Black-Litterman Framework to Test the Effectiveness of Technical Indicators in Portfolio Optimization

14 Pages Posted: 1 Dec 2019

See all articles by Alexander Joshua Michael Fish

Alexander Joshua Michael Fish

Cornell University - College of Engineering

Anant Patel

Cornell University; Cornell University - College of Engineering

Shankar Venkatraman

Cornell University - College of Engineering

Date Written: May 17, 2019

Abstract

This project explored the Black-Litterman framework to construct a portfolio of stocks listed in the S&P 100 index, and tracked the performance of the efficient portfolio against the S&P 100 index. Thirteen technical indicators (viz. RSI, Bollinger Bands, MACD etc.) were used to incorporate the investor’s personal views into the Bayesian framework. Performance of an equally-weighted linking matrix was compared against the performance of a distributed linking matrix in the training period of January 2009 to December 2013, wherein the constructed portfolios minimized Expected Shortfall under the said probabilistic model. Excluding transaction costs, the resulting optimized portfolio outperformed the benchmark during the testing period of January 2014 to December 2018 on both nominal and risk-adjusted basis as measured by overall performance and Sharpe ratio.

Keywords: Portfolio Management, Optimization, Computational Finance, Black-Litterman, Technical Analysis, Finance, Investments

JEL Classification: G11

Suggested Citation

Fish, Alexander and Patel, Anant and Venkatraman, Shankar, Black-Litterman Framework to Test the Effectiveness of Technical Indicators in Portfolio Optimization (May 17, 2019). Available at SSRN: https://ssrn.com/abstract=3487589

Alexander Fish (Contact Author)

Cornell University - College of Engineering ( email )

Ithaca, NY 14853
United States

Anant Patel

Cornell University

30-09 Broadway
Apt 3
Astoria, NY New York 11106
United States

Cornell University - College of Engineering ( email )

Ithaca, NY 14853
United States

Shankar Venkatraman

Cornell University - College of Engineering ( email )

Ithaca, NY 14853
United States

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