BVX Knowledge Base
BVX Knowledge Base
M&A University

BVX® Iterative Method

BVX® uses Income Approach and Discounted Cash Flow (DCF) method. It applies DCF method to buyer's actual post-acquisition cash flow by discounting it at buyer's expected equity rate of return. BVX® does not include the Market Approach or the Asset Approach.


BVX® values 100% Enterprise Value.  Equity Value determination and application of minority discounts, marketability discounts, etc, is done outside of BVX®.  BVX® does not use any market data, nor does it use any valuation formulas, nor does it use traditional valuation methods. Expected EBITDA and cost of equity are inputs provided by the user.  BVX® does not use the weighted average cost of capital (WACC) because WACC ignores priority of debt over equity. 


BVX® is not a forecasting tool hence it does not have input for historical financials, nor is BVX® a recasting tool. Value of the business depends on its expected future performance, not on its past.  BVX® requires data for expected future performance (Pro-forma projections) of the business. 


BVX® valuation is independent of the type of the business.  BVX® indirectly captures the type of the business through its balance sheet and its ability to leverage the balance sheet in the capital markets.  BVX® valuation is independent of the size of the business.  Size may impact variables like expected ROE, financing, organization form, etc.  These variables can be changed to reflect the size of the business. 


BVX® iterative method consists of three steps. Step 1 consists of calculating post-acquisition financial statements for the buyer using normal accounting standards. The user provides all inputs for the financial statements, except two values; they are purchase price multiple and buyer equity (these two are called unknown variables). In the first iteration random values are assigned to the unknown variables (price multiple and buyer equity) to generate a complete set of financial statements. Step 2 consists of testing the results of step 1 to see if various conditions are satisfied (see details below). Step 3 consists of changing the values of the unknown variables (price multiple and/or buyer equity) if all conditions in Step 2 are not satisfied and then repeating Step 1 with new values for the unknown variables. BVX® keeps iterating until all conditions of Step 2 are satisfied. The values for the unknown variables (price and equity) in the last iteration when all conditions are satisfied are the BVX® - Best Value and the associated required buyer equity. 


[Note: a) Actual BVX® has 3 unknown variables. They are price multiple, buyer equity and mezzanine equity, b) All the conditions of Step 2 are detailed below, c) BVX® uses commercially available non-linear programming (NLP) algorithm during the 3 steps to help converge on the optimal solution.] 

The iterative valuation approach permits complete elimination of the . The valuation results are highly objective because 1) BVX® inputs are no different than the ones needed to create pro-forma statements, 2) the financials are calculated using normal standards used by accountants and lenders, 3) ROI is calculated based on actual cash delivered to the investor, 4) cash flow is discounted using equity ROI, rather than with WACC, 5) financials are based on buyer's post-acquisition projected performance, and 6) all factors that affect cash flow i.e. debt repayment, deal structure, organization form, purchase price allocation, etc. are accounted for.


BVX® theory is "Value of a firm is the Equilibrium of Price, Terms, and Deal Structure that satisfies the needs of all parties to an M&A transaction." More specifically BVX® assumes that a) the Seller wants the best price for the business, b) the Buyer is willing to pay the price and has the necessary equity that meets his/her ROI objective and meets all his/her financial commitments." Specifically, these criterions are implemented within BVX® through more detailed assumptions, some of which are:


1) Seller wants the maximum price, and

2) Seller wants to maximize goodwill allocation to reduce taxes, and

3) Buyer wants to achieve a certain minimum ROI, and

4) Buyer wants to meet all financial obligations to creditors, and 

5) Buyer wants to pay proper taxes, and

6) Buyer wants to maintain a safety cash reserve at year end, and

7) Buyer wants to meet all the cash needs of the operation, and

8) Buyer does not want negative cash flow (See - Cash Flow), and

9) Buyer does not want to plan for equity infusion after the acquisition, and

10) There is ample acquisition capital available in the market place and that a Buyer can be found with the necessary equity to complete the acquisition, and

11) Buyer wants to maximize bank borrowing, and

12) Buyer can find cash flow lenders or can negotiate necessary Seller Financing, and

13) Buyer wants to first borrow against the A/R, then against the Inventory, then against the Fixed Assets (see ), and

14) Buyer is willing to accept negative taxable income as long as the cash flow is not negative (later on this requirement will be made optional, and

15) Buyer wants to use excess cash flow to pay down debt as described in , and

16) Lenders will not refinance the term loans during the planning horizon; however, they would permit borrowing against new fixed assets, etc.

17)   Seller expects the buyer to maximize debt borrowing under prevailing market conditions and to make efficient use of funds.


BVX® converts above requirements into a series of mathematical equations (also known as "constraints" in the non-linear programming field.) Then BVX® uses the non-linear programming algorithm to determine the optimal values for the unknown variables (price, buyer equity and mezzanine equity) that satisfy all these requirements. The number of iterations and the time it takes to determine the optimal value can vary from few seconds to few minutes. Time to determine optimal value is different every time Deal Optimizer is activated even if the data input is not changed. The number of calculations ranges from 500,000 to several millions for each optimal value determination.