├── README.md
├── 7 Debt.ipynb
├── 9 WACC.ipynb
├── 8 Unlevered Equity .ipynb
├── 12 Relative Valuation.ipynb
├── 3 Other Profit Measures.ipynb
├── 13 Forward Contracts.ipynb
├── 11 WACC Challenge.ipynb
├── 1 Present Value.ipynb
├── 10 Debt Effect Analysis.ipynb
├── 5 Cash Flow Challenges.ipynb
├── 6 CAPM.ipynb
├── 4 Depreciation.ipynb
└── 2 NPV vs. IRR.ipynb


/README.md:
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1 | # FinanceAndPython.com-CorporateFinance


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/7 Debt.ipynb:
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 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {},
 7 |    "outputs": [
 8 |     {
 9 |      "name": "stdout",
10 |      "output_type": "stream",
11 |      "text": [
12 |       "0.05\n",
13 |       "0.055\n",
14 |       "0.07500000000000001\n"
15 |      ]
16 |     }
17 |    ],
18 |    "source": [
19 |     "print(.05 + 0*.5)\n",
20 |     "print(.05 + .01*.5)\n",
21 |     "print(.05 + .05*.5)"
22 |    ]
23 |   }
24 |  ],
25 |  "metadata": {
26 |   "kernelspec": {
27 |    "display_name": "Python 3",
28 |    "language": "python",
29 |    "name": "python3"
30 |   },
31 |   "language_info": {
32 |    "codemirror_mode": {
33 |     "name": "ipython",
34 |     "version": 3
35 |    },
36 |    "file_extension": ".py",
37 |    "mimetype": "text/x-python",
38 |    "name": "python",
39 |    "nbconvert_exporter": "python",
40 |    "pygments_lexer": "ipython3",
41 |    "version": "3.6.1"
42 |   }
43 |  },
44 |  "nbformat": 4,
45 |  "nbformat_minor": 1
46 | }
47 | 


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/9 WACC.ipynb:
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 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {},
 7 |    "outputs": [
 8 |     {
 9 |      "name": "stdout",
10 |      "output_type": "stream",
11 |      "text": [
12 |       "0.07833333333333334\n"
13 |      ]
14 |     }
15 |    ],
16 |    "source": [
17 |     "def WACC(price,outstanding,debtValue,debtYield,stockReturn,taxRate): \n",
18 |     "    equity = price*outstanding \n",
19 |     "    total = equity+debtValue \n",
20 |     "    return (equity/total)*stockReturn+(debtValue/total)*debtYield*(1-taxRate) \n",
21 |     "print(WACC(20.0,100,1000.0,.05,.1,.3))"
22 |    ]
23 |   }
24 |  ],
25 |  "metadata": {
26 |   "kernelspec": {
27 |    "display_name": "Python 3",
28 |    "language": "python",
29 |    "name": "python3"
30 |   },
31 |   "language_info": {
32 |    "codemirror_mode": {
33 |     "name": "ipython",
34 |     "version": 3
35 |    },
36 |    "file_extension": ".py",
37 |    "mimetype": "text/x-python",
38 |    "name": "python",
39 |    "nbconvert_exporter": "python",
40 |    "pygments_lexer": "ipython3",
41 |    "version": "3.6.1"
42 |   }
43 |  },
44 |  "nbformat": 4,
45 |  "nbformat_minor": 2
46 | }
47 | 


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/8 Unlevered Equity .ipynb:
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 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {},
 7 |    "outputs": [
 8 |     {
 9 |      "name": "stdout",
10 |      "output_type": "stream",
11 |      "text": [
12 |       "0.08\n",
13 |       "0.12\n",
14 |       "0.16\n",
15 |       "0.2\n"
16 |      ]
17 |     }
18 |    ],
19 |    "source": [
20 |     "def unlever(r_a,r_d,E,D):\n",
21 |     "    return r_a + (r_a-r_d)*(D/E)\n",
22 |     "print(unlever(.08,.04,100,0))\n",
23 |     "print(unlever(.08,.04,100,100))\n",
24 |     "print(unlever(.08,.04,100,200))\n",
25 |     "print(unlever(.08,.02,100,200))"
26 |    ]
27 |   },
28 |   {
29 |    "cell_type": "code",
30 |    "execution_count": 2,
31 |    "metadata": {},
32 |    "outputs": [
33 |     {
34 |      "name": "stdout",
35 |      "output_type": "stream",
36 |      "text": [
37 |       "0.12\n",
38 |       "0.08\n",
39 |       "0.06\n"
40 |      ]
41 |     }
42 |    ],
43 |    "source": [
44 |     "print(unlever(.08,.04,100,100-0))\n",
45 |     "print(unlever(.08,.04,100,100-100))\n",
46 |     "print(unlever(.08,.04,100,100-150))"
47 |    ]
48 |   }
49 |  ],
50 |  "metadata": {
51 |   "anaconda-cloud": {},
52 |   "kernelspec": {
53 |    "display_name": "Python 3",
54 |    "language": "python",
55 |    "name": "python3"
56 |   },
57 |   "language_info": {
58 |    "codemirror_mode": {
59 |     "name": "ipython",
60 |     "version": 3
61 |    },
62 |    "file_extension": ".py",
63 |    "mimetype": "text/x-python",
64 |    "name": "python",
65 |    "nbconvert_exporter": "python",
66 |    "pygments_lexer": "ipython3",
67 |    "version": "3.6.1"
68 |   }
69 |  },
70 |  "nbformat": 4,
71 |  "nbformat_minor": 1
72 | }
73 | 


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/12 Relative Valuation.ipynb:
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 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {
 7 |     "collapsed": true
 8 |    },
 9 |    "outputs": [],
10 |    "source": [
11 |     "import numpy as np\n",
12 |     "A = np.array( [ [100,10,5],[0,100,5],[0,0,100] ] )\n",
13 |     "B = np.array([200,300,400])"
14 |    ]
15 |   },
16 |   {
17 |    "cell_type": "code",
18 |    "execution_count": 2,
19 |    "metadata": {},
20 |    "outputs": [
21 |     {
22 |      "name": "stdout",
23 |      "output_type": "stream",
24 |      "text": [
25 |       "[ 1.52  2.8   4.  ]\n"
26 |      ]
27 |     }
28 |    ],
29 |    "source": [
30 |     "x = np.linalg.solve(A, B)\n",
31 |     "print(x)"
32 |    ]
33 |   },
34 |   {
35 |    "cell_type": "code",
36 |    "execution_count": 3,
37 |    "metadata": {},
38 |    "outputs": [
39 |     {
40 |      "name": "stdout",
41 |      "output_type": "stream",
42 |      "text": [
43 |       "[ 136.8  280.   440. ]\n",
44 |       "856.8\n"
45 |      ]
46 |     }
47 |    ],
48 |    "source": [
49 |     "print(x*[90,100,110])\n",
50 |     "print(sum(x*[90,100,110]))"
51 |    ]
52 |   }
53 |  ],
54 |  "metadata": {
55 |   "anaconda-cloud": {},
56 |   "kernelspec": {
57 |    "display_name": "Python 3",
58 |    "language": "python",
59 |    "name": "python3"
60 |   },
61 |   "language_info": {
62 |    "codemirror_mode": {
63 |     "name": "ipython",
64 |     "version": 3
65 |    },
66 |    "file_extension": ".py",
67 |    "mimetype": "text/x-python",
68 |    "name": "python",
69 |    "nbconvert_exporter": "python",
70 |    "pygments_lexer": "ipython3",
71 |    "version": "3.6.1"
72 |   }
73 |  },
74 |  "nbformat": 4,
75 |  "nbformat_minor": 1
76 | }
77 | 


