Python for Finance

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Chapter 6ReferencesPlease refer to the following articles:• Carhart, Mark M., 1997, On Persistence in Mutual Fund Performance, Journalof Finance 52, 57-82.• Fama, Eugene and Kenneth R. French, 1993, Common risk factors in thereturns on stocks and bonds, Journal of Financial Economics 33, 3056.• Fama, Eugene and Kenneth R. French, 1992, The cross-section of expectedstock returns, Journal of Finance 47, 427-465.• String manipulation: http://www.pythonforbeginners.com/basics/string-manipulation-in-pythonAppendix A – data case #3 - beta estimationObjective: hands-on experience to estimate the market risk for a given set ofcompanies:1. What are alpha and beta for those companies?2. Comment on your results.[ 207 ]
Capital Asset Pricing Model3. Based on your monthly returns, what are the means of annual returns forS&P500 and risk-free rate?4. If the expected market return is 12.5% per year and the expected risk-free rateis 0.25% per year, what are the costs of equity for those companies?5. What is the portfolio beta?Computational tool: PythonPeriod: From 1/2/ 2011 to 12/31/2016 (the last five years).Technical details:i Company name Ticker Industry Shares1 Wal-Mart Stores Inc. WMT Superstore 10002 Apple Inc. AAPL Computer 20003 International Business Machine IBM Computer 15004 General Electric Company GE Technology 30005 Citigroup C Banking 1800Procedure for data downloading and manipulation:1. Stock monthly price data is from Yahoo finance (http://finance.yahoo.com).2. Calculate monthly returns from monthly prices.3. S&P500 is used as the market index and its ticker is ^GSPC.4. Risk-free rate from Prof. French monthly dataset is used as our risk-free rate.5. When merging those datasets, please pay attention to the order of their dates.Note 1 – how to download data? Here we use S&P500 as an example (ticker is^GSPC):1. Go to Yahoo Finance (http://finance.yahoo.com).2. Enter ^GSPC.3. Click Historical Prices.4. Choose starting date and ending dates. Click Get Prices.5. Go to the bottom of the page and click Download to spreadsheet.6. Give a name, such as sp500.csv.[ 208 ]
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Python for FinanceSecond EditionFin
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CreditsAuthorYuxing YanProject Coor
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I would like to thank Ben Amoako-Ad
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www.PacktPub.comeBooks, discount of
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Table of ContentsPrefaceixChapter 1
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Table of ContentsAppendix F -how to
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Table of ContentsAppendix C - data
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Table of ContentsCapital budgeting
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PrefaceIt is our firm belief that a
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PrefaceChapter 2, Introduction to P
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PrefaceChapter 13, Credit Risk Anal
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ConventionsIn this book, you will f
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PrefaceErrataAlthough we have taken
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Python BasicsThere are several reas
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Python BasicsThe top-left panel (wi
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Python Basics4. From the IPython sh
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Python BasicsIn the preceding codes
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Python Basics(1+r)^nfv: fture value
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Python BasicsAssuming that we inves
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Python BasicsBy using a loop, we ca
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Python BasicsThe print() function c
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Python BasicsWe could retrieve data
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Python BasicsData typesDescriptioni
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Python BasicsFor example, from the
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Python BasicsIn Chapter 3, Time Val
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Python BasicsTo retrieve the file,
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Python BasicsHere PV is the present
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Introduction toPython ModulesIn thi
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Chapter 2In addition, when a module
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Chapter 2Recall that in Chapter 1,
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Chapter 2To find a specific module,
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Chapter 2In the previous example, d
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Chapter 2----------a : array_likeCa
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Chapter 2If n returns are given, th
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Chapter 2Similarly, we could save a
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Chapter 2The next example presents
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Chapter 2Introduction to statsmodel
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Chapter 2From the preceding output,
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Chapter 2'core', 'crosstab', 'cut',
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x[bad] = np.nan# missing code is np
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[ 57 ]Chapter 2For finance, time se
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Chapter 2Stock A Stock B0 0.452820
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Chapter 22010-01-08 110.929466>> >d
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Chapter 2After clicking the logo sh
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For a Mac, we have the following co
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Chapter 2After answering y, the fol
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Chapter 213. What is wrong with the
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Time Value of MoneyIntroduction to
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Time Value of MoneyLater in this ch
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Time Value of MoneyNote that in the
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Time Value of MoneyFor perpetuity,
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Time Value of MoneyExample 1: >>>pv
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Time Value of MoneyThe following ta
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Time Value of MoneyExample 1: >>>pv
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Time Value of MoneyIf using the sam
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Time Value of MoneyWhile using just
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Time Value of MoneyThe related grap
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Time Value of MoneyNow, let's write
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Time Value of MoneyFor annuity due,
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Time Value of MoneyThe total future
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Time Value of MoneyNormally, if a s
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Time Value of MoneyAn annuity is de
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Time Value of MoneyTo find out the
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Time Value of MoneyThe output is sh
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Time Value of Moneydef pvGrowingAnn
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Time Value of Money15. We know that
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Sources of DataDiving into deeper c
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Sources of DataTo find the usage of
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Sources of DataTo view the first an
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Sources of DataIn the preceding pro
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Sources of Data>>>ret[0:3]array([-0
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Sources of DataAssume that two pric
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Sources of Datad0=x.datefor i in ra
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Sources of DataThe so-called candle
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Sources of DataRetrieving data from
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Sources of DataAssume that the data
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Sources of DataGenerating two dozen
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Sources of Data15 spreadBBB Moody's
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Sources of Data0 201512311 20130228
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Sources of DataAssume that TORQcq.p
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Sources of Datasigma = np.std(ret)x
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Sources of DataAppendix C - Python
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Sources of DataThe corresponding gr
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Sources of DataAppendix F -how to g
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Sources of Data3. Manually download
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Bond and Stock ValuationBond or fix
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Chapter 5In Chapter 3, Time Value o
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Chapter 5On the bottom, we have the
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Chapter 5By applying equation (8),
Page 184 and 185: Chapter 5Note that Rm=APR/m is from
Page 186 and 187: Chapter 5Interest rate quotationEff
Page 188 and 189: Chapter 5The related graph is given
Page 190 and 191: Chapter 5For this reason, a Python
Page 192 and 193: Chapter 5>>>w20.4878049>>>w1*1 + w2
Page 194 and 195: Chapter 5If a bond holder could con
Page 196 and 197: Based on this result, the YTM is 7.
