<p><div><div><div><div data-width="100" data-show-faces="false" data-send="true" data-layout="button_count" data-height="21" data-href="https://www.r-bloggers.com/stock-prices-analysis-part-2-exercises/"></div></div><div><a data-url="https://www.r-bloggers.com/stock-prices-analysis-part-2-exercises/" data-counturl="https://www.r-bloggers.com/stock-prices-analysis-part-2-exercises/" data-count="horizontal" data-text="Stock prices analysis part 2 exercises" class="twitter-share-button" data-via="rbloggers" href="https://twitter.com/share"></a></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://r-exercises.com/2016/08/02/stock-prices-analysis-part-2-exercises/"> R-exercises</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p><a target="_blank" href="http://r-exercises.com/wp-content/uploads/2016/08/bargain-1457948_960_720.png?1704f8"><img sizes="(max-width: 289px) 100vw, 289px" srcset="http://r-exercises.com/wp-content/uploads/2016/08/bargain-1457948_960_720-289x300.png 289w, http://r-exercises.com/wp-content/uploads/2016/08/bargain-1457948_960_720-600x622.png 600w, http://r-exercises.com/wp-content/uploads/2016/08/bargain-1457948_960_720.png 694w" alt="bargain-1457948_960_720" class="wp-image-1055" src="https://i1.wp.com/r-exercises.com/wp-content/uploads/2016/08/bargain-1457948_960_720-289x300.png?resize=289%2C300" data-recalc-dims="1"></a>This is the second part of the exercises dedicated to analysis of stock prices. In this part we will provide exercises for plotting, fitting linear model and predicting stock prices.</p>
<p>You dont need to be an expert stock’s trader in order to understand the examples, but you should go through <a target="_blank" href="http://r-exercises.com/2016/07/26/stock-prices-analysis-part-1-exercises/">part 1</a>, since we shall use some of the solutions as starting point for some of the exercises.</p>
<p>The data set for the exercises is the same as for part 1 and you can download it <a target="_blank" href="http://r-exercises.com/wp-content/uploads/2016/07/data.csv">here</a>. If you solved the exercises from part 1, you can use data frames from exercise 6 and 7 as input for the exercises in part 2.</p>
<p>Answers to the exercises are available <a target="_blank" href="http://r-exercises.com/2016/08/02/stock-prices-analysis-part-2-solutions/">here</a>. If you have a different solution, feel free to post it.</p>
<p><strong>Exercise 1</strong></p>
<p>Plot a polygon showing closing prices of stock X for the last 90 trading days.</p>
<p><strong>Exercise 2</strong></p>
<p>Suppose you have equal amounts of each stock in the data frame. Calculate and plot the average daily return of your portfolio. (Tip: <i>daily return on stock is calculated in part 1, exercise 7.</i>)</p>
<p><strong>Exercise 3</strong></p>
<p>Plot pairwise scatter plots comparing returns on all stocks in data set.</p>
<p><strong>Exercise 4</strong></p>
<p>Fit a linear model of the form <code>Y=a+bX</code> to closing prices of YHOO and plot it on a polygon together with actual closing prices of YHOO.</p>
<p><strong>Exercise 5</strong></p>
<p>How much of the variation in closing prices of GE is explained by the linear model of the form <code>Y=a+bX</code> that fits to daily returns of GE.(<i>Tip: you need to calculate r<sup>2</sup>.</i>)</p>
<p><strong>Exercise 6</strong></p>
<p>When you fit a simple linear model of the form <code>Y=a+bX</code> to closing prices of YHOO in 2016, is the coefficient b statistically significant on the level of <code>α=0.05</code>?</p>
<ol>
<li>Yes</li>
<li>No</li>
</ol>
<p><strong>Exercise 7</strong></p>
<p>Find the linear model <code>Y=a+bX</code> that fits to closing prices of GOOG in 2016.</p>
<p><strong>Exercise 8</strong></p>
<p>Calculate 95% confidence interval for linear model <code>Y=a+bX</code> that fits to closing prices of GOOG in 2016. Save the value in a variable for later use.</p>
<p><strong>Exercise 9</strong></p>
<p>With fit linear model <code>Y=a+bX</code>, predict GOOG closing price with 95% confidence interval for ten days ahead of the last recorded price, based on closing prices in 2016. Save the predicted values in a variable for later use.</p>
<p><strong>Exercise 10</strong></p>
<p>Plot on the same graph:</p>
<ol>
<li>the closing price of GOOG in 2016</li>
<li>fitted values for model <code>Y=a+bX</code> for closing prices of GOOG in 2016 (from exercise 7)</li>
<li>95% confidence interval for model <code>Y=a+bX</code> for closing price of GOOG in 2016 (from exercise 8)</li>
<li>prediction of GOOG closing price for next ten days with 95% confidence interval (from exercise 9)</li>
</ol>
<p>Image by Maklay62 (Pixabay post) [<a target="_blank" href="https://pixabay.com/static/uploads/photo/2016/06/15/00/47/bargain-1457948_960_720.png">CC0 Public Domain </a>], <a target="_blank" href="https://pixabay.com/static/uploads/photo/2016/06/15/00/47/bargain-1457948_960_720.png">via Pixabay</a>.</p>
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