*3.3. The Strategies*

To approximate the behavior of traders in real markets, we implemented trading strategies based on commonly understood behaviors of traders in the market.

Fundamental traders trade based on what they believe to be the fundamental value of a stock. In our simulation at any given time, each fundamental trader is endowed with a random perceived fundamental value that is within 30% of the actual fundamental value. Differences between the perceived value and current price, drive trade decisions. If current price is greater than value, the trader sells the stock, and if current price is lower, she buys.

Our simulations are based on practically used fundamental trading strategies from the market. A few examples will help illustrate that fundamental traders' decision-making process is in fact very similar to the simulations in our paper. The basic strategy in the paper consists of two steps: (1) arriving at a perceived value for the asset; and (2) trading when the asset deviates from this perceived value. This is paralleled in the real world by common strategies used by fundamental traders, for example, the commonly employed fundamental strategy of buying shares when they fall below a certain percentage of the perceived value, or when the stock is sold once it reaches its perceived value (Regan 2019). Another example is when fundamental traders employ "trailing stops", where they sell any stock that falls by a certain percentage.

Note that fundamentals changing news may arrive only sporadically. However, actual fundamental trading frequency depends on how often, and in which direction, the price deviates from the trader determined perceived value. Price deviations away from perceived value are determined by the actual

trades in the market, by all types of traders. Therefore, a fundamental trader actually trades based on the number of times the price changes in a significant way with respect to the perceived value, and not necessarily based on the frequency of fundamental news arrival. For example, some news may not even result in any trade, if the change of the perceived value is such that there is no incentive for the trader to change their position in the market. On the other hand, even without any news arrival, movement in market prices, due to other traders' activities, may lead the fundamental trader to trade.

Technical traders' strategy is to consider the di fference between their perceived value and the current price of the stock. They use the 20-day moving average (MA) price of the stock, which updates at every second based on the last 20 days' worth of seconds, as their perceived value for the stock. Trading decision is based on the di fference between this perceived value and the current price. Specifically, a rise in current price above the MA triggers a buy order and the opposite triggers a sell order.

Following a statistical arbitrage strategy, a pairs trader trades one stock based on the movements of the correlated stock. Our goal is to determine the impact on the market when a pairs trader mistakenly believes two stocks are positively correlated, and begins trading them as though they are paired, even though they are fundamentally unrelated. So, our pairs trader will assume two unrelated stocks are positively and closely correlated. Then, if one stock moves a significant amount (±1%) since the trader checked last (20 s ago) the trader will buy if there was a rise and sell if there was a fall, the other stock at an appropriate price and volume.

Our simulations therefore mimic one of the most common algorithmic trading strategies: statistical arbitrage (Brogaard 2010; Zhang 2010; Froot et al. 1992). Statistical arbitrage strategies use short-term correlations among security prices to make short-term price predictions and trade to profit from these predictions.
