Quantitative investing, or the process of using mathematical models and algorithms to identify investment opportunities, reduces the likelihood of emotions and biases clouding investment decision-making. That should translate to a more rational and consistent approach to investing, which could drive better performance overall.
Benefits of Quantitative Investing
Quantitative investment strategies, which include statistical arbitrage, factor investing, risk parity, machine learning techniques, and artificial intelligence approaches, offer several key benefits to investors and their advisors:
- Data-driven decision-making: As we mentioned earlier, data, including mathematical models and algorithms, is the foundation of quantitative investment strategies, thereby reducing or removing the influence of emotions and biases in investment decision-making.
- Diversification and risk management: Because these strategies involve trading various asset classes or financial instruments, they can also help diversify portfolios.
- Efficiency and adaptability: Quantitative investment tools can process vast amounts of data quickly and be applied to different market conditions, making the approach highly efficient.
- Transparency and demonstrability: The rules-based nature of quantitative investment strategies empowers advisors with more transparency into its approaches. Historical data allows the strategies to be stringently backtested to evaluate their effectiveness.
Challenges of Quantitative Investing
Considering the implications of emotional investing, which can cost investors as much as 3% in lost returns per year, particularly in the face of ongoing market volatility, quantitative investing presents an appealing antidote to fear-driven trading.
And with research continuing to prove the potentially costly impact of emotions on investment decision-making and portfolio performance, why would advisors not leverage the rational, consistent, and logical approach of quantitative investing?
In spite of its many benefits, historically, quantitative investing hasn’t been without limitations. Key challenges to implementing a quantitative investing approach include:
- Model risk: Quantitative models are based on historical data; therefore, they have assumptions that may not hold in the future, and erroneous models can lead to significant losses. Overfitting is a common problem that occurs when the model performs well on past data but poorly when presented with new events.
- Data quality and availability: The effectiveness of a quantitative strategy is highly dependent on the quality, timeliness, and reliability of the data used. Inaccurate or incomplete data can significantly impact the strategy’s performance.
- Computational complexity: Not all advisors have access to the significant computational power, programming expertise and robust data science required to implement quantitative investing strategies successfully.
- Market impact and liquidity: Some quantitative investment strategies can impact market prices, leading to higher transaction costs. There’s also potential risk associated with trading in less liquid markets, which some strategies require.
- Regulatory and ethical concerns: Using algorithms in trading has raised concerns about market fairness and integrity. Regulatory changes can also impact the viability of certain strategies.
Offering Advisors the Power of Quantitative Investing
But historical challenges don’t have to hold modern advisors back. Helios blends deep, ongoing quantitative investment research with a powerful suite of tools designed to put the power of quantitative investing at advisors’ fingertips, while alleviating many of the roadblocks associated with quantitative strategies.
The Helios Tools platform organizes a universe of investment research into decision-ready analysis of thousands of mutual funds and ETFs, helping advisors easily create and manage quantitative investment models. The fund universe offers access to over 27,000 tickers scored via our proprietary confidence rating system, as well as a full report on each ticker, including a complete, decision-ready set of analytics.
Our proprietary confidence rating process compares funds within the same asset class to help advisors determine the best fit for client needs, while also streamlining investment committee meetings and maintaining organized documentation of due diligence and fund research.
Model customization capabilities make it easy for advisors to incorporate multiple quantitative techniques into models while also accounting for unique client preferences and specifications, backed by Helios’ deep quantitative research.
For more information on Helios’ suite of quantitative investing solutions, click here.