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APPENDIX B (CONFIDENTIAL) – FURTHER CONSIDERATIONS 

Risk mitigation purpose of the Contract: The link between political polling and economic risk, 

particularly for equities, is a well-known one. This makes strong theoretical sense. Political polls 

are projections of the political future; stock prices are expected, and have been documented, to 

impute expectations of the future. 1 Because equities have political exposure (e.g. the future cash 

flows of firms are subject to regulations, laws, and more imposed by the executive), political 

polls can create an important price-basing function for equities. Consequently, there is economic 

risk embedded in equities (and other assets, services, commodities, and financial contracts) 

dependent on political polling. This Contract is designed for parties to hedge risks associated 

with those polls. However, more than theory, there is robust, replicated academic evidence 

proving as much. This research has been replicated across countries, across political parties, and 

across elections (i.e. primaries and general elections). 

Because markets reflect real-time changes in information, polling directly affects market prices. 

An increase in the polling of a protectionist candidate winning can reduce the prices of stocks for 

trade-dependent firms and emerging market equities as soon as a new poll is released. This 

Contract is specifically about polling numbers and not about who wins a given election cycle. 

Below, detail is provided of evidence describing how markets reflect and respond to changes in 

polling. 

For example, in 2011, Ejara et al. (“Opinion Polls and the Stock Market: Evidence from the 2008 

U.S. Presidential Election”) found a consistent link between election polling and the stock 

market. They observed data from the 2008 general election, testing the link between polling 

changes for the major party candidates and major equity indices. They wrote, 

Stock markets anticipate the impact of events on future cash flows. Current values depend on future cash

flows and risk prospects. We posit that election polls are indications of the political platform that is

expected to prevail in presidential elections. Given the traditional philosophical differences between the

Republican and the Democratic Parties, and the specific campaign promises of the U.S. presidential

candidates in the most recent (2008) election, we hypothesized that stock market reacts negatively to the

prospect of Obama winning the election. We test this hypothesis by relating daily stock index returns to a

lag value of differences in election polls that show Obama’s advantage over McCain. The results

consistently show that when Obama has a poll advantage over McCain, the stock market reaction is more

negative than when McCain has a poll advantage over Obama.

2 

That paper focuses on the market as a whole. However, some equities are undoubtedly more 

exposed than others. For example, Blau et al. (2019)’s paper, “Information in stock prices: the 

case of the 2016 U.S. presidential election” details how pharmaceutical company stocks are 

2 https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1931742

1 https://www.princeton.edu/~ceps/workingpapers/91malkiel.pdf

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positively correlated with Donald Trump’s odds of victory. 

3 Pham et al. (2018) found that 

insurance companies’ stock prices were very negatively correlated with positive news for Trump 

because of his promise to dismantle the Affordable Care Act. 4 Similarly, in Harold et al.’s 2016 

paper, “Do opinion polls move stock prices? Evidence from the US Presidential Election in 

2016” the answer was a clear yes. They first found that some firms (representing around 30% of 

total market capitalization) are especially exposed to electoral outcomes, and constructed a 

successful trading strategy which achieved above-market returns by investing in firms that 

benefited from Trump doing better-than-average in pre-election polling. 5

In fact, America’s influence is so great that changes in election polling even affect the stock 

markets of other countries. Kim and Kim’s 2021 paper, “U.S. presidential election polls and the 

economic prospects of China and Mexico”, found that unexpected jumps in Trump, “generate 

significantly negative long-term effects on their [China and Mexico’s] home currency and the 

stock prices, while the default probability responds significantly positively in the long run.” 6

Many American investors have major exposure to the Chinese, Mexican, and other markets, 

whether through exchange-traded financial instruments (like emerging market exchange-traded 

funds) or directly through foreign direct investment. Kim and Kim specifically found effects on 

Mexican and Chinese currency ratios to the U.S. dollar, on credit default swaps for Mexican and 

Chinese government debt, and the two countries’ equity markets. Thus, the Contract could be 

very helpful for candidates who wish to hedge against a particular candidate that is more likely to 

produce hostile policy towards other countries. For instance, Donald Trump is running for 

President again in the 2024 Republican Primary. 

