Study on Evaluation Index System for Enterprise Value

Study on Evaluation Index System for Enterprise Value
When Merger and Acquisition
KONG Xiangwei, ZHAO Kun
School of Economic & Management, Beijing Jiaotong University, P.R.China, 100044
School of Logistics, Beijing Wuzi University, P.R.China, 101149
Abstract: Based on previous research, after discuss four main enterprise valuation approaches in use,
this paper presents a new index system for measuring enterprise value, when merger and acquistion. The
system framework is presented by: enterprise character, industry situation, and macro-economic. By
using this new index system, target enterprise can by be evaluated more objectively. At the end, by using
new Data-Mining methods optimize calculation result.
Keywords: enterprise value, evaluation, index system, data mining
With the coming of value management era, and valuation theory and method has become the focus,
which attracts more attention of people increasingly. Enterprise value, which base in modern economy,
is a perspective,compounding and real function of enterprise goal. Economic development goes through
agricultural economy,industrial economy,service economy and experience economy. In different eras,the
wealth in the mechanism changes greatly, with which the subject of value source changes.In the
economic era of agriculture labor creates value,in the industrial economy capital means value.While the
latter two types of economy rely on the fact that the customer gives value.Based on current literatures, it
is found that the available method is rarely to assess enterpise value specially and systematically.
1. Research summary
Mostly, enterprise's value theory has gone through three stages: supply determining, character by labor
value theory and key element value theory; demand determining character by the value theory of utility;
supply and demand determining charater by new classical value. The course of the progress in value
theory reflected the course of social production development and environmental changes of economy.
Generally, there are four main evaluation approaches about enterprise value in use: the valuation
approach based on assets, the relative comparison valuation approach, the income present value
valuation approach and the real option valuation approach. All those approaches were invented during
the history of M&A in developing countries.
Nowadays, there are many innovations about evaluation measure of enterprise value: NPV(Net Present
Value) is equal to the present value of all future free cash flows less the investment’s initial outlay;
FCFF(Free Cash Flow of Firm); EVA (Economic Value Added); ROIC(Return on Investment Capital);
ROV real option valuation method ; and so on.
（
）
2. Influential factors of enterprise value
There are many studies on influence factors of enterprise value when merger and acquisition, scholars
inducing from different springboard. For example, Tang guang(2006) classified enterpise value into
operating performance, core competence, and enterprise external environment.
Based on the procreant reason of enterprise value, this paper classified into three sorts, figured out by
Figure -1:
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Figure-1 Evaluation model of enterprise value
1. Enterprise character
Enterprise value is a systematic and holistic evaluate result, caused from accouting value, future profit,
innovation capacity, etc. Including: (1) profit ability, enterprise gain benefit with a certain period. To
acquire benefit is the purpose of an enterprise, and the most enterpirse value determined by their profit
ability. (2) increase ability, sustainable growth generally reflect the development situation of enterprise.
It’s the main factor for most analyser to evaluate enterprise value. (3) innovation capability, includeing,
technology innovation, enterprise can update new technology to improve their efficiency or improve
new techonolgy or product; management innovation, enterprise invent new measure, process to carry
business.
2. Industry situation
Every enterprise has it’s industry, which determines the expansibility of this enterprise. The industry
development phase, competition, degree of concentration, technology level will influence every
enterprise within this industry, therefore industry situation is the main factor to evaluate an enterprise
value when merger and acquisition.
3. Micro-economic
Micro-economic will influence the value of enterprise greatly. When stock market index in high level,
the price of enterprise for exchange will rise great than normal. On the contrary, when stock market
index in low level, it’s hard to evaluate the exchange price of enperprise, since the value of reference
enterprice also in low level lost fair and square.
3. The structure of enterprise value evaluation index system
According to enterprise value evaluation model, this paper constructs an index system with a prime
objective level, 3 objective level, 13 primary indexes, and 24 target indexes. Table 2 shows us the
specific indexes.