Page 198 and 199: Chapter 5Stock valuationThere are s
Page 200 and 201: Chapter 5……(23)The selling pric
Page 202 and 203: Chapter 5Here,Equity is the market
Page 204 and 205: Here, PV is the present value, R is
Page 206 and 207: Chapter 5Appendix C - Python progra
Page 208 and 209: Chapter 5sellingPrice/(1+R)**nselli
Page 210 and 211: Chapter 5A3/A- 32.00 49.00 59.00 72
Page 212 and 213: Capital Asset Pricing ModelCapital
Page 214 and 215: Chapter 6Here is the covariance bet
Page 216 and 217: Chapter 6Here, yi is the ith observ
Page 218 and 219: default to the md5 hash or the url
Page 220 and 221: Chapter 6(beta,alpha,r_value,p_valu
Page 222 and 223: Chapter 60 1.0 NaN 1.51 1.5 1.0 2.0
Page 224 and 225: Chapter 6The first 10 lines are sho
Page 226 and 227: Chapter 6Saving our data to an Exce
Page 228 and 229: Simple string manipulationFor Pytho
Page 230 and 231: Chapter 6The output is shown here:T
Page 232 and 233: Chapter 6The two most used panels a
Page 236 and 237: Chapter 6Note 2 - how to download a
Page 238 and 239: Chapter 616. From this chapter, we
Page 240 and 241: Multifactor Models andPerformance M
Page 242 and 243: The pandas DataFrame is used to con
Page 244 and 245: Chapter 7import statsmodels.api as
Page 246 and 247: Chapter 7When ratios of equity book
Page 248 and 249: Chapter 7To save space, we will not
Page 250 and 251: Chapter 7The corresponding output i
Page 252 and 253: Chapter 7The output is shown here:P
Page 254 and 255: Chapter 7The only modification is t
Page 256 and 257: Chapter 7y = pd.DataFrame({'key': [
Page 258 and 259: Chapter 7'SP500': [0.1,0.17, -0.05,
Page 260 and 261: Chapter 7Sometimes, we need to merg
Page 262 and 263: Appendix A - list of related Python
Page 264 and 265: Chapter 7The first and last several
Page 266 and 267: Chapter 7• http://canisius.edu/~y
Page 268 and 269: 14. Data source: http://mba.tuck.da
Page 270 and 271: Time-Series AnalysisIn finance and
Page 272 and 273: There is one end of chapter problem
Page 274 and 275: Chapter 8In the preceding program,
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Page 278 and 279: Chapter 8The output is shown here:P
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2016-09-01 0.0025 0.0200 -0.0134 0.
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Chapter 8import numpy as npticker='
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Chapter 8Some books distinguish the
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Chapter 8ticker='ibm'begdate=(2013,
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Chapter 852-week high and low tradi
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Chapter 8The covariance between the
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ff=pd.read_pickle('c:/temp/ffDaily.
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Chapter 8The following Python progr
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Chapter 8Here, n is the number of o
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Chapter 8x=np.array(pd.read_csv(pat
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Chapter 8The output is shown here:[
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Chapter 8In the preceding example,
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Chapter 8• George, T.J., and Hwan
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The key part of the program used to
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Chapter 8For readers from schools w
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11. In addition, generate the simil
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Portfolio TheoryUnderstanding portf
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Chapter 9Here, is the portfolio var
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Chapter 9var=w1**2*var1 +w2**2*var2
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Chapter 9The graph is shown here:To
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Chapter 9Optimization terminated su
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Chapter 9Here, U is the utility fun
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Chapter 9print("meanAnnual, varAnnj
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[ 303 ]Chapter 9'SCD' 'SEF' 'SI' 'S
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output.columns=[ticker]return outpu
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The key command used is ret.T*ret.