There is also extensive evidence specifically regarding polling margins and stock market 

volatility. Volatility affects and matters to investors–it’s also the impetus for less volatile 

investment vehicles like fund-of-funds (hedge funds that invest in other hedge funds). Other 

investors own products directly tied to volatility. The VIX index, which is calculated based on 

the prices of S&P 500 options, has become a major part of U.S. financial markets. Li and Born 

(2006) found that closer elections – estimated through polling margins – increased stock market 

volatility and average returns. 7Increased certainty and smaller polling margins was also found to 

be effective on stock markets by both Smales (2015) and Szelim (2023). 89 Leblang and 

Mukherjee (2017) even found a connection between lower volatility and particular candidates 

winning, specifically Democratic ones. 10

10 https://www.cambridge.org/core/journals/political-analysis/article/abs/presidential-elections-and-the-stock-marketcomparing-markovswitching-and-fractionally-integrated-garch-models-of-volatility/2E1DEFE2B86909278B798FF

8996AB9F6

9 https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1766375&dswid=-412

8 https://onlinelibrary.wiley.com/doi/abs/10.1111/acfi.12107

7 https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1475-6803.2006.00197.x

6 https://www.tandfonline.com/doi/abs/10.1080/00036846.2021.1937501

5 https://www.sciencedirect.com/science/article/abs/pii/S1062976921000582

4 https://www.sciencedirect.com/science/article/abs/pii/S0264999318300191

3 https://www.tandfonline.com/doi/abs/10.1080/00036846.2019.1591608

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The evidence above – that election polling affects equity prices in real time – has been replicated 

dozens of times across different elections and countries, including by Mattozzi (2008) in the 

United States, Ionnadis and Thompson (1986) in the United States and the United Kingdom, 

Brander (1989), Gwilm and Buckle (2010) in the United Kingdom, Forsythe et al. (1995), and 

Beaulieu et al. (2006) in Canada, Shawl and Suffian (2004) in Malaysia, Jensen and Schmith 

(2005) in Brazil, Murekachiro (2016) in Zimbabwe, and Chen (2022) in Taiwan. 111213141516171819

These conclusions are not exclusive to university-sponsored research. Instead, the popular press 

frequently describes how election polling impacts equities and derivatives products. A sample of 

financial press pieces include: 

1. Yahoo Finance: Stocks fall as Trump's re-election odds drop 20

2. CNBC: Biden’s big lead in the polls could be partly behind market’s drop 21

3. CBS: As Trump keeps rising in polls, stocks keep falling 22

Much of the research above is tested on polling regarding general elections, not primary 

elections, as this Contract relates to. However, plenty of evidence on the matter – both from 

academics and the press – is specific to primaries. For example, Pham et al.’s 2018 paper cited 

above which documented the effects of Donald Trump’s polling successes during his 2016 

primary election as having effects on stocks, especially the insurance industry. Moreover, even if 

the only hard evidence was for general elections, the Contract would still be useful. Primaries are 

the determinations of who disputes the general election; they patently inform the market and 

create risk as long as general election polling does. 

Polls are not perfect predictors of the future, nor are they perfect estimations of the present. 

However, polls are nonetheless useful indicators of current public election sentiment (especially 

in the absence of large, liquid markets for such events). Even as individual polls face issues, the 

Source Agency – FiveThirtyEight – is so widely quoted and read because of their efforts to 

maximize the value of polls. This is done through strict quality control: understanding particular 

polling firms’ house effects (their natural tendency to support one candidate, or party, more than 

22 https://www.cbsnews.com/news/as-trump-keeps-rising-in-polls-stocks-keep-falling/

21 https://www.cnbc.com/2020/06/24/bidens-big-lead-in-the-polls-could-be-partly-behind-markets-drop-and-may-lea

d-to-more-weakness.html

20 https://www.yahoo.com/video/stocks-fall-as-trumps-reelection-odds-drop-analyst-194203254.html

19 https://www.businessperspectives.org/images/pdf/applications/publishing/templates/article/assets/16867/IMFI_202

2_03_Chen.pdf

18 https://journals.sagepub.com/doi/10.1177/0010414005279790

17 https://ir.uitm.edu.my/id/eprint/44859/

16 https://onlinelibrary.wiley.com/doi/abs/10.1111/j.0008-4085.2006.00363.x

15 https://www.jstor.org/stable/135930

14 https://www.nber.org/system/files/working_papers/w3073/w3073.pdf

13 https://www.tandfonline.com/doi/abs/10.1080/758536472

12 https://onlinelibrary.wiley.com/doi/abs/10.1002/mde.4090070409

11 https://www.jstor.org/stable/40270850

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others), sample sizes, and methodology. FiveThirtyEight ’s polling methodology is in full 

provided in Appendix C. Polling is a science, and one that has improved over the years rather 

than weakened despite misses. 

Independent of the above, there is also a strong use case for the Contract by those who are 

directly involved with a particular campaign. Many vendors and contractors who have a business 

relationship with a campaign could use the Contract to hedge against the risk that the candidate 

falls in polling to levels that might mean they drop out, or lose. This includes: campaign 

consultants, advertising agencies, public relations firms, printers and promotional goods (e.g. 

t-shirts and clothing) vendors, security firms, and photographers. 