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/3 Other Profit Measures.ipynb:
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 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {
 7 |     "collapsed": true
 8 |    },
 9 |    "outputs": [],
10 |    "source": [
11 |     "class Cashflow:\n",
12 |     "    def __init__(self, val,t,r):\n",
13 |     "        self.val = val\n",
14 |     "        self.t = t\n",
15 |     "        self.r = r\n",
16 |     "        self.PV = self.valAt(0)\n",
17 |     "    def valAt(self,time):\n",
18 |     "        return self.val*(1+self.r)**(time-self.t)\n",
19 |     "def NPV(arr):\n",
20 |     "    return sum([x.PV for x in arr])\n",
21 |     "def pIndex(flows):\n",
22 |     "    pos = [x for x in flows if x.val>0]\n",
23 |     "    neg = [x for x in flows if x.val<0]\n",
24 |     "    return NPV(pos)/-NPV(neg)"
25 |    ]
26 |   },
27 |   {
28 |    "cell_type": "code",
29 |    "execution_count": 2,
30 |    "metadata": {},
31 |    "outputs": [
32 |     {
33 |      "name": "stdout",
34 |      "output_type": "stream",
35 |      "text": [
36 |       "1.239606953892668\n"
37 |      ]
38 |     }
39 |    ],
40 |    "source": [
41 |     "flows = [Cashflow(-1500,0,.05),Cashflow(1000,1,.05),Cashflow(1000,2,.05)]\n",
42 |     "print(pIndex(flows))"
43 |    ]
44 |   },
45 |   {
46 |    "cell_type": "code",
47 |    "execution_count": 3,
48 |    "metadata": {},
49 |    "outputs": [
50 |     {
51 |      "name": "stdout",
52 |      "output_type": "stream",
53 |      "text": [
54 |       "10.0\n",
55 |       "12.210884353741495\n",
56 |       "7.908098991675274\n"
57 |      ]
58 |     }
59 |    ],
60 |    "source": [
61 |     "print(Cashflow(20,0,.05).PV+Cashflow(-10,0,.05).PV)\n",
62 |     "print(Cashflow(30,2,.05).PV+Cashflow(-15,0,.05).PV)\n",
63 |     "print(Cashflow(40,4,.05).PV+Cashflow(-25,0,.05).PV)"
64 |    ]
65 |   }
66 |  ],
67 |  "metadata": {
68 |   "anaconda-cloud": {},
69 |   "kernelspec": {
70 |    "display_name": "Python 3",
71 |    "language": "python",
72 |    "name": "python3"
73 |   },
74 |   "language_info": {
75 |    "codemirror_mode": {
76 |     "name": "ipython",
77 |     "version": 3
78 |    },
79 |    "file_extension": ".py",
80 |    "mimetype": "text/x-python",
81 |    "name": "python",
82 |    "nbconvert_exporter": "python",
83 |    "pygments_lexer": "ipython3",
84 |    "version": "3.6.1"
85 |   }
86 |  },
87 |  "nbformat": 4,
88 |  "nbformat_minor": 1
89 | }
90 | 


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/13 Forward Contracts.ipynb:
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 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {},
 7 |    "outputs": [
 8 |     {
 9 |      "name": "stdout",
10 |      "output_type": "stream",
11 |      "text": [
12 |       "27.36322655556311\n"
13 |      ]
14 |     }
15 |    ],
16 |    "source": [
17 |     "class Cashflow:\n",
18 |     "    def __init__(self, val,t,r):\n",
19 |     "        self.val = val\n",
20 |     "        self.t = t\n",
21 |     "        self.r = r\n",
22 |     "        self.PV = self.valAt(0)\n",
23 |     "    def valAt(self,time):\n",
24 |     "        return self.val*(1+self.r)**(time-self.t)\n",
25 |     "def NPV(arr):\n",
26 |     "    return sum([x.PV for x in arr])\n",
27 |     "flows = [Cashflow(-150,0,.1), Cashflow(50*1.05,1,.1), Cashflow(50*1.1,2,.1), Cashflow(50*1.15,3,.1), Cashflow(50*1.2,4,.1)]\n",
28 |     "print(NPV(flows))"
29 |    ]
30 |   },
31 |   {
32 |    "cell_type": "code",
33 |    "execution_count": 2,
34 |    "metadata": {},
35 |    "outputs": [
36 |     {
37 |      "name": "stdout",
38 |      "output_type": "stream",
39 |      "text": [
40 |       "-12.260091523803048\n"
41 |      ]
42 |     }
43 |    ],
44 |    "source": [
45 |     "flows = [Cashflow(-150,0,.1), Cashflow(50*.8,1,.1), Cashflow(50*.85,2,.1), Cashflow(50*.9,3,.1), Cashflow(50*.95,4,.1)]\n",
46 |     "print(NPV(flows))"
47 |    ]
48 |   },
49 |   {
50 |    "cell_type": "code",
51 |    "execution_count": 3,
52 |    "metadata": {},
53 |    "outputs": [
54 |     {
55 |      "name": "stdout",
56 |      "output_type": "stream",
57 |      "text": [
58 |       "39.62331807936616\n"
59 |      ]
60 |     }
61 |    ],
62 |    "source": [
63 |     "flows = [Cashflow(50*(1.05-.8),1,.1), Cashflow(50*(1.1-.85),2,.1), Cashflow(50*(1.15-.9),3,.1), Cashflow(50*(1.2-.95),4,.1)]\n",
64 |     "print(NPV(flows))"
65 |    ]
66 |   }
67 |  ],
68 |  "metadata": {
69 |   "anaconda-cloud": {},
70 |   "kernelspec": {
71 |    "display_name": "Python 3",
72 |    "language": "python",
73 |    "name": "python3"
74 |   },
75 |   "language_info": {
76 |    "codemirror_mode": {
77 |     "name": "ipython",
78 |     "version": 3
79 |    },
80 |    "file_extension": ".py",
81 |    "mimetype": "text/x-python",
82 |    "name": "python",
83 |    "nbconvert_exporter": "python",
84 |    "pygments_lexer": "ipython3",
85 |    "version": "3.6.1"
86 |   }
87 |  },
88 |  "nbformat": 4,
89 |  "nbformat_minor": 1
90 | }
91 | 


