A Dilemma of Self-interest vs. Ethical Responsibilities in Political Insider Trading (2024)

Political Ethics, Social Contract Theory, and Virtue Ethics

From an ethics perspective, it is widely documented that most forms of insider trading are unethical because it generates profits at other parties’ expense by exploiting information advantages gained through means of position or association (i.e., connections) instead of through public channels (Bhattacharya & Daouk, 2002; Christensen et al., 2017; Gao & Huang, 2016; Jerke, 2010). Indeed, some studies suggest that the buying or selling of securities by insiders with access to non-public information is unethical, unfair, or even illegal (Blau et al., 2021; Brudney, 1979; Klaw & Mayer, 2021; Werhane, 1989, 1991). In contrast, others (e.g., see Manne (1966), Gilson and Kraakman (1984), Meulbroek (1992), Lin and Rozeff (1995), and Bainbridge (2006), among many others) question whether insider trading is economically harmful or advance amoral arguments concerning its impact on the informativeness of prices. Therefore, it is crucial to re-engage in the debate about whether profiting by exploiting political information is unethical. For this task, the political ethics literature allows us to enjoin aspects of ethical expectations derived under social contracting theory with certain aspects of virtue ethics.

Under the STOCK Act, politicians have a disqualifying conflict of interest in a political decision if it is foreseeable that the decision will directly impact their private finances. A conflict of interest arises when the private interests of a politician or official clash with that of the public. The key ethical question is whether private interest could influence, or appear to influence, the voting decisions officials make in their working lives. Henceforth, public officials should not take unfair advantage of their position by using non-public information that could benefit them at the expense of others.

Social contract theory suggests thatall stakeholders should live together in society following an agreement/contract that establishes moral and political rules of behavior. For example, if politicians live according to a social contract (Hobbes, 1651; Locke, 2003; Rousseau, 1987), they can live morally by their own choice. Social contracts can be explicit, such as laws, or implicit, such as raising one’s hand in class to ask questions. The U.S. Constitution is often mentioned as an explicit example of part of America’s social contract. It sets out what the government can and cannot do. People who choose to live in America agree to be governed by the moral and political obligations outlined in the Constitution’s social contract. Indeed, whether social contracts are explicit or implicit, they provide a valuable framework for harmony in society. Political ethics requires politicians’ trades to meet higher ethical standards than would be necessary for the personal standard of pursuing financial profit, mainly due to potential conflict of interest concerns (Blau et al., 2021; Jensen & Meckling, 1976; Rawls, 1971; Stark, 2003). Such conflicts of interest can undermine trust (Blau et al., 2021) and make the public lose faith in the integrity of governmental decision-making processes, leading to perceptions of corruption, lack of accountability, and social distrust.

As discussed above, inconsistencies between personal (or private) and political morality in political ethics can be viewed as a “conflict of interest.” However, it is essential to recognize that these two concepts of morality can also maintain a typical positive relationship. Someone who learned the skills necessary in the political sector may apply these qualities in a setting outside of politics, often viewed as a private setting. In contrast, someone entering the political ground may already have the qualities and virtues expected in a professional environment. Reciprocity, as in the context of deriving those traits, is commonly present when entering the field if one did not already learn the qualities. Although both concepts of morality include different expectations, there is a correlation between the two. Whether the virtues and values were acquired or previously held, they factor in and apply to both settings. Those who have emerged into the intense political domain should understand that virtues and morals can influence each other (Mendeluk, 2018). We assert that building one’s character by viewing political morality above private morality can be substantially more ethical when we contemplate the issue of politicians’ insider trading.

Salbu (1995) analyzes the expansive and constricted approaches of insider trading regulation within a framework of basic tenets of the American capitalist social contract regarding the legitimacy of property claims. The insider trading law has progressed from an expansive approach under which corporate outsiders, including politicians, are permitted to trade on non-public information provided such trading does not breach a fiduciary duty. Salbu (1995) further claims that the existing constricted approach to the regulation of insider trading is deficient in meeting the expectations of two core components of the social contract. It discourages procedural equality of opportunity and endorses claims to property not characterized by legitimate methods of acquisition or transfer. Because the old, expansive regulatory interpretation was more consistent with the terms of the social contract concerning property claims, it served our economic and ethical expectations more effectively than the current system. Accordingly, Salbu (1995) suggests that the expansive approach to regulating insider trading be reestablished under United States law.

We apply social contract theory to consider whether it provides a sound ethical foundation for prohibiting trading on material non-public information. Social contract theory shares the premise that whether a practice is “ethical” or “unethical” depends upon whether those governed by such rules have consented or would consent (Dunfee & Donaldson, 1995). We contend that stock trading based on material non-public information by politicians would not be an ideal or even an ethical practice to which most investors would agree because it runs directly counter to the traditional concept of the ethics of fairness (Klaw & Mayer, 2021; Salbu, 1995; Zingales, 2012).

Based on Salbu (1995), Fandos (2020), and Mesiya (2021) further argue that the STOCK Act is an inappropriate and ineffective legal mechanism for remedying issues of political ethics. As a result, concerning political insiders, we consider that an ethical approach might be better than the legal approach to dealing with financial conflicts of interest. In addition, Jerke (2010) advocates for public disclosure of political intelligence-gathering activities by outside actors, such as lobbyists and hedge funds. Jerke (2010) further contends against prohibiting trading on political intelligence by external actors because these actors are merely the Washington equivalents of security analysts, whose information-gathering functions are legitimate, if not desirable. However, unlike Jerke (2010), we maintain that political insider trading is unethical to the extent that it generates profits at other parties’ expense by exploiting information advantages gained through political position or connection (Bhattacharya & Daouk, 2002; Christensen et al., 2017; Gao & Huang, 2016). It is unethical mainly due to conflict of interest even if politicians pass through the hurdle of the legitimacy of property claims.