Price basing/price discovery utility of the Contract: The Contract represents a market-based 

probability that a given candidate for the Republican nomination polls above, below, or between 

a given threshold. This information would provide significant utility for those trying to price 

assets, services, and financial contracts. As described above at length, there is a meaningful and 

statistically significant relationship between election polling and U.S. equity prices and other 

financial instruments (including foreign equity prices). The Contract could also be useful for 

pricing services for a campaign – if a campaign is expected to succeed (or fail) in the polls that 

could be useful information for charging them for goods and services, such as security, 

promotional goods, or photography services. 

As described in detail above, the Contract has important risk mitigation and price basing/price 

discovery utility. The Contract does not relate to the enumerated categories of contracts listed in 

Section 5c(c)(i) of the Act. Additionally, the Exchange has not determined such contracts to be 

contrary to the public interest and there has been no determination by the Commission that such 

contracts would be contrary to the public interest. The Contract provides a means for managing 

and assuming price risks, discovering prices, and disseminating price information on the 

Exchange’s fair and financially secure trading facility. 

The Contract has bona fide risk mitigation and price basing utility for participants with 

underlying economic exposure, as described above. The Contract is not merely recreational, as 

the discussion of risk mitigation and price basing/price discovery utility demonstrates. The 

outcome of the Contract is not predominantly determined by chance and depends on a variety of 

economic and political factors. Finally, it is possible for traders to use skill and effort to gain 

knowledge and information about the likelihood of the event. For example, traders can gain 

information about the likelihood of the event by following the political news cycle and polling. 

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APPENDIX C (CONFIDENTIAL) – SOURCE AGENCY 

The data which is used to determine the Expiration Value of the Contract is published by 

FiveThirtyEight . The methodology used by the Source Agency to calculate the polling average is 

described below: 

Almost since its founding, FiveThirtyEight has published comprehensive averages of polls for a wide

variety of questions related to U.S. politics. In June 2023, we debuted a new set of models for these

averages that aims to improve the models’ accuracy and how the results are visually conveyed to our

readers.

The most important differences from our old polling-average model are:

- We now use separate models for each type of polling average. Polling averages need to process

information differently in different contexts: For example, presidential approval ratings and

horse-race polls can change faster than favorability ratings and generic ballot polls. To account for

these variations, we now derive separate sets of parameters to control the aggressiveness of each

of our polling averages for presidential and vice-presidential approval, politician favorability and

different types of horse-race averages (presidential elections, presidential primaries, senate and

gubernatorial general and primary elections, and the generic congressional ballot), using our

historical database of polls.

- House effects can change over time, and have more uncertainty. We don’t want to make

unnecessarily large adjustments to a pollster’s house effect as a result of a poll conducted when

one candidate happened to be surging, so our house effects are now calculated using the value of

the polling average on each day of the time series, rather than the average over the entire time

period. Our house effects are also now formally Bayesian, which means the value we end up using

in our final average is higher when (a) we have more polls for that pollster and (b) the individual

differences from the average for each poll are more narrowly distributed.

- We dynamically adjust our average based on two different aggregation models. We want to

avoid scenarios where there aren’t many polls for a while, then a deluge of new data creates a

whiplash in the average. To help with that, when we have more data in a recent time period, we

rely less on our slow-to-update exponentially weighted moving average and more on our more

aggressive polynomial regression trendline. This also has the benefit of giving us averages that are

more responsive to quick movement in the data when multiple polls reflect that movement,

without reacting too aggressively to any one individual survey showing a big change.

- We calculate uncertainty for every average. Previously, we only displayed uncertainty intervals

for our presidential approval average. Now, to visualize the noisiness and estimated error across

each of our models, we show uncertainty intervals for all the various types of averages we run.

Here are all the steps we take to calculate our averages:

Which polls we include

FiveThirtyEight’s philosophy is to collect as many polls as possible for every topic or race we’re actively

tracking — so long as they are publicly available and meet our basic criteria for inclusion. After

determining that a poll meets our standards, we have to answer a few more questions about it before

sending it off to the various computer programs that power our models.

- Which version should we use? If a pollster releases multiple versions of a survey — say, an

estimate of President Biden’s approval rating among all adults and registered voters — we choose

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the survey that best matches either the breakdown of polls in our historical database or the

preferred target population for that type of poll. In practice, that means if historical polls on a

particular topic (for example, presidential approval or favorability ratings) were mostly published

among all adults, we will prefer polls of all adults to polls of registered voters and polls of

registered voters to polls of likely voters. But for polls of a primary or general election, where we

are mainly interested in the subpopulation of Americans who are likely to (or at least able to) vote,

we prefer polls of likely voters to polls of registered voters and polls of registered voters to polls

of all adults.

