Quantitative investment strategies (QIS): A smart, systematic approach to investing

What are QIS, why do they matter, and how are they shaping modern investing? This article explores their origins, benefits, and growing relevance – from transparency and governance to the role of AI.

Robert Roe, CFA Executive Director, Quantitative Investment Strategies,         Leonteq

What are QIS?

They are rules-based constructs that determine the buying/selling of financial assets according to mathematical signals and/or time stamps. They are developed based on empirical evidence and qualitative economic research.

Are QIS a new trend in asset management or do they have a long history?

QIS are nothing new. In 1952, Markowitz introduced modern portfolio theory and the benefits of diversification. Sharpe later developed the capital asset pricing model. These advances, combined with increased computing power, enabled “quants” to create algorithms and models. In the 1980s, hedge funds popularized QIS, albeit with high fees and limited transparency. The Global Financial Crises reshaped the investment landscape in many ways, with demand shifting from high-fee hedge funds to more transparent strategies. Investment banks met the demand for QIS by offering investible indices with transparent methodologies. This also coincided with the rise of ETFs for passive investments.

Why are QIS interesting from a portfolio manager perspective?

Just as structured products on underlyings like stocks offer many avenues for customization, QIS are flexible and can be adapted to reflect a view. Combining the two layers, the structured payoff on a QIS gives complementary benefits, allowing for tailored exposure to a particular market, geography or even arbitrage opportunity at a set price.  

What are the (dis)advantages of QIS from a product provider and an investor perspective?
QIS can be used to reduce certain risks. For example, a volatility target mechanism removes the volatility risk from the exposure to an asset provided by the product. If the volatility of the asset increases, the QIS will systematically deleverage and then re-leverage once volatility subsides. This mechanism fixes the volatility of the underlying and provides price stability for both the investor and the product provider. The complexity of QIS can vary significantly but they are designed to offer the following benefits for investors:

• Enhance beta exposure by reducing volatility and drawdowns, which can lead to outperformance in the medium to long term.
• Profit from systematic opportunities that exist in certain market conditions.

QIS are, however, slightly more expensive than passive investments.

What are the foundations of an index business?

At Leonteq, we focus on robustness, scalability, flexibility, transparency, and effective governance with the aim of building a sustainable offering for our investors in a transparent and flexible way. We want to give investors access to an informed and transparent offering based on clear objectives and clear implementation. At the same time, we want to create a sustainable business requiring limited, targeted investment for the benefit of our company and its shareholders.

You said that Leonteq is focusing on robustness, among other things. What do you mean by that?

Before launching an index for a client, several departments validate its set-up from different perspectives, including risk, methodology, contractual aspects, and governance. The most critical validation comes from the Model Validation team, which confirms that the Index Rule Book is aligned with the index and risk system set-up and verifies the accuracy of new QIS models. Once live, each calculation undergoes multiple validation layers to ensure that market data are reliable.

What does ‘model validation’ involve?

It follows the same core elements as for valuation and hedging models. It evaluates conceptual soundness and implementation by assessing model design and construction quality. It also ensures that the judgment exercised in model design and construction is well informed, carefully reviewed, and consistent with industry practice and published research. In view of its features and specifics, however, a tailored QIS model validation process has been established. In particular, the QIS Template checks coherence with the Index Rule Book, a task performed by the Structuring and Trading department. The Risk Control team prepares a conclusive document supporting the validation results.

You referred to effective governance earlier. Could you explain what this entails?

To promote effective governance, Leonteq has established a permanent and effective cross-functional Approval Committee to ensure oversight of all aspects of the provision of QIS in accordance with Regulation (EU) 2016/1011 (BMR). This Committee helps to ensure that decisions are not made unilaterally and that any discretionary decisions are aligned with each index’s rules-based methodologies and policies.

What are the risks from a trading point of view?

QIS depend on vast amounts of historical and real-time data for decision-making. If the data is incomplete, inaccurate, or delayed, the trade executions may be flawed. Execution risk arises when there are delays, errors, or inefficiencies in the process of executing trades based on QIS signals. Bugs and system failures could also disrupt trading.

Does AI have an impact on QIS?

AI, particularly machine learning algorithms, are highly effective in analyzing and identifying patterns in varied data. AI should mean a quicker time-to-market for any index by reducing the time taken to validate a model. AI could improve risk evaluation and monitoring by assessing volatility, liquidity risk, and market correlations in real time. However, AI models are only as good as the data they are trained on and could lead to overfitted models. AI is likely to play a dominant role in QIS in the future but challenges such as data quality, model interpretability, and regulatory issues remain.

Biography

Robert Roe is a Structurer specializing in quantitative investment strategies with around 16 years of experience in financial markets. At Leonteq, he is primarily responsible for the development of the different areas of the QIS platform, whose investible indices are underlyings for tailored structured products.