To optimize its portfolio allocation strategies, Aberdeen needed to create model portfolios in which individual asset classes such as equities, commodities, bonds, and property are overweight or underweight compared with a benchmark. These decisions are partially based on complex relational patterns linking the behavior of factors that influence markets and their impact on future asset performance. Aberdeen wanted to apply machine learning algorithms to characterize these relationships, understand their patterns, and produce trading decisions based on them.  

Aberdeen analysts needed to train and backtest the machine learning algorithms using available market data. Recognizing that the more data they had, the more evidence they would have to support their results, the group wanted to use market data stretching back more than 15 years. Backtesting with this much data on a multidimensional problem was too slow for local PCs, and they needed to speed the process using a computing cluster.

Abderdeen used MATLAB, Parallel Computing Toolbox™, and MATLAB Parallel Server to implement machine learning algorithms for asset allocation and run them in the Microsoft Azure cloud.

Working in MATLAB, Llorente-Cano and his team developed a set of classification models. Each was based on a different machine learning algorithm from Statistics and Machine Learning Toolbox™ and Deep Learning Toolbox™, including neural networks, decision trees, and support vector machines (SVMs).

They trained the models using factors such as monetary policy, corporate profits, interest rates, and implied volatilities.  He accessed market data using Datafeed Toolbox™.

The team backtested the trained models on more than 15 years of historical data. The tests, which are performed repeatedly as new methods are explored and new data becomes available, took up to a full day to complete.

To speed this process, James Mann, solution architect at Aberdeen, prototyped a parallel implementation on the desktop with Parallel Computing Toolbox and then used MATLAB Parallel Server to run the parallel execution on an onsite computer cluster with 80 workers.

Later, Mann redeployed the models to the same number of workers running on Microsoft Azure virtual machines (VMs). He wrote a script that allows MATLAB users to start up the VMs in the cloud, where MATLAB Parallel Server provides the machine learning algorithms access to the workers. Once finished, the users run another script to shut down the VMs.

Llorente-Cano continues to refine machine learning models for asset allocation. He is currently using MATLAB to develop trading strategies based on econophysics-inspired change-point analysis methods as well as global optimization methods in Global Optimization Toolbox.

• Portfolio performance goals supported. “We’ve based many portfolios on the asset allocation process developed with MATLAB machine learning algorithms,” says Llorente-Cano. “These algorithms help us determine whether portfolios will be overweight or underweight compared with our benchmarks.”
• Processing times cut from 24 hours to 3. “Our processing times went from 24 hours to 3 when we started running on the Azure cloud with MATLAB Distributing Computing Server,” notes Mann. “Because the job scheduler is integrated into MATLAB, it’s easy to take advantage of parallel computing just by opening a pool and using parfor loops.”
• Results confirmed with multiple machine learning techniques. “We believe that different approaches to learning bring different types of knowledge,” says Llorente-Cano. “With MATLAB, we present the same data to neural networks, SVMs, and classification trees, and it gives us a great deal of confidence when these different models come to the same trading decision.” 

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