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Table. 1 The evaluation index system of enterprise value
objective
item
Enterprise
character
(A1)
primary
Item
Accounting
value(B1)
Present value
of profit (B2)
Innovation-capabil
ity (B3)
Hard capability
(B4)
Soft capability(B5)
Total object
Industry
situation
(A2)
Micro
-economic
(A3)
Industry
attraction(B6)
Industry maturity
(B7)
Industry growth
(B8)
Industry
concentration(B9)
GDP growth B10
Money-supply
(B11
Interest rate level
B12
Consumption
growth B13
target
item
Static accounting value (C1)
Retained profits growth (C2)
Future retained profits (C3)
Income growth rate (C4)
Sci-tech award and patent (C5)
New product and Published thesis (C6)
Innovation resource (C7)
Personnel structure (C8)
Innovation and decision-making mechanism,. (C9)
Environment of science and technology development (C10)
Industry profit situation (C11)
Whole industry venture (C12)
Industry development phase (C13)
Industry dimensions (C14)
Industry income growth rate (C15)
Industry technology level(C16)
Market occupancy situation(C17)
Industry competition degree (C18)
GDP growth(C19)
Currency supply growth(C20)
M1/M2 (C21)
Interest rate (C22)
Fiscal police (C23)
（ ）
）
（ ）
（ ） Consumption increase rate (C24)
4. Support vector machines for semi-supervised multi-label classification
We want to evaluate the enterprise value using index system in section 3. Multi-label learning refers to
the classification problems where each example can be assigned to multiple different classes. It has
found applications in many real-world problems. Because the index system above is layered, we will
transform it to a multi-label classification problem. For the prime objective level, every object has a
label subset, then for the integrated object, that is its enterprise value, every enterprise has a label set
with three label subsets. Due to enterprise’s data is hard to get, we can only get a little of number
enterprise’s data, we would better to use semi-supervised multi-label classification model to evaluate it.
The following terminology and notations will be used throughout the rest of the paper. Let D =(x1,
x2, . . . , xn) denote the entire dataset, where n is the total number of examples, including both the labeled
ones and the unlabeled ones. We assume that the first nl examples are labeled ones, and their label
information is presented in the binary matrix T ∈ {0,1} nl × m where m is the number of classes. Let the
similarity of all the examples denoted by a matrix A = [Ai,j ]n×n, where element Ai,j ≥ 0 represents the
similarity between two examples based on their input patterns. We denote by Ti,k ≥ 0 the confidence
score of assigning the k-th class label to the i-th example, and by ti = (Ti, 1, Ti,2, . . . , Ti,m)> the confidence
scores of assigning each class to the i-th example. Finally, the matrix T = [Ti,k]n×m denotes the
confidence scores of assigning every class label to all examples.
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To capture the correlation among different classes, we introduce matrix B = [Bk,l]m×m for the class
similarities. Each element Bk,l ≥ 0 represents the similarity between two classes. Then, instead of
computing the class-based similarity between two examples by the direct dot product, we compute it by
a weighted dot product. Then, following the assumption stated above, we would expect
T
Ai , j ≈ t i Bt j if the class assignments ti and tj are appropriate for examples xi and xj . This leads to the
following optimization problem:
An alternative optimization approach is adopted to solve the constrained NMF. In particular, we will
solve the optimization problem by alternatively fixing one set of label confidences and finding the
optimal solution for another set of label confidences. More specifically, we first fix the normalized label
confidence matrix Tˆ and the scaling factors α j ’s, and search for the abnormalized label confidence
m
Ti,j that optimizes (3). To this end, we upper-bound the term ( Ai , j −
∑T
i ,k
Bk ,l T j ,l ) 2 as follows
k ,l =1
In the above, Tˆ refers to the matrix T from the last iteration. We use the convexity of the quadratic
function in the first step of the derivation, and the concaveness of the logarithm function in the third step
of the derivation. Then, we can upper-bound the first term in the function (3) as
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By combining the above two bounds, we have the upper bound for the objective function in (3). Taking
the derivative of the bounding function with respect to Tj,l, we have
which leads to the following solution
In the second step, we fix the abnormalized label confidence Ti,k and search for the normalized label
~
confidence Ti , k that optimizes the problem in (3), which leads to the following optimal solution:
In summary, the iterative steps solving the optimization problem (3) could be formulated as the
following algorithm
Step 1 randomly initialize T and Tˆ subject to the constraints in (3)
Step 2 until convergence, do 1. Fix all α j ’s and Tˆ , update T using Equation (4); 2. Fix T, update Tˆ
using Equation (5); 3. Fix T; update all α j ’s using Equation (6)
5 Conclusion
In this paper, with the help of AHP method, based on analysing the properties and influence factor of
enterprise value, presents a new index system for measuring the enterprise value in order to take
corresponding measures to assess objective enterprise value when merger and acquisition. This index
system has the following advantages: (1) It integrates non-linear relationships among various factors
internally influencing an enterprise value quite well. The assessment becomes comprehensive and
objective. (2) This index system is concise, comprehensive and feasible.
At last, by using new Data-Mining methods optimize calculation result more precisely.
Acknowledgements:
Supported by Funding Project for Academic Human Resource Development in Institutions of Higher
Learning Under the Jurisdiction of Beijing Municipality(PHR200907134)
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Kong Xiangwei is a post PH. D at School of Economic & Management Beijing Jiaotong University,
Beijing, China, 100044. Tel: 010-51688378. Email: [email protected] .
，
，
Zhao Kun is a lecturer of School of Logistics Beijing Wuzi University Beijing China 101149. Tel: 010-89534018.
Email: [email protected].
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