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The recent cyclical changes in the Brazilian economy caused a massive entry of individuals into the stock market. Due to the country’s historical high interest rates, these investors used to invest their money in government bonds or in savings accounts, but, once the inflation seems to be under control since 2016, the returns of lower risk investments began to be compromised as the central bank lowered the basic interest rate (known as SELIC).
As we can see in the graph below, the return on an LTN bond, which used to have a return above 10% per year, currently yields no more than 6% in nominal terms.
As Brazilian investors started looking for new alternatives to invest their money, the Ibovespa (the main index in the Brazilian market) started to rise inexorably, until the recent outbreak of Coronavirus. As we can see in the chart below, the Brazilian stock market recovered significantly fast, following the trend in other world markets, after the coronavirus outbreak.
#data-analysis
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Mobiweb Technologies is considered as an expert stock market betting software development company that offers an extensive and wide coverage betting software to empower the bettors. Our Team of top level management and expert sports betting software developers design unbeatable software development strategies that compete with other betting software in the market. People who believe in investment consider gambling as a source of profit generation and for this, Mobiweb’s betting software offers one touch betting facility to place the wager. Most importantly, it offers best-in-class user experience and provides a user engaging atmosphere. If you are looking for developing stock market betting software then you don’t need to hesitate, give this opportunity to Mobiweb and get on time delivery of your project under minimum possible cost.
#stock market betting software development #stock market betting software #stock market betting app development #stock market betting app developers #stock market betting software providers
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Is the stock market crashing in 2021? In this video, we are going over why and when the stock market will crash, what you can do to prepare for it and profit, and what stocks I’m buying right now that are great long term holds.
Make sure to watch until the end, as I will go over what’s happening with the markets right now, what’s causing it, and my sentiment about why a crash will occur.
📺 The video in this post was made by Charlie Chang
The origin of the article: https://www.youtube.com/watch?v=vOIxDL5XCjY
🔺 DISCLAIMER: The article is for information sharing. The content of this video is solely the opinions of the speaker who is not a licensed financial advisor or registered investment advisor. Not investment advice or legal advice.
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Many investors consider fundamental analysis as their secret weapon to beat the stock market. You can perform it using many methods, but one thing they have in common. They all need data about companies’ financial statements.
Luckily all stocks traded on US stock markets must quarterly report to the Securities and Exchange Commission (SEC). Every quarter SEC prepares a comfortable CSV package to help all the investors in their quest for the investment opportunity. Let’s explore how to get valuable insights from these .csv files.
In this tutorial, we will use python’s pandas library which ideal for parsing CSV files, and we will learn how to:
We will process the data and:
As usual, you can follow the code in the notebook shared on GitHub.
There doesn’t seem to be any problem. You simply download the quarterly package from the SEC dataset page, you sort the values from the financial statements in descending order and pick the stocks on the top. The reality isn’t that straightforward. Let’s have a look and explore 45.55MB big zip file with all SEC filings for the first quarter of 2020.
The package for every quarter contains 5 files. Here’s an example of 2020 Q1:
Unzipped files in the SEC quarterly data dump
This article will only deal with the submission master because it contains more than enough information for one article. Follow-up story will examine the data in more detail. Let’s begin.
In the first quarter of 2020, the companies have submitted 13560
files and the sub.txt gathers 36 columns about them.
# load the .csv file into pandas
sub = pd.read_csv(os.path.join(folder,"sub.txt"), sep="\t", dtype={"cik":str})
# explore number of rows and columns
sub.shape
[Out]: (13560, 36)
I always start with a simple function that reviews each column of the data frame, checks the percentage of empty values, and how many unique values appear in the columns.
Explore the sub.txt file to see what data each column contain
Let me highlight a few important columns in the SEC submission master.
Example of the quick file overview in pandas
Based on the analysis, we see that the 2020Q1 submission contains 23 unique types of financial reports. Investors’ primary interest lies in the 10-K report, which covers the annual performance of the publically traded company. Because this report is expectedly delivered only once a year, important is also 10-Q report showing quarterly changes in the company’s financials.
10-K
Annual report of US-based company10-Q
Quarterly report and maybe20-F
Annual Reports of a foreign company40-F
Annual Reports of a foreign company (Canadian)Let’s see which forms are the most common in the dataset. Plotting of the form types in the 2020Q1 will show this picture:
Using Plotly’s low level API to produce bar and pie subplots
Different submission types reported by the companies in 2020Q1 using visualization in Plotly
The dataset contains over 7000 8-K reports notifying about important events like agreements, layoffs, usage of material, modification of shareholder rights, change in the senior positions, and more (see SEC’s guideline). Since they are the most common we should spend some time exploring them.
#stocks #exploratory-data-analysis #python #data-analysis #stock-market #data analysis
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Sentiment analysis or opinion mining is a simple task of understanding the emotions of the writer of a particular text. What was the intent of the writer when writing a certain thing?
We use various natural language processing (NLP) and text analysis tools to figure out what could be subjective information. We need to identify, extract and quantify such details from the text for easier classification and working with the data.
But why do we need sentiment analysis?
Sentiment analysis serves as a fundamental aspect of dealing with customers on online portals and websites for the companies. They do this all the time to classify a comment as a query, complaint, suggestion, opinion, or just love for a product. This way they can easily sort through the comments or questions and prioritize what they need to handle first and even order them in a way that looks better. Companies sometimes even try to delete content that has a negative sentiment attached to it.
It is an easy way to understand and analyze public reception and perception of different ideas and concepts, or a newly launched product, maybe an event or a government policy.
Emotion understanding and sentiment analysis play a huge role in collaborative filtering based recommendation systems. Grouping together people who have similar reactions to a certain product and showing them related products. Like recommending movies to people by grouping them with others that have similar perceptions for a certain show or movie.
Lastly, they are also used for spam filtering and removing unwanted content.
NLP or natural language processing is the basic concept on which sentiment analysis is built upon. Natural language processing is a superclass of sentiment analysis that deals with understanding all kinds of things from a piece of text.
NLP is the branch of AI dealing with texts, giving machines the ability to understand and derive from the text. For tasks such as virtual assistant, query solving, creating and maintaining human-like conversations, summarizing texts, spam detection, sentiment analysis, etc. it includes everything from counting the number of words to a machine writing a story, indistinguishable from human texts.
Sentiment analysis can be classified into various categories based on various criteria. Depending upon the scope it can be classified into document-level sentiment analysis, sentence level sentiment analysis, and sub sentence level or phrase level sentiment analysis.
Also, a very common classification is based on what needs to be done with the data or the reason for sentiment analysis. Examples of which are
Based on what needs to be done and what kind of data we need to work with there are two major methods of tackling this problem.
#machine learning tutorials #machine learning project #machine learning sentiment analysis #python sentiment analysis #sentiment analysis
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The last thing you want to do is to dissatisfy your customers. It is quite disappointing for online shoppers to want to purchase a product and they end up discovering that it is out of stock.
One thing that is common among Shopify stores is that they usually experience stockouts. A stockout occurs when inventory gets finished. If customers want to handle issues concerning stock outs effectively, then, they should use Shopify product back-in-stock alerts App.
What can back in stock alerts help you do? It can help customers notify shoppers when products are available if they subscribe to it using the back in stock notification app.
Learn More : https://hubifyapps.com/back-in-stock-notification-app/
#back in stock notification app #back in stock alert #in stock alert #in stock #back in stock #stock alert app