Strathclyde Business School, finTech MScBecoming an effective technology analyst - fall 2018Olivier Bauthéac01/11/2018As part of the fall 2018 iteration of the ‘becoming an effective technoloy analyst’ class of the StrathclydeBusiness School finTech MSc program coursework, below are the instructions for your data-science financeassignment. Examples solutions in both the R and Python programming languages will be provided in duetime.Full stack data-science finance (small) projectPreprocessing (ELT)ExtractMinimum requiredIn an excel woorkbook, query Bloomberg for historical (bdh) as well as contemporaneous (bdp) data for amarket index as well as a broad cross-section of U.S. stocks. Historical data should be retrieved from October1st 2016 to today at the daily frequency on individual ticker specific sheets (one sheet per name). All names’contemporaneous data, on the other hand, should sit on a single sheet. The Bloomberg ticker for the marketindex is ‘RAY Index’ while those for the corporation names are listed below:BBG stock tickersADM US Equity CIVI US Equity GBX US Equity LIND US Equity SERV US EquityAE US Equity CLGX US Equity GDI US Equity LZB US Equity SGA US EquityAGCO US Equity CLR US Equity GHC US Equity MAN US Equity SITE US EquityAJRD US Equity COMM US Equity GME US Equity MEI US Equity SMP US EquityALG US Equity CRL US Equity GOLF US Equity MLR US Equity SPXC US EquityAMD US Equity CTB US Equity GPN US Equity MRC US Equity STRT US EquityAMOT US Equity CTLT US Equity GTLS US Equity MTD US Equity SUPN US EquityASGN US Equity CTXS US Equity HFC US Equity MTZ US Equity TAST US EquityATRO US Equity DHI US Equity HOFT US Equity NC US Equity TMO US EquityAVT US Equity DKS US Equity HPE US Equity NGVT US Equity TNET US EquityAWI US Equity EBIX US Equity HURC US Equity NHC US Equity TPB US EquityBBBY US Equity EEFT US Equity HWKN US Equity NUE US Equity UBNT US EquityBFAM US Equity ELF US Equity HY US Equity OSIS US Equity UFPI US EquityBID US Equity ELVT US Equity IAC US Equity OSK US Equity UFS US EquityBIG US Equity EML US Equity IART US Equity PFGC US Equity USAK US EquityBKNG US Equity ENTG US Equity IBP US Equity PGTI US Equity VLGEA US EquityBLD US Equity ERI US Equity IDTI US Equity PKI US Equity VLO US EquityBSET US Equity ETH US Equity INT US Equity PLPC US Equity VRSK US EquityBWA US Equity FICO US Equity IOSP US Equity PRAH US Equity WBC US EquityBYD US Equity FISV US Equity ITRI US Equity PSX US Equity WERN US EquityCAL US Equity FL US Equity JLL US Equity RBC US Equity WGO US EquityCBRE US Equity FLR US Equity KHC US Equity RS US Equity WRK US EquityCENTA US Equity FLT US Equity KSU US Equity RXN US Equity XPO US Equity1BBG stock tickersCHEF US Equity FTV US Equity LGND US Equity SCL US Equity ZBRA US EquityThe historical time series should include the following market & book data fields:Field Bloomberg symbolclose price PX_LASTbook value per share BOOK_VAL_PER_SHearnings per share TRAIL_12M_EPSdividend per share TRAIL_12M_DVD_PER_SHdebt SHORT_AND_LONG_TERM_DEBTequity TOTAL_EQUITYcurrent assets BS_CUR_ASSET_REPORTcurrent liabilities BS_CUR_LIABsales SALES_REV_TURNContemporaneous data on the other hand should include the number of shares outstanding, number ofdirectors on the board, number of women on the board, number of board meetings per year, long companyname and company description. Explore Bloomberg to find the corresponding field symbols.Going further Using VBA, make your workbook updatable. Ammend your workbook so that it retrieves up to datedata in one clic. I.e. if in the future you open the workbook you created today, the workbook should beable to retrieve up to date data.– Hint 1. Update doesn’t necessarily mean adding most recent values to an existing time series.Requerying the whole data up to the most recent date would work as well.– Hint 2. Inspect the BQL syntax in Bloomberg formula cells, ammend accordingly. Using VBA, make your workbook flexible. Ammend your workbook so that it can retrieve data for anyset of stocks/indexes & market/book fields at various frequencies (year, month, week, day), from andto any date. The user should only have to list the tickers/fields and set the parameters on one sheet.– Hint 1. Object oriented programming could help; excel table objects in particular.– Hint 2. Create an ‘update’ sheet with tickers list, parameters (frequency, start and end dates) andfields. This sheet could also be used to host the contemporaneous dataset. Using VBA, make your workbook fully portable. If you open your workbook without a live Bloombergconnection you’ll notice you loose the contemporaneous dataset; try to fix that problem somehow.