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Synthetic Controls for Causal Inference in Investing

Updated: Mar 16



Synthetic controls are an increasingly popular method in causal inference that can be highly useful for investors when evaluating the impact of major events or policy changes. The goal of synthetic controls is to construct a comparative case that acts as a counterfactual representing what would have happened without the intervention. This allows investors to isolate the causal effect of the event.



How Synthetic Controls Work


The key idea behind synthetic controls is to create a weighted combination of similar comparison units that closely match the pre-intervention characteristics and outcomes of the affected unit. This synthesized control group acts as the counterfactual for the post-intervention period. For example, imagine a major tax policy change affects Industry X. To evaluate the causal impact on Company A in Industry X, we would construct synthetic Industry X by weighting other industries with similar prior performance. This synthetic industry mimics Industry X in the absence of the policy shift. By comparing Company A to synthetic Industry X post-intervention, we can estimate the effect of the tax policy.


Advantages for Investors


Synthetic controls offer several benefits for investors seeking to make data-driven decisions:


  • Isolate the true causal impact of events on asset prices, removing biases from underlying trends or external factors

  • Customize counterfactuals by incorporating available data on relevant predictor variables

  • Estimate effects even if there is only one affected unit, unlike typical difference-in-differences analyses

  • Visualize alternative timelines and trajectories


Example Synthetic Control for Investors


Imagine we want to study the effect of the 2016 Olympics on tourism stocks in Brazil. We construct a synthetic Brazil tourism industry index by weighting tourism industries from other South American countries. This index closely tracks Brazil’s tourism industry before 2016 but diverges after, suggesting a positive Olympic effect. Comparing individual Brazilian tourism stocks like Hotel Co. to the synthetic provides stock-specific Olympic estimates for investors.


Example Synthetic Control for Merger Analysis


Synthetic controls can also be useful for investors specifically analyzing the impact of mergers and acquisitions. For example, imagine Company A acquires Company B in early 2018. We want to evaluate the causal effect of this merger on Company A’s stock price. To construct a synthetic control, we would weight a combination of similar companies that mirror Company A’s pre-2018 stock price trends and financial metrics like revenue, debt level, market share, etc. This synthesized Company A minus Company B tracks closely up until the merger and then diverges, with the gap representing the estimated merger effect. The synthetic shows what would have happened to Company A without acquiring Company B. Comparing the synthetic to the real Company A stock post-merger indicates whether the deal added or destroyed value for shareholders. Investors can decompose this total effect into proposed channels like cost synergies, market power gains, increased debt capacity, and managerial efficiencies. This application demonstrates how investors could use synthetic controls to regularly evaluate M&A outcomes. Determining what deals worked and why would allow investors to better predict future merger success and make smarter investments in consolidating industries.


Limitations to Consider


While very useful, synthetic controls do have limitations. They rely on having quality pre-intervention data to tune the comparator units. They also assume the intervention effect emerges immediately rather than delayed over time. Finally, they face challenges extrapolating estimates beyond the observed post-intervention period. Despite these limitations, synthetic control methods remain on the frontier of causal inference tools for investors seeking to quantify the impacts of new policies, events, or corporate actions on asset prices. Combining synthetic counterfactuals with a broad toolkit allows investors to become central actors in value creation through data-driven decision making.


The flexibility of synthetic controls makes them applicable to studying any major event, like new regulations, interest rate changes, elections, etc. Their ability to generate data-driven counterfactuals makes them a valuable causal inference technique for informed investment analysis.

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