The Rise of Quantitative Investment Strategies

The Rise of Quantitative Investment Strategies

by SigTech

Quantitative investment strategies incorporate a wide range of mathematical and statistical techniques. They provide a systematic and disciplined approach to taking investment decisions. By leveraging the power of computer algorithms and big data, they identify and exploit known - yet oftentimes neglected - and previously unknown patterns in the financial markets. Furthermore, factors which many investment managers may have believed were essential to understanding asset price movements but which defied quantification have been operationalised with the availability of new alternative datasets and novel analytical methodologies1. The prominence of quantitative investing has risen steadily in recent decades, with its popularity evident in the allocation of assets to quant strategies. In SigTech’s Hedge Fund Research Report 2021, 80% of hedge fund managers expected institutional investors to increase their allocation to systematic funds. 86% predicted a rise in the number of quantitative fund managers over the next five years2.


Systematising Discretion

Quantitative investment is often contrasted with the traditional discretionary approach; where portfolio managers and traders predict asset price movements with more subjective analyses. However, conceptualising investment as an enduring dichotomy between discretionary and systematic approaches fails to recognise the unique possibilities presented by systematic methods. These techniques promise to assist and enhance any investment process. One of the major trends in the contemporary investment management industry is the increasing use of quantitative data and analytics by discretionary fund managers. It is no longer a question of whether a systematic approach should be applied, but rather to what degree it should direct analysis and investment decisions.


The Quantitative Investment Process

A quant investment process typically follows a sequential set of steps from idea generation to live deployment:

  1. After formulating an investment idea, the strategy’s investment universe is defined.

  2. The factors (e.g. value, carry and momentum) assessing the attractiveness of various assets are selected.

  3. Using e.g. a straightforward ranking methodology or more sophisticated scoring systems, these factors are used to generate signals intended to exploit market inefficiencies.

  4. An optimal portfolio construction methodology is then defined. This includes a decision on how to weight the portfolio’s constituents, thus defining the degree to which the strategy should be buying or selling an asset. Examples of techniques include equal-weighted, mean-variance, equal contribution to risk, and market capitalisation.

  5. The strategy is then backtested. Using extensive time series data and a wide range of analytical tools, the historical performance of the strategy is calculated in order to discern its portfolio characteristics and likely future performance.

  6. Once a potentially profitable trading strategy has been created, it can be released into a live production environment.

Access to a comprehensive technology infrastructure including clean, validated, and operationally-ready data is essential to each step in the creation of quantitative investment strategies.


The Advantages of Quantitative Investment Strategies

Quant strategies are consistent, cost-efficient, easily customisable, and immune from the distortionary effects of emotion. These advantages stem from their rational, objective, rules-based approach to investment.

Their rationality derives from their systematic nature. This ensures consistency and discipline by maintaining a drift-free investment process, with trades following predetermined signals and rules. The rules-based approach also facilitates the easy customisation of strategies to suit specific investor needs. This inherent scalability provides greater operational efficiency.

The objectivity of quantitative investing rests upon its clinical approach; removing the distortionary effects of emotion which may influence discretionary managers and which are amplified during periods of high market uncertainty.

Quantitative investment strategies are also well suited to assessing risk. Investment risk is often generated by complex and mutable factors. Systematic strategies are better able to monitor variations in the market and model their associated risks.

Another advantage of quantitative investment is its performance. An emerging body of evidence exists demonstrating that automated investment processes outperform those more reliant on a discretionary investment process3.


Common pitfalls

Conversations surrounding technological improvements can be tinged with a perverse scientism; where a tendency emerges to glorify new concepts and methodologies as magical solutions to once intractable problems. Quantitative investment is not immune from this reflex. Investors should not assume that systematic investment strategies per se will be superior over both short term and long term investment horizons. It is thus important to formulate an investment thesis before conducting backtests and to take measures to avoid issues related to, among others, overfitting and data mining.

A major source of concern for quant strategies stem from issues related to data. Access to clean, validated, and operationally-ready data is essential to ensuring reliable investment. Using low quality data greatly influences both the accuracy and performance of a strategy. While fund managers find both the negotiation process with data vendors and the onboarding of datasets highly frustrating, they believe that access to high quality data is key to achieving superior returns4.

Furthermore, systematic investment strategies are - like discretionary strategies - not immune to sudden and unexpected shocks to financial markets. However, a systematic approach - if followed stringently - has the ability to efficiently process the large volume of data necessary to interpret and adapt to movements in the financial markets in a speedy manner.


Building Your Quantitative Investment Strategies with SigTech

Quantitative investment approaches are expected to continue their transformation of the financial services industry and the portfolio management techniques used by investors. No longer applicable to systematic investing purposes only, today they are utilised by both systematic and discretionary investors. Asset management firms, hedge funds, mutual funds, and pension funds increasingly recognise the need to systematise their analytics and investment processes. More and more, institutional investors are demanding the application of systematic approaches. They use the presence or absence of these technologies as part of a due diligence process when making an investment decision about allocating to a fund manager.

For investment managers seeking to develop a quantitative trading infrastructure, SigTech offers a future-proof quant technology platform. Our research environment allows you to combine a wealth of clean, curated, and interoperable datasets with a comprehensive library of over 300 pre-built and fully customisable investment strategies. Our next-gen backtest engine facilitates the historical modelling of strategies trading across a range of financial instruments and markets, and accounts for both direct and indirect trading costs.

A feedback loop between research and production maintains consistent results between backtesting and live trading. Finally, effortless integration with third-party or in-house order and execution management systems improves performance and minimises slippage. SigTech eliminates the expensive upfront costs of infrastructure build-out and provides everything you need to construct, backtest, optimise, and deploy your trading strategies. Leave the complex configuration to us and start chasing alpha today.



References

  1. SigTech (2021): Tapping into social media to predict the market
  2. SigTech (2021): Hedge Fund Research Report 202
  3. Grobys et al. (2022): Man versus machine: on artificial intelligence and hedge funds performance
  4. SigTech (2022): Financial Data Impact Report 2022