For those that are up-to-date with the investment industry, it would be discovered that tons of financial data are being generated by the internet, communication devices, and social media platforms. This data can enhance the methodology used by investors and financial analysts in their decision making. Nowadays, new businesses take their time to gather data on every activity carried out to build up their businesses.
With the compilation of more data, the potential of what can be done with these data continue to increase. For these reasons, some advancements have introduced several tools, such as sentiment analysis and machine learning. Investors are looking for avenues of maximizing these tools to gain leverage over their competitors who depend solely on traditional data.
Structuring The Financial Data System
In times past, computers could only read structured or quantifiable data. Nowadays, most of the data that are being generated are not composed and are in an unquantifiable format. One of such data is the textual news data. This type of data does not appear under a company or title making its mapping process complicated. Even financial analysts face the heat when trying to work on unstructured textual data. Since this is not organized, how then can a machine understand and interpret these data correctly? This has been a major challenge that has deterred many investors from using textual data to get the latest updates.
There is a need for making use of the right tool to about complicated issues such as missing, incomplete, or duplicated data. Only the right tool can enable investors to gain access to clean data to improve their investment strategies. This can only be achieved by filtering and reducing the tons of data received daily and narrow down to the right data.
For this reason, every investor needs an accurate tool or indicator that helps to detect when to make a withdrawal or invest in a particular stock.
The Advent Of StockBrain Data Pipeline
At this juncture, after a clear analysis of the challenge faced by investors, StockBrain, a system that can help to enhance equity, investors’ investment strategy was developed. StockBrain can assist investors in these four different ways highlighted below:
- Collection of relevant data
Daily, StockBrain gathers several news articles from multiple sources and in several languages. These news data are then filtered to get rid of unwanted and irrelevant content from its database.
- Extraction of topic and knowledge
StockBrain has a sophisticated extract function that can distinguish similar phrases from every article collected. These phrases are now used to classify and put each article into their corresponding categories. The Vector Space Model (VSM) and Latent Dirichlet Allocation (LDA) approach are utilized to achieve this goal. Also, curated dictionaries are used to recognize data connected to a business, investment, and economic trends. That is why StockBrain can detect any news topic that is trending when a stock price change is initiated.
- Modeling of news sentiment
Stochastic and statistical strategies based on the latest sentiment dictionaries like VADER or NTUSD-Fin are used by StockBrain to model news sentiment. This approach helps to increase the accuracy of the previously extracted news articles. By so doing, a single overall sentiment value is generated for a news story. Alongside, detect the weight and measure the sentiment of every sentence.
- Calculation of stock sentiment
Every sentiment data from a particular company is gathered in a single metric, starting from the weakest to the strongest. Also, current topics and news stories are displayed to enable actionable insights.