Author: FundFront– FundFront.com
- In times of market uncertainty, traditional investment strategies may not be sufficient to protect portfolios.
- Quantitative and systematic investment strategies use mathematical models and scientific approaches that can potentially make more well-informed decisions.
- By following a predetermined set of rules, systematic investment strategies may help to maintain a consistent investment approach over time and add diversification benefits to investment portfolios.
In times of market uncertainty, traditional investment strategies, such as diversifying across a large basket of stocks, may no longer be sufficient to weather the storm. As a result, investors may be looking for ways to navigate the uncertainty and finding themselves turning to liquid alternative investment strategies to protect their portfolios.
One approach in particular that may be useful in these situations is the use of quantitative and systematic investment strategies. These strategies involve the use of mathematical models and algorithms to make investment decisions, rather than relying on subjective judgment or intuition. By taking a systematic and data-driven approach, these strategies can potentially make more well-informed decisions and reduce the impact of emotions on their investments. In this article, we’ll give an overview of quantitative and systematic investment strategies and how they can benefit investor portfolios.
Quantitative and systematic investment strategies are often based on the idea that financial markets are driven by predictable patterns, and that these patterns can be identified and exploited using mathematical models. In fact, algorithms and models are at the heart of systematic strategies, impacting nearly all aspects of the investment process.
- Decision-making – Systematic strategies use algorithms to analyze data and make predictions about the future performance of different assets. These algorithms consider a wide range of factors, such as historical price data, market conditions, and economic indicators, to identify the most promising investment opportunities.
- Risk management – Algorithms can be used to monitor and manage risk in real-time. These algorithms help identify potential risks and take appropriate action to mitigate them. For example, they may automatically sell off assets that are losing value or diversify the portfolio to reduce exposure to a particular risk.
- Trading – Systematic portfolio managers often utilize automated trading systems that can execute trades more quickly and accurately than humans. These algorithms can analyze market conditions and execute trades based on pre-defined rules and parameters. This can help ensure that the portfolio is always optimally positioned to take advantage of market opportunities.
- Portfolio construction – Portfolio optimization models can help systematic managers to construct and manage their strategies in a way that maximizes returns while minimizing risk by selecting the right mix of assets to achieve a desired investment goal. This can help portfolio managers to better align their portfolios with their investment objectives.
The role of financial research & development
Given the significant reliance on big data, sophisticated algorithms and statistical models, it is easy to see that financial research and development plays a critical role in the creation and implementation of these strategies. Many systematic investment strategies are managed by experienced professionals who have specialized knowledge in fields such as data science and will often use scientific methods to develop their processes.
- Research – Systematic portfolio managers collect a vast amount of data on different assets and markets, including historical price data, economic indicators, and other relevant information. They use this data to identify patterns and trends that can provide insight into the behavior of different assets.
- Signal generation – With the data that has been collected and analyzed, systematic portfolio managers look to identify trade signals. These signals can indicate that a particular asset is likely to move in a certain direction. For example, a trade signal might be a trend in the price of a stock or an indicator of future market volatility. Systematic portfolio managers use algorithms to scan the data and identify these trade signals in real-time.
- Strategy testing – Beyond identifying market patterns, there is usually a heavy emphasis on testing and validating the models that have been created. This involves conducting extensive back-testing and simulation to ensure that the models and algorithms are effective and accurate, and can consistently generate positive returns in a wide range of market conditions.
- Adaptation – As market conditions evolve, the performance of different investment strategies can change. AI algorithms are increasingly being used to help systematic portfolio managers identify these changes and adjust their models accordingly. This can help ensure that the portfolio is always positioned to take advantage of market opportunities and manage risk effectively.
Examples of quantitative and systematic strategies
Quantitative & systematic strategies can be found in a number of different investment styles depending on the type of opportunities they’re seeking to exploit. It’s worth highlighting some of the most common types of quantitative & systematic strategies to gain a better understanding of how they work and some of the key distinguishing characteristics of each approach.
One of the most well-known quantitative and systematic strategies is trend following. This strategy involves using algorithms to identify trends in the market and make investment decisions accordingly. For example, an algorithm may identify a trend of rising prices in a particular stock and signal that it’s time to buy. By following trends, portfolio managers can potentially capitalize on the momentum of the market.
Another common strategy is global macro. This strategy involves taking a broad view of the global economy and making investment decisions based on macroeconomic trends. For example, a portfolio manager using a global macro strategy may look at trends in global GDP growth, inflation, and interest rates to identify potential investment opportunities. Similarly, managed futures strategies, also known as commodity trading advisor (CTA) strategies, trade futures contracts, taking positions in a wide range of assets including contracts for agricultural, energy, metals, broad indexes and more.
Relative value trading strategies take long and short positions in assets in order to profit from the difference in their prices. These strategies often involve identifying assets that are mispriced relative to each other and taking positions that will benefit from changes in their prices. For example, an investor using a relative value strategy might take a long position in a stock that’s undervalued and a short position in a stock that’s overvalued, in the expectation that the prices of the two stocks will converge over time. Market neutral strategies are somewhat similar in that they seek to remove exposure to market risk by balancing long and short positions. This means that the overall portfolio is designed to have little or no exposure to broad market movements, allowing it to potentially perform well regardless of whether the overall market is going up or down.
It’s also worth mentioning statistical arbitrage and algorithmic trading strategies which use algorithms to execute trades automatically, often at a high speed and in large volumes with the goal of exploiting pricing inefficiencies across capital markets. There are even more examples of quantitative and systematic strategies that investors can consider but they generally all tend to use objective data and analysis to help identify trends and patterns in the market.
How quantitative and systematic strategies can complement investor portfolios
One of the key benefits of using quantitative and systematic approaches is the ability to reduce emotional biases in investment decision-making process. It is well-known that emotions can play a significant role in investment decisions, leading to suboptimal outcomes. Fear and greed can drive investors to make rash decisions that are not in their best interests. By relying on a process and data-driven approach, systematic portfolio managers can potentially reduce the impact of these emotional biases and behave more logically.
Additionally, because quantitative and systematic strategies are quite niche by nature, they tend to offer low correlations to major asset classes. This means that they are less likely to move in the same direction as traditional stocks and bonds, providing investors with the opportunity to diversify their portfolios and reduce their overall risk. This is partly due to their ability to control volatility and drawdowns using embedded risk management techniques. This practice of hedging and mitigating the effects of market downturns may lead to more consistent return profiles, which can be particularly attractive to investors who are looking to manage their overall risk.
Quantitative and systematic investment strategies can be useful for investors looking for ways to navigate market uncertainty. These strategies use mathematical models and algorithms to make investment decisions, rather than relying on subjective judgment or intuition. By taking a systematic and data-driven approach, these strategies can potentially make more informed decisions, reducing the impact of emotions on investments and offering investors a number of potential benefits, including low correlations to major asset classes, controlled volatility and drawdowns, and more consistent return profiles.
Disclaimer – FundFront Ltd., does not provide advice and the information in this article should not be construed as such. FundFront Ltd., is registered in England and Wales, and its Registered Office is at C/O Zeeta House, 200 Upper Richmond Road, London, United Kingdom, SW15 2SH. Company Number: 13711456. FundFront Ltd., is an Appointed Representative of Brooklands Fund Management Limited, which is authorised and regulated by the Financial Conduct Authority with the firm reference number 757575 and the Securities and Exchange Commission with the registered number 286221.