Let’s suppose 10 traders are given similar trading capital and same brokers and they are told to follow similar indicators; their performances will still be different. If we are to follow market stats that state that 90% of retail traders lose money, then we can be sure that only one out of the 10 traders will make good money. The rest will incur losses.
Why is that? The answer lies in their behavior and psychological biases. Every trader will respond to similar situations differently, which will impact their trading decisions and thus, their performances will vary.
Every trader (or human being) has specific biases and their actions and observations are a result of different filters formed by their own experiences. Though it is almost impossible to be unbiased in trading decision-making, the goal is certainly achievable with the help of technology. Here are some of the important psychological biases:
- Representativeness Bias: It arises when traders start labeling a trade based on their recent performance and extrapolate future performances accordingly. They initiate buy orders in a trade and expect the asset or market to go further up as the price might have been rising continuously in the recent past.
- Loss-aversion Bias: You initiate a buy trade and expect it to go up, but it starts going down. But you still don’t sell it because of the difficulty in digesting the loss. Traders hold on to the trade and eventually book the loss. They simply fail to realize that money can be reinvested in a more quality stock but they keep on hoping that the loss can be recovered some day in the future.
- Overconfidence Bias: Traders are so sure of their strategy and skill that believe they have some secret edge in the market. A person from the auto sector may be tempted to believe that they have an upper hand in trading auto-stocks. However, the market usually does not respect such correlations and such traders may find themselves incurring heavy losses. With technology, such biases can be eliminated.
- Endowment Bias: This is similar to loss aversion wherein traders believe what they own is more valuable than what they don’t and thus, they continue to hold on to losing stocks beyond what is necessary.
Can Technology Overcome Human Biases in Trading?
The answer is a resounding YES, if the latest research studies are to be believed. Though most of these researches are done in other areas and not in trading, the conclusion of these several studies is the same: Algorithms are less biased than humans and they can take better decisions.
Since traders suffer from emotional biases, which seriously affects their performance, algorithmic trading can help minimize these bias-based issues to a great extent. Algorithmic trading is based on rule-based trading. It is completely emotion-free, has minimum bias, and is based on validated mathematical models of market data. It also helps in minimizing risks that arise from faulty position sizing of trades, which causes overexposure and magnification of the impact of any losses sustained. Some traders have a tendency to trade on the long side or short side and they prefer trading in one of the directions only without considering whether the overall market is bullish or bearish.
Algorithms analyze work on the basis of pre-defined parameters and these parameters can be purely fundamental, pattern–based, or a combination of both. In this, emotional, subjective, and irrelevant factors are weeded out. Suitable strategies can be executed based on the overall market direction and save you from huge losses.
Here is how technology can help conquer different types of biases:
- Minimize risks: Algorithmic and AI-driven systems can help implement trading strategies based on certain rules which will help conquer market volatility and also ensures that any losses are booked quickly as per pre-defined rules. So even if the number of winning trades is low, they eventually end up becoming hugely profitable because the quantum of loss in loss-making trades is low. These systems don’t suffer from endowment bias, loss-aversion or overconfidence biases. Rules can be framed to counter the emotional biases in trader. For example, if a trader has a tendency to book early profit, technology can help get rid of the problem. In other words, algorithms and AI-driven systems actually cuts losses and lets profits run, which bypasses the human tendency to do otherwise.
- Help implement trading discipline: A system can be devised after understanding your risk-appetite and trading goals. This can help implement your trade plan effectively resolving a big problem for traders who don’t stick to a plan.
Though people don’t trust technology completely, but with time AI-driven technologies will mature, the trust levels will build up and trades can be done without any human intervention.
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