Although buzzwords such as “investment factors,” “factor funds,” “smart/strategic beta” have recently gained popularity, factor investing was pioneered back in the 1960s. This era gave birth to many modern finance theories, including the seminal Capital Asset Pricing Model (CAPM). Factor investing, a methodical approach to choosing investments, has seen significant evolution since then.

CAPM and future advancements:

Factor investing traces its origins to the 1960s, particularly the Capital Asset Pricing Model (CAPM). Developed by academics like Sharpe, Lintner, and Mossin, CAPM emphasised the role of market returns in explaining stock returns by claiming that a stock's expected return depends on its beta, a measure of its responsiveness to market movements.

Despite its contributions, CAPM faced challenges. CAPM explained a small portion of the returns, largely due to its many theoretical assumptions. CAPM’s failure to explain various market phenomena, such as the Value Effect, Size Effect, and Momentum Effect, motivated academics to develop more sophisticated multi-factor models which explain risk and return based on factors other than beta.

Expanding the Paradigm with Fama and French:

In response to CAPM's limitations, Fama and French introduced their seminal three-factor model in 1992. Going beyond market risk, it considered size and value factors as well. These developments can be considered as the official inception of multi-factor investing. Later, in 2015, they further refined the model with two additional factors: profitability and investment. This refinement allows for a broader understanding of expected asset returns, considering not just market dynamics but also size, value, profitability, and investment characteristics.

Growing Factors and Competing Models:

Factor investing continued to evolve with the addition of momentum in 1997 by Carhart and the introduction of alternative models like the Hou, Xue, and Zhang q-factor model.

Factor investing is a rapidly evolving domain marked by the continuous introduction of new factor models and parameters. This expansion brings forth both opportunities and challenges. Navigating the diverse array of factor parameters demands a discerning approach. The critical need is to distinguish genuine sources of excess returns from those potentially arising from data mining. This consideration holds utmost significance for researchers and practitioners, underscoring the need for rigorous methodologies and meticulous analysis.

Rise of Smart Beta Investment solutions:

The term "smart beta" emerged in the early 2000s to describe factor-based investment strategies. This shift empowered investors to actively incorporate specific factors into their investment portfolios. Smart beta strategies introduced a systematic method for diversifying portfolios by focusing on targeted risk factors, with the objective of augmenting potential returns. Originating as alternative investment products, they later transitioned into more accessible formats such as ETFs and mutual funds which has seen a tremendous rise since the credit crisis of 2008.

Impact of Technology and future developments:

Today, factor investing has become increasingly popular, with practitioners utilising and developing factor-based products due to their transparent and systematic rules and relatively low costs. The next few years will be interesting as we see how factor investing continues to evolve. Technological and computational advancements have profoundly impacted factor investing, enhancing the capacity to process and analyse vast datasets, often referred to as big data, and identify complex patterns and relationships that drive asset returns. Machine learning and artificial intelligence (AI) have further revolutionised this field, allowing for the examination of non-linear relationships and the potential discovery of new, subtle factors. These technologies facilitate the development of sophisticated, data-driven investment strategies which also benefits risk management by improving the modelling of factor exposures and correlations. However, these technological advances also introduce new challenges, such as the risk of overfitting and the need for clear interpretability of complex models.

Factor investing has been pivotal in reshaping asset pricing and portfolio management. Originating from the market-centric view of CAPM, it has evolved into a multifactor perspective, notably influenced by Fama-French models. While successful in explaining market anomalies and aiding portfolio diversification, challenges persist—factor timing, market crowding, and the integration of non-financial data, such as ESG factors, pose complexities. The surge in factors and parameters raises concerns about data-snooping and the stability of factor premiums. Nevertheless, factor investing remains a vibrant area of research and practice, adapting to a changing financial landscape. The future will likely witness continued adaptation, leveraging technological advancements and data analysis to refine and potentially redefine critical factors for investment success. In this complex landscape, the connection between theoretical rigour and practical application remains crucial for investment success.