- Is it an especially large survey? When polls are fed into the model, we decrease the effective

sample sizes of large surveys. Leaving these large numbers as they are would give those polls too

much weight in our average. As a default, we cap sample sizes at 5,000. Then, for all polls

conducted for a given context (say, approval ratings), we use a method called winsorizing to limit

extreme values.

- Do we know the sample size? Some pollsters do not report sample sizes with their surveys,

especially for polls released a long time ago. While we can usually obtain this number for recent

surveys by calling up the firm, we have to make informed guesses for past data. First we assume

that a missing sample size is equal to the median sample size of other polls from that same pollster

on the same topic (i.e., favorability, approval or horse race). If there are no other polls conducted

by that firm in our database, we use the median sample size of all other polls for that poll type.

- Does this matchup reflect something that could happen in reality? For horse-race polls, we

exclude polls that ask people how they would vote in hypothetical matchups if those matchups

have already been ruled an impossibility, such as after each party has chosen its nominee or if the

matchup doesn’t include an incumbent who’s announced a reelection bid. We also exclude polls

that survey head-to-head matchups in races with more than two major candidates or polls that pit

members of a ticket against each other (e.g., 2024 Democratic primary polls that include both

Biden and Vice President Kamala Harris).

- Is this a tracking poll? Some pollsters release daily results of surveys that may overlap with each

other. We account for this potential overlap in these “tracking” polls by running through our

database every day and dynamically removing polls that have field dates that overlap with each

other until none are overlapping and we have retained the greatest number of polls possible for

that series and firm, paying special attention to include the most recent poll.

- Is there any other problem with this survey? In addition to excluding all polls for all pollsters

that don’t meet our standards, individual surveys may also be excluded for other methodological

reasons, which we explain in detail on our polls policy page.

How we weight and adjust polls

After all this data is in our database, we compute two weights for each survey that control how much

influence it has in our average, based on the following factors:

- Sample size. We weight polls using a function that involves the square root of its sample size.1

We want to account for the fact that additional interviews have diminishing returns after a certain

point. The statistical formula for a poll’s margin of error — a number that pollsters (usually)

release that tells us how much their poll could be off due to random sampling error alone — uses a

square-root function, so our weighting does, too.

- Multiple polls in a short window. We want to avoid a situation where a single pollster “floods” a

race with its data, overwhelming the signal from other pollsters. To do that, we decrease the

weight of individual surveys from pollsters that release multiple polls in a short time period. If a

pollster releases multiple polls within a 14-day window, those polls together receive the weight of

one normal poll.2 That means if a pollster releases two polls in two weeks, each would receive a

weight of 0.5. If it releases three polls, each would receive a weight of 0.33.

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Once we have these weights, we calculate a cumulative weight by multiplying the two weights. We then

test and adjust for any factors that could be systematically shifting groups of polls in one direction. We

consider three main adjustments here.

- Population adjustments. For each type of survey, we have a preferred sample universe — for

example, likely voters for horse-race polling or all adults for presidential approval. Not every poll

will use that preferred sample universe, though, so we adjust to minimize variation between

different population groups. These adjustments come from a generalized additive model that

predicts poll results using variables for the population of each survey, its methodology (whether

people were reached online, by phone via a live interviewer, by phone via automated dialer, etc.)

and the end date, which the model transforms using a spline (you may also have heard this referred

to as a “piecewise polynomial”). The result is an estimate of how much polls from each population

category differ from each other on each of the possible candidates or responses. We use those

values to adjust polls from populations we are not interested in to look more like the one we are

targeting.

- House-effect adjustments. Second, we adjust polls for “house effects,” or the tendency for certain

polling firms to produce polls that consistently lean one way or another relative to the average poll

conducted around the same time. We estimate house effects using a similar formula to the

population adjustment explained above, but we use a statistical technique called Bayesian

updating to make sure the adjustments for a given pollster are not sensitive to noise in the data.

That’s because what looks like a house effect in an individual poll could just be abnormal amounts

of random sampling or other error. This added step shrinks our model’s initial estimate of a

pollster’s house effect back toward zero. Our assumption here is that house effects may look large

at the beginning of a series but will diminish over time in the absence of other data. Specifically,

the regression we run in the adjustment model gives us both an estimated mean and standard

deviation for the house effect for each pollster, which we use to update a normal distribution with

a mean of 0 and a standard deviation of 3. For national averages, we estimate house effects using

only national polls. For state-level averages, we estimate house effects from both national polls

and state-level polls, since there typically aren’t enough state-level surveys from an individual

pollster to reliably calculate a state-level house effect.

