Up: 1:2
Published: Abstract, PDF.
Archived: SSRN.
The Iowa Electronic Markets are small, real-money financial markets designed to aggregate information about future events. The market microstructure of these markets is studied and a market making model is developed to provide liquidity for one set of securities offered by this exchange. A computer program was created to employ the market making model and profit from the market’s inefficiencies. Using invested capital, the system traded 34% of the total market volume and achieved a Sharpe ratio of 9.9. This paper reveals the details of how this algorithmic trader worked to show how it functioned and the value it added to the Iowa Electronic Markets.
This chart shows historical data derived from market activity and the algorithmic trader's own trading history. The chart is annotated with significant political events to show how the election process affected market activity and the algorithmic trader's profitability.
Explore the interactive chart below to learn about the algorithmic trader's market making model and to understand how the model's parameters affected its behavior. Read the research paper to learn how this model was derived and to see an analysis of the trading results.
| Min | Max | Value | Description | ||
| -100 | +100 | A= | Market Maker's current position | ||
| 2 | 6 | t= | Expected holding period for position | ||
| 1% | 3% |  = |
Estimated one-day volatility of the market asset | ||
| 6 | 10 |  = |
Risk aversion parameter | ||
| 75 | 150 | Y= | Estimated limit order size posted by other traders | ||
| 2.5e-5 | 5.0e-5 |  = |
Market impact parameter |
The Market Marker used two equations to determine how many shares it was willing to buy or sell at any price. These equations were functions of parameters that included the Market Maker's current position, its risk aversion and the expected volatility of the securities.
The goal of this market making model is to repeatedly buy securities at prices just below fair value and to sell securities at prices just above fair value, making a profit as a result. The difference between fair value and the price the Market Maker can buy or sell is the potential spread profit, and is represented by the parameter s in the below equations. As s gets larger, the profit opportunities available to the Market Maker get larger and the Market Maker becomes more willing to trade a larger number of shares.
The above chart shows the number of shares the Market Maker was willing to buy or sell at any price on the x-axis given an expected fair value of 0.5. The size of the buy and sell orders were calculated using these equations:
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Read the information in the other tabs to learn more about the purpose of the other parameters and how they affected the Market Maker's behavior.
provides the model with an estimate of the expected volatility of the securities. As you change , observe that when it gets larger (smaller) the Market Maker is less (more) willing to either buy or sell.
quantifies the Market Maker's risk aversion. As you change , observe that when it gets larger (smaller) the Market Maker is less (more) willing to either buy or sell.
quantifies the expected information content of the humans' trades and the expected adverse price move. I gradually increased this parameter as the time till the election decreased because I expected the information content of the humans' trades to go up. As you change , observe that when it gets larger (smaller) the Market Maker is less (more) willing to either buy or sell.