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Chapter 9def sharpe(R,w):var = port
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2. Code for defining two functions:
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Chapter 9In one of the previous pro
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Chapter 9The output is shown here:A
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Chapter 9ret = sp.array(mean_return
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Chapter 9x = getData(ticker,begdate
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Chapter 9Here is the output:Since t
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Chapter 97. Click CSV on the right-
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Chapter 90 10006 19570301 198407181
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Chapter 911. For several stocks suc
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Chapter 910. Update yanMonthly.pkl,
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Chapter 916. Estimating the Sharpe
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Options and Futures• Using the bi
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Options and FuturesTaking the natur
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Options and Futuress0=1.25rHome=0.0
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Options and FuturescontractFuturesS
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Options and FuturesTo create a grap
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Options and FuturesA graphical repr
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Options and FuturesEuropean versus
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Options and FuturesGenerating our o
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Options and FuturesThe first line o
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Options and FuturesThe related grap
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Options and Futuresx3=60; c3=5y1=(a
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Options and FuturesThe correspondin
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Options and FuturesNote that in the
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Options and FuturesOn the other han
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Options and FuturesThe following co
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Options and FuturesThe correspondin
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Options and Futuresplt.figtext(0.75
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Options and FuturesHere, u is the u
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Options and Futures# at level 2su=r
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Options and Futuresd = 1.0 / ua = e
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Options and FuturesFor the currency
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Options and FuturesSimilarly, we co
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Options and FuturesvolHigh=1.0cLow=
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Options and Futuresmin_value=1000fo
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Options and FuturesAppendix A - dat
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Options and FuturesNote that -1 mea
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Options and Futures16. The current
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Value at RiskIn finance, implicitly
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Chapter 11d1=stats.norm.pdf(0,0.1,0
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Chapter 11s=sp.arange(-10,z,delta)f
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Chapter 11ret=f(x)plt.plot(x,ret)x2
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Chapter 11z=norm.ppf(1-confidence_l
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Chapter 11#print(retNdays.head())po
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Chapter 11p =getData(ticker, begdat
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Chapter 11n=5000000#ret = random.no
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Chapter 11VaR based on sorted histo
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leftTail=int(n*(1-confidence_level)
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std=np.std(ret)#n_simulation=5000sp
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for i in np.arange(n):stock=tickers
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Chapter 11Again, we expect to see 2
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Chapter 11#sum=0.0for i in np.arang
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Chapter 11Estimation of an n-day Va
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Chapter 1110. When we estimate a Va
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Monte Carlo SimulationMonte Carlo S
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Chapter 12This program is equivalen
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Chapter 12Here, f(x) is the density
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Chapter 12import matplotlib.pyplot
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Chapter 12Using simulation to estim
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Chapter 12Generating random numbers
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Chapter 12#for i in range(len(nonSt
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Chapter 12The constraint specified
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Chapter 12Simulation of stock price
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Chapter 12Graphical presentation of
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Chapter 12y2=rho*x1+sp.sqrt(1-rho**
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dt=T/n_stepscall=sp.zeros([n_simula
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Chapter 12Based on the preceding re
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Chapter 12We have the following equ
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print("max NPV of project=",round(m
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Chapter 12One alternative is a so-c
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Chapter 12totalCost=salaryCost + ad
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[ 455 ]Chapter 12In other words, we
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Chapter 12Constructing an efficient
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Chapter 12The graph is shown here:I
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Chapter 12The following program ref
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Chapter 12On the other hand, if we
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4. To convert random numbers from a
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Credit Risk AnalysisThe objective o
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Chapter 136 A3 A- A- 1 17 Baa1 BBB+
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Chapter 13Source: Moody's (2007).No
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Chapter 13A secured debt is a debt
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If P is the default probability we
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14 B2/B 455.800 481.600 505.200 531
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Chapter 13Using the KMV model to es
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Chapter 13The following Python prog
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Chapter 13The related graph is show
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Chapter 13output.append(round(R,5))
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Chapter 13CDS P CDS P CDS P CDS P C
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8. What are uses of the term struct
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Exotic OptionsIn Chapter 10, Option
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Chapter 14x=40. # exercise priceT=6
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Chapter 14Here, call (T) is the cal
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Chapter 14v[i][j]=max(v1,v2)return
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Chapter 14n = mu.shape[0]Sigma_det
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Chapter 14First, we should study th
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Chapter 14sigma1=0.15sigma2=0.20pri
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Chapter 14The final price would be
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Chapter 14total+=sTprice_average=to
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Chapter 14#if sT<barrier:in_=True#p
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Chapter 14c=p4f.bs_call(s,x,T,r,sig
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Chapter 14The Python code for this
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Chapter 14If we use futures to hedg
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Chapter 14Here, T is the maturity d
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Chapter 14SummaryThe options we've
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Volatility, Implied Volatility, ARC
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IndexSymbols2-Step Approach 1522-st
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merging 228-234merging, based on da
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Monte Carlo Simulationabout 408, 42
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Python SimPy module 449QQuasi Monte
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