Quantitative investing — also called “quant investing” — isn’t entirely new, but it is becoming a dominant strategy for hedge funds and asset managers in the 21st century.
In essence, quantitative investment strategies are rules-based approaches that rely on statistics, probability and mathematical modeling to invest across asset classes and outperform benchmarks, manage risk or diversify a portfolio. The popularity of such quant strategies has been significantly influenced by the modern rise of Big Data analytics, as well as the progress in computing power and disruptive technologies.
In fact, Magma Capital Funds utilizes quant strategies built on artificial intelligence (AI), machine learning (ML) and neural networks to find opportunities and edge, but also to mitigate risk.
Let’s explore the issue in greater detail, the advantages and disadvantages, and some examples of overarching quantitative investment strategies.
A definition of quant investing
Quantitative investing uses quantitative analysis to make investment decisions. This approach is based on rigorous statistical analysis and often involves developing complex models and algorithms that assess markets, asset valuations, volatility, company technicals and various investing factors (e.g., price, value, growth).
Typically, these strategies are constructed to isolate and identify factors that lead to outperformance. With this context in hand, a quant trading strategy could be implemented to capitalize on that edge to generate excess returns or to improve risk management, portfolio management or asset allocation.
Quantitative analysis is most commonly contrasted with fundamental analysis. Whereas quant strategies rely on computer models, hard data and programmatic algorithms, fundamental analysis is built on human interpretation of various signals that inform investment decisions.
The key difference is that fundamental analysis is perceived as being more subjective. Quant strategies are built to be systematic and to remove emotion, whereas fundamental analysts may follow gut intuition in scrutinizing a particular security or investing factor, even after doing a vast amount of research. This, in effect, illustrates the difference between quantitative strategies and qualitative strategies.
How do quantitative investing strategies work?
Importantly, the process of developing and implementing a quant strategy entails rigorous back-testing to ensure that a trading hypothesis can be proved before being pursued. At a high level, the steps to creating a quant strategy include:
- Sifting through data to find anomalies or criteria that can be tied to outperformance or better risk management.
- Developing the model and back-testing it. A back-test is a simulation using historical data that indicates how well the quant strategy or model would perform over a period of time. For example, a back-test may use historic stock market prices over a five-year time period to test whether a model would have outperformed benchmarks or better managed portfolio risk due to market volatility.
- Implementing the strategy using a defined set of rules to screen assets and construct an ideal portfolio. Continuous maintenance and updating of the model — and portfolio — are also necessary.
Advantages and disadvantages of quant investment strategies
There are many draws to adopting quant strategies:
Automated analysis of vast data sets
The universe of securities and financial data is immense and grows every second with each tick of the stock market ticker. That can present obvious challenges for analysis, especially from a fundamental investing perspective.
However, quant strategies are designed to ingest enormous data sets and synthesize precise insights according to the model parameters. The innovations in high-speed, high-power computing continue to make quantitative analysis more efficient and accessible to quant funds. Artificial intelligence and machine learning, in particular, are key tools quant funds use to filter through data noise and find relevant insights.
Ability to analyze alternative data
Relevant financial data goes well beyond price and other technicals. Increasingly, alternative data streams are being leveraged for an investing edge. These data sources include earning call transcripts, satellite GIS imaging, credit card transactions and social media chatter. While unstructured data are less quantitative than hard numbers, quant funds can still develop models that analyze such data to inform investment decision-making.
Back-testing plays a crucial role in the quantitative investing process and also provides the model hypothesis with evidence of success. Such objectivity is valuable in investment decision-making, especially considering the various behavior pitfalls asset managers are at risk of falling into. Back-testing not only definitively proves that the strategy can generate alpha beyond expected returns, but it also provides an evidence-based roadmap for disciplined implementation of the strategy.
With all that said, however, there are some potential disadvantages to quantitative investment. These include:
- The purported “black box” effect: Some asset managers may be hesitant to adopt quant strategies for fear of handing over decision-making to a “black box” of complex, secretive algorithms that they can’t actually see. This isn’t the case for every quant fund, but the perceived lack of control can be seen as a disadvantage.
- Overfitting: This can occur when a model bases its assumptions too heavily on historical data and produces an outcome uniformly expected from its inputs. The problem with overfitting it does not take future uncertainty into account very well.
- Selection bias: An analyst may actively seek out a pattern or criterion, but the presence of such a factor is not evidence of its relation to outperformance. This type of bias may manifest relationships between data and outcomes that aren’t there during the sheer pursuit of isolating the criterion.
Examples of quantitative investing strategies
There is limitless potential for a diversity of quant strategies given the breadth of data, human talent and computing power available. Some of the most well-known quantitative investment strategies include:
Factor investing is a forerunner of the modern quant strategy — having been popularized in the 1990s. The approach essentially seeks to identify, isolate and target various factors in the same way quant strategies mine data for patterns or criteria of excess returns. Common factors include value, growth, size, quality and momentum.
This strategy seeks to take long positions in underpriced stocks, while short-selling other securities deemed overpriced. This approach is designed to capitalize on both short-term and long-term opportunities in the stock market and price movements.
Pair trading is a type of long/short strategy that aims to identify two or more securities with a price relationship and which are trading outside of a historical range. After extensive analysis and formulation of selection criteria, a quant fund may bet that the prices of those securities will converge or diverge. Which security is bought and which is shorted depends on the type of correlation.
Often called StatArb, statistical arbitrage is a systematic trading approach to exploiting mispricing in similar assets. Typically, StatArb portfolios are constructed with a large number of securities, which are often held for very short amounts of time. The intense data mining and analysis demands of StatArb make it ripe for artificial intelligence and machine learning, as the strategy is dependent on running mean reversion analysis to uncover, measure and exploit mispricings.
Markets move in the wake of big news, whether that is a C-suite shakeup or an earnings report that soars past the mark. However, there’s precious little time to take a position or make a trade that capitalizes on that news. AI and ML can be used to track real-time events and market responses. A natural language processing tool can be used to scan and interpret the transcript of an earnings call and quantify what it means in terms of various phrases or words that turn up.
Many other applications of AI and ML are possible with quantitative analysis and events-based trading. For example, a model can be constructed to ingest and interpret satellite imaging that shows crop yields. A quant fund can then design a trading strategy around expected yields. The automation provided by such technologies is critical to enabling firms to take advantage of fleeting windows of opportunity.
Also known as HFT, high-frequency trading often falls in the arbitrage bucket. This programmatic approach is centered around a holistic analysis of multiple markets and typically aims to complete a large number of transactions in mere seconds. This strategy depends heavily on computing power and algorithms to not only find opportunities, but to quickly execute trades.
Learn more about how Magma treats quant investing
Quantitative investing has roots that date back to the CAPM model of the 1960s, and it continues to become an established approach as more quant funds adopt such strategies. Even retail traders are moving in that direction thanks to the growing accessibility of financial data and computing power.
Magma leverages a quant approach to investment decision-making by assessing a variety of signals and volatility indicators to inform portfolio management. We use AI and ML to dynamically adjust investments and find the most opportune asset classes during any given market conditions.
Contact us today to learn more about Magma’s approach to quantitative investing.
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