1. Introduction

The purpose of this assignment is to apply the knowledge you have acquired so far in this module to a real-world scenario. This is a group assignment. It facilitates the application of several techniques, namely linear regression, time-series analysis, and linear programming, and serves to evaluate your ability to use Excel, and interpret and present Business Analytics results.

2. Financial Data

Buying and selling stocks (or bonds and other securities) is a potentially profitable business, but without proper analytics, it can lead to big financial losses. When designing a stock portfolio, some of the decisions to take are:
• The capital to invest;
• The risk the investor is willing to take;
• The time the investor will wait for his/her returns.
The use of analytics methods can help with some of these decisions.

3. Assignment Description

This project is a (simple) simulation of modelling and optimising an investment portfolio, stated as follows: The substantial stimulus packages employed by governments to combat the effect of COVID-19 had very positive effects on the financial markets. As a result of this scenario, you were tasked with setting up a portfolio of companies, modelling their price behaviour, and optimising a two-week investment strategy, from the 12th July 2021 to the 23rd July 2021, while subject to specific risk constraints.

3.1 Establish a Portfolio of Companies

Your portfolio must be composed of twice as many companies as there are members in your group (e.g. eight companies for a group of four members). The first letter of the Ticker code of each company must match the first letter of each of your team member’s first (given) name, as registered with UCD. For example, Grace Hopper can choose GOOGL and GILD, while Alfred Kinsey can choose AAPL and AMZN). All companies must trade on the NASDAQ stock exchange.

3.2 Data Gathering

Download daily data for each of your companies, for the period from the 10th May 2021 until the 9th July 2021 (inclusive). Based on performance during this period, choose companies likely to give a positive return on investment. Acquire your data from Yahoo Finance: http://finance.yahoo.com/ 1

3.3 Descriptive Analytics

For each company, sequentially number each daily entry as a Trading Day (TD) (i.e. data for the 10th May 2021 is T D = 1). Then create a line plot of TD versus Adjusted Closing Price (ACP), for the period from the 10th May 2021 to the 25th June 2021), along with a 5-day and a 10-day Simple Moving Average (SMA(5) and SMA(10)) of ACP, all in the same plot.

3.4 Predictive Analytics

For each company, perform linear regression on the Adjusted Closing Price (ACP) variable, using TD as the predictor. Use the period from the 10th May 2021 until the 25th June 2021 (inclusive) as your training data, and from the 28th June 2021 until the 9th July 2021 as your test data. Record the slope of the model as the predicted Daily Price Increase (DPI) for each company. Report the train and test the RMSE of each model.

3.5 Prescriptive Analytics

The strategy you will employ is to invest a maximum of $10,000 (USD) per company composing your portfolio, deciding how many days to invest in each company (you can ignore transaction costs).

The objective is to maximise the expected return of the investment:

– The expected return of each company is calculated by multiplying the number of days of investment in the company, the number of shares bought for that company, and the predicted DPI of the company.

– Add the expected return of investment figures for all companies, to obtain the total expected return of the investment.

• Buy the maximum number of shares of each company, such that the total invested in that company does not exceed $10,000, using the closing price of the 9th July 2021 as the share price for each company (e.g. if the price is $3,000, then you can buy three shares).
• Ensure that the number of days of investment in each company is a whole number2
• In order to control the risk of your investment strategy, use the following constraints:
• Ensure the average daily invested amount across the 10 days of the investment period does not exceed $40,000 ($50,000 for teams with five members):
• The daily invested amount for each day of the investment period is the amount invested across all companies that are still being invested upon.
• Relate the precision of your models to how long you should invest in each company: Calculate a Daily Precision Loss (DPL) approximation for each company as 1  R2.
• using the calculated training R2 Ensure that the DPL value of each company is multiplied by the number of days of investment in that company does not exceed 3.5.

Calculate the optimum point (number of days to invest in each company), using Linear Programming. Report your solution, along with the average daily invested amount, and the total expected return.

Report the actual return of the investment using real data:

• Download the actual market data for the investment period (12th July 2021 to 23rd July 2021), and calculate the actual return for the two week investment period.

Use the open price of the 12th July 2021 as the stock price for each company.3
– Use the closing price corresponding to the last investment day (e.g. 16th July 2021 for a company in which you will invest 5 days).

Calculate the gain/loss per company using the observed closing price after the number of investment days decided upon.

Compare and analyse the difference between both figures.