One competing alternative to the social contract theory related to politicians’ insider trading is the theory of virtue ethics. Virtue ethics emphasize the “right” way to be is that, which stimulates the development of personal characteristics to attain the “good” or “honesty” (Aristotle, 1926; Klaw & Mayer, 2021; Maclntyre, 1984, 1988; Murphy, 1999). For example, Aristotle (1926) proposed nine critical virtues: wisdom, prudence, justice, fortitude, courage, liberality, magnificence, benevolence, and temperance. While Aristotle’s list of virtues does not contain “creating efficiencies” in the financial market, virtue theory can broaden insider trading ethics even if insider trading were legal.

We consider that politicians who trade on material non-public information would need to reconcile their behavior with the virtue of “honesty.” It is crucial to address whether political insider traders are honest. It would be hard to imagine that they are without disclosure to the financial markets and all the market participants. However, political insider traders are unlikely to be honest to all stakeholders because they neither need to take full responsibility for their trading actions nor treat other stakeholders fairly.

While virtue theory is a plausible alternative to social contract theory regarding politicians’ insider trading, we consider social contract theory as more relevant to politicians’ insider trading than virtue theory. That is primarily because virtue theory is mute on price efficiency in financial markets. Thus, the theory of virtue ethics does not precisely predict abnormal financial returns or information asymmetry related to politicians’ insider trading other than the possible existence of the politicians’ dishonesty that could create distrust between shareholders (Blau et al., 2021).

Political realists (Korab-Karpowicz, 2010) criticize that ethics has no place in politics. They claim that moral rules should not bind politicians if they are effective in the political sphere. They further claim that politicians should put more emphasis on the national interest. However, Walzer (1977) points out that if realists are asked to justify their claims, they will almost always appeal to their moral principles. Another criticism comes from those who argue that one should not pay so much attention to politicians and policies but instead look more closely at the larger structures of society where the most severe ethical problems lie (Barry, 2005). However, we maintain that while one should not ignore structural injustice, too much emphasis on structural injustice issues could potentially lead us to neglect both the practice of information exploitation for private benefits and the issues arising from conflict of interest. We further contend that the current practices of politicians’ insider trading are creating social distrust among market participants.

Politicians and Insider Information Trading

Early evidence of potential conflict of interest consistent with political insider trading is provided by Boller and Ward (1995). They show that out of a random sample of 111 members of Congress and Senate, 25% of members’ stock transactions were directly linked with legislative activity. After that, several studies attempted to quantify the performance of politicians’ stock investments and to answer whether they make abnormal profits, but the results were mixed. Ziobrowski et al. (2004) show that between 1993 and 1998, senators’ stock trades outperform the market by approximately 10% per year. Yet, Eggers and Hainmueller (2014) show that between 2004 and 2008, Congress members’ trades underperform the market by, on average, 2–3% annually. Perhaps, pre-STOCK Act data can generate conflicting results depending on the period of the analysis because “it is also possible that members simply stopped reporting their incriminating transactions once they realized that academics and the media were watching them” (Kim, 2013c, p. 170).

Huang and Xuan (2019) show that in the year following the establishment of the STOCK Act, there were no surprise mergers or earnings announcements that politicians took advantage of, and theysuggest that the STOCK Act might have stopped politicians’ insider information trading. Yet, given the plethora of anecdotal evidence in 2020 alone and the fact that many senators need to file corrections every year, it is still unclear whether the STOCK Act has effectively increased transparency and reduced insider trading. Blau et al. (2021) empirically examine the sample of stocks held by members of Congress compared to control stocks and find that liquidity significantly worsens, and stock volatility substantially increases during the post-STOCK Act amendment period. They conclude that restrictions on non-corporate insider trading are associated with unfairness and harmfulness.

Hypothesis Formation

Social contract theory provides a sound ethical foundation for prohibiting insider trading in the U.S. (Klaw & Mayer, 2021; Salbu, 1995). As Salbu (1995) notes, insider trading profits generally render the transaction unfair because a trader with inside information does not deserve the gains she receives by utilizing that information. Given the opportunity, market participants would generally refuse to agree to a system that permits insider trading (Tramontano, 2017; Zingales, 2012) because earned merit has little to do with rewarding those who trade with the unearned advantage of inside information. We posit that the same ethical problem applies to politicians, as trading on inside political information may create unearned benefits.⁹

We also maintain that political insider trading by the Senate has the potential to disrupt fair equality of opportunity. We postulate that political insider trading can pose potential conflicts of interest among firms, shareholders, politicians, and financial intermediaries. The breach of the fair equality of opportunity increases social distrust and raises the potential for conflict of interest among stakeholders, thereby adversely affecting financial efficiencies. However, gauging the true extent of political insider information trading is not easy. Kim (2013b) points out that the STOCK Act is not binding for all legislators.¹⁰ Thus, the trading activity of senators (and potentially House members) should be viewed as a subset or as a proxy for all others who have access to the same information but do not file their transactions (Kim, 2013b). As a result, there is a potential for politicians who have access to material non-public information to disseminate it in violation of some of social contract theory’s four ethical propositions (i.e., fairness, harmlessness, honoring property rights, and fiduciary relationships). Second, to the extent that the four ethical principles are adhered to, it should not be possible for politicians to generate high abnormal profits simply because they access insider information. For example, abnormal returns could be possible when investing in industries that fall under the jurisdictions of their committees (Karadas, 2018) or even without a committee link when investing in local firms (Eggers & Hainmueller, 2014) because in these cases, politicians may be in a better position to exploit public information.