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/11 WACC Challenge.ipynb:
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 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {
 7 |     "collapsed": true
 8 |    },
 9 |    "outputs": [],
10 |    "source": [
11 |     "def unlever(r_a,r_d,E,D):\n",
12 |     "    return r_a + (r_a-r_d)*(D/E)"
13 |    ]
14 |   },
15 |   {
16 |    "cell_type": "code",
17 |    "execution_count": 2,
18 |    "metadata": {
19 |     "collapsed": true
20 |    },
21 |    "outputs": [],
22 |    "source": [
23 |     "a = [.08,.03,100,100]\n",
24 |     "b = [.1,.04,100,0]\n",
25 |     "c = [.09,.02,200,100]\n",
26 |     "d = [.08,.03,100,200]"
27 |    ]
28 |   },
29 |   {
30 |    "cell_type": "code",
31 |    "execution_count": 3,
32 |    "metadata": {},
33 |    "outputs": [
34 |     {
35 |      "name": "stdout",
36 |      "output_type": "stream",
37 |      "text": [
38 |       "0.13\n",
39 |       "0.1\n",
40 |       "0.125\n",
41 |       "0.125\n",
42 |       "The average is:\n",
43 |       "0.12\n",
44 |       "The WACC is:\n",
45 |       "0.07975806451612902\n"
46 |      ]
47 |     }
48 |    ],
49 |    "source": [
50 |     "import numpy as np\n",
51 |     "\n",
52 |     "print(unlever(a[0],a[1],a[2],a[3]))\n",
53 |     "print(unlever(b[0],b[1],b[2],b[3]))\n",
54 |     "print(unlever(c[0],c[1],c[2],c[3]))\n",
55 |     "print(unlever(c[0],c[1],c[2],c[3]))\n",
56 |     "print(\"The average is:\")\n",
57 |     "rates = []\n",
58 |     "rates.append(unlever(a[0],a[1],a[2],a[3]))\n",
59 |     "rates.append(unlever(b[0],b[1],b[2],b[3]))\n",
60 |     "rates.append(unlever(c[0],c[1],c[2],c[3]))\n",
61 |     "rates.append(unlever(c[0],c[1],c[2],c[3]))\n",
62 |     "print(np.mean(rates))\n",
63 |     "def WACC(price,outstanding,debtValue,debtYield,stockReturn,taxRate): \n",
64 |     "    equity = price*outstanding \n",
65 |     "    total = equity+debtValue \n",
66 |     "    return (equity/total)*stockReturn+(debtValue/total)*debtYield*(1-taxRate) \n",
67 |     "print(\"The WACC is:\")\n",
68 |     "print(WACC(10.5,20,(300-200),.04,.1025,.2))"
69 |    ]
70 |   }
71 |  ],
72 |  "metadata": {
73 |   "anaconda-cloud": {},
74 |   "kernelspec": {
75 |    "display_name": "Python 3",
76 |    "language": "python",
77 |    "name": "python3"
78 |   },
79 |   "language_info": {
80 |    "codemirror_mode": {
81 |     "name": "ipython",
82 |     "version": 3
83 |    },
84 |    "file_extension": ".py",
85 |    "mimetype": "text/x-python",
86 |    "name": "python",
87 |    "nbconvert_exporter": "python",
88 |    "pygments_lexer": "ipython3",
89 |    "version": "3.6.1"
90 |   }
91 |  },
92 |  "nbformat": 4,
93 |  "nbformat_minor": 1
94 | }
95 | 


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/1 Present Value.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "class Cashflow:\n",
 12 |     "    def __init__(self, val,t,r):\n",
 13 |     "        self.val = val\n",
 14 |     "        self.t = t\n",
 15 |     "        self.r = r\n",
 16 |     "        self.PV = self.valAt(0)\n",
 17 |     "    def valAt(self,time):\n",
 18 |     "        return self.val*(1+self.r)**(time-self.t)"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 2,
 24 |    "metadata": {},
 25 |    "outputs": [
 26 |     {
 27 |      "name": "stdout",
 28 |      "output_type": "stream",
 29 |      "text": [
 30 |       "1859.410430839002\n"
 31 |      ]
 32 |     }
 33 |    ],
 34 |    "source": [
 35 |     "flows = [Cashflow(1000,1,.05),Cashflow(1000,2,.05)]\n",
 36 |     "total =0\n",
 37 |     "for f in flows:\n",
 38 |     "    total+= f.PV\n",
 39 |     "print(total)"
 40 |    ]
 41 |   },
 42 |   {
 43 |    "cell_type": "code",
 44 |    "execution_count": 3,
 45 |    "metadata": {},
 46 |    "outputs": [
 47 |     {
 48 |      "name": "stdout",
 49 |      "output_type": "stream",
 50 |      "text": [
 51 |       "[952.3809523809523, 907.0294784580498]\n",
 52 |       "1859.410430839002\n"
 53 |      ]
 54 |     }
 55 |    ],
 56 |    "source": [
 57 |     "total = [f.PV for f in flows]\n",
 58 |     "print(total)\n",
 59 |     "print(sum(total))"
 60 |    ]
 61 |   },
 62 |   {
 63 |    "cell_type": "code",
 64 |    "execution_count": 4,
 65 |    "metadata": {},
 66 |    "outputs": [
 67 |     {
 68 |      "name": "stdout",
 69 |      "output_type": "stream",
 70 |      "text": [
 71 |       "2152.5\n",
 72 |       "[1102.5, 1050.0]\n",
 73 |       "2152.5\n"
 74 |      ]
 75 |     }
 76 |    ],
 77 |    "source": [
 78 |     "total =0\n",
 79 |     "for f in flows:\n",
 80 |     "    total+= f.valAt(3)\n",
 81 |     "print(total)\n",
 82 |     "total = [f.valAt(3) for f in flows]\n",
 83 |     "print(total)\n",
 84 |     "print(sum(total))"
 85 |    ]
 86 |   }
 87 |  ],
 88 |  "metadata": {
 89 |   "anaconda-cloud": {},
 90 |   "kernelspec": {
 91 |    "display_name": "Python 3",
 92 |    "language": "python",
 93 |    "name": "python3"
 94 |   },
 95 |   "language_info": {
 96 |    "codemirror_mode": {
 97 |     "name": "ipython",
 98 |     "version": 3
 99 |    },
100 |    "file_extension": ".py",
101 |    "mimetype": "text/x-python",
102 |    "name": "python",
103 |    "nbconvert_exporter": "python",
104 |    "pygments_lexer": "ipython3",
105 |    "version": "3.6.1"
106 |   }
107 |  },
108 |  "nbformat": 4,
109 |  "nbformat_minor": 1
110 | }
111 | 


