Introduction

The Efficient Market Hypothesis (EMH), a pillar of financial theory, asserts that security prices instantaneously reflect all publicly available information, rendering persistent abnormal returns unattainable through historical analysis. Yet this assumption faces a critical stress test in the actions of corporate insiders—executives and directors whose trades are mandated for disclosure but rooted in privileged access to material non-public information. By analyzing the Thomson Reuters Insiders Data Feed (2010–2023), augmented with CRSP market returns, this study interrogates whether insider transactions contain predictive signals that systematically defy semi-strong form efficiency.

Insider trading provides a uniquely potent lens for testing EMH. First, insiders’ legal obligation to disclose transactions under Section 16 of the Securities Exchange Act creates a structured dataset with precise event dates (e.g., transaction vs. filing lags) and quantifiable metrics, filtered through Thomson’s proprietary cleanse indicators to isolate high-confidence records. Second, the hierarchical nature of insider roles—from CEOs to mid-level officers—allows us to test whether informational advantages correlate with organizational seniority. Third, the dataset’s granularity, including split-adjusted prices and ownership types (direct vs. indirect), enables robust controls for confounding factors like survivorship bias or estate-related noise.

Our analysis focuses on three EMH conflict points: (1) whether cumulative abnormal returns (CAR) around insider transactions exceed chance expectations, (2) whether post-disclosure price drifts persist beyond regulatory filing dates, and (3) whether returns vary predictably with insider hierarchy and trade characteristics. By integrating cleansed transaction data (e.g., H/R-coded records) with CRSP’s factor models, we isolate alpha specific to insider activity rather than broader market or style effects.

If significant abnormal returns emerge—particularly among high-confidence CEO/CFO trades—this would challenge the semi-strong EMH by demonstrating that markets fail to fully price insider intent, even after disclosures. Conversely, null findings would reinforce EMH’s resilience in an era of increasing regulatory transparency. Beyond theoretical implications, our results carry practical stakes for policymakers evaluating disclosure latency rules and enforcement prioritization.

The following sections detail our methodology, beginning with data alignment between Thomson Reuters and CRSP, followed by event study results, robustness checks, and a discussion of regulatory ramifications.

Methods

This package uses the same workflow approach as the R Markdown you know and love. Basically it goes from RMarkdown > Knitr > Markdown > Pandoc > HTML/CSS > PDF. You can even use the bibliography the same way (Thorne 2025).

Results

Usually you want to have a nice table displaying some important results that you have calculated. In posterdown this is as easy as using the kable table formatting you are probably use to as per typical R Markdown formatting.

You can reference tables like so: Table 1. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aliquam placerat augue at velit tincidunt semper. Donec elementum porta posuere. Nullam interdum, odio at tincidunt feugiat, turpis nisi blandit eros, eu posuere risus felis non quam. Nam eget lorem odio. Duis et aliquet orci. Phasellus nec viverra est.

Or with figures: Figure 1, or Figure 2.

Figure 1: Great figure!

data <- iris

plot(x = data$Sepal.Length, 
     y = data$Sepal.Width, 
     col = data$Species,
     pch = 19, 
     xlab = "Sepal Length (cm)",
     ylab = "Sepal Width (cm)")

Figure 2: Amazing, right?!

Next Steps

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Conclusion

Try posterdown out! Hopefully you like it!