Nevertheless, Kocieniewski and Farrell (2020) document that members of Congress with oversight of industries such as financial services, defense contracting, and health care earned huge gains recently through well-timed trades in the market. Given their advantages in terms of oversights of such industries, local firms, and timing, it is questionable whether the system should allow substantial financial gains to politicians. Moreover, most current literature on government insiders fails to consider differences in financial know-how between corporate and government insiders. The latter, e.g., a legislator, can use her inside information to make an informed trade. Still, alack of skill might prevent her from making abnormal returns achieved by a financial analyst trading on the same information, as Ferguson and Voth (2008) showed.

Social contract theory assumes that whether a practice is right or wrong depends upon whether those governed by such rules have consented or would consent (Dunfee & Donaldson, 1995). Thus, we would argue that high financial gains by politicians should not be a practice most people would agree with because it runs counter to traditional capitalist concepts of the moral equality of opportunity and associating moral desert with merit (Blau et al., 2021; Salbu, 1995; Zingales, 2012).

Initially, we contemplate whether the Senate’s inside trading could generate a positive financial gain. One can expect politicians’ trading to be free of systematic use of legislative and political information after the STOCK Act’s enactment. Belmont et al. (2020) analyze the buy-and-hold abnormal returns of politicians between March 2012 and 2020, and they confirm the previous results of Eggers and Hainmueller (2014) by showing that the actual returns of politicians outperform the market returns but fail to outperform the industry-size benchmark. Their results support the premise that senators possess valuable legislative information while lacking company-related inside information.¹¹

To the extent that the social contract theory is valid, we speculate that politicians’ investments should not generate high abnormal returns.

Concept of Abnormal Idiosyncratic Volatility (AIV)

Yang et al. (2020) show that their price-based measure of information risk, AIV, relates to insider trading,¹² institutional trading activities, and short selling. There is a positive relationship between the AIV and the size of informed return run-ups before the earnings announcements, suggesting that some traders are informed about the earnings before the public. Our approach is inspired by their suggestion that the AIV measure of information risk “may also be applied to other information events such as mergers and acquisitions, product recalls, and patent applications” (Yang et al., 2020, p. 530). We modify their measure to compare the volatility surrounding the stock transaction dates of politicians to that during the rest of the year. This modification is made because, unlike quarterly earnings, where the start of the information event is clearly defined, politicians’ trades are not necessarily the start of the event. As in Yang et al. (2020), the idiosyncratic volatility is measured on the three-factor model of Fama and French (1993).

where Ri,t is the daily excess return on the stock i at time t, MKT is the value-weighted market portfolio excess return over the risk-free rate, SMB is the size factor, and HML is the value factor.

To compute abnormal idiosyncratic volatility (AIV), we need to specify the time windows for the calculations. Following Ali and Hirshleifer (2017) and Yang et al. (2020), we define the 5-day window (t − 2, t + 2) surrounding the politician’s stock trade event (t) to minimize possible noise. For every transaction, we create two sub-periods. First, the around-the-trade (ATT) period includes the 5days in the transaction window. Second, the non-around-trade (NAT) period is the whole year period before the trade, excluding the ATT days. To compute the AIV, we run the Fama–French three-factor regression for each stock using the previous year’s daily data. We obtain an estimated daily residual ∈i and then compute the annualized idiosyncratic volatility of stock separately for the ATT and NAT using the following formulae:

where ln stands for the natural logarithm and nATT (nNAT) is the number of days in the around-the-trade (non-around-trade) period. Finally, we define the abnormal idiosyncratic volatility (AIV) as the difference between the volatility around the trade (ATT) and the rest of the year (NAT):

Note that the AIV is calculated for each stock and trade separately. This approach allows us to estimate information asymmetry associated with the transaction (i.e., the extent of political insider trading). In contrast with the previously used return analyses of politicians’ trades, the AIV-based approach captures unusual stock price patterns around their trades. Using the AIVs around the dates of politicians’ stock trades, we can detect informed trading activity by people who do not disclose their trading activity but still have access to the same information.¹³

Testing Buy-and-Hold Portfolios of Political Insiders

Several papers have attempted to estimate the investment returns of politicians. For example, Ziobrowski et al. (2004) use a technique known as calendar-time transaction-based analysis (Odean, 1999). They create portfolios by purchasing stocks on the same day as the politicians, which will be then sold 12months later. These synthetic portfolios built from politicians’ transactions outperform a passive market index by 12% per year in the Senate (1993–1998) and 6% in the House (1985–2001). However, Eggers and Hainmueller (2013) criticize their findings. Since Congress members do not hold these synthetic portfolios, the actual returns that members earned with their portfolios can be substantially different from expected returns. In addition, they note that results from such synthetic portfolios can be susceptible, and their returns can vary significantly across other specifications. Using the data before the STOCK Act, Eggers and Hainmueller (2013) show no significant outperformance of politicians.

To test Hypothesis 1 and Corollary 1 by avoiding the criticism associated with a technique that leads to the aforementioned, divergent conclusions we compute buy-and-hold market-adjusted and industry-adjusted returns over several holding periods. We follow each politician’s stock trade using information from electronic reports of senators during the January 2012–December 2019 period. For several periods of a buy-and-hold strategy, we define the buy-and-hold abnormal return on a politician’s investment as

where ri,t is the daily return on firm i at time t. rmkt,t and rind,t are the daily returns of S&P500 and industry, respectively. The Fama–French (1997) 48 industry classification is used to define an industry. For the period p, we consider 1w, 2w, 1m, 2m, and 3m, representing periods of 1week, 2two weeks, 1month, 2months, and 3months, respectively. We acknowledge that our empirical tests are not free from the criticism of Eggers and Hainmueller (2013). Then, the empirical version of H1 on whether or not a politician’s investment outperforms the market is

H10:BHARt,pimkt≤0. Alternatively, H1A:BHARt,pimkt>0.

