ABS-5232.0-AustralianNationalAccounts-FinancialAccounts-FinancialAssetsLiabilitiesCentralBorrowingAuthorities-AmountsOutstanding-Liabilities-Derivatives-RestWorld-A3370486W.svg_.pngEconomy Elections & Institutions Polarizing Political Economy 

Corruption in the Age of Derivatives

By James H. Nolt

During the presidential campaign and now after the election, there has been a strong push for Donald Trump to release his tax returns, as all other presidential candidates have for decades. Similar issues of financial disclosure have been demanded of Trump’s nominees for cabinet and other senior posts, with somewhat more success. During the campaign, Trump promised he would release his tax returns when the Internal Revenue Service completes its audit. After the election, former Trump campaign manager and current counselor to the president, Kellyanne Conway, announced that now that the election is over, Trump never will release his taxes, despite his promise and polls showing that an overwhelming majority of Americans wants him to do it.

Many press articles have examined obvious conflict of interest concerns, such as Trump now being both landlord and tenant of his Washington hotel—which occupies a leased government property—or the many countries where Trump has business deals or properties whose value may be influenced by his governmental initiatives. These sorts of potential conflicts of interest are just the tip of the iceberg.

Not only in the U.S. but worldwide, financial derivatives are the greatest vehicle today for corrupt dealings, in both the public and private sectors. Anyone anywhere in the world who makes decisions that predictably affect the value of any particular asset can make enormous profits by first betting which way that asset price will move using a derivative position, then taking an action that moves the price as predicted. This is why it is so dangerous to the public interest for wealthy people to hold office, especially when they are able to hide their assets from public view. No public official in the world has more power to move asset prices than the president of the United States.


Public knowledge of a president’s annual tax returns before, during, and after the term of office could, at the very least, show the public how much wealth the president has. It could also indicate, because of capital gains taxes, the specifics of profitable trades. Of course, an office holder could still try to hide assets and trades, or to confine them to the accounts of family members and business associates, but that would require violating tax and insider trading laws.

In ancient Athens, the birthplace of democracy, most magistrates served one-year terms. At the end of their terms, they were routinely audited to investigate whether they had gained any wealth using corrupt methods while in office. Those found to be corrupt would be punished. Modern democracy has a similarly negative moral view of corruption, but the complexity of financial transactions today make laws against corruption very difficult to enforce, particularly when, as in Trump’s case, the office holder’s assets and income sources are largely unknown to the public.

There are several characteristics of financial derivatives that make them perfect vehicles for corruption. First, profits can be made very quickly, often within a single day. Second, since ownership of derivatives is typically brief, the “paper trail” quickly disappears unless auditors can access the transaction data of the financial institution (often an investment bank) that facilitated the transaction. Even then, derivatives once issued can be laundered from one owner to another. Ownership can also be exchanged privately, outside public markets. Third, derivatives leverage profits. Prices are typically a very small fraction of the face value. So with a certain quantity of capital, say $1 million, an investor could bet on the price movement of a much larger quantity of assets, perhaps $50 to $200 million, depending on the specific price of the bet.

For example, on Wednesday Trump met with pharmaceutical executives and told them drug prices are too high. That might appear to be a threat to their monopoly profits (based on patents), so you might expect their stock prices to fall. Actually, Trump’s promise to them was to speed up regulatory approval of new drugs, which would reduce costs without interfering with their monopoly power. According to the economic theory of monopolies, such action will not lower prices but increase the profits of pharmaceutical companies. Thus, predictably, the stock prices of pharmaceutical companies increased immediately after the meeting. If Trump or any of his associates bought call options or long futures on pharmaceutical stocks prior to the meeting, they could have made enormous profits. Typically the price of such derivatives is a very small fraction of its face value, so the gain from any price movement in the right direction is magnified significantly.

Consider a somewhat arbitrary but realistic example. Suppose the pharmaceutical company stock price today is $100 per share. Perhaps I can buy an at-the-money call option with a 30-day maturity for $2 a share. Saying “at-the-money” means the strike price, the price I have the right to buy it at, is the same as the current market price. If within the 30-day life of this option, the price of the stock rises to, let’s say, $104 per share, my call option gives me the right to buy the stock at the strike price of $100. Since I can then immediately sell it at $104, the current market price, my profit would be what I gained by buying the stock at $100 and selling it at $104, minus the $2 I paid for the option, which equals $2 profit per option. My profit is greatly magnified over what it would have been had I just bought the stock. If I have $1000 to spend, I could have bought 10 shares of stock and earned $40, or 4 percent profit, if I sold it at $104. However, if I spent the same $1000 on 500 of the call options I described above, my profit would be $2 times 500 options, which equals $1000. Instead of a 4 percent profit on my capital, I gain 100 percent over a very short period of time. This is the power of derivative leverage. Of course, if the price never goes up within the 30 days of the option, I lose the entire $1000. It pays to make sure bets.

The only real limit on derivative bets is having sufficient counterparties willing to own the opposite side of the bet. It is these investors who pay you when your bet wins. Therefore, any investors who know they are betting against an insider, say a crony of Trump, would be a fool to take the bet. The problem is that often the owners of the other side of the bet do not know who the counterparty is. They deal only with the investment bank that sells them the wager. In some cases, as during the financial crash of 2008, the “sucker side” of derivative bets (in that case, bullish bets on real estate mortgages) was packaged into synthetic bonds and sold to unsuspecting investors, in many cases pension accounts, that lost heavily when the crash came. The bearish insiders walked away with highly leveraged profits.

It is certain that there are many top public officials around the world, or often their family members or associates, who are making a killing using derivatives in this way. It typically does involve violations of laws, though in notoriously corrupt countries, like Russia, there is hardly any chance that top officials or their cronies will ever be brought to justice. It is unproven whether or not Trump or his associates are engaged in such activities, but the more obscure his business relations are, the harder it would be for the public to know. It is also suspicious that he likes to announce new initiatives suddenly, with little warning, since this sort of behavior would make it easier for insiders with prior knowledge of forthcoming action to make quick profits. Next week, I will consider more about how this sort of potential corruption could polarize the business elite.



James H. Nolt is a senior fellow at World Policy Institute and an adjunct associate professor at New York University.

[Photo courtesy Australian Bureau of Statistics]

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