The landscape of the investment industry is rapidly shifting due to the influence of cutting-edge technologies. Among these, machine learning (ML) is making a significant impact on the traditional processes involved in portfolio construction. Asset managers are increasingly turning to ML algorithms to optimize portfolios efficiently, moving away from conventional methods like mean-variance optimization (MVO). This shift necessitates a closer examination of how ML is revolutionizing portfolio construction.
Understanding the basics of ML algorithms and their implications for portfolios is crucial for investors. The strategies employed by asset managers to build client portfolios directly affect investors’ outcomes. Therefore, investors should acquaint themselves with these emerging methods, which are gaining traction in the industry. This article endeavors to offer an overview of how ML algorithms are reshaping the landscape of portfolio optimization.
Background:
The inception of ‘machine learning’ traces back to 1959 when A.L. Samuel experimented with teaching a computer to play checkers. The successful results of this experiment laid the groundwork for further exploration and the subsequent evolution of highly sophisticated ML algorithms over the years. Numerous sectors, including investment management, have embraced these technologies in recent times to enhance their processes.
ML algorithms prove particularly useful for analyzing high-dimensional or nonlinear datasets, a scenario increasingly common with the proliferation of unstructured and alternative data sources. These algorithms fall into two primary categories: supervised learning and unsupervised learning. While supervised learning uncovers patterns between input variables and known outcomes, unsupervised learning uses unlabeled data to identify patterns within the dataset. Exhibit 1 showcases some common ML algorithms prevalent in investment management.
Exhibit 1: Common Machine Learning Algorithms in Investment Management:
- Least Absolute Shrinkage and Selection Operator (LASSO): Reduces the complexity of regression models by incorporating a penalty term for additional features.
- K-Means Clustering: Divides data into distinct clusters based on similar characteristics.
- Hierarchical Clustering: Groups data incrementally into larger or smaller clusters.
- Artificial Neural Networks (ANNs): Utilizes nodes to process input data and generate desired outputs.
Investment professionals foresee that new analytical methods, predominantly ML, will disrupt the industry significantly in the years ahead. A survey of CFA Institute members revealed that participants expect ML to be the most disruptive factor influencing job roles within the next five to ten years, as shown in Exhibit 2.
Portfolio Optimization:
The advent of neural networks in the 1960s paved the way for alternative portfolio optimization methods leveraging ML. The rise of “expert systems” has encouraged industry professionals to rely more on machine assistance in addressing complex challenges. ML algorithms have gained traction in portfolio construction to help boost returns and sharpen competitive edges. These algorithms offer solutions to the limitations of traditional portfolio optimization methods like MVO.
MVO’s inadequacies, such as neglecting skewness and inhibiting the expression of future asset performance views, have prompted the adoption of alternative approaches like mean-variance-skewness optimization models and the integration of Black-Litterman models with ANNs. Reverse optimization and utilizing ML algorithms for predicting returns have emerged as viable methods to enhance portfolio efficiency. LASSO models, in particular, play a crucial role in estimating the covariance matrix for high-dimensional data, a vital component in portfolio optimization.
Implications and Conclusion:
The evolving landscape of the investment industry underscores the necessity for professionals to adapt to emerging technologies. ML is anticipated to revolutionize job roles and portfolio optimization methodologies in the coming years. By leveraging ML algorithms, asset managers strive to craft portfolios with superior returns for similar risk levels, transcending the limitations of traditional optimization techniques. Investors stand to benefit from staying informed about these trends to comprehend the impact of innovative optimization methods on their portfolios.
Investment professionals and investors alike must navigate this changing landscape to stay ahead of the curve and maximize portfolio performance in the digital age.