The empirical version of Corollary 1 for whether or not a politician’s investment exceeds the industry portfolio returns is

C10:BHARt,piind≤0. Alternatively, C1A:BHARt,piind>0.

Based on H1 and C1, we expect four different scenarios: (1) outperforming the market and industry portfolios, (2) outperforming the market portfolio and underperforming the industry portfolio, (3) underperforming the market portfolio and outperforming the industry portfolio, and (4) underperforming the market and industry portfolios.

Note that calculating actual returns is almost impossible for two reasons. First, politicians can choose whether it is a partial sale or a complete sale when politicians report their transactions, but there are no clear definitions. Moreover, matching buys with the corresponding sales is nearly impossible because they must report ranges of total dollars invested instead of exact dollar amounts. Second, many politicians dilute their transactions, i.e., they split their purchases and sales into several smaller transactions rather than reporting one significant transaction. Hence, it is difficult to calculate the actual returns precisely, and more significant transactions could be noisy.¹⁴

Testing Abnormal Idiosyncratic Volatility

To test H2 and H3, we employ the information risk measure of AIV with the modifications described earlier. In H2, we investigate whether the probability of informed trading (i.e., AIV) is higher during periods surrounding dates with stock transactions by senators.

H2a0:AIV≤0. Alternatively, H2aA:AIV>0.

As Yang et al. (2020) mention, the calculated AIV might be lower than in reality because there may be several potential events during the other period (NAT) where information asymmetry plays a role (e.g., quarterly earnings, mergers and acquisitions, product recalls, etc.).

Next, we examine whether politicians are in the first group of insiders. One can conjecture that senators are informed, but it is unclear whether they are generally among the first group of people trading on the information. To test this, we modify the computation of the AIV described earlier by shifting the five-day event window. We define AIVF (i.e., politicians trade first), where the five-day window includes the transaction day and the following 4days. Similarly, we define AIVL (i.e., politicians trade last) as 4days before the transaction and the transaction day. If politicians’ trades are genuinely in the “first wave” of informed trades, the 5-day window following their trade will contain most of the informed trades. Thus, AIVF is expected to be greater than AIV. Conversely, if their trades are lagging behind those of other informed traders about a certain, AIVL will present a larger value than AIV.

H2b10:AIVF≤AIV. Alternatively, H2b1A:AIVF>AIV.

H2b20:AIVL≤AIV. Alternatively, H2b2A:AIVL>AIV.

Table 1

Distribution of senators’ trades by security type and year

Panel A reports the total amounts of transactions by year and security type, with the last column and row reporting the total by security type and year, respectively. In contrast, Panel B reports the distribution of each security and direction of trade by year (row percentages). The last column contains the total number of transactions over the entire period and represents a hundred percent base for scaling

Buy-and-Hold Stock Returns Following Senator’s Trades

To examine buy-and-hold excess return (market), we conduct two tests for mean values (t tests) and median values (sign tests). H1 is set to test whether stock returns outperform the market in each given year and for each holding period commencing with the day of a senator’s trade. In Table ​Table2,2, the first set of rows tests whether the average abnormal return is different from zero, while the second set of rows contains the sign tests (also known as the median tests).

Overall, Table ​Table22 results reject the first null hypothesis (H1₀) and support H1_A, which states that politicians’ buy-and-hold returns outperform the market. Except for the shortest holding period, we find evidence that senators’ stock purchases outperform the market at the 1% significance. The average buy-and-hold abnormal return (market) is about 0.5% for two weeks and increases to 4.9% for 3months.

When we analyze the patterns of the excess market returns by year, we still observe that the averages are positive and significant at a 1% level in most cases. However, a few negative and significant excess returns are present for some holding periods in 2015 and 2018. We also note that in both 2015 and 2018, the largest portion of stocks was sold, and most transactions were diluted.¹⁸ In addition, we examine excess returns using a different benchmark such as CRSP value-weighted market index and find that the results remain the same.

Overall, the observed patterns of the abnormal market returns are amplified with the more extended hold periods. This longevity suggests that value-relevant political information may take a while to become incorporated into stock prices. Moreover, a non-parametric version of our test procedure, the sign test of Snedecor and Cochran (1989), confirms and magnifies the results of the t tests. Thus, the combined results do not support the prediction based on the social contract theory.

We examine industry-adjusted returns for stock purchases and report the results in Table ​Table3.3. The mean excess returns are not significantly different from zero for a large portion of the results. The lack of significance for the t test could be explained by the high variation of the excess returns (industry). On average, outperforming the industry portfolio would be less common than outperforming the market since corporate insider information is primarily available only to a limited number of senators who, for example, may sit on essential committees and observe corporate lobbying activities (Eggers & Hainmueller, 2014). Thus, the results of industry-adjusted returns are not conclusive.

Abnormal Idiosyncratic Volatility

This section aims to analyze if politicians’ trades are associated with periods of higher information asymmetry. Since such information asymmetry cannot be simply attributed to a few recorded politicians’ transactions, high AIV values during periods around politicians’ trades would imply that either many more people at Capitol Hill are relying on the inside political information or this valuable inside information could be leaked to other market participants. Accordingly, we test H2 by the decomposition of factors that drive abnormal idiosyncratic volatility (AIV).