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/10 Debt Effect Analysis.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stdout",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "1100.0\n"
 13 |      ]
 14 |     }
 15 |    ],
 16 |    "source": [
 17 |     "import numpy as np\n",
 18 |     "returns = [1000,1100,1200]\n",
 19 |     "expected = np.mean(returns)\n",
 20 |     "print(expected)"
 21 |    ]
 22 |   },
 23 |   {
 24 |    "cell_type": "code",
 25 |    "execution_count": 2,
 26 |    "metadata": {},
 27 |    "outputs": [
 28 |     {
 29 |      "name": "stdout",
 30 |      "output_type": "stream",
 31 |      "text": [
 32 |       "1000.0\n"
 33 |      ]
 34 |     }
 35 |    ],
 36 |    "source": [
 37 |     "print(expected/1.1)"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "code",
 42 |    "execution_count": 3,
 43 |    "metadata": {},
 44 |    "outputs": [
 45 |     {
 46 |      "name": "stdout",
 47 |      "output_type": "stream",
 48 |      "text": [
 49 |       "0.1\n"
 50 |      ]
 51 |     }
 52 |    ],
 53 |    "source": [
 54 |     "def unlever(r_a,r_d,E,D):\n",
 55 |     "    return r_a + (r_a-r_d)*(D/E)\n",
 56 |     "print(unlever(.1,.04,1000,0))"
 57 |    ]
 58 |   },
 59 |   {
 60 |    "cell_type": "code",
 61 |    "execution_count": 4,
 62 |    "metadata": {},
 63 |    "outputs": [
 64 |     {
 65 |      "name": "stdout",
 66 |      "output_type": "stream",
 67 |      "text": [
 68 |       "0.16\n"
 69 |      ]
 70 |     }
 71 |    ],
 72 |    "source": [
 73 |     "print(unlever(.1,.04,500,500))"
 74 |    ]
 75 |   },
 76 |   {
 77 |    "cell_type": "code",
 78 |    "execution_count": 5,
 79 |    "metadata": {},
 80 |    "outputs": [
 81 |     {
 82 |      "name": "stdout",
 83 |      "output_type": "stream",
 84 |      "text": [
 85 |       "[480, 580, 680]\n"
 86 |      ]
 87 |     }
 88 |    ],
 89 |    "source": [
 90 |     "returns_after = [x-520 for x in returns]\n",
 91 |     "print(returns_after)"
 92 |    ]
 93 |   },
 94 |   {
 95 |    "cell_type": "code",
 96 |    "execution_count": 6,
 97 |    "metadata": {},
 98 |    "outputs": [
 99 |     {
100 |      "name": "stdout",
101 |      "output_type": "stream",
102 |      "text": [
103 |       "580.0\n",
104 |       "1.16\n"
105 |      ]
106 |     }
107 |    ],
108 |    "source": [
109 |     "expected = np.mean(returns_after)\n",
110 |     "print(expected)\n",
111 |     "print(580.0/500.0)"
112 |    ]
113 |   }
114 |  ],
115 |  "metadata": {
116 |   "anaconda-cloud": {},
117 |   "kernelspec": {
118 |    "display_name": "Python 3",
119 |    "language": "python",
120 |    "name": "python3"
121 |   },
122 |   "language_info": {
123 |    "codemirror_mode": {
124 |     "name": "ipython",
125 |     "version": 3
126 |    },
127 |    "file_extension": ".py",
128 |    "mimetype": "text/x-python",
129 |    "name": "python",
130 |    "nbconvert_exporter": "python",
131 |    "pygments_lexer": "ipython3",
132 |    "version": "3.6.1"
133 |   }
134 |  },
135 |  "nbformat": 4,
136 |  "nbformat_minor": 1
137 | }
138 | 


