Automated Portfolio Management Using Treynor-Black Model and ResNet

In the previous chapter, we covered the basic concepts of investment banking. We also learned about the concepts of Mergers and Acquisitions (M&A) and Initial Public Offering (IPO). We examined the clustering model, which is a modeling technique of AI. We looked at detailed steps and examples to solve the problem with auto syndication. We implemented an example that identified acquirers and targets. So, the previous two chapters were intended for the issuers on the securities side of investment banking.

In this chapter, we will look at the dynamics of investors. Investors drive investment behavior strategically. The issuance of equity or debt can be done in either of two ways—via the primary market or the secondary market. The role of the primary market is to issue new securities on behalf of companies, the government, or other groups in order...

Financial concepts

In this section, we will explore various financial concepts. For an in-depth survey of the domain knowledge, you are encouraged to refer to the syllabus of the Chartered Finance Analyst (CFA).

Alpha and beta returns in the capital asset pricing model

According to the capital asset pricing model (CAPM), investment return equals the risk-free rate + alpha + beta * market return + noise (with a mean of zero). Alpha is the return earned by the superior performance of the firm or investors, while beta is the riskiness of the asset in comparison to the overall market return. Beta is high when the risk of the investment is riskier than the average market. Noise is the random movement or luck that has a long-term return of zero.

The asset management industry, especially professional investment managers, is commonly charging clients based on alpha. That explains why people pay so much attention to alpha.

Realized and unrealized investment...

Understanding the Markowitz mean-variance model

The objective of portfolio management is to minimize risk in order to ascertain the target return, given that, for the specific investor, we have the target return and risk tolerance captured from the IPS and historical returns. Typical portfolio optimization models used in the industry include the Markowitz mean-variance model and the Treynor-Black model.

An economist, named Harry Markowitz, introduced mean-variance analysis, which is also known as Modern Portfolio Theory (MPT), in 1952. He was awarded a Nobel Prize in Economics for his theory.

The mean-variance model is a framework for assembling asset portfolios so that a return can be maximized for a given risk level. It is an extension of investment diversification. Investment diversification is an idea that suggests investors should invest in different kinds of financial assets. Investment diversification is less risky in comparison to investing in only one...

Exploring the Treynor-Black model

Due to the instability of the Markowitz mean-variance model in managing problems associated with multi-asset class portfolios, the Treynor-Black model was established. Treynor-Black's model fits the modern portfolio allocation approach where there are certain portfolios that are active and others that are passive. Here, passive refers to an investment that follows the market rate of return—not to beat the market average return but to closely follow the market return.

An active portfolio refers to the portfolio of investment in which we seek to deliver an above-market average return. The lower the market return with a market risk level, the higher the portfolio. Then, we allocate the total capital to an active portfolio. So, why take more risk if the market return is good enough? The Treynor-Black model seeks to allocate more weight to the asset that delivers a higher return/risk level out of the total risk/return level of the...

Portfolio construction using the Treynor-Black model

Let's say we are given 10 days of pricing data, and the work of technical analysis is to draw the lines on the right to make sense of the trend in order to generate the next day's pricing for the 11^th day. It is quite obvious to find that it is indeed what a convolutional neural network could tackle.

Knowing that, practically, the time unit we are looking at could be per 100 ms or 10 ms instead of 1 day, but the principle will be the same:

Let's continue with the Duke Energy example. In this hypothetical case, we assume that we are the treasurer running the pension fund plan of Duke Energy with a total asset size of 15 billion USD with a defined contribution plan. Presumably, we know what our IPS is in digital format:

• Target return = 5% of real return (that means deducting the inflation of goods)
• Risk = return volatility equals 10%
...

Predicting the trend of a security

In the preceding example, we played the role of a trader who followed the portfolio allocation set by the treasurer. Assuming that our job is to follow the securities required by the treasurer, the profit and loss of the trader hinges on how can we profit from buying low and selling high. We took the daily pricing history of securities as the data to build our model. In the following section, we will demonstrate how to predict the trend before making buy decisions for assets.

Solution

There are two major processes—one on model development and another on model backtesting. Both processes include a total of eight steps for real-time deployment, which we will not include here. However, it is very similar to model backtesting. The following diagram illustrates the flow of the process:

Loading, converting, and storing data

In this step, we will load the data, convert...

Summary

In this chapter, we learned a number of portfolio management techniques. We combined them with AI to automate the decision-making process when buying assets. We learned about the Markowitz mean-variance model and the Treynor-Black model for portfolio construction. We also looked at an example of portfolio construction using the Treynor-Black model. We also learned how to predict trends in the trading of a security.

In the next chapter, we will look at the sell side of asset management. We will learn about sentiment analysis, algorithmic marketing for investment products, network analysis, and how to extract network relationships. We will also explore techniques such as Network X and tools such as Neo4j and PDF Miner.