Table ​Table44 below contains mean AIVs based on a 5-day window around the stock trade across years and different types of characteristics. For the sake of space, we report only the 5-day results, but the complete set of results using alternative windows is available upon request. We find that information asymmetry associated with stock trades by senators is considerably high. It is, on average, 3.6%, which is higher than the average AIV of 1.1% related to quarterly earnings announcements in Yang et al. (2020). Thus, our results on the AIV do not support the social contract theory prediction of H2. Table ​Table44 presents the average value of the AIV computed across years, personal characteristics, and account types. The highest AIV is associated with the joint accounts (9.7%), followed by spouse-owned accounts (−1.8%), and self-owned accounts (−3.0%), with the child accounts representing a very low (large negative) value of the AIV (−12.5%). These results suggest that politicians may refrain from using their self-owned accounts when trading based on potential insider information. A child’s account is also limited, unlike corporate insider trading found in Berkman et al. (2014). Berkman et al. (2014) find that the guardians behind under-aged accounts successfully pick stocks. Moreover, they show that the guardians tend to channel their best trades through children’s accounts.

Regarding the direction of trade, we observe that partial sales are associated with a higher AIV even when compared to purchases. This could, however, be caused by the lack of legislative activity in 2014, when the mean AIV for purchases was low (and negative). Moreover, senators can use their insider information to minimize losses. For example, in early 2020, senators with access to information about the incoming measures against COVID-19 took their money out of the market (Ziolkowski, 2020).

The effect of diluted transactions could be more complex because of a wider window and a higher noise associated with diluted dealings. The results are very similar when we use other windows (e.g., 7 days) for the calculation of AIV¹⁹ but with lower mean values, suggesting that increasing the time window prevents us from capturing more information asymmetry and raises the noise in the measurement.

One potential problem with interpreting an increase in the AIV around politicians’ trades as evidence of political insider trading could be related to other major corporate announcements on dividends, earnings, mergers and acquisitions, and stock repurchases. These events may convey no important political information but lead to high AIV. We examine the frequencies of trades made during the major firm-specific announcements to verify if this is the case. Thus, we want to compare how the information asymmetry during the senator stock transaction period to that around quarterly earnings announcements.

In Table ​Table5,5, we report that these events, on average, represent only around 4.2% of the overall senators’ trades. Moreover, we conduct an additional test after removing the transactions that occur during the events and find that our results are similar to those obtained from the whole sample, presented in the following sections.²⁰

Alternative Measures of Abnormal Idiosyncratic Volatility

In this section, we define modified versions of the AIV to analyze if politicians are in the first wave of informed traders and to compare the extent of the AIV associated with the politician’s trade with the AIV connected with the quarterly earnings, as defined by Yang et al. (2020). First, we calculate AIV around the quarterly earnings for every year and thestock for which a senator transaction occurred. We denote this abnormal idiosyncratic volatility as AIVE (earnings) to differentiate it from other calculated measures. To test H2(b1) and H2(b2), we define AIVF (first informed) and AIVL (last informed) using different event windows.

Given the fact that while AIV, a price-based measure of informed trading, in the context of Yang et al. (2020), is measured surrounding a widely announced corporate event (i.e., earnings report), there is a caveat concerning the AIV measured around a very quiet (non-public) senator trade as a proxy of information asymmetry. Market microstructure literature has introduced several quantity-based measures of asymmetric information, the most prominent of which is the Easley et al. (1996) measure of Probability of Informed Trading (PIN) and the closely related VPIN measure of Easley et al. (2012). Although these measures have also been criticized in the literature,²¹ they could allow us to test the robustness of our findings using the quantity-based measures of informed trading. Unfortunately, we lack access to the high-frequency trading data necessary to construct the PIN (or VPIN) measure. Instead, we construct three alternative proxies for information asymmetry, which have been used in prior literature (e.g., Brennan & Subrahmanyam, 1996; Armstrong et al., 2011; Yang et al., 2020). Spread is computed as (asking price–bidding price)/traded price, multiplied by the number of trades. Abnormal volume is computed as in Yang et al. (2020). We compare trading volume (in millions) in the 5-day window surrounding the politician’s trade to trading volume in the rest of the year as we did for AIV. Abnormal turnover is the difference in turnover (= trading volume/shares outstanding) between the 5-day window and the rest of the year. We observe that these proxies are highly correlated with AIV.

In Table ​Table6,6, we show the means of the AIV, AIVF, and AIVL along with AIVE throughout the years. The highest AIV is associated with the regular definition of the AIV, followed by AIVL and then by AIVF. This evidence suggests that senators are in the middle of the pack of informed traders and rarely the first or the last to trade on the news, negating H2(b1) and H2(b2).

The observed differences across years could partly be due to different levels of legislative activity or changes in the ruling party/administration. For example, the lowest level of legislative activity is found in 2014, implying the least amount of potential political inside information available to senators. This explains the negative mean values of the AIV in 2014. In 2017, the first year of the Trump administration, information that could affect the market might have been driven mainly by the president himself, potentially leading to the observed negative values for AIV.

To formally test H2(b), we use both the t test and the sign test, as in the previous section. We report the battery of tests in Table ​Table7,7, documenting the differences between the average values of various AIVs. It confirms that the regular AIV has the highest values. We also observe that the AIV associated with politicians’ trades is higher than that around earnings announcements (AIVE as defined in Yang et al., 2020). This result implies that, on average, information asymmetry is greater during periods around senator transactions than during periods around quarterly earnings announcements. More importantly, this result supports our view that there is a flow of valuable private information from Capitol Hill and that senators’ trading activity is only the tip of the iceberg. Most individuals who have access to the information do not have to report their investments. We also find that three alternative variables present similar patterns. Similar to AIV measures, the average values are relatively higher in 2015, 2016, and 2018 and lower in 2014 and 2017.