- Trendline adjustments. Finally, for averages of state polls, we apply a trendline adjustment to

control for movement in the national political environment between the time the poll was taken

and whatever day the aggregation model is run on. This adjustment gives us a better estimate of

public opinion in states with sparse polling data. Imagine it’s the 2016 election and you only had

polls from Pennsylvania up to Oct. 15, but national polls released up until Election Day. An

average of national polls would have shown significant tightening in the race over the last three

weeks of the campaign, but an unadjusted average of the Pennsylvania polls would have been

stuck at the value of polls taken in mid-October. This simple average would thus have been highly

misleading if taken at face value.

How we average polls together

Once we have collected our polls and adjusted them, we can finally calculate a polling average. Our final

polling average is actually an average of two different methods for calculating a trend over time.

The first is an exponentially weighted moving average, or EWMA (a popular tool in financial analysis).

The EWMA calculates an average for any given day by calculating a weight for each poll based on how old

it is, multiplying the poll result by that weight, then adding the values together. We select the value for a

parameter called decay, which determines the rate at which older data points are phased out of the average

according to an exponential function.

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The second is a trend through points, calculated using a methodology similar to that of the now-defunct

Huffington Post Pollster website and the forecasting methodology used by The Economist. We fit this trend

using our custom implementation of a kernel-weighted local polynomial regression, which is just a fancy

way to calculate a line through points. The trendline and weight on any given poll in this regression depend

on two parameters that we also have to set: the bandwidth of the kernel and the degree of the polynomial.3

Once these two trendlines are calculated, we calculate a mixing parameter to determine how much weight

to give each trendline in our final average. This weight depends on the number of polls conducted over the

last month. We put more weight on the polynomial regression when there is more data available to estimate

it. That has the benefit of giving us less noisy averages, because the local polynomial regression detects

movement quicker than the EWMA, which is useful when we have news events that move public opinion

and coincide with a big dump of new data.

Finally, we use a technique called optimization to test the calibration of our model by calculating thousands

of different averages for each politician and race in our historical database using different values for each of

our four hyperparameters (the parameters that govern the behavior of a model): decay, bandwidth, degree

and the mixing parameter. For each type of polling average, our model picks the set of parameters that

generate the optimal values for two measures of accuracy:

- The mean absolute error our polling average has in predicting future real poll results. For every

time series in our historical database, we calculate an average on every day in the series and then

take the average difference between every poll result and the calculated polling average 28 days

earlier.

- Error autocorrelation, which captures how well we can predict the differences between polls and

the average on a given day based on previous differences between the polls and the average. This

ensures that the model strikes the right balance between predicting future poll results and

describing past data; a polling average shouldn’t bounce around to match the value of every poll

on every day, and neither should it be a straight line on a graph. When autocorrelation is too high,

a model is not reacting enough to movement in the underlying data. Too low, and it’s reacting too

much.

In 2023, we started calculating these hyperparameters values separately for each type of polling average

(that is, presidential approval ratings, favorability ratings and horse-race polling averages). That means that

we are always specifying the type of aggregation model that minimizes these two measures of error for that

type of polling average. This results in averages that are more reactive to changes in the horse race, which

tend to happen as a result of real campaign events, and less reactive to changes in favorability rating polls,

which are due more often to noise.

And that’s basically it! FiveThirtyEight’s polling averages can really be thought of as two different models:

one that measures any biases resulting from the polls’ underlying data-generating process, and another to

aggregate polls after adjusting for those biases.

There is one last feature of note. As with any model we run, polling averages contain uncertainty. There is

error in the individual polls, error in our adjustments and error in selecting the hyperparameters that

produce the optimal trendlines. Starting in 2023, all our polling averages4 convey this uncertainty by

calculating and displaying the 95th-percentile difference between the polling average on every day and the

polls published those days. This “error band” represents the uncertainty in that average.

23 

23 https://fivethirtyeight.com/methodology/how-our-polling-averages-work/

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Additionally, FiveThirtyEight’s pollster rankings – part of this process – can be found directly at 

this link . 

24

24 https://projects.fivethirtyeight.com/pollster-ratings/

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APPENDIX D (CONFIDENTIAL) – COMPLIANCE WITH CORE PRINCIPLES 

Compliance with Core Principles 

The Exchange has conducted a comprehensive analysis of the designated contract market core 

principles (“Core Principles”) as set forth in Part 38 of the Act. The Core Principles relevant to 

the Contract are outlined and discussed in further detail below: 

Core Principle 2 - Compliance with Rules and Impartial Access: The Exchange has adopted 

the Rulebook, which provides the requirements for accessing and trading on the Exchange. 

