ON EXCEL:

$3\mathrm{.}3$ Descriptive Analytics

For each company, do the following:

$1.$ Sequentially number each daily entry as a Trading Day $($TD$)($i$.$e$.$ data for $30$th Oct $2023$ is T D $=1).$

$2.$ Calculate the $5-$day Least Squares Moving Average $($LSMA$(5))$ of the Adjusted Closing Price $($ACP$).$

The LSMA$(5)$ at day t is calculated as follows:

LSMA$(5)$t $=$ LR$($ACP :T D$,5)($t$)(1)$

where LR$($ACP :T D$,5)$ is a linear regression model of ACP given TD$,$ for the $5$ previous days. In other words, to calculate the LSMA$(5)$ for day t$,$ build a linear regression predicting ACP given TD$,$ using data from t $5$ to t $1,$ and make a prediction for day t using the model

$3.$ Create a line plot of TD versus ACP, for the period from TD$=1$ until TD$=29,$ with a second line $($in the same plot$)$ showing the calculated LSMA$(5)$ data for TD $=\{6...29\}.$

$4.$ Calculate the Daily Gain $($DG$)$ for TD $=\{2...29\}.$ The DG for day t is calculated simply as:

DGt $=$ ACPt $$ ACPt$1(2)$

$5.$ The volume of shares traded can be an indicator of market confidence. A price upwards$/$downwards trend with a volume increase can indicate market confidence on the trend; conversely, decreasing traded volumes over time might indicate lack of confidence on the current price trend. Capture this effect

by calculating the correlation $($r$)$ between volume and the DG data calculated previously, for TD $=\{2...29\}$:

V OL$/$DGr $=$ r$($V OL$,$ DG$)$