Event-Driven and Alternative Data Strategies

Event-Driven Strategy

An event-driven strategy typically refers to a strategy that is activated only under specific circumstances. A common practice is to trade after the release of important economic data or significant meetings. Generally, event-driven strategies have low correlation with typical trend-following, mean-reversion, or pairs trading strategies. Furthermore, event-driven strategies tend to have low correlation with each other, thus helping the construction of diversified investment portfolios.

Based on information such as economic data, prices, trading volume, volatility, etc., various types of event-driven strategies can be designed. In financial markets, the tradable assets and signals are numerous, allowing traders to design their own set of event-driven strategies based on their expertise and experience.

Alternative Data Strategy

When developing quantitative trading strategies, besides focusing on prices and trading volumes of the underlying assets, we can also incorporate various types of data based on the characteristics of different financial instruments. For example, we can analyze the relationship among financial instruments of the same underlying asset, such as spot, futures, or options. Alternatively, we might utilize the price of one asset to generate trading signals in another, or even consider macroeconomic data for trading decisions. From these data, we can come up with strategies including trend-following, mean-reversion, pairs-trading, and more strategies.

Such strategies often serve to diversify risk. However, in formulating these strategies, several considerations may require further examination. Firstly, the quality, completeness, and accuracy of the data need thorough evaluation. Secondly, the frequency at which data is generated should align with the strategy being developed. Finally, the latency from data publishment to trading signal generation must be assessed.

Although alternative data strategies utilize different sets of data, they face similar challenges as those encountered in conventional trading strategies because they still trade the same financial assets.