Xnet Stock
Sandee Embree
These 6 additional grades for each stock are a compliment to the overall POWR Rating shown to the left. Investors should first focus their research on stocks with an overall Buy rating of A or B. Then, and only then, use the component grades to drill down to the stocks that meet your unique investing style. In general, the more component grades of A or B the better the odds that the stock should outperform.
Everyone loves growth stocks. However, there is more than one way to measure the health and consistency of that growth pointing to stocks likely to outpace the pack. All in all we have found 13 diverse growth factors that each are beneficial in finding stocks ready to outperform. These 13 factors are combined together in this Growth rating. However, be sure to first focus on stocks with an overall POWR Rating of A or B. Then, and only then, consider the added benefit found in a Growth rating of A or B.
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Xunlei (XNET) has a beta rating of 1.14. This means that it is more volatile than the market, on average. A beta of 1 would indicate the stock moves in-line with the market, while a beta of 2 would indicate the stock moves twice as much as the market.
CI&T (NYSE:CINT) is an AI-powered digital products company that I believe could deliver multibagger returns from its current cheap valuation. In addition to AI, the company is involved in other hot growth sectors like cloud services. The stock has been consolidating sideways for the past couple of months but appears to be bottoming out. I expect shares to start cruising higher again soon on the back of robust growth and expanding margins.
CuriosityStream (NASDAQ:CURI) is a subscription video streaming service specializing in documentaries and other factual content like science, history, and nature programming. I view it as a differentiated player in the crowded streaming wars. As digital media consumption continues rising, I believe CuriosityStream is well-positioned to benefit from its unique content catalog sourced from some top documentary creators.
Xunlei Limited (NASDAQ:XNET) operates an internet platform in China, leveraging cloud technology. The company focuses on shared cloud computing and blockchain technology to provide solutions for digital content.
Xunlei also heavily invests in R&D, which could pay off big time if it can become a tech powerhouse in the future. Regardless of the R&D potential though, I find XNET shares to be extremely undervalued right now. This is a profitable tech company with a huge runway for growth, yet it trades at a measly 7 times trailing earnings. In my view, the stock is a coiled spring ready to deliver multibagger returns going forward.
TrueCar (NASDAQ:TRUE) is a digital automotive marketplace aiming to be the most transparent brand in the industry. It empowers car buyers by showing them what others paid for the vehicle they want, helping them recognize a fair price. TrueCar connects these consumers to certified dealers, facilitating a personalized and efficient car buying experience.
The earnings estimates for TrueCar indicate a gradual return to profitability over the next several years. While a small loss of 4 cents per share is projected for 2024, earnings are expected to flip to positive in 2025 and grow steadily from there, reaching 35 cents per share in 2029. Revenue growth is also forecasted to be robust, with double-digit increases each year, hitting $317 million by 2029 from $183 million in 2024.
The improving fundamentals make it unlikely TRUE shares remain stuck in their current range as the core business rapidly grows. The stars are aligning for this automotive marketplace to shift into a higher gear.
Blend Labs (NYSE:BLND) provides a cloud-based software platform for financial services firms in the U.S. It operates two main segments: Blend Platform and Title365. The Blend Builder Platform offers products powering digital-first consumer journeys for mortgages, home equity loans, vehicle loans, and more. The company also provides title search procedures and other professional services.
On the date of publication, Omor Ibne Ehsan did not hold (either directly or indirectly) any positions in the securities mentioned in this article. The opinions expressed in this article are those of the writer, subject to the InvestorPlace.com Publishing Guidelines.
Omor Ibne Ehsan is a writer at InvestorPlace. He is a self-taught investor with a focus on growth and cyclical stocks that have strong fundamentals, value, and long-term potential. He also has an interest in high-risk, high-reward investments such as cryptocurrencies and penny stocks. You can follow him on LinkedIn.
So close, so close, but missing data shuts down Mathematica's functions. My data, the first sector -- AdvertisingAgencies -- for example, 133 stocks, when filtered for nyse & nasdaq a mere 33. Problems with missing data so filtered again for 2021 at 193 days, and eight more bit the dust. The list:
I wanted to look at seasonality -- I first tried "Return", but 20 cent stocks raised a dollar or two had 164% returns which skewed the data, "Price" was used. I wanted to check seasonality over a ten year period, 2021 worked but was only ten months, so 2020 was tried for 12 months.
Missing data in the first stock of the list, but if I kept deleting stocks for missing data soon there would not be any data to analyze. I tried using Table to reorganize the data by month, worked for 2021, but not for 2020 with missing data
How does one work with FinancialData with all of its flaws???? I can not keep deleing stocks or else there will be so few, the data will be meaningless. If I can not take away, documentation says to replace the missing data with the mean of what is left -- but that is simply beyond my coding ability. Is there a better way???
I don't completely understand what you are trying to do, however, you are running into issues because you are trying to select data by indexing months and that will fail if there is no data for a particular month/stock. A way to avoid that is to group the data by month so missing months are not part of the group if data is missing. An example using the code from my previous post.
You can try it on your full list of stocks and see if the result looks reasonable. If it does, you can remove the year selection and generate the chart for all years combined. Or compare the last 2 years with all previous years or ...
Interesting, January and February did well and the summer months showed more negative returns, but I needed to look at, at least ten years of data. I must first simplify the data before I learned how to wrap the years. I took the mean of the reordered data, which I now believe was a mistake as mean averages columns not rows.
Same data but the charts look quite different. The top BarChart is based on the percent difference between the mean of the 25 stocks for each of the ten months in 2021 against the mean price of all the stocks over ten months. Whereas the bottom BarChart is the mean of the average "Return" for each stock for each month. One of the problems is that I used mean on the reordered data which remains and still needs to be investigated. And I did not know what Nov to Dec looked like, so I had to look at 2020. I tried the same template as I had used for 2021, but I was hampered by missing data
Pick[] worked up to k = 3 , but failed at k = 4 because of the missing data in stock ADV. What I was hoping for was a BarChart similar to the first one posted in this dialogue, with each bar representing one month's average difference ( ("montlyAverageStocksPrice" - "yearlyAverageStocksPrice") / "yearlyAverageStocksPrice" ) for each of the last ten years and plotting the 12 months in one chart. So the first ten bars would be January's averagePriceDifference for the years 2011 to 2021 and the next ten bars would be February's averagePriceDifference for the years 2011 to 2021. With this information I could determined if the advertising dollar changed throughout the year that influenced stock price. But the same problem remains, missing data kills functions......
Can you explain what you mean by "I wanted to look at seasonality". If you want to compare the monthly price for various stocks across years then it would be better to organize the data to make it easier to do that. One option using a subset of your list.
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Friday was a positive day for the stock market, as investors celebrated the traditionally quiet day after Thanksgiving with solid gains. Sentiment among market participants has been positive throughout 2017, and with early holiday shopping activity showing little sign of any imminent change in that trend, the few investors who were trading in the holiday-shortened session today generally had an upbeat mood. There wasn't much news to move individual stocks, but a few companies did show good gains. SandRidge Energy (SD -2.06%), Advanced Semiconductor Engineering (ASX -0.19%), and Xunlei (XNET -0.72%) were among the best performers on the day. Below, we'll take a closer look to see what made these stocks do so well.
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