,
8. Asset Turnover (Net Sales/Total Assets)
While there are competing explanations for the effect that a firm’s undervaluation
has on the likelihood of its acquisition by a bidder, there is consistent agreement
across all explanations that the greater the level of undervaluation then the greater the
likelihood a firm will be acquired. The hypothesis that embodies the impact of these
competing explanations is as follows:
H2: Undervaluation of a firm will lead to an increased likelihood of acquisition.
The explanatory variable suggested by this hypothesis is:
9. Market to book ratio (Market Value of Securities/Net Assets)
The Price Earnings (P/E) ratio is closely linked to the undervaluation and
inefficient management hypotheses. The impact of the P/E ratio on the likehood of
acquisition is referred to as the P/E hypothesis:
H3: A high Price to Earnings Ratio will lead to a decreased likelihood of acquisition.
It follows from this hypothesis that the P/E ratio is a likely candidate as an
explanatory variable for inclusion in models for the prediction of potential takeover
10. Price/Earnings Ratio
The growth resource mismatch hypothesis is the fourth hypothesis. However, the
explanatory variables used in models specified to examine this hypothesis capture
growth and resource availability separately. This gives rise to the following:
H4: Firms which possess low growth / high resource combinations or,
alternatively, high growth / low resource combinations will have an increased
likelihood of acquisition.
The following explanatory variables suggested by this hypothesis are:
11. Growth in Sales (Operating Revenue) over the past year
12. Capital Expenditure/Total Assets
13. Current Ratio (Current Assets/Current Liabilities)
14. (Current Assets – Current Liabilities)/Total Assets
15. Quick Assets (Current Assets – Inventory)/Current Liabilities
The behaviour of some firms to pay out less of their earnings in order to maintain
enough financial slack (retained earnings) to exploit future growth opportunities as
they arise, has led to the dividend payout hypothesis:
H5: High payout ratios will lead to a decreased likelihood of acquisition.
The obvious explanatory variable suggested by this hypothesis is:
16. Dividend Payout Ratio
Rectification of capital structure problems is an obvious motivation for takeovers.
However, there has been some argument as to the impact of low or high leverage on
acquisition likelihood. This paper proposes a hypothesis known as the inefficient
financial structure hypothesis from which the following hypothesis is derived.
H6: High leverage will lead to a decreased likelihood of acquisition.
The explanatory variables suggested by this hypothesis include:
17. Net Gearing (Short Term Debt + Long Term Debt)/Shareholders Equity
18. Net Interest Cover (EBIT/Interest Expense)
19. Total Liabilities/Total Assets
20. Long Term Debt/Total Assets
The existence of Merger and Acquisition (M&A) activity waves, where takeovers
are clustered in wave-like profiles, have been proposed as indicators of changing
levels of M&A activity over time. It has been argued that the identification of M&A
waves, with the corresponding improved likelihood of acquisition when the wave is
surging, captures the effect of the rate of takeover activity at specific points in time,
and serves as valuable input into takeover prediction models. Consistent with M&A
activity waves and their explanation as a motivation for takeovers is the industry
H7: Industry merger and acquisition activity will lead to an increased likelihood
An industry relative ratio of takeover activity is suggested by this hypothesis:
21. The numerator is the total bids launched in a given year, while the
denominator is the average number of bids launched across all the industries in
Size will have an impact on the likelihood of acquisition. It seems plausible that
smaller firms will have a greater likelihood of acquisition due to larger firms
generally having fewer bidding firms with the resources to acquire them. This gives
rise to the following hypothesis:
H8: The size of a firm will be negatively related to the likelihood of acquisition.
Explanatory variables that can be employed to control for size include:
21. Log (Total Assets)
22. Net Assets
4. Data and Method
The data requirements for the variables defined above are derived from the
financial statements and balance sheet date price information for Australian listed
companies. The financial statement information was sourced from the AspectHuntley
data base which includes annual financial statement data for all ASX listed companies
between 1995 and 2006. The database includes industry classifications for all firms
included in the construction of industry relative ratios. Lists of takeover bids and their
respective success were obtained from the Connect4 database. This information
enabled the construction of variables for relative merger activity between industries.
Additionally, stock prices from the relevant balance dates of all companies were
sourced from the AspectHuntley online database, the SIRCA Core Price Data Set and
4.1 The Discrete Choice Modelling Framework
The modelling procedure used is the nominal logit model, made popular in the
bankruptcy prediction literature by Ohlson (1980) and, subsequently, in the takeover
prediction literature by Palepu (1986). Logit models are commonly utilised for
dichotomous state problems. The model is given by equations  to  below.
The logit model was developed to overcome the rigidities of the Linear
Probability Model in the presence of a binary dependent variable. Equations  and
 show the existence of a linear relationship between the log-odds ratio (otherwise
known as the logit Li) and the explanatory variables. However, the relationship
between the probability of the event and acquisition likelihood is non-linear. This
non-linear relationship has a major advantage that is demonstrated in equation .
Equation  measures the change in the probability of the event as a result of a small
increment in the explanatory variables, .
When the probability of the
event is high or low, the incremental impact of a change in an explanatory variable on
the likelihood of the event will be compressed, requiring a large change in the
explanatory variables to change the classification of the observation. If a firm is
clearly classified as a target or non-target, a large change in the explanatory variables
is required to change its classification.
4.2 Sampling Schema
Two samples were used in the model building and evaluation procedure. They
were selected to mimic the problem faced by a practitioner attempting to predict
takeover targets into the future.
The first sample was used to estimate the model and to conduct in-sample
classification. It was referred to as the Estimation Sample. This sample was based on
financial data for the 2001 and 2002 financial years for firms that became takeover
targets, as well as selected non-targets, between January, 2003 and December, 2004.
The lag in the dates allows for the release of financial information as well as allowing
for the release of financial statements for firms whose balance dates fall after the 30th
June. Following model estimation, the probability of a takeover offer was estimated
for each firm in the entire sample of firms between January, 2003 and December,
2004 using the estimated model and each firm’s 2001 and 2002 financial data. Expost
predictive ability for each firm was then assessed.
A second sample was then used to assess the predictive accuracy of the model
estimated with the estimation sample data. It is referred to as the Prediction Sample.
This sample includes the financial data for the 2003 and 2004 financial years, which
will be used in conjunction with target and non-target firms for the period January,
2005 to December, 2006. Using the model estimated from the 2001 and 2002
financial data, the sample of firms from 2005 and 2006 were fitted to the model using
their 2003 and 2004 financial data. They were then classified as targets or non-targets
using the 2005 and 2006 data. This sampling methodology allows for the eva
Ultimo aggiornamento 2014-02-23
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Ultimo aggiornamento 2013-01-18
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