This presentation is about incorporation risk through simulation.

1. Incorporation Risk
Through Simulation

2. Presentation forming group
• Zulfiqar Ansari 12IN118
• Shehroze Mughal 12IN01
• Jazib Zai 12IN126
• Zahid Ali Abbasi 12IN127
• Mohsin Shaikh 12IN132
• Serwan Ali Memon 12-11IN54

3. Contents
• Introduction
• Incorporation
• Risk
• Simulation
• Risk and Simulation
• Example
• Risk in Decision Making
• Risk Analysis
• Monte Carlo Simulation
• Problem
• Conclusion

4. Introduction
The term risk today has many different meaning. In financial circles however the
term risk has a definite and distinct meaning. Risk refers to the situation where
decision are based on the calculation of probabilities that certain outcomes will
materialize or where probabilities based on historical information and statistical
frequency distribution are known

5. Incorporation
• Incorporation is the forming of a new corporation
• The process of legally declaring a corporate entity as separate from its owners
• A corporation is a company or group of people authorized to act as a single
entity (legally a person) and recognized as such in law

6. Risk
• Risk is potential of losing something of valuable
• Risk is a consequence of action taken in spite of uncertainty
• The probability or threat of quantifiable damage, injury, liability, loss, or any
other negative occurrence that is caused by external or internal vulnerabilities,
and that may be avoided through preemptive action

7. Simulation
• The act or process of pretending
• The technique of representing the real world by a computer program
• The act of giving a false appearance
• An attempt to model a real-life or hypothetical situation on a computer so that it
can be studied to see how the system works

8. Risk and Simulation
• Simulation allows you to evaluate, compare and optimize alternatives designs,
plans and policies through visualization
• The act of simulating the probabilistic data into statistical form
• Simulation is used when the level of uncertainty is high and the alternatives are
not quite feasible and taking any action will cause the heavy damages
• The complexity of the uncertainties requires the visualization of future
consequences

9. Example 1
A fisheries biologist could dynamically simulate the salmon population in a river in
order to predict changes to the population and quantitatively understand the
impacts on salmon of possible action (e.g. fishing, loss of habitat)to ensure that
they don't go extinct at some point in the future

10. Example 2
• When implementing a strategic plan for a company the impacts are likely to take
months (or years) to materialize. Simulation is particularly valuable when there
is significant uncertainty regarding the outcome or consequences of a particular
alternative under consideration. Probabilistic simulation allows dealing with this
uncertainty in a quantifiable way

11. Example 3
• The newsvendor problem is one in which a retailer for instance needs to
purchase some quantity of an item prior to demand being known. If too few are
ordered must be heavily discounted. This fundamental problem commonly
occurs in retailing such as when a store needs to order seasonal or perishable
merchandise. However it also exists for manufacturers of products that decline
in value after they are produced. For example, in the fast changing electronics
industry, if a company commits to manufacturing too many handled computers it
will be left with product that is worth only a fraction of what is was worth when
produced. Even transpiration services such as airlines face a form of this
problem since airlines must decide ahead of time how frequently to fly a give
route and which type of aircraft to use.

12. Risk in Decision Making
• The unpredictable consequence as a result of taking decision
• Decision making requires an understanding of the requirements and objectives,
their relative importance, and how to assess options and make the 'best'
decision
• For a successful decision making, understanding the level of risk and its
consequence is needed to select the best possible solution

13. Risk Analysis
Risk Analysis involves following steps
Define the
problem
Construct
the Model
Assess
Input
Variables
Calculate
Interpret
the Model
Output
Use the
Risk
analysis in
Decision
Making

14. Monte Carlo Simulation
• Monte Carlo methods are quite useful for simulating systems with many coupled
degrees of freedom
• Monte Carlo methods can be used to solve any problem having a probabilistic
interpretation
• Monte Carlo simulation uses repeated sampling to determine the properties of
some phenomenon (or behavior).
• Monte Carlo simulations sample are taken from a probability distribution for
each variable to produce hundreds or thousands of possible outcomes
• Monte Carlo methods are especially useful for simulating phenomena with
significant uncertainty in inputs and systems with a large number of coupled
degrees of freedom

