finTech MSc留学生代做、代写Python程序语言、代写MSc program、代做Python设计

日期：2018-11-22 10:10

Strathclyde Business School, finTech MSc

Becoming an effective technology analyst - fall 2018

Olivier Bauthéac

01/11/2018

As part of the fall 2018 iteration of the ‘becoming an effective technoloy analyst’ class of the Strathclyde

Business School finTech MSc program coursework, below are the instructions for your data-science finance

assignment. Examples solutions in both the R and Python programming languages will be provided in due

time.

Full stack data-science finance (small) project

Preprocessing (ELT)

Extract

Minimum required

In an excel woorkbook, query Bloomberg for historical (bdh) as well as contemporaneous (bdp) data for a

market index as well as a broad cross-section of U.S. stocks. Historical data should be retrieved from October

1

st 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 market

index is ‘RAY Index’ while those for the corporation names are listed below:

BBG stock tickers

ADM US Equity CIVI US Equity GBX US Equity LIND US Equity SERV US Equity

AE US Equity CLGX US Equity GDI US Equity LZB US Equity SGA US Equity

AGCO US Equity CLR US Equity GHC US Equity MAN US Equity SITE US Equity

AJRD US Equity COMM US Equity GME US Equity MEI US Equity SMP US Equity

ALG US Equity CRL US Equity GOLF US Equity MLR US Equity SPXC US Equity

AMD US Equity CTB US Equity GPN US Equity MRC US Equity STRT US Equity

AMOT US Equity CTLT US Equity GTLS US Equity MTD US Equity SUPN US Equity

ASGN US Equity CTXS US Equity HFC US Equity MTZ US Equity TAST US Equity

ATRO US Equity DHI US Equity HOFT US Equity NC US Equity TMO US Equity

AVT US Equity DKS US Equity HPE US Equity NGVT US Equity TNET US Equity

AWI US Equity EBIX US Equity HURC US Equity NHC US Equity TPB US Equity

BBBY US Equity EEFT US Equity HWKN US Equity NUE US Equity UBNT US Equity

BFAM US Equity ELF US Equity HY US Equity OSIS US Equity UFPI US Equity

BID US Equity ELVT US Equity IAC US Equity OSK US Equity UFS US Equity

BIG US Equity EML US Equity IART US Equity PFGC US Equity USAK US Equity

BKNG US Equity ENTG US Equity IBP US Equity PGTI US Equity VLGEA US Equity

BLD US Equity ERI US Equity IDTI US Equity PKI US Equity VLO US Equity

BSET US Equity ETH US Equity INT US Equity PLPC US Equity VRSK US Equity

BWA US Equity FICO US Equity IOSP US Equity PRAH US Equity WBC US Equity

BYD US Equity FISV US Equity ITRI US Equity PSX US Equity WERN US Equity

CAL US Equity FL US Equity JLL US Equity RBC US Equity WGO US Equity

CBRE US Equity FLR US Equity KHC US Equity RS US Equity WRK US Equity

CENTA US Equity FLT US Equity KSU US Equity RXN US Equity XPO US Equity

1

BBG stock tickers

CHEF US Equity FTV US Equity LGND US Equity SCL US Equity ZBRA US Equity

The historical time series should include the following market & book data fields:

Field Bloomberg symbol

close price PX_LAST

book value per share BOOK_VAL_PER_SH

earnings per share TRAIL_12M_EPS

dividend per share TRAIL_12M_DVD_PER_SH

debt SHORT_AND_LONG_TERM_DEBT

equity TOTAL_EQUITY

current assets BS_CUR_ASSET_REPORT

current liabilities BS_CUR_LIAB

sales SALES_REV_TURN

Contemporaneous data on the other hand should include the number of shares outstanding, number of

directors on the board, number of women on the board, number of board meetings per year, long company

name 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 date

data in one clic. I.e. if in the future you open the workbook you created today, the workbook should be

able 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 any

set of stocks/indexes & market/book fields at various frequencies (year, month, week, day), from and

to 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) and

fields. 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 Bloomberg

connection 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 financial data extraction tool and now it’s time to

use it.

Load

Using R or Python (example solutions will be provided for both programming languages), load the workbook

data 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 58.4400

2

## 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 71.077 135.381

The static dataset on the other hand should be row-indexed by tickers and have columns hosting the

corresponding static data fields. For static data, only numeric fields should be loaded with long company

name and description fields left to the excel workbook for reference.

Transform

Market betas

Minimum required

Using the most recent samples in the time series data, calculate the individual 1-year market betas for the

stocks. Show calculations and comment. Comments should include a detailled discussion on what market

betas are, what they represent for stocks as well as details about the corresponding model. Plot your results

as a histogram and comment. Hint: there are 252 trading days in a year.

Going further

Using all the time series samples, calculate the individual rolling 1-year market betas for the stocks. Randomly

select five stocks and display their corresponding rolling beta time series on the same lineplot.

3

Features interactions

Using the most recent samples in the time series dataset, for each name construct a set of feature

interactions that include the following popular financial ratios: price to book, price to earnings, dividend

yield and gearing. Show calculations and discuss these concepts from a corporate finance standpoint.

Explore this new dataset. Hint: use visualization tools.

Modeling

Minimum required

Cluster analysis (unsupervised learning)

Hierarchical clustering

After normalizing the ratios dataset above to zero means and unit variances, apply hierachical clustering

and 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 the

two classes. Hint: how would Warren Buffett most likely answer this?

Label individual names accordingly in a new ‘classes’ dataframe.

Going further

Classification (supervised learning)

Create a betas dataset that subsets the most recent (last sample date) samples from the rolling maket

betas 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 attributed

above. 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-test

sets split and 5-fold cross-validation.

For each model:

– Show training and test set confusion matrices and calculate corresponding precision & recall

indicators; 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 to

model fine-tunning.

– Find model optimal parameters using gridsearch and run model accordingly. Show corresponding

learning curves.