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32 Cards in this Set
- Front
- Back
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1.4 essential functions and formulas in Excel for financial modelling |
SUM & SUMPRODUCT | IF& IFERROR | NPV & IRR | VLOOKUP & INDEX-MATCH | PMT & FV | COUNT, COUNTIF & COUNTIFS |
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1.5 steps in building financial model |
define the objective| gather data| structure the model | identify variables and assumptions | Build formulas & calculations |validate and test | document assumptions and methodology | analyze and interpret results| present findings| review and update |
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1.6 advanced financial modelling techniques |
scenario analysis | Monte Carlo simulation |sensitivity and breakeven analysis | capital budgeting techniques | Complex valuation methods | Dynamic Financial modelling| data analysis and visualisation |
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1.7 characteristics of financial modelling |
based on historical data| forward looking| assumptions and inputs| dynamic | scenario based| sensitivity analysis |
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1.8 how to learn financial modelling |
basic accounting knowledge| finance fundamentals| Excel proficiency |data analysis skills | knowledge of corporate finance and valuation | practice and hands on experience |
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1.9 types of financial model |
financial statement m.| discounted cash flow DCF m.| mergers and acquisitions M&A m.|LBO leveraged buyout m.| project finance m.| sensitivity and scenario m. |risk modeling | option pricing m. |Monte Carlo simulation m. |
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1.10 components of a financial model |
historical data| assumptions| formulas| calculations| outputs |
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1.11 how to build a financial model? Step by step |
historical results and assumptions| make income statement| make balance sheet| build the supporting schedules| complete the income statement and balance sheet| bulid the cash flow statement | perform the discounting cash flow analysis | add sensitivity analysis and scenarios| build charts and graphs| stress test and Audit the model |
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1.1 what is modelling |
is the process of creating a representation of a company's financial situation. It involves the development of a mathematical model that helps in making financial decisions. |
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1.2 objectives of financial modelling |
To analyze the historical performance... | to develop forecasts & scenarios for future performance... | to identify trends in financial data. .. | to assess risk & evaluate potential outcomes | to compare company to each other... | to facilitate financial planning, budgeting, forecasting... |
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2.1 scenario analysis |
scenario analysis is a process of examining and evaluating possible events or scenarios that could take place in the future to understand potential outcomes under various conditions. |
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2.2 types of scenario analysis |
base scenario, best scenario and worst scenario| macro environmental | Industry specific | operational | global |
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2.3 characteristics of scenario analysis |
Plausibility | consistency |diversity| dynamic in nature| quantitative & qualitative elements |
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2.4 application of scenario analysis |
strategic planning | risk management | investment decision making | supply chain management | policy planning |
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2.5 necessity of scenario analysis in today's business world |
uncertainty | Rapid change| globalization |strategic agility তৎপরতা | complex interconnectedness |
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2.6 assumptions of scenario analysis |
plausible বিশ্বাসযোগ্য variability | independence of variables | consistency |identification of Key drivers |
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2.8 drawbacks of scenario analysis |
requires a high level of skill| unforeseen outcomes | cannot model every scenario |
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2.15 steps of scenario analysis in Microsoft Excel |
identify key variables for SA |set up a base s. | create copies for s. | adjust assumptions for each s.| Link s. to a summary sheet |use data tables for sensitivity| create charts and graphs | iteration and refinement |
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2.7 steps to performing scenery analysis |
list the assumption... | copy and paste the list of assumption... | fill in all details of each scenario | ensure the layout of all three scenery... | create a new section... | use excel choose function | link the... |
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2.9 sensitivity analysis |
is a tool used in financial modeling to analyze how the different values of a set of independent variables affect a specific dependent variable under certain specific conditions |
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2. 11 advantages of financial sensitivity analysis |
adds reliability... | flexible with the boundaries...| helps one make informed choices |
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3.1 simulation |
is a large scale system of men, machines, materials and information operating in a real world environment aiming for optimal alternative through trial and error |
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3.2 scope of simulation techniques |
Healthcare simulator | computer simulator | military| finance | flight| engineering, technology or process |
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3.3 phases of simulation |
identify the problem | identify the decision variables, decide the performance criterion and decision rule | constract a numerical model | validate the model |design the experiments| Run simulation model | examine the results in terms of problem solution |
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3.4 applications of simulation |
queuing ptroblems | job shop scheduling | inventory p. |network m. |financial m.| marketing m. |
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3.5 types of simulation |
deterministic and probabilistic s. | time dependent and time independent s. | visual interactive s. | business game| corporate and financial s. |
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3.6 simulation models - mainly two types| broadly 4 types |
continuous |discrete models|_| deterministic| stochastic সম্ভাব্যতার সূত্রাবলি | static | dynamic models |
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3.8 Monte Carlo simulation |
is a statistical technique that involves replacing a complex system with a probabilistic model and drawing random samples from the model using random numbers |
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3.8.1 Monte Carlo simulation procedure |
clearly define the problem | construct an appropriate model | prepare the model for experimentation | using steps 1-3 experiment with the model | summarize and examine the results | evaluate the results of the simulation |
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3.9. construct an appropriate model under simulation |
identify the variables and the parameters| formulate an appropriate decision rule | identify the type of distribution that will be used | specify the manner in which time will be charged | define the relationship between variable and parameters |