– Hint 1. VBA events could help.You now have a fully portable, customizable Bloomberg finTech MSc留学生作业代做、代写Python程序语言作业、代写MSc program作业、代做Python课程financial data extraction tool and now it’s time touse it.LoadUsing R or Python (example solutions will be provided for both programming languages), load the workbookdata in memory. Organise the data in two dataframes, one for the historical times series, the other for static(contemporaneous) data. The time series dataframe should have a two-level row index including tickers &dates while columns should host the corresponding time series; the dataframe should broadly look like this:## ticker Date PX_LAST BOOK_VAL_PER_SH TRAIL_12M_EPS## 1: RAY Index 2016-10-04 1273.897 476.0300 58.4400## 2: RAY Index 2016-10-05 1279.598 476.0500 58.44002## 3: RAY Index 2016-10-06 1279.430 476.0500 58.4300## 4: RAY Index 2016-10-07 1274.602 476.0700 58.4800## 5: RAY Index 2016-10-10 1281.312 476.0800 58.4700## ---## 62692: USAK US Equity 2018-10-17 17.830 8.4359 0.7559## 62693: USAK US Equity 2018-10-18 17.310 8.4359 0.7559## 62694: USAK US Equity 2018-10-19 17.410 8.4359 0.7559## 62695: USAK US Equity 2018-10-22 18.240 8.4359 0.7559## 62696: USAK US Equity 2018-10-23 17.310 8.4359 0.7559## TRAIL_12M_DVD_PER_SH SHORT_AND_LONG_TERM_DEBT TOTAL_EQUITY## 1: NA 566.360 504.630## 2: NA 566.370 504.650## 3: NA 566.340 504.640## 4: NA 566.360 504.660## 5: NA 566.350 504.660## ---## 62692: 0 88.958 70.125## 62693: 0 88.958 70.125## 62694: 0 88.958 70.125## 62695: 0 88.958 70.125## 62696: 0 88.958 70.125## BS_CUR_ASSET_REPORT BS_CUR_LIAB SALES_REV_TURN## 1: 339.210 234.730 NA## 2: 339.340 234.820 NA## 3: 339.340 234.820 NA## 4: 339.580 234.980 NA## 5: 339.610 235.000 NA## ---## 62692: 78.798 71.077 135.381## 62693: 78.798 71.077 135.381## 62694: 78.798 71.077 135.381## 62695: 78.798 71.077 135.381## 62696: 78.798 71.077 135.381The static dataset on the other hand should be row-indexed by tickers and have columns hosting thecorresponding static data fields. For static data, only numeric fields should be loaded with long companyname and description fields left to the excel workbook for reference.TransformMarket betasMinimum requiredUsing the most recent samples in the time series data, calculate the individual 1-year market betas for thestocks. Show calculations and comment. Comments should include a detailled discussion on what marketbetas are, what they represent for stocks as well as details about the corresponding model. Plot your resultsas a histogram and comment. Hint: there are 252 trading days in a year.Going furtherUsing all the time series samples, calculate the individual rolling 1-year market betas for the stocks. Randomlyselect five stocks and display their corresponding rolling beta time series on the same lineplot.3Features interactions Using the most recent samples in the time series dataset, for each name construct a set of featureinteractions that include the following popular financial ratios: price to book, price to earnings, dividendyield and gearing. Show calculations and discuss these concepts from a corporate finance standpoint. Explore this new dataset. Hint: use visualization tools.ModelingMinimum requiredCluster analysis (unsupervised learning)Hierarchical clusteringAfter normalizing the ratios dataset above to zero means and unit variances, apply hierachical clusteringand draw the corresponding dendogram. What seems to be the optimal number of clusters for this dataset?Explain.K-means Implement a two-cluster k-means analysis on this dataset. Explore the resulting cluster characteristics:calculate the cluster specific means for each ratio. Comment on the results and propose labels for thetwo classes. Hint: how would Warren Buffett most likely answer this? Label individual names accordingly in a new ‘classes’ dataframe.Going furtherClassification (supervised learning) Create a betas dataset that subsets the most recent (last sample date) samples from the rolling maketbetas dataset above. Merge the classes, ratios, static and betas datasets together. Implement a classification analysis on the resulting dataset where the target is name’s class as attributedabove. Use various classifiers including logistic regression, k-nearest-neighbours, support vector machines,decision tree, random forest and neural network (multi-layer perceptrons). Use 75-25% for training-testsets split and 5-fold cross-validation. For each model:– Show training and test set confusion matrices and calculate corresponding precision & recallindicators; comment. Your comments should include a discussion on precision and recall.– Explain what the model does and how. Discuss model paramaters and how they contribute tomodel fine-tunning.– Find model optimal parameters using gridsearch and run model accordingly. Show correspondinglearning curves.转自：http://ass.3daixie.com/2018112339684092.html