Pursuant to Chapter 3 of the Rulebook, Members must utilize the Exchange’s services in a 

responsible manner, comply with the rules of the Rulebook (“Rules”), cooperate with Exchange 

investigations, inquiries, audits, examinations and proceedings, and observe high standards of 

integrity, market conduct, commercial honor, fair dealing, and equitable principles of trade. 

Chapter 3 of the Rulebook also provides clear and transparent access criteria and requirements 

for Exchange Members. Trading the Contract will be subject to all the rules established in the 

Rulebook, which are aimed at enforcing market integrity and customer protection. 

In particular, Chapter 5 of the Rulebook sets forth the Exchange’s Prohibited Transactions and 

Activities and specifically prescribes the methods by which Members trade contracts, including 

the Contract. Pursuant to Rule 3.2, the Exchange has the right to inspect Members and is 

required to provide information concerning its business, as well as contracts executed on the 

Exchange and in related markets. Chapter 9 of the Rulebook sets forth the Exchange’s Discipline 

and Rule Enforcement regime. Pursuant to Rule 9.2, each Member is required to cooperate with 

an Exchange investigation by making their books and records available to the Exchange. The 

Exchange’s Market Regulation Department performs trade practice surveillance, market 

surveillance, and real-time market monitoring to ensure that Members adhere to the Rules of the 

Exchange. The Market Surveillance Department reserves the authority to exercise its 

investigatory and enforcement power where potential rule violations are identified. 

Core Principle 2 also stipulates that an exchange shall establish means to provide market 

participants with impartial access to the market. Chapter 3 of the Rulebook, and Rule 3.1 in 

particular, provides clear and transparent access criteria and requirements for Members. The 

Exchange will apply access criteria in an impartial manner, including through the application 

process described in Rule 3.1. 

Core Principle 3 - Contract not Readily Subject to Manipulation: 

Core Principle 3 and Rule 38.200 provide that a DCM shall not list for trading contracts that are 

readily susceptible to manipulation. The Exchange’s marketplace and contracts, including this 

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Contract, have been designed in accordance with this fundamental principle. The Exchange 

maintains various safeguards against outcome manipulation and other forms of manipulation, 

including, (i) automatic trade surveillance and suspicious behavior detection, (ii) Rulebook 

prohibition, Member certification, and notification, (iii) Member monitoring and 

know-your-customer verification, and (iv) sanctions. These safeguards render the Contract not 

readily susceptible to manipulation. 

(i) Automatic trade surveillance and suspicious behavior detection : The Exchange’s trade 

monitoring and market surveillance systems compute statistics using information from all trades 

that occur on the Exchange over a range of timeframes, ranging from per trade to the full history 

of trading activity. These statistics are geared towards identifying unusual trading activity and 

outlier behaviors. If the trade monitoring and market surveillance system identifies behavior 

deemed to be unusual, the Exchange’s compliance personnel have the ability to investigate and 

determine applicable sanctions, including limits to or suspension of a Member’s access to the 

Exchange. 

(ii) Rulebook prohibition, member certification and notification: The Exchange’s Rulebook 

includes various provisions that prohibit manipulative behaviors. As noted above in the 

discussion of Core Principle 2, the Exchange’s Rulebook gives the Exchange the authority to 

investigate potential violations of its rules. Pursuant to Rule 3.2, the Exchange has the right to 

inspect Members’ books and records, as well as contracts executed on the Exchange and in 

related markets. Pursuant to Rule 9.2, each member is required to cooperate with an Exchange 

investigation by making their books and records available to the Exchange for investigation. The 

Exchange’s Market Regulation Department performs trade practice surveillance, market 

surveillance, and real-time market monitoring to ensure that Members adhere to the Exchange’s 

rules. The Rulebook also imposes sanctions on Members who break rules. Potential penalties 

include fines, disgorgement, and revocation of membership in Kalshi. Only Members are 

allowed to trade on the Exchange, and the Exchange requires its Members to strictly comply with 

the Rulebook. Members cannot complete the account creation process and trade on the Exchange 

until they certify that they have read the Exchange’s rules and agree to be bound by them. 

In addition, the Exchange requires applicants for membership to represent and covenant that the 

applicant will not trade on any contract where they have access to material non-public 

information, may exert influence on the market outcome, or are an employee or affiliate of the 

Source Agency. In order to further reduce the potential for manipulation, the Exchange maintains 

a dedicated page on the trading portal that lists all the source agencies and their associated 

contracts, together with a warning that employees of those companies, persons with access to 

material non-public information, and persons with an ability to exert influence on the underlying 

of a contract are prohibited from trading on those contracts. This page is intended to serve as an 

effective means of raising Members’ awareness of these rules and prohibitions, further reducing 

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the potential for manipulation. Similarly, the Exchange places a prominent notice on each 

contract page that notifies Members of the prohibition on trading the Contract while employed 

by its Source Agency, trading the Contract on the basis of non-public information, and trading 

the Contract while having the ability to exert influence on the Contract’s Market Outcome. 