--------------------------------------------------------------------------------
/5 Cash Flow Challenges.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stdout",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "116473.83353154093\n"
 13 |      ]
 14 |     }
 15 |    ],
 16 |    "source": [
 17 |     "class Cashflow:\n",
 18 |     "    def __init__(self, val,t,r):\n",
 19 |     "        self.val = val\n",
 20 |     "        self.t = t\n",
 21 |     "        self.r = r\n",
 22 |     "        self.PV = self.valAt(0)\n",
 23 |     "    def valAt(self,time):\n",
 24 |     "        return self.val*(1+self.r)**(time-self.t)\n",
 25 |     "def NPV(arr):\n",
 26 |     "    return sum([x.PV for x in arr])\n",
 27 |     "flows1 = [Cashflow(-100000,0,.05),Cashflow(50000,1,.05),Cashflow(50000,2,.05),Cashflow(50000,3,.05),Cashflow(50000,4,.05),Cashflow(50000,5,.05)]\n",
 28 |     "print(NPV(flows1))"
 29 |    ]
 30 |   },
 31 |   {
 32 |    "cell_type": "code",
 33 |    "execution_count": 2,
 34 |    "metadata": {},
 35 |    "outputs": [
 36 |     {
 37 |      "name": "stdout",
 38 |      "output_type": "stream",
 39 |      "text": [
 40 |       "116473.83353154096\n"
 41 |      ]
 42 |     }
 43 |    ],
 44 |    "source": [
 45 |     "NCS = [Cashflow(-100000,0,.05)]\n",
 46 |     "OCF = [Cashflow(50000,1,.05),Cashflow(50000,2,.05),Cashflow(50000,3,.05),Cashflow(50000,4,.05),Cashflow(50000,5,.05)]\n",
 47 |     "print(NPV(NCS)+NPV(OCF))"
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "code",
 52 |    "execution_count": 3,
 53 |    "metadata": {},
 54 |    "outputs": [
 55 |     {
 56 |      "name": "stdout",
 57 |      "output_type": "stream",
 58 |      "text": [
 59 |       "94826.45017838685\n"
 60 |      ]
 61 |     }
 62 |    ],
 63 |    "source": [
 64 |     "NCS = [Cashflow(-100000,0,.05)]\n",
 65 |     "OCF = [Cashflow(45000,1,.05),Cashflow(45000,2,.05),Cashflow(45000,3,.05),Cashflow(45000,4,.05),Cashflow(45000,5,.05)]\n",
 66 |     "print(NPV(NCS)+NPV(OCF))"
 67 |    ]
 68 |   },
 69 |   {
 70 |    "cell_type": "code",
 71 |    "execution_count": 4,
 72 |    "metadata": {
 73 |     "collapsed": true
 74 |    },
 75 |    "outputs": [],
 76 |    "source": [
 77 |     "def NPV2(arr,t):\n",
 78 |     "    return sum([x.PV*(1-t) for x in arr])"
 79 |    ]
 80 |   },
 81 |   {
 82 |    "cell_type": "code",
 83 |    "execution_count": 5,
 84 |    "metadata": {},
 85 |    "outputs": [
 86 |     {
 87 |      "name": "stdout",
 88 |      "output_type": "stream",
 89 |      "text": [
 90 |       "55861.1601427095\n"
 91 |      ]
 92 |     }
 93 |    ],
 94 |    "source": [
 95 |     "NCS = [Cashflow(-100000,0,.05)]\n",
 96 |     "OCF = [Cashflow(45000,1,.05),Cashflow(45000,2,.05),Cashflow(45000,3,.05),Cashflow(45000,4,.05),Cashflow(45000,5,.05)]\n",
 97 |     "print(NPV(NCS)+NPV2(OCF,.2))"
 98 |    ]
 99 |   },
100 |   {
101 |    "cell_type": "code",
102 |    "execution_count": 6,
103 |    "metadata": {
104 |     "collapsed": true
105 |    },
106 |    "outputs": [],
107 |    "source": [
108 |     "def taxShield(initial,salvage,years,taxRate,r):\n",
109 |     "    dep = (initial-salvage)/years\n",
110 |     "    taxShield = dep*taxRate\n",
111 |     "    return [Cashflow(taxShield,t,r) for t in range(1,years+1)]"
112 |    ]
113 |   },
114 |   {
115 |    "cell_type": "code",
116 |    "execution_count": 7,
117 |    "metadata": {},
118 |    "outputs": [
119 |     {
120 |      "name": "stdout",
121 |      "output_type": "stream",
122 |      "text": [
123 |       "73179.06682523277\n"
124 |      ]
125 |     }
126 |    ],
127 |    "source": [
128 |     "print(NPV(NCS)+NPV2(OCF,.2)+NPV(taxShield(100000,0,5,.2,.05)))"
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 8,
134 |    "metadata": {},
135 |    "outputs": [
136 |     {
137 |      "name": "stdout",
138 |      "output_type": "stream",
139 |      "text": [
140 |       "-10823.691676577058\n",
141 |       "62355.375148655716\n"
142 |      ]
143 |     }
144 |    ],
145 |    "source": [
146 |     "NWC = [Cashflow(-50000,0,.05),Cashflow(50000,5,.05)]\n",
147 |     "print(NPV(NWC))\n",
148 |     "print(NPV(NCS)+NPV2(OCF,.2)+NPV(taxShield(100000,0,5,.2,.05))+NPV(NWC))"
149 |    ]
150 |   },
151 |   {
152 |    "cell_type": "code",
153 |    "execution_count": 9,
154 |    "metadata": {},
155 |    "outputs": [
156 |     {
157 |      "name": "stdout",
158 |      "output_type": "stream",
159 |      "text": [
160 |       "99140.93946256468\n"
161 |      ]
162 |     }
163 |    ],
164 |    "source": [
165 |     "NCS = [Cashflow(-100000,0,.05),Cashflow(50000,5,.05)]\n",
166 |     "OCF = [Cashflow(45000,1,.05),Cashflow(45000,2,.05),Cashflow(45000,3,.05),Cashflow(45000,4,.05),Cashflow(45000,5,.05)]\n",
167 |     "NWC = [Cashflow(-50000,0,.05),Cashflow(50000,5,.05)]\n",
168 |     "Shield = taxShield(100000,50000,5,.2,.05)\n",
169 |     "print(NPV(NCS)+NPV2(OCF,.2)+NPV(Shield)+NPV(NWC)+NPV2([Cashflow(10000,5,.05)],.2))"
170 |    ]
171 |   }
172 |  ],
173 |  "metadata": {
174 |   "anaconda-cloud": {},
175 |   "kernelspec": {
176 |    "display_name": "Python 3",
177 |    "language": "python",
178 |    "name": "python3"
179 |   },
180 |   "language_info": {
181 |    "codemirror_mode": {
182 |     "name": "ipython",
183 |     "version": 3
184 |    },
185 |    "file_extension": ".py",
186 |    "mimetype": "text/x-python",
187 |    "name": "python",
188 |    "nbconvert_exporter": "python",
189 |    "pygments_lexer": "ipython3",
190 |    "version": "3.6.1"
191 |   }
192 |  },
193 |  "nbformat": 4,
194 |  "nbformat_minor": 1
195 | }
196 | 


--------------------------------------------------------------------------------
/6 CAPM.ipynb:
--------------------------------------------------------------------------------
 1 | {
 2 |  "cells": [
 3 |   {
 4 |    "cell_type": "code",
 5 |    "execution_count": 1,
 6 |    "metadata": {
 7 |     "collapsed": true
 8 |    },
 9 |    "outputs": [],
10 |    "source": [
11 |     "def CAPM(rf,market,beta): \n",
12 |     "    return rf+(beta*(market-rf))"
13 |    ]
14 |   },
15 |   {
16 |    "cell_type": "code",
17 |    "execution_count": 2,
18 |    "metadata": {},
19 |    "outputs": [
20 |     {
21 |      "data": {
22 |       "image/png": 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QtDCEEPHSWrNgwQKKFy/ODz/8wMCBA9m1a5cki1RIWhhCiMc6e/YsnTp14pdf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\n",
23 |       "text/plain": [
24 |        "<matplotlib.figure.Figure at 0x1104d34e0>"
25 |       ]
26 |      },
27 |      "metadata": {},
28 |      "output_type": "display_data"
29 |     }
30 |    ],
31 |    "source": [
32 |     "import matplotlib.pyplot as plt \n",
33 |     "\n",
34 |     "betas = [] \n",
35 |     "returns = [] \n",
36 |     "beta = 0 \n",
37 |     "rf = .02 \n",
38 |     "market = .08 \n",
39 |     "for x in range(31): \n",
40 |     "    betas+=[beta] \n",
41 |     "    r = CAPM(rf,market,beta) \n",
42 |     "    returns +=[r] \n",
43 |     "    beta+=.1 \n",
44 |     "plt.plot(betas,returns,\"blue\") \n",
45 |     "betas = [] \n",
46 |     "returns = [] \n",
47 |     "beta = 0 \n",
48 |     "rf = .04 \n",
49 |     "market = .08 \n",
50 |     "for x in range(31): \n",
51 |     "    betas+=[beta] \n",
52 |     "    r = CAPM(rf,market,beta) \n",
53 |     "    returns +=[r] \n",
54 |     "    beta+=.1 \n",
55 |     "plt.plot(betas,returns,\"red\") \n",
56 |     "\n",
57 |     "betas = [] \n",
58 |     "returns = [] \n",
59 |     "beta = 0 \n",
60 |     "rf = .02 \n",
61 |     "market = .12 \n",
62 |     "for x in range(31): \n",
63 |     "    betas+=[beta] \n",
64 |     "    r = CAPM(rf,market,beta) \n",
65 |     "    returns +=[r] \n",
66 |     "    beta+=.1 \n",
67 |     "plt.plot(betas,returns,\"black\") \n",
68 |     "plt.xlabel(\"Beta\")\n",
69 |     "plt.ylabel(\"Return\")\n",
70 |     "plt.title(\"Capital Asset Pricing Model\")\n",
71 |     "plt.legend([\"Baseline\",\"Higher Risk Free\",\"Higher Risk Premium\"])\n",
72 |     "plt.show()"
73 |    ]
74 |   }
75 |  ],
76 |  "metadata": {
77 |   "kernelspec": {
78 |    "display_name": "Python 3",
79 |    "language": "python",
80 |    "name": "python3"
81 |   },
82 |   "language_info": {
83 |    "codemirror_mode": {
84 |     "name": "ipython",
85 |     "version": 3
86 |    },
87 |    "file_extension": ".py",
88 |    "mimetype": "text/x-python",
89 |    "name": "python",
90 |    "nbconvert_exporter": "python",
91 |    "pygments_lexer": "ipython3",
92 |    "version": "3.6.1"
93 |   }
94 |  },
95 |  "nbformat": 4,
96 |  "nbformat_minor": 2
97 | }
98 | 


