lec5 slides_with_some_answers - Econ 138 Financial and...

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Econ 138 Financial and Behavioral Economics Lecture 5: Financing Constraints Ulrike Malmendier UC Berkeley Tu, January 31, 2008
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Organization issues Clari fi cation on course content: Corporate Finance (CF) and Behavioral (CF) with an emphasis on eco- nomic grounding Not covering the Asset Pricing (AP) side of Finance or Behavioral AP The Behavioral Finance part of this course focuses on managerial bi- ases (overcon fi dence / hubris, sunk-cost fallacy) not on investor biases (noise traders −→ some treatment in Econ 119) Some treatment of investor biases to the extent that they are relevant to CF, e.g., managers responding to investors’ overvaluation of their stock by issuing stock, the so-called market timing literature.
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Posted slides: I am generally trying to post updated slides a day after class or so, sometimes even right after class, sometimes one or two days later. The updated notes correct some typos and, most importantly, include some of our calculations. I even ventured into drawing our fancy graphs from lecture 2 and including them in the updated notes . . . I can’t promise though that I will always be able do that!
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1 Follow-up 1: joint maximization Question in last class: ‘In the case I > C , how do we know that old shareholders don’t prefer NOT to issue equity and to just invest up to I = C rather than getting diluted? Answer: For I > C , the maximization program was max I s s + s 0 ( A + R ( I )) s.t. s 0 s + s 0 · ( A + R ( I )) = I C if I > C and, after plugging in s 0 = s I C A + R ( I ) I + C became the unconstrained program max I A + R ( I ) I + C .
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and we are back to the simple maximization program in the case of ‘su cient cash to fi nance anything’ ( I C for any desired I ). It gave us a solution I with R 0 ( I ) = 1 and the value of objective function is A + R ( I ) I + C . If we did not want to issue shares, instead, we would maximize A + R ( I ) I + C with the constraint I C : max I A + R ( I ) I + C s.t. I C The resulting maximum of the constrained optimization program (s.t . ... ) can only be smaller than the maximum of the unconstrained maximization program. (In fact, since ( A + R ( I ) I + C ) /∂I > 0 for all I < I , it is optimal to go to the maximum I, i.e., I = C. Hence the value of the objective function is A + R ( C ) C + C < max I A + R ( I ) I + C .)
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Graph! What is the surplus we lose? C A + C A + C + R(I) I
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Additional Material Summarizing both cases in one maximization program: Rather than splitting up the cases of “su cient cash” ( I C ) and “insu cient cash” ( I > C ), we could have considered one unifying maximization program. Denote the price for newly issued shares s 0 by P . Then, we can write the maximization program as follows: max I,s 0 s s + s 0 h A + C + s 0 P + R ( I ) I i s.t. s 0 P = s 0 s + s 0 h A + C + s 0 P + R ( I ) I i s 0 P I C s 0 P 0
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Solving the fi rst constraint for s 0 P (1 s 0 s + s 0 ) = s 0 s + s 0 [ A + C + R ( I ) I ] ⇐⇒ s s + s 0 s 0 P = s 0 s + s 0 [ A + C + R ( I ) I ] and plugging into the objective function, we obtain s s + s 0 [ A + C + R ( I ) I ] + s 0 s
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