Determinants of Political Information Risk and Politicians’ Stock Performance

We now estimate the regression models of political information risk (AIV) and politicians’ stock trade performance (buy-and-hold returns). Again, our stock performance analysis does not extend over periods longer than 3months as in other tests. Both models include an extensive list of standard control variables. Still, our focus is on political variables (i.e., politicians’ personal and legislative characteristics) that may reveal possible channels for the information asymmetry around senators’ stock transaction dates.

Several variables are related to Senate legislative activity. The Legislative effectiveness score (LES)²² measures how well the senator advances her agenda items through the legislative process and into law. A high LES score implies that the senator has greater influence and better connections within the U.S. Senate (Fowler, 2006a, 2006b) and is better informed. Senators can also gather information by serving on various committees. With better access to important news (e.g., on the policy agenda in general or specific pieces of legislation) and with the benefit of observing relevant individual firm lobbying, committee service can give them further valuable insight (Eggers & Hainmueller, 2014). To capture this effect, we include five committees based on the power ranking defined by Stewart (2012): Finance, Appropriations, Rules and Administration, Armed Services, and Foreign Relations. We include the Number of bills lobbied for (reported in hundreds) and the Number of lobbyists on the day of the transaction (reported in thousands) to explain the importance of the current legislative events. For every transaction date, we sum the number of introduced bills that have at least one lobbyist.

Additionally, we sum the total amount of lobbyists across all bills introduced on the transaction date. Finally, there is some evidence that investments in local firms could result in higher returns (Eggers & Hainmueller, 2014). We, therefore, include the Home bias indicator variable equal to 1 when a senator’s stock trade involves a firm from their home state and 0 otherwise. We also add two industry-related variables. Industry concentration is the value of the Herfindahl index that is computed by each firm’s total stock value within the same industry. The weight of each stock in the given industry based on stock value is included.

As firm controls, we employed standard variables used in asset pricing models: Past profitability, Illiquidity, Firm size, Book-to-market ratio, and Beta. Past profitability is measured using four separate past stock returns, following Brennan et al. (2012). Similarly, we use a group of variables Rm-1,Rm-3,m-2,Rm-6,m-4,Rm-12,m-6, which stand for returns over the last month, previous months 3–2, previous months 6–4, and previous months 12–6, respectively. Illiquidity is measured following Amihud (2002) as a sum of absolute values of daily returns divided by daily volume for the year, multiplied by 10^6. Firm size is the log of the market value of equity. The book-to-market ratio is the log of the book-to-market ratio. Beta is calculated using the Fama–French (1993) three-factor model over the past 6months. However, Beta is only used as a control variable for the AIV regression. We provide detailed descriptions (Table 11) and summary statistics (Table 14) in the internet appendix.

In Table ​Table8,8, we report results for three AIV regression specifications. Column (1) contains both tenure and age variables. In column (2), we omit the tenure and keep the age variables. This alternative specification serves as a sensitivity check since tenure and age are highly correlated. Finally, in column (3), we use a subsample that includes only stock purchases. Table ​Table88 reveals some interesting results. First, senator tenure and age are the only personal characteristics that remain statistically significant when we include the other control variables. Specifically, early-career and mid-career senators’ trades generate significantly higher informed trading than late-career senators. More specifically, in column (1), senators with shorter tenure (0–4years) present an 11.8 percentage points higher AIV, on average, than senators with longer tenure (14–35years).

The Number of bills coefficient is positive and significant at the 5% level, suggesting that higher levels of overall legislative activity generate more insider information, increasing information asymmetry. The coefficient of Finance Committee (the most powerful committee based on the ranking in Stewart (2012)) is also positive and significant. This indicates that trades by the finance committee members are significantly more informative than the rest, suggesting that such a committee might be privy to a substantial amount of value-relevant information.

The negative Home bias effect may appear surprising at first glance. However, this effect may imply that senators may be reluctant to widely share insider information about firms in their home state. As mentioned previously, the AIV captures the abnormal stock activity resulting in departures from the Fama–French asset pricing model. Therefore, changes in AIV cannot merely be caused by a few reported trades but rather require substantial additional trading activity. Hence, we would not detect information asymmetry when inside information is available to a limited number of market participants. Alternatively, the negative coefficient of Home bias may indicate that senators deliberately avoid trading on insider information about home state firms.

We see that the Number of lobbyists has a negative and significant effect on AIV, which is consistent with the view that lobbyists reduce information risk by disseminating value-relevant information to the market. In sum, information risk around senator stock trade dates appears to increase with the senatorial activity (Number of bills) and decrease with lobbying activity (Number of lobbyists). Thus, overall, we have some supporting but mixed evidence of H3.

Industry concentration is positively related to AIV. The estimated coefficient is consistently significant across three models at the 1% level. We interpret this as evidence that, if a few large firms dominate the industry, there are a number of small-sized firms that tend to present high levels of information asymmetry.

Note that stocks with many politicians’ trades over the past year would have fewer NAT days by construction. This feature could potentially distort the value of AIV and hence consequently affect the results. To account for this data-specific aspect and test our results’ robustness, we also run a weighted OLS regression, using the ratio of NAT days to ATT days as weights. The results are similar to those in the main specification, showing that our results are not biased by a larger number of ATT days. The detailed results are not presented here; they are available in the internet appendix (Table 15).