--------------------------------------------------------------------------------
/4 Depreciation.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {
  7 |     "collapsed": true
  8 |    },
  9 |    "outputs": [],
 10 |    "source": [
 11 |     "def depreciation(initial,salvage,years):\n",
 12 |     "    return (initial-salvage)/years"
 13 |    ]
 14 |   },
 15 |   {
 16 |    "cell_type": "code",
 17 |    "execution_count": 2,
 18 |    "metadata": {},
 19 |    "outputs": [
 20 |     {
 21 |      "data": {
 22 |       "image/png": 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GfAM8AGBmDYDuQMNgnkFmlhzMMxjoBdQNuuxl3gj86O51gAHAEzHbkkKgQqmiPNn9DF6+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\nkTw8AAAAAElFTkSuQmCC\n",
 23 |       "text/plain": [
 24 |        "<matplotlib.figure.Figure at 0x107069630>"
 25 |       ]
 26 |      },
 27 |      "metadata": {},
 28 |      "output_type": "display_data"
 29 |     }
 30 |    ],
 31 |    "source": [
 32 |     "import matplotlib.pyplot as plt\n",
 33 |     "\n",
 34 |     "def depreciationFull(initial,salvage,years):\n",
 35 |     "    values=[initial]\n",
 36 |     "    value=initial\n",
 37 |     "    periods=[0]\n",
 38 |     "    charge = depreciation(initial,salvage,years)\n",
 39 |     "    for y in range(1,years+1):\n",
 40 |     "        periods+=[y]\n",
 41 |     "        value-=charge\n",
 42 |     "        values +=[value]\n",
 43 |     "        \n",
 44 |     "    return values,periods\n",
 45 |     "values,periods = depreciationFull(15000,0,3)\n",
 46 |     "plt.plot(periods,values)\n",
 47 |     "plt.xlabel(\"Year\")\n",
 48 |     "plt.ylabel(\"Equipment Value\")\n",
 49 |     "plt.title(\"Straight Line Depreciation\")\n",
 50 |     "plt.show()"
 51 |    ]
 52 |   },
 53 |   {
 54 |    "cell_type": "code",
 55 |    "execution_count": 3,
 56 |    "metadata": {},
 57 |    "outputs": [
 58 |     {
 59 |      "data": {
 60 |       "image/png": 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GZDYZllREJA/wDPBCRp3zElWdoKqNVbVx8eLFM/r0/1AwTyBv9KnHl/ddw8XYOG75cAnP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\n5j87zPRlwqC8UwAAAABJRU5ErkJggg==\n",
 61 |       "text/plain": [
 62 |        "<matplotlib.figure.Figure at 0x107069240>"
 63 |       ]
 64 |      },
 65 |      "metadata": {},
 66 |      "output_type": "display_data"
 67 |     }
 68 |    ],
 69 |    "source": [
 70 |     "import matplotlib.pyplot as plt\n",
 71 |     "values,periods = depreciationFull(15000,0,3)\n",
 72 |     "plt.plot(periods,values)\n",
 73 |     "values,periods = depreciationFull(15000,3000,3)\n",
 74 |     "plt.plot(periods,values)\n",
 75 |     "plt.xlabel(\"Year\")\n",
 76 |     "plt.ylabel(\"Equipment Value\")\n",
 77 |     "plt.title(\"Straight Line Depreciation\")\n",
 78 |     "plt.legend([\"Equipment 1\",\"Equipment 2\"])\n",
 79 |     "plt.show()"
 80 |    ]
 81 |   }
 82 |  ],
 83 |  "metadata": {
 84 |   "anaconda-cloud": {},
 85 |   "kernelspec": {
 86 |    "display_name": "Python 3",
 87 |    "language": "python",
 88 |    "name": "python3"
 89 |   },
 90 |   "language_info": {
 91 |    "codemirror_mode": {
 92 |     "name": "ipython",
 93 |     "version": 3
 94 |    },
 95 |    "file_extension": ".py",
 96 |    "mimetype": "text/x-python",
 97 |    "name": "python",
 98 |    "nbconvert_exporter": "python",
 99 |    "pygments_lexer": "ipython3",
100 |    "version": "3.6.1"
101 |   }
102 |  },
103 |  "nbformat": 4,
104 |  "nbformat_minor": 1
105 | }
106 | 


