Algorithmic trading, often referred to as "algo trading", is based on a multitude of strategies designed to maximize returns while minimizing risk. One such strategy is "Assets Class Picking". In this article, we take a closer look at this technique, which is essential for all investors seeking to optimize their portfolios.

👉 Asset class picking strategies can be implemented as algo trading strategies. Here are some examples:

An asset class is a group of securities or investments with similar financial characteristics. This can include, among others, equities, bonds, commodities or currencies. The way these asset classes are combined in a portfolio largely determines its performance.

These strategies are based on long-term market projections. For example, a common strategy is to increase the allocation to equities during periods of economic growth and to favor bonds during recessions. Commodities can be used to hedge against inflation, while liquidity is often held in cash to reduce overall portfolio risk.

In the context of tactical strategies, exploiting market inefficiencies by combining several asset classes is at the heart of algo trading.

The majority of a portfolio's returns come from the allocation between different asset classes, rather than from the selection of individual assets. Indeed, assets within the same class are often highly correlated. Therefore, in algo trading, strategic allocation between different asset classes is paramount.

In particular, it has been observed that certain factors, such as momentum or value, perform better when applied jointly to different asset classes.

Studies such as that by Blitz and Vliet (2008) have documented the importance of momentum in asset allocation. Others, such as that by Asness, Moskowitz and Pedersen (2012), have highlighted the significant abnormal returns obtained by combining different strategies across multiple asset classes.

The research also shows the importance of standardization, such as the use of the Z-score, to directly compare valuation measures across asset classes.

Recent studies conclude that asset classes within a country are integrated, as are asset markets between countries. This integration plays a crucial role in explaining returns on market anomalies across asset classes.

Asset Class Picking is an essential element of algo trading. It requires an in-depth understanding of market dynamics and a mastery of algorithmic tools. By better understanding this technique and judiciously integrating it into their strategies, investors can greatly improve the performance of their portfolios.

The world of algorithmic trading is evolving rapidly. To stay at the cutting edge, it's crucial to keep abreast of new techniques and research.

💡 Read more: