Sensing Market Sentiment for Algorithmic Marketing at Sell Side

In the previous chapter, we learned about investment portfolio management. We also learned some of the portfolio management techniques, such as the Markowitz mean-variance model and the Treynor–Black model for portfolio construction. We also learned about how to predict a trend for a security. So, the previous chapter was based on the buy side of a market. It depicted the behavior of portfolio managers or asset managers.

In this chapter, we will look at the sell side of the market. We will understand the behavior of the counterpart of the portfolio managers. Sell side refers to securities firms/investment banks and their main services, including sales, trading, and research. Sales refers to the marketing of securities to inform investors about the securities available for selling. Trading refers to the services that investors use to buy and sell off...

Understanding sentiment analysis

Sentiment analysis is a technique in which text mining is done for contextual information. The contextual information is identified and extracted from the source material. It helps businesses understand the sentiment for their products, securities, or assets. It can be very effective to use the advanced techniques of artificial intelligence for in-depth research in the area of text analysis. It is important to classify the transactions around the following concepts:

• The aspect of security the buyers and sellers care about
• Customers' intentions and reactions concerning the securities

Sentiment analysis is known to be the most common text analysis and classification tool. It receives an incoming message or transaction and classifies it depending on whether the sentiment associated with the transaction is positive, negative, or neutral. By using the sentiment analysis technique, it is possible to...

Sensing market requirements using sentiment analysis

One of the key requirements of a security firm/investment bank on the sell side is to manufacture the relevant securities for the market. We have explored the fundamental behaviors and responsibilities of companies in Chapter 4, Mechanizing Capital Market Decisions, and Chapter 5, Predicting the Future of Investment Bankers. We learned about the momentum approach in Chapter 6, Automated Portfolio Management Using the Treynor–Black Model and ResNet. While the market does not always act rationally, it could be interesting to hear about the market's feelings. That is what we will be doing in this chapter.

In this example, we will be playing the role of the salesperson of an investment bank on the trading floor, trading in equities. What we want to find out is the likes and dislikes regarding securities so that they can market the relevant securities, including derivatives. We got our insights from Twitter Search...

Network building and analysis using Neo4j

As sell-side analysts, besides finding out the primary impact of news on the company, we should also find out the secondary effect of any news. In our example, we will find out the suppliers, customers, and competitors of any news on the stocks.

We can do this using three approaches:

• By means of direct disclosure, such as annual reports
• By means of secondary sources (media reporting)
• By means of industry inferences (for example, raw materials industries, such as oil industries, provide the output for transportation industries)

In this book, we use direct disclosure from the company to illustrate the point.

We are playing the role of equity researchers for the company stock, and one of our key roles is to understand the relevant parties' connections to the company. We seek to find out the related parties of the company—Duke Energy—by reading the company...

Summary

In this chapter, we learned about the behavior of the sell side of a market. We learned about what sentiment analysis is and how to use it. We also looked at an example to sense market needs using sentiment analysis. We learned about network analysis using Neo4j, which is a NoSQL database technique. We learned about text mining using the PDF miner tool.

In the next chapter, we will learn how to use bank APIs to build personal wealth advisers. Consumer banking will be a focus of the chapter. We will learn how to access the Open Bank Project to retrieve financial health data. We will also learn about document layout analysis in the chapter. Let's jump into it without any further ado.