15. Problem
Consider a firm that has three unrelated product lines and needs to forecast its
total net profit for the coming year. Uncertainty exists in the revenue and cost
structures of each product; however, the three divisional managers have
assessed their profit distributions as shown in table. These profit distributions are
assumed to be
Table: Profit Distributions for three products
Product 1 Product 2 Product 3
Profit Probability Profit Probability Profit Probability
800000 01
75000 0.2 850000 0.3 500000 0.5
125000 0.5 990000 0.5 550000 0.5
175000 0.3 950000 0.1
∑=1 ∑=1 ∑=1

16. Table: Evaluation of all possible outcomes
Product 1 Product 2 Product 3 Total Probability
75 800 500 1375 0.2*0.1*0.5=0.01
75 800 550 1425 0.2*0.1*0.5=0.01
75 850 500 1425 0.2*0.3*0.5=0.03
75 850 550 1475 0.2*0.3*0.5=0.03
75 900 500 1475 0.2*0.5*0.5=0.05
75 900 550 1525 0.2*0.5*0.5=0.05
75 950 500 1525 0.2*0.1*0.5=0.01
75 950 550 1575 0.2*0.1*0.5=0.01
125 800 500 1425 0.5*0.1*0.5=0.025
125 800 550 1475 0.5*0.1*0.5=0.025
125 850 500 1475 0.5*0.3*0.5=0.075
125 850 550 1525 0.5*0.3*0.5=0.075
125 900 500 1525 0.5*0.5*0.5=0.125
125 900 550 1575 0.5*0.5*0.5=0.125
125 950 500 1575 0.5*0.1*0.5=0.025

17. 125 950 550 1625 0.5*0.1*0.5=0.025
175 800 500 1475 0.3*0.1*0.5=0.015
175 800 550 1525 0.3*0.1*0.5=0.015
175 850 500 1525 0.3*0.3*0.5=0.045
175 850 550 1575 0.3*0.3*0.5=0.045
175 900 500 1575 0.3*0.5*0.5=0.075
175 900 550 1625 0.3*0.5*0.5=0.075
175 950 500 1625 0.3*0.1*0.5=0.015
175 950 550 1675 0.3*0.1*0.5=0.015

18. Total profit Distribution
Profit(thousands) Probability
1375 0.01
1425 0.065
1475 0.195
1525 0.32
1575 0.26
1625 0.115
1675 0.015
∑=1

19. Assignment of Random Numbers to profit Values
Random Numbers Trial profit Values
0,1 75
2,3,4,5,6 125
7,8,9 175
Random
Numbers
Trial Profit
0 800
1,2,3 850
4,5,6,7,8 900
9 950
Random
Numbers
Trial Profit
0,1,2,3,4 500
5,6,7,8,9 550
Product 2 Product 3

20. Table: Monte Carlo Results for the Total Profit Distribution
Profit 1375 1425 1475 1525 1575 1625 1675 Approximant Computer
Time Per Run
True
Probabilities
0.01 0.065 0.195 0.32 0.26 0.115 0.015
No: of Trials
10 0 0 1 2 4 3 0
10 0 0 1 6 2 1 0 < 1 Second
10 0 1 2 4 2 1 0
100 0 4 24 28 34 9 1
100 2 6 22 26 27 17 0 1 ¼ Seconds
100 1 5 15 34 28 15 2
100 0 9 26 31 24 8 2
1000 7 63 191 318 279 120 22 11 Seconds
1000 11 62 207 317 283 101 19
1000 6 77 198 299 290 108 22

21. 10000 97 659 1955 3228 2744 1151 166 < 2 minutes
10000 104 646 1945 3208 2833 1108 158
10000 103 637 1945 3196 2803 1155 161
100000 1025 6393 19622 32028 28074 11276 1582 < 16 minutes

22. Conclusion
Use of the simulations can be helpful in solving complex decisions but they are
very expensive and need additional training to use them. Selecting the simulation
is also the a big problem because they are not made for the short term decisions .
Monte Carlo decision model is helpful very much and mostly it has been used
because it provides us an accurate results