When everything goes algorithmic nowadays, why not Portfolio Management?
“Algorithmic Portfolio Management” gets a few thousand results on Google, compared to about 9 million results for “Algorithmic Trading” and in LinkedIn training, one gets zero results as on date!
In algorithmic trading or algo trading for short, preprogrammed algorithms or set of processes execute the trades. Its volumes have steadily increased over years, reaching about 60-80% of the total trading volumes depending on the markets, higher in advanced equity and forex markets and with about 40-50% of trading volume being generated in commodity markets. It also increases volatility and certain risks with millions to billions of market value getting wiped off within minutes and then recovering.
The top reasons for using algo trading are – ease of use, improved trader productivity, consistency of execution performance, lower costs/commissions, better monitoring and high speed/lower latency. Money management fund managers use algo trading to implement their investment decisions. There are traditional strategies such as mean reversion, price or earnings momentum, value and multi-factor or combination of multiple strategies and machine learning based ones such as artificial neural networks, k-NN and Bayes etc.
One specific trend over the years has been diminishing alpha and it is increasingly becoming difficult for actively managed funds to beat their benchmark indices, after expenses. ETFs are making a comeback or gaining mind and market share in the recent years. In the US, passive ETFs have attracted more investments than passive mutual funds. In order to keep the growing tendency of operational and management costs going up, there is an increasing need to leverage technology to be more efficient and effective.
Then there are quant funds, in which securities to invest are chosen through quantitative analysis based on numerical data and without any subjective intervention. While their cost of management is lower as fund managers’ efforts and interventions are much lower, their performance has not been consistent over long time.
So, how is Algorithmic Portfolio Management different from algo trading and is there a case for it to be similarly popular going forward, in this algorithm driven world? It is likely to be so, and let us look at it, along with the causes and trends that would drive it up in future.
Robo-advisory services, which provide algorithmic financial planning services to individuals after collecting their information, have been getting popular. They started with passive indexing strategies and moved onto more sophisticated optimization with variants of modern portfolio theory, tax loss harvesting and retirement planning.
With the advent of ever-increasing computational power and availability of broader and deeper data, Machine Learning brings in more sophistication to the algorithms. Machine Learning (ML) and Artificial Intelligence (AI) make analysis of new forms of data such as unstructured ones, hitherto not practical. While absence of investors’ human biases and subjective judgements are touted as advantages, AI/ML models can have their own biases depending on the data fed to them, deficiencies and limitations of the algorithms used and may even reflect the biases and preferences of algorithm constructors.
In Algorithmic Portfolio Management, a portfolio of assets and sub-assets need to be managed for better risk adjusted returns. That is a key difference when compared to algo trading, which is more of one dimensional, focused on single security at a time. So, the key aspects of Algorithmic Portfolio Management are:
- Asset Allocation
- Portfolio Construction
- Portfolio Execution
- Performance Monitoring and Evaluation
Asset allocation is the single biggest factor that determines a large percentage of the returns or variance in the returns of the portfolio over long periods. Efficiency Frontier can optimize the portfolio for low risk and high expected return or vice versa. Diversification with negatively or low correlated securities lower the standard deviation or the risk. Monte Carlo simulation is used for risk analysis by producing distributions of possible outcomes. Apart from these age old and traditional techniques, principal component analysis can be used for feature selection i.e. to choose the parameters and aspects that matter and ML algorithms can be used for better optimization. While there are diminishing benefits with greater diversification, machine driven algorithmic approach can make management of higher number of securities more effective and easier than with human based processes.
Portfolio construction involves careful selection of securities for better risk adjusted returns. Algorithmic frameworks including macro and micro level decisions can be used for greater alignment of investment objectives and risk profiles.
Portfolio execution in terms of buying and selling securities can leverage algo trading for lower market impact and better outcomes. Higher percentage of larger ticket size trades of institutions tend to use algo trading more than for smaller trades.
With the availability real time and near real time data and computational power, portfolio performance monitoring and evaluation can be more frequent and thus triggering effective rebalancing in near real time based on market data for more optimal returns.
Passive rebalancing including calendar and/or percentage-based rebalancing is used for robo-advisory approaches. Algorithmic management can bring in more sophisticated and optimization for active and dynamic asset allocation and rebalancing driven by them. Dynamic asset allocation is not driven by fixed percentage allocations, but involves a more nuanced approach of changing the securities and their constitution based on analysis or algorithmic output.
In conclusion, fund managers are expected to leverage algorithmic portfolio management to complement subjective decisions, to reduce the costs of management and for greater alpha, though portfolios may not be driven entirely by algorithms in near future.