Vague Expert Information/Recommendation in Portfolio Optimization-An Empirical Study

Marcin Bartkowiak,Aleksandra Rutkowska

doi:10.3390/axioms9020038

Abstract

In a real market, the quantity of information and recommendations is constantly increasing. However, recommendations are often in linguistic form and no one recommendation is based on a single piece of information. Predictions of individuals and their confidence can vary greatly. Thus, a problem arises concerning different (disjointed or partially coherent) vague opinions of various experts or information from multiple sources. In this paper, we introduce extensions of the Black—Litterman model with linguistic expressed views from different experts/many sources. The study focuses on empirical analysis of proposed fuzzy approach results. In the presented modification every expert presents its opinion about particular assets according to intervals, and then an experton for each asset is built. In the portfolio optimization, we use aggregated views presented by interval, which is the mean value of the experton built on particular views. In an empirical study, we built and tested 10,000 portfolios based on recommendation from EquityRT, which was made by 14–49 experts monthly between November 2017 and June 2019 for the 29 biggest companies from the US market and different sectors. The annual average return from portfolios is 9.5–11.8%, depending on the width of the intervals and additional constraints. This approach allows people to formulate intuitive views and view the opinions of a group of experts.

Highlights

The quantity of information and recommendations relating to investment is constantly increasing.The services such as SeekingAlpha and StockTwits, where independent analysts and retail investors can contribute and share analysis for free, are rapidly growing
We use aggregated views presented by interval, which is the mean value of the experton built on particular views
Recommendations are often in linguistic form and no one recommendation is based on a single piece of information

Summary

Introduction

The quantity of information and recommendations relating to investment is constantly increasing. The services such as SeekingAlpha and StockTwits, where independent analysts and retail investors can contribute and share analysis for free, are rapidly growing. SeekingAlpha reports more than 3 million users and 9 million unique visits monthly This represents a significant portion of the US investment market, where more than 50 million households invest in mutual funds or equities. Many professional services, such as Bloomberg or Reuters Thompson, allow access to the predictions of various experts, and investment firms pay their own experts. We introduce extensions of the Black–Litterman model (further BL model) with linguistically expressed views from different experts/many sources

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Conclusion