(iii) Member monitoring and know-your-customer verification (“KYC”) : The Exchange has 

a robust KYC process. The KYC process is an important tool that helps flag and uncover higher 

risk traders before they become Members of the platform. The Exchange’s KYC process 

leverages technology to develop a clear and proper understanding of its members, and the 

various risks they may pose with respect to market integrity and fairness, including 

manipulation. During the application process, applicants are required to share personally 

identifiable information, such as their full legal name, identification number, date of birth, and 

address with the Exchange. Additionally, applicants are required to provide a government issued 

photo ID (passport, drivers license, etc.) that is used to validate the personally identifiable 

information shared by the applicant during the application process. Applicant information is run 

through a comprehensive set of databases that are actively compiled and maintained by an 

independent third party. The databases are utilized by the Exchange to identify applicants that 

are employees or affiliates of various governments and other agencies. Moreover, the databases 

can identify known close relatives and associates of such people as well. Applicants that are 

flagged go through enhanced due diligence, including manual review, as part of the onboarding 

process. 

Additionally, as part of the KYC process, the Exchange runs applicants through adverse media 

databases. The adverse media dataset is a real-time structured data feed of companies and 

individuals subject to adverse media. Monitoring thousands of news sources, business and trade 

journals, in addition to local, regional and national newspapers, the adverse media feed isolates 

and highlights any entities or individuals subject to a range of adverse media. The Exchange 

utilizes the database to trigger enhanced due diligence, because applicants with adverse media 

may be more likely to engage in certain types of unlawful activity including market 

manipulation. 

The Exchange engages in active and continuing KYC checks. The KYC checks are initially 

performed upon application, and the Exchange then monitors its Members on an ongoing basis 

by running member information through the KYC databases. If material new information 

concerning an existing Member is at some point added to a database, the Exchange’s system will 

flag the Member even if the cause for the flag was not extant at the time of the Member’s 

application. That Member will then go through enhanced due diligence. 

(iv) Sanctions : Exchange Members must agree to the terms and conditions of the Exchange’s 

Rulebook before being allowed to trade. As a result, Members are subject to disciplinary actions 

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and fines for engaging in improper market conduct that is prohibited by the Exchange’s 

Rulebook. In the event that suspicious trading activity is detected and results in an investigation 

initiated by the Exchange, market participants are required to provide the Exchange with 

information relevant to the scope of the investigation under Rule 3.2. Chapter 9 of the 

Exchange’s Rulebook details the process for discipline and rule enforcement. Disciplinary action 

can range from a letter of warning to fines to referral to governmental authorities that can result 

in criminal prosecution. 

In addition to these global policies and safeguards, there are a number of contract specific 

attributes and considerations that render the Contract not readily susceptible to manipulation. 

FiveThirtyEight employees who work on the polling aggregator would be unable to manipulate 

the contract. The information used to calculate the polling average is publicly available (i.e. it is 

the set of polls from a pre-set group of reputable polling companies that are posted on those 

companies’ websites) and the methodology by which the average is calculated is publicly 

available so no FiveThirtyEight employee would have inside information about the results of the 

average. In addition, any discrepancy between the results of a poll and its value as recorded on 

FiveThirtyEight would be swiftly identified, as these polls are both high-profile (and are reported 

on by major news outlets and agglomerated by rival aggregators like RealClearPolitics) and 

explicitly linked in the FiveThirtyEight aggregator. 

Polling companies whose polls form the basis of the approval ratings cannot meaningfully 

manipulate the average. As described at length in Appendix C, there is extensive quality control 

conducted by FiveThirtyEight to prevent polling organizations from manipulating the value of 

the Underlying. This sort of edge is how and why the publication has succeeded and is the basis 

upon which it has differentiated itself; dedication to high-quality data-based journalism is the 

core of the publication’s reputation. One of the strengths of the FiveThirtyEight model is that it is 

an aggregate--indeed, it has dozens of pollsters whom they include in their aggregate. In 

addition, it phases out polls by the previous pollster when newer polls are released. Therefore, on 

expectation, the timing of a poll’s release should not affect the aggregate (i.e. a polling firm 

cannot skew the average by publishing several times in the period before Expiration). This 

approach also helps reduce any “house effects” (e.g. some pollsters have consistently more 

Democratic or Republican-leaning results) since a) one poll has only a slight effect on the 

average and b) any new poll from a firm just replaces the firm’s old polls so the net change of 

adding a new poll would not affect the overall skew of the numbers. Thus having advance 

knowledge of the poll’s results--considering how many polls are factored into the 

average--would not be sufficient information to affect the market. The most important pollsters 

also have codes of conduct that prohibit their employees from trading on their material 

non-public information. For example, the Washington Post has their code of conduct below: 

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This news organization is pledged to avoid conflicts of interest or the appearance of conflict of interest

wherever and whenever possible. We have adopted stringent policies on these issues, conscious that they

may be more restrictive than is customary in the world of private business.