Now, we will analyze the abnormal market returns to determine the main drivers of individual profitability for the other stock purchases. Table ​Table99 Panel A depicts the market-adjusted buy-and-hold returns analysis results for the five different periods over 3months. Interestingly, stocks bought by early- or mid-career senators have higher market-adjusted returns over longer holding periods. Contrary to the AIV regressions, age is a positive and significant predictor of future returns. These results can suggest that older senators’ trades are associated with more information asymmetry, but the younger senators are making more profitable investments.

The legislative effectiveness score (LES) coefficient is not significant for the short holding period models but turns positive and significant for the more extended period models (≥ 1month). This suggests that senators who are better connected within the legislative network may possess information that takes at least 2months to be incorporated into market prices. Home state stocks bought by senators also perform significantly better over more extended holding periods, which supports the findings of Eggers and Hainmueller (2014). Coupled with the fact that the trades of home state stocks are associated with lower AIV (reported in Table ​Table8),8), this result implies that senators may not share value-relevant information about home state firms with many others. We find that AIV is a significant and positive predictor of two-week and 1-month returns. Interestingly, this time period is matched with a period of 30–45days for senators to report their transactions.

In addition, we analyze the industry-adjusted returns and report the analysis in Table ​Table99 Panel B. The results are generally consistent with the market’s abnormal returns. We observe that politicians with a high Legislative effectiveness score (LES) outperform even the industry for longer term periods. Furthermore, we find that both home bias and AIV remain strong predictors of industry-adjusted returns. These results suggest that even though senator trades, on average, do not outperform the industry portfolios, there might be some specific instances where senators will have a superior stock picking ability.

Overall, our buy-and-hold returns analysis nicely complements the findings of AIV regressions. Our results show that senators’ stock trading activity reported in electronic filings is information-driven and that the magnitude of information asymmetry predicts future returns. Nevertheless, the politically informed trading we uncover could be the tip of the iceberg since many more people have access to the same information but do not have to file their trades.²³

Sale Transactions

While purchases signal a positive view of the stock, sales trades include a more complex and diverse set of possible motivations. For example, the decision to sell could not only be driven by negative information but more commonly, by liquidity needs or by the decision to secure a profit or minimize a loss. Moreover, the timing of a sale can be distorted by decision bias like the disposition effect.

The STOCK Act does not require senators to report the exact amount invested or the price at which they purchased (and sold) the security, making it impossible to accurately calculate realized returns on their investments. This, in turn, makes it hard to disentangle the different motives for selling thoroughly. As a result, even though there are documented cases of senators’ informed selling to protect their investments (e.g., selling their stocks before the incoming measures against COVID-19), proper identification of such a subsample of sales transactions remains elusive given the current data. Moreover, the analysis of sale transactions needs to be performed separately using different methods. It is because neither buy-and-hold of stocks after senators’ stock sales nor a portfolio analysis after selling by senators (as reported by Eggers & Hainmueller, 2013) can distinguish among sales motivated by insider information, disposition effect, liquidity-based trading, or other motivations.

Table ​Table1010 provides a robustness test of politicians’ stock trade performance using an alternative measure based on the premise that the average sale should be less informed than the average purchase trade. We match every purchase transaction with the most recent sale transaction by the same politician that occurred within a week of the purchase. We then compute the difference of the market-adjusted returns by subtracting the return of the stock the senator sold from the return of the stock just purchased.²⁴

Therefore, positive and significant returns would suggest that stocks that senators purchase perform better than those they just sold. We conduct two tests for mean values (t-tests) and median values (sign tests) to examine this. For the mean value tests, the t tests show that returns are higher after the purchases in general (except in 2014), and the effect increases with the longer holding periods. The median tests (sign tests) also present similar patterns. Consequently, we conclude that the primary reason for selling stocks is to secure profits and reserve liquidity for the next investments. That is, sale transactions may not be driven by negative stock information. We leave a more rigorous analysis of sale transactions for future research.

Internet Appendix

See Fig. A.1.

See Tables ​(https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html)11,11, ​scaling12,12, ​respectively.13,13, ​1114,14, ​respectively1515.

Conflict of Interest

All authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

¹There is a plethora of popular press articles on politicians’ trading occurring prior to major news announcements. Some examples include the recent covid-19 onset and related lockdowns (e.g., see https://www.thedailybeast.com/sen-kelly-loeffler-dumped-millions-in-stock-after-coronavirus-briefing; https://www.ajc.com/news/state--regional-govt--politics/david-perdue-stock-trading-saw-uptick-coronavirus-took-hold/MRWmzwXeHgxi6IcmBbPgaN/; https://www.businessinsider.com/coronavirus-david-perdue-bought-stock-company-producing-ppe-after-briefing-2020-4; https://www.cnbc.com/2020/03/20/coronavirus-gop-sen-hoeven-bought-up-to-250000-in-health-fund-after-briefing.html; https://www.opensecrets.org/news/2020/03/burr-unloaded-stocks-before-coronavirus/), failed drug trials (e.g., https://nypost.com/2018/08/08/gop-congressman-busted-for-insider-trading/), the invasion of Ukraine by Russia (https://finance.yahoo.com/news/10-stocks-us-politicians-bought-164447897.html), or upcoming Obama Care legislation (https://www.wsj.com/articles/SB10001424052970204844504577100260349084878).

