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The Barchart Technical Opinion widget shows you today's overally Barchart Opinion with general information on how to interpret the short and longer term signals. Unique to Barchart.com, Opinions analyzes a stock or commodity using 13 popular analytics in short-, medium- and long-term periods. Results are interpreted as buy, sell or hold signals, each with numeric ratings and summarized with an overall percentage buy or sell rating. After each calculation the program assigns a Buy, Sell, or Hold value with the study, depending on where the price lies in reference to the common interpretation of the study. For example, a price above its moving average is generally considered an upward trend or a buy.
Netflix (NFLX) stock is notoriously volatile. And while some nimble traders have surely used NFLX's gut-wrenching swings to their advantage over the years, plenty of punters with less fortunate timing have just as assuredly had their faces ripped off.
On the plus side, Netflix is the king of on-demand streaming entertainment, serving TV series, films and games via 270 million paid memberships in more than 30 languages and 190 countries. It furthermore lays claim to arguably the best brand in the industry.
On the downside, Wall Street puts relentless pressure on the company to grow its subscriber base. As a consequence, Netflix must spend tens of billions of dollars on content to attract and retain viewers. Competition from the likes of Walt Disney (DIS), Apple (AAPL), Paramount (PARA), Amazon.com (AMZN) and others have forced Netflix to splurge on efforts to acquire, license and produce content over the past several years.
After all, nothing hurts NFLX stock like losing subscribers. Recall that in April 2022, shares plunged after Netflix reported its first loss of subscribers in more than a decade. The company shed in excess of $50 billion in market value overnight.
It's also worth recalling that Netflix stock was already in a steep decline at that point. Sluggish subscriber growth and rising costs had long knocked it off its perch. Indeed, shares hit an all-time closing high of $691.69 back in November 2021.
Of the 47 analysts issuing opinions on NFLX surveyed by S&P Global Market Intelligence, 23 rate it at Strong Buy, six say Buy and 16 call it a Hold. One analyst rates it at Sell, while one says it's a Strong Sell.
Dan Burrows is Kiplinger's senior investing writer, having joined the august publication full time in 2016.\n\nA long-time financial journalist, Dan is a veteran of SmartMoney, MarketWatch, CBS MoneyWatch, InvestorPlace and DailyFinance. He has written for The Wall Street Journal, Bloomberg, Consumer Reports, Senior Executive and Boston magazine, and his stories have appeared in the New York Daily News, the San Jose Mercury News and Investor's Business Daily, among other publications. As a senior writer at AOL's DailyFinance, Dan reported market news from the floor of the New York Stock Exchange and hosted a weekly video segment on equities.\n\nOnce upon a time \u2013 before his days as a financial reporter and assistant financial editor at legendary fashion trade paper Women's Wear Daily \u2013 Dan worked for Spy magazine, scribbled away at Time Inc. and contributed to Maxim magazine back when lad mags were a thing. He's also written for Esquire magazine's Dubious Achievements Awards.\n\nIn his current role at Kiplinger, Dan writes about equities, fixed income, currencies, commodities, funds, macroeconomics, demographics, real estate, cost of living indexes and more.\n\nDan holds a bachelor's degree from Oberlin College and a master's degree from Columbia University.\n\nDisclosure: Dan does not trade stocks or other securities. Rather, he dollar-cost averages into cheap funds and index funds and holds them forever in tax-advantaged accounts. "}), " -0-11/js/authorBio.js"); } else console.error('%c FTE ','background: #9306F9; color: #ffffff','no lazy slice hydration function available'); Dan BurrowsSocial Links NavigationSenior Investing Writer, Kiplinger.comDan Burrows is Kiplinger's senior investing writer, having joined the august publication full time in 2016.
A long-time financial journalist, Dan is a veteran of SmartMoney, MarketWatch, CBS MoneyWatch, InvestorPlace and DailyFinance. He has written for The Wall Street Journal, Bloomberg, Consumer Reports, Senior Executive and Boston magazine, and his stories have appeared in the New York Daily News, the San Jose Mercury News and Investor's Business Daily, among other publications. As a senior writer at AOL's DailyFinance, Dan reported market news from the floor of the New York Stock Exchange and hosted a weekly video segment on equities.
Here we are starting with the simplest possible line graph using geom_line. For this simple graph, I chose to only graph the size of the first tree. I used dplyr to filter the dataset to only that first tree. If you're not familiar with dplyr's filter function, it's my preferred way of subsetting a dataset in R, and I recently wrote an in-depth guide to dplyr filter if you'd like to learn more!
First, I call ggplot, which creates a new ggplot graph. It's essentially a blank canvas on which we'll add our data and graphics. In this case, I passed tree_1 to ggplot, indicating that we'll be using the tree_1 data for this particular ggplot graph.
Next, I added my geom_line call to the base ggplot graph in order to create this line. In ggplot, you use the + symbol to add new layers to an existing graph. In this second layer, I told ggplot to use age as the x-axis variable and circumference as the y-axis variable.
You'll note that this geom_line call is identical to the one before, except that we've added the modifier color = 'red' to to end of the line. Experiment a bit with different colors to see how this works on your machine. You can use most color names you can think of, or you can use specific hex colors codes to get more granular.
Now, let's try something a little different. Compare the ggplot code below to the code we just executed above. There are 3 differences. See if you can find them and guess what will happen, then scroll down to take a look at the result.
This change is relatively straightforward. Instead of only graphing the data for a single tree, we wanted to graph the data for all 5 trees. We accomplish this by changing our input dataset in the ggplot() call.
Effectively, we're telling ggplot to use a different color for each tree in our data! This mapping also lets ggplot know that it also needs to create a legend to identify the trees, and it places it there automatically!
This ggplot + geom_line() call is identical to the one we just reviewed, except we've substituted linetype for color. The graph produced is quite similar, but it uses different linetypes instead of different colors in the graph. You might consider using something like this when printing in black and white, for example.
We just saw how we can create graphs in ggplot that map the Tree variable to color or linetype in a line graph. ggplot refers to these mappings as aesthetic mappings, and they encompass everything you see within the aes() in ggplot.
x and y are what we used in our first ggplot + geom_line() function call to map the variables age and circumference to x-axis and y-axis values. Then, we experimented with using color and linetype to map the Tree variable to different colored lines or linetypes.
The group mapping allows us to map a variable to different groups. Within geom_line, that means mapping a variable to different lines. Think of it as a pared down version of the color and linetype aesthetic mappings you already saw. While the color aesthetic mapped each Tree to a different line with a different color, the group aesthetic maps each Tree to a different line, but does not differentiate the lines by color or anything else. Let's take a look:
You'll note that the 5 lines are separated as before, but the lines are all black and there is no legend differentiating them. Depending on the data you're working with, this may or may not be appropriate. It's up to you as the person familiar with the data to determine how best to represent it in graph form!
In our Orange tree dataset, if you're interested in investigating how specific orange trees grew over time, you'd want to use the color or linetype aesthetics to make sure you can track the progress for specific trees. If, instead, you're interested in only how orange trees in general grow, then using the group aesthetic is appropriate, simplifying your graph and discarding unnecessary detail.
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