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/2 NPV vs. IRR.ipynb:
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  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "metadata": {},
  7 |    "outputs": [
  8 |     {
  9 |      "name": "stdout",
 10 |      "output_type": "stream",
 11 |      "text": [
 12 |       "359.41043083900206\n"
 13 |      ]
 14 |     }
 15 |    ],
 16 |    "source": [
 17 |     "class Cashflow:\n",
 18 |     "    def __init__(self, val,t,r):\n",
 19 |     "        self.val = val\n",
 20 |     "        self.t = t\n",
 21 |     "        self.r = r\n",
 22 |     "        self.PV = self.valAt(0)\n",
 23 |     "    def valAt(self,time):\n",
 24 |     "        return self.val*(1+self.r)**(time-self.t)\n",
 25 |     "def NPV(arr):\n",
 26 |     "    return sum([x.PV for x in arr])\n",
 27 |     "print(NPV([Cashflow(-1500,0,.05),Cashflow(1000,1,.05),Cashflow(1000,2,.05)]))"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "code",
 32 |    "execution_count": 2,
 33 |    "metadata": {
 34 |     "collapsed": true
 35 |    },
 36 |    "outputs": [],
 37 |    "source": [
 38 |     "class Cashflow:\n",
 39 |     "    def __init__(self, val,t,r):\n",
 40 |     "        self.val = val\n",
 41 |     "        self.t = t\n",
 42 |     "        self.r = r\n",
 43 |     "        self.PV = self.valAt(0)\n",
 44 |     "    def valAt(self,time):\n",
 45 |     "        return self.val*(1+self.r)**(time-self.t)\n",
 46 |     "    def tryR(self,r):\n",
 47 |     "        return self.val*(1+r)**(0-self.t)"
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "code",
 52 |    "execution_count": 3,
 53 |    "metadata": {
 54 |     "collapsed": true
 55 |    },
 56 |    "outputs": [],
 57 |    "source": [
 58 |     "def NPV2(r,arr):\n",
 59 |     "    return sum([x.tryR(r) for x in arr])"
 60 |    ]
 61 |   },
 62 |   {
 63 |    "cell_type": "code",
 64 |    "execution_count": 4,
 65 |    "metadata": {
 66 |     "collapsed": true
 67 |    },
 68 |    "outputs": [],
 69 |    "source": [
 70 |     "from scipy.optimize import fsolve\n",
 71 |     "def findIRR(arr):\n",
 72 |     "    return fsolve(NPV2, 0,(arr))"
 73 |    ]
 74 |   },
 75 |   {
 76 |    "cell_type": "code",
 77 |    "execution_count": 5,
 78 |    "metadata": {},
 79 |    "outputs": [
 80 |     {
 81 |      "name": "stdout",
 82 |      "output_type": "stream",
 83 |      "text": [
 84 |       "[ 0.21525044]\n",
 85 |       "-8.417373464908451e-10\n"
 86 |      ]
 87 |     }
 88 |    ],
 89 |    "source": [
 90 |     "print(findIRR([Cashflow(-1500.0,0.0,.05),Cashflow(1000.0,1.0,.05),Cashflow(1000.0,2.0,.05)]))\n",
 91 |     "print(NPV2(.215250437022,[Cashflow(-1500.0,0,.05),Cashflow(1000.0,1,.05),Cashflow(1000.0,2,.05)]))"
 92 |    ]
 93 |   },
 94 |   {
 95 |    "cell_type": "code",
 96 |    "execution_count": 6,
 97 |    "metadata": {},
 98 |    "outputs": [
 99 |     {
100 |      "name": "stdout",
101 |      "output_type": "stream",
102 |      "text": [
103 |       "500.0 Yes Yes\n",
104 |       "470.39505930791086 Yes Yes\n",
105 |       "441.56093810073037 Yes Yes\n",
106 |       "413.46969554152145 Yes Yes\n",
107 |       "386.0946745562128 Yes Yes\n",
108 |       "359.41043083900206 Yes Yes\n",
109 |       "333.3926664293341 Yes Yes\n",
110 |       "308.01816752554805 Yes Yes\n",
111 |       "283.264746227709 Yes Yes\n",
112 |       "259.11118592711034 Yes Yes\n",
113 |       "235.53719008264443 Yes Yes\n",
114 |       "212.52333414495604 Yes Yes\n",
115 |       "190.05102040816348 Yes Yes\n",
116 |       "168.10243558618538 Yes Yes\n",
117 |       "146.66051092643897 Yes Yes\n",
118 |       "125.70888468809096 Yes Yes\n",
119 |       "105.23186682520816 Yes Yes\n",
120 |       "85.21440572722634 Yes Yes\n",
121 |       "65.64205688020695 Yes Yes\n",
122 |       "46.50095332250555 Yes Yes\n",
123 |       "27.777777777777942 Yes Yes\n",
124 |       "9.459736356806275 Yes Yes\n",
125 |       "-8.465466272507342 No No\n",
126 |       "-26.009650340405756 No No\n",
127 |       "-43.184183142559846 No No\n",
128 |       "-60.0 No No\n",
129 |       "-76.46762408667178 No No\n",
130 |       "-92.59718519437047 No No\n",
131 |       "-108.3984375 No No\n",
132 |       "-123.88077639564938 No No\n",
133 |       "-139.05325443787 No No\n",
134 |       "-153.92459646873726 No No\n",
135 |       "-168.50321395775939 No No\n",
136 |       "-182.79721861043595 No No\n",
137 |       "-196.8144352862555 No No\n",
138 |       "-210.56241426611814 No No\n",
139 |       "-224.04844290657456 No No\n",
140 |       "-237.2795567158613 No No\n",
141 |       "-250.2625498844783 No No\n",
142 |       "-263.003985300968 No No\n",
143 |       "-275.5102040816328 No No\n",
144 |       "-287.7873346411148 No No\n",
145 |       "-299.84130132910155 No No\n",
146 |       "-311.67783265685375 No No\n",
147 |       "-323.3024691358027 No No\n",
148 |       "-334.7205707491084 No No\n",
149 |       "-345.9373240758117 No No\n",
150 |       "-356.95774908602925 No No\n",
151 |       "-367.7867056245438 No No\n",
152 |       "-378.4288995991174 No No\n"
153 |      ]
154 |     }
155 |    ],
156 |    "source": [
157 |     "flows = [Cashflow(-1500,0.0,.05),Cashflow(1000,1.0,.05),Cashflow(1000,2.0,.05)]\n",
158 |     "cutoff = findIRR(flows)[0]\n",
159 |     "r=0\n",
160 |     "decision1 =\"Yes\"\n",
161 |     "decision2 =\"Yes\"\n",
162 |     "for _ in range(50):\n",
163 |     "    value = NPV2(r,flows)\n",
164 |     "    if value>0:\n",
165 |     "        decision1 =\"Yes\"\n",
166 |     "    else:\n",
167 |     "        decision1 =\"No\"\n",
168 |     "    if r<cutoff:\n",
169 |     "        decision2 =\"Yes\"\n",
170 |     "    else:\n",
171 |     "        decision2 =\"No\"\n",
172 |     "    r+=.01\n",
173 |     "    print(value,decision1,decision2)"
174 |    ]
175 |   },
176 |   {
177 |    "cell_type": "code",
178 |    "execution_count": 7,
179 |    "metadata": {},
180 |    "outputs": [
181 |     {
182 |      "name": "stdout",
183 |      "output_type": "stream",
184 |      "text": [
185 |       "-500.0 No Yes\n",
186 |       "-470.39505930791086 No Yes\n",
187 |       "-441.56093810073037 No Yes\n",