²Some studies show that investments by members of Congress outperform the market (Gao and Huang 2016; Jeng et al., 2003; Ziobrowski et al., 2004), presumably due to their significant informational advantage (Gao and Huang 2016; Ziobrowski et al., 2004). On the other hand, others suggest that stocks purchased by senators, on average, underperform the market (Belmont et al., 2020; Eggers and Hainmueller 2013). For instance, using portfolio analysis, Ziobrowski et al. (2004) show that government insiders outperform the market by 10% per year on average. Christensen et al. (2017) find more profitability from the recommendation revisions issued by analysts who are employed at politically connected brokerage houses.

³Conflict of interest in different contexts is discussed in Jensen and Meckling (1976), Boller and Ward (1995), Stark (2003), Rawls (1971), and Blau et al. (2021), among many others.

⁴Such political connections shaped the 2008 financial crisis bailout (Duchin and Sosyura 2012) and politicians’ support for the Economic Emergency Stabilization Act (Hassan et al., 2019).

⁵In his 2011 testimony on the STOCK Act proposal, Robert Khuzami states that “[t]here is no reason why trading by Members of Congress or their staff members would be considered “exempt” from the federal securities laws, including the insider trading prohibitions, though the application of these principles to such trading, particularly in the case of Members of Congress, is without direct precedent and may present some unique issues.” See https://www.sec.gov/news/testimony/2011/ts120611rk.htm.

⁶As described in Christensen et al (2017) political information encompasses “information about many types of issues, such as prospective legislation, upcoming government actions (e.g., interest rate cuts, military strikes), failed negotiations with other countries, unfolding economic crises (e.g., Great Recession), etc.”.

⁷For example, a 2013 Washington Post article states: “An April 1 alert to stock traders that predicted the outcome of a key Medicare funding decision has gained intense legal and public scrutiny. But a series of events in Washington and on Wall Street in the weeks before the alert raises the possibility that information related to the government’s decision may have previously circulated and moved the market, according to a trail of e-mails and market data.” (ElBoghdady and Hamburger 2013).

⁸In our application, we focus on senators because past results indicate that senators tend to perform better than representatives (e.g., Kim 2013a). In general, senators are more experienced and powerful, and they are better able to obtain inside information.

⁹The theory of virtue ethics (Aristotle 1926; Klaw and Mayer 2021; Maclntyre 1984, 1988; Murphy 1999) might be a plausible alternative perspective to the social contract theory, but nevertheless less suitable for interpreting empirical results as it does not have an accurate prediction of abnormal returns or information asymmetry.

¹⁰There were more than 7000 legislators in 2012 that had access to political insider information and could still trade on it without any repercussions after the passing of the STOCK Act (Kim 2013c). Currently, congressional aides and staffers do not have to disclose their trading activities (Lawder and Cowan 2012).

¹¹Our study is different from Belmont et al. (2020) in that we mainly examine whether information asymmetry (AIV) is related to politicians’ trading. We contend that return analysis may not be ideal to conclude whether senators are informed or not. Our findings imply that the mass of unfiled transactions using the same inside information remains undetected. Therefore, examining abnormal idiosyncratic volatility at the stock level is more appropriate than tracing a politician’s investment performance only.

¹²In the context of corporate insiders, possible information trading can also be detected using the Easley and O’hara (1987) measure of the probability of information-driven trading (PIN). The various modifications of this technique, using a sequence of buy and sell signals, have become a benchmark and are frequently used (Hasbrouck 1991; Brennan and Subrahmanyam 1996; Easley et al., 2001; Aktas et al., 2008; Collin‐Dufresne and Fos 2015).

¹³We provide detailed descriptions of the variables including data sources in the internet appendix (Table 11).

¹⁴We describe the distribution and magnitude of diluted trades in the data section.

¹⁵We find 374 paper-based transactions for the sample period (2014 to 2019) that we use to analyze AIV and buy-and-hold abnormal returns. We find that our results are qualitatively similar even after adding these paper transactions to the sample.

¹⁶By the same token, we do not compare our results with some existing data fragments before the enactment of the STOCK Act.

¹⁷Though it may be hard to take advantage of insider information in a stock market since it requires more financial knowledge, it can be much easier in the bonds market. This may be why bonds are the second most traded category after stocks. This paper will not examine the issue more closely, but the problem warrants closer examination.

¹⁸This pattern seems to imply that senators’ decisions to sell are less informative, perhaps because of behavioral bias, such as the disposition effect. Another potential explanation for the negative returns in 2015 and 2018 is that senators were more focused on protecting their wealth when a regime shift in the upcoming election became more likely. This could be further supported by a different pattern for municipal and corporate bonds.

¹⁹Results are available in the internet appendix (Table 13).

²⁰The negligible role of non-political information is also visible from the comparison of the AIV associated with the political trades and with the earning announcements. For the interest of the space, we do not present repeated tests and tables here. All results are available upon request.

²¹Duarte and Young (2009) argue that it is PIN’s liquidity component, rather than its information risk component, that is priced. In addition, Mohanram and Rajgopal (2009) and Lai et al. (2014) question the robustness of PIN’s return predictability in extended samples. Finally, PIN estimation has become increasingly challenging due to the ever-growing number of trades and high-frequency algorithmic trading (see Kim and Stoll 2014; Brogaard et al., 2019; Duarte et al., 2020).

²²The methodology along with the data can be found at https://thelawmakers.org/.

²³We also examine two interacted terms (AIV*Number of bills and AIV*Number of lobbyists) and find significantly positive effects for longer period excess returns (1month to 3months). This evidence implies that the actual effect of legislative/lobbying activity made via information asymmetry may not be properly captured in a short period (< 1month).

²⁴If the senator sold multiple stocks on the same day, we take their average return.

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