25 

Individual poll respondents also could not meaningfully manipulate the market. Polls have 

thousands of responses and FiveThirtyEight includes a large number of polls so one individual 

could not meaningfully affect any poll, let alone the polling aggregate. The aggregate also only 

includes the most accurate polls so polls that matter most are the polls least likely to be 

manipulated. 

Political campaigns themselves will not behave differently because of the Contract. Their 

incentive is always to maximize their polling numbers in order to win the election. Campaigns 

have little ability to directly influence their poll numbers, moreover – but to the extent they do, 

they are already maximizing those channels. Finally, employees from FiveThirtyEight and the 

polling organizations cited by them (available on their website) are prohibited from trading on 

the Contract. 

Further, as part of the Exchange’s KYC verification and monitoring system, the Exchange also 

cross-checks applicants against comprehensive databases. In particular, the Exchange will check 

whether any Members trading on this Contract are on databases of Politically Engaged Persons. 

The Exchange further cross checks applicants against databases of family members and close 

associates of Politically Engaged Persons. These checks help to further reduce the potential for 

trading violations and further increase the integrity of this Contract. 

Core Principle 4 - Prevention of Market Disruption: Trading in the Contracts will be subject 

to the Rules of the Exchange, which include prohibitions on manipulation, price distortion, and 

disruption to the cash settlement process. Trading activity in the Contract will be subject to 

monitoring and surveillance by the Exchange’s Market Surveillance Department. In particular, 

the Exchange’s trade surveillance system monitors the trading on the Exchange to detect and 

prevent activities that threaten market integrity and market fairness including manipulation, price 

distortion, and disruptions of the settlement process. The Exchange also performs real-time 

market surveillance. The Exchange sets position limits, maintains both a trade practice and 

market surveillance program to monitor for market abuses, including manipulation, and has 

disciplinary procedures for violations of the Rulebook. 

Core Principles 7 and 8 - Availability of General Information and Daily Publication of 

Trading Information: Core Principles 7 and 8, implemented by Regulations Sections 

Subsections 38.400, 38.401, 38.450, and 38.451, require a DCM to make available to the public 

accurate information regarding the contract terms and conditions, daily information on contracts 

25 https://www.washingtonpost.com/policies-and-standards/

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such as settlement price, volume, open interest, and opening and closing ranges, the rules, 

regulations, and mechanisms for executing transactions on or through the facilities of the 

contract market, and the rules and specifications describing the operation of the contract market's 

electronic matching platform. 

Rule 2.17 of the Rulebook sets forth the rules for publicizing information. The Rulebook and the 

specifications of each contract are made public on the Exchange website and remain accessible 

via the platform. The Exchange will post non-confidential materials associated with regulatory 

filings, including the Rulebook, at the time the Exchange submits such filings to the 

Commission. Consistent with Rule 2.17 of the Rulebook, the Exchange website will publish 

contract specifications, terms, and conditions, as well as daily trading volume and open interest 

for the Contract. Each contract has a dedicated “Market Page” on the Kalshi Exchange platform, 

which will contain the information described above as well as a link to the Underlying used to 

determine the Expiration Value of the Contract. Chapter 5 sets forth the rules, regulations and 

mechanisms for executing transactions, and the rules and specifications for Kalshi’s trading 

systems. 

Core Principle 11 - Financial Integrity of Transactions: Each Member must be in good 

standing and in compliance with the Member eligibility standards set forth in Chapter 3 of the 

Rulebook. All contracts offered by the Exchange, including the Contract, are cleared through the 

Clearinghouse, a Derivatives Clearing Organization (“DCO”) registered with the CFTC and 

subject to all CFTC Regulations related thereto. The Exchange requires that all trading be fully 

cash collateralized. As a result, no margin or leverage is permitted, and accounts must be 

pre-funded. The protection of customer funds is monitored by the Exchange and ensured by the 

Clearinghouse as “Member Property.” 

All Remaining Requirements: All remaining Core Principles are satisfied through operation of 

the Exchange’s Rules, processes, and policies applicable to the other contracts traded thereon. 

Nothing in this contract requires any change from current rules, policies, or operational 

processes. 

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