188 |       "-413.46969554152145 No Yes\n",
189 |       "-386.0946745562128 No Yes\n",
190 |       "-359.41043083900206 No Yes\n",
191 |       "-333.3926664293341 No Yes\n",
192 |       "-308.01816752554805 No Yes\n",
193 |       "-283.264746227709 No Yes\n",
194 |       "-259.11118592711034 No Yes\n",
195 |       "-235.53719008264443 No Yes\n",
196 |       "-212.52333414495604 No Yes\n",
197 |       "-190.05102040816348 No Yes\n",
198 |       "-168.10243558618538 No Yes\n",
199 |       "-146.66051092643897 No Yes\n",
200 |       "-125.70888468809096 No Yes\n",
201 |       "-105.23186682520816 No Yes\n",
202 |       "-85.21440572722634 No Yes\n",
203 |       "-65.64205688020695 No Yes\n",
204 |       "-46.50095332250555 No Yes\n",
205 |       "-27.777777777777942 No Yes\n",
206 |       "-9.459736356806275 No Yes\n",
207 |       "8.465466272507342 Yes No\n",
208 |       "26.009650340405756 Yes No\n",
209 |       "43.184183142559846 Yes No\n",
210 |       "60.0 Yes No\n",
211 |       "76.46762408667178 Yes No\n",
212 |       "92.59718519437047 Yes No\n",
213 |       "108.3984375 Yes No\n",
214 |       "123.88077639564938 Yes No\n",
215 |       "139.05325443787 Yes No\n",
216 |       "153.92459646873726 Yes No\n",
217 |       "168.50321395775939 Yes No\n",
218 |       "182.79721861043595 Yes No\n",
219 |       "196.8144352862555 Yes No\n",
220 |       "210.56241426611814 Yes No\n",
221 |       "224.04844290657456 Yes No\n",
222 |       "237.2795567158613 Yes No\n",
223 |       "250.2625498844783 Yes No\n",
224 |       "263.003985300968 Yes No\n",
225 |       "275.5102040816328 Yes No\n",
226 |       "287.7873346411148 Yes No\n",
227 |       "299.84130132910155 Yes No\n",
228 |       "311.67783265685375 Yes No\n",
229 |       "323.3024691358027 Yes No\n",
230 |       "334.7205707491084 Yes No\n",
231 |       "345.9373240758117 Yes No\n",
232 |       "356.95774908602925 Yes No\n",
233 |       "367.7867056245438 Yes No\n",
234 |       "378.4288995991174 Yes No\n"
235 |      ]
236 |     }
237 |    ],
238 |    "source": [
239 |     "flows = [Cashflow(1500,0.0,.05),Cashflow(-1000,1.0,.05),Cashflow(-1000,2.0,.05)]\n",
240 |     "cutoff = findIRR(flows)[0]\n",
241 |     "r=0\n",
242 |     "decision1 =\"Yes\"\n",
243 |     "decision2 =\"Yes\"\n",
244 |     "for _ in range(50):\n",
245 |     "    value = NPV2(r,flows)\n",
246 |     "    if value>0:\n",
247 |     "        decision1 =\"Yes\"\n",
248 |     "    else:\n",
249 |     "        decision1 =\"No\"\n",
250 |     "    if r<cutoff:\n",
251 |     "        decision2 =\"Yes\"\n",
252 |     "    else:\n",
253 |     "        decision2 =\"No\"\n",
254 |     "    r+=.01\n",
255 |     "    print(value,decision1,decision2)"
256 |    ]
257 |   },
258 |   {
259 |    "cell_type": "code",
260 |    "execution_count": 8,
261 |    "metadata": {},
262 |    "outputs": [
263 |     {
264 |      "data": {
265 |       "image/png": 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fnH/PjGX4nJ2s2n+WD7qGyn7u+d35g7BpnLHxU1oSVGgJdV+E8k1kYmAeZWbHusLo87ik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\ncMJj1oQXAAAAAElFTkSuQmCC\n",
266 |       "text/plain": [
267 |        "<matplotlib.figure.Figure at 0x1093982e8>"
268 |       ]
269 |      },
270 |      "metadata": {},
271 |      "output_type": "display_data"
272 |     }
273 |    ],
274 |    "source": [
275 |     "import matplotlib.pyplot as plt\n",
276 |     "\n",
277 |     "flows = [Cashflow(-1500,0,.05),Cashflow(1000,1,.05),Cashflow(1000,2,.05)]\n",
278 |     "\n",
279 |     "r = 0\n",
280 |     "rates=[]\n",
281 |     "values=[]\n",
282 |     "for _ in range(50):\n",
283 |     "    rates+=[r]\n",
284 |     "    values+=[NPV2(r,flows)]\n",
285 |     "    r+=.01\n",
286 |     "plt.plot(rates,values)\n",
287 |     "\n",
288 |     "flows = [Cashflow(1500,0,.05),Cashflow(-1000,1,.05),Cashflow(-1000,2,.05)]\n",
289 |     "\n",
290 |     "r = 0\n",
291 |     "rates=[]\n",
292 |     "values=[]\n",
293 |     "for _ in range(50):\n",
294 |     "    rates+=[r]\n",
295 |     "    values+=[NPV2(r,flows)]\n",
296 |     "    r+=.01\n",
297 |     "plt.plot(rates,values)\n",
298 |     "plt.xlabel(\"r\")\n",
299 |     "plt.ylabel(\"NPV\")\n",
300 |     "plt.legend([\"Cash Flows 1\",\"Negative Cash Flows 1\"])\n",
301 |     "\n",
302 |     "plt.show()"
303 |    ]
304 |   },
305 |   {
306 |    "cell_type": "code",
307 |    "execution_count": 9,
308 |    "metadata": {},
309 |    "outputs": [
310 |     {
311 |      "data": {
312 |       "image/png": 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PMmzKUrKyrZCYknfWb0ARqRWoIKFkX2YWKWlH7dReE/T6dKzLi/06sGDzAYZP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\nN38pAAAAAElFTkSuQmCC\n",
313 |       "text/plain": [
314 |        "<matplotlib.figure.Figure at 0x10939bf28>"
315 |       ]
316 |      },
317 |      "metadata": {},
318 |      "output_type": "display_data"
319 |     }
320 |    ],
321 |    "source": [
322 |     "flows = [Cashflow(-1200,0,.05),Cashflow(2000,3,.05),Cashflow(2000,6,.05),Cashflow(-3000,10,.05)]\n",
323 |     "\n",
324 |     "r = 0\n",
325 |     "rates=[]\n",
326 |     "values=[]\n",
327 |     "for _ in range(50):\n",
328 |     "    rates+=[r]\n",
329 |     "    values+=[NPV2(r,flows)]\n",
330 |     "    r+=.01\n",
331 |     "plt.axhline(0, color='black')\n",
332 |     "plt.plot(rates,values)\n",
333 |     "plt.xlabel(\"r\")\n",
334 |     "plt.ylabel(\"NPV\")\n",
335 |     "plt.title(\"Cash Flows with Mixed Signs\")\n",
336 |     "plt.show()"
337 |    ]
338 |   }
339 |  ],
340 |  "metadata": {
341 |   "anaconda-cloud": {},
342 |   "kernelspec": {
343 |    "display_name": "Python 3",
344 |    "language": "python",
345 |    "name": "python3"
346 |   },
347 |   "language_info": {
348 |    "codemirror_mode": {
349 |     "name": "ipython",
350 |     "version": 3
351 |    },
352 |    "file_extension": ".py",
353 |    "mimetype": "text/x-python",
354 |    "name": "python",
355 |    "nbconvert_exporter": "python",
356 |    "pygments_lexer": "ipython3",
357 |    "version": "3.6.1"
358 |   }
359 |  },
360 |  "nbformat": 4,
361 |  "nbformat_minor": 1
362 | }
363 | 


--------------------------------------------------------------------------------