AI meets Trading Psychology: Data shows +63% performance boost for traders struggling with cognitive biases
Study reveals 63% better trade performance if trades are AI-filtered for cognitive biases and emotions.
This article is for everyone interested in AI advancements, active trading (Forex, CFDs, Crypto, Stocks, etc.) as well as Trading Psychology!
The Hoc-trade AI is capable of detecting behaviors, emotions, and cognitive biases of retail traders. It has been shown to identify detrimental behaviors leading to significant underperformance.
This represents a groundbreaking advancement in two aspects: Firstly, previously qualitative applications of trading psychology now become quantitative, allowing traders and investors to understand the effect of their emotions on their performance. Secondly, Hoc-trade AI operates in real-time, enabling it to alert or prevent trades, the moment of trades opening.
With significance demonstrated across millions of trades, a wide range of applications for retail traders awaits.
With all the latest available resources on fundamental analysis and technical analysis at hand, more than 90% of retail traders are still experiencing losses. It has become abundantly clear that there must be another factor leading to the consistent underperformance of retail traders & investors. Leading sources have consistently highlighted trading behaviors and trading psychology as the primary factors behind traders’ losses, however, there has been no software or tool to directly link those. Drawing upon our wealth of trading experience at Hoc-trade, we were convinced of the validity of this issue. Hence, we began the development of Hoc-trade, an AI-powered trading analytics tool designed to monitor detrimental and emotional trading behaviors.
Background
The core of our product is designed to identify detrimental trading behaviors resulting from emotional and/or cognitive psychology biases. These thorough assessments are generated in a form of comprehensive reports called TradeMedic, the equivalent of medical report for retail traders that outlines identified trading behaviors, severity level, performance impacts, as well as the underlying context.
We leverage AI to bridge the gap between behavioral finance concepts and trading and investment decisions. While many behavioral finance concepts, such as Kahneman’s Prospect Theory, Gambler’s Fallacy, Pessimism Bias, etc., have been scientifically studied and explained, Hoc-trade AI is the first to identify markers of such behavior in personal trading and market data. Consequently, it can detect and derive performance effects.
By consolidating this data into a user-friendly and intuitive TradeMedic report, we empower brokers, prop firms, and exchanges to deliver robust and unparalleled behavioral insights and education to their traders.
Data & Analysis Result
We are extremely pleased with the result of our first large scale analysis on the efficacy of identifying destructive behaviors. In this analysis, our AI categorizes trades into two distinct possibilities:
a) Those exhibiting behavioral issues
b) Those that do not
It’s crucial to emphasize that this analysis is conducted at the initiation of each individual trade, without any prior knowledge of how the trade will unfold.
The result: Trades free from issues demonstrate significantly better performance, by 63% on average, than those exhibiting problems. This concludes that our AI can reliably predict, on average and at the point of trade entry, which trades are likely to underperform due to behavioral or emotional issues.
Furthermore, this solidly reinforces the hypothesis on how trading psychology contributes to a trader’s diminished performance, affirming that we are pinpointing the precise behaviors responsible for such underperformance.
With more than 90% of traders losing in FX trading, it’s not surprising to see that the average performance per trade is negative. In case our AI classified a trade as having an issue, the average performance of such trade is -1.15% of the traders’ balance. In case the trade doesn’t exhibit any issue, the average performance shows an improvement at -0.43%. While a trade without issues has not yet reached a profitable outcome, there is already a significant uplift in performance.
While our AI hasn’t covered all potential behavioral issues, it’s crucial to recognize that its impact on enhancing traders’ performance has already demonstrated significance. There are good reasons why there is still a negative performance for trades classified as free of issues.
Firstly, we are currently focusing on behaviors detectable at trade entry and excluded those identified by our AI but indeterminable at that point. Instances of such behaviors include prematurely cutting profits — a common and notable behavior influenced by loss aversion, resulting in smaller profits due to a humans’ larger fear of losing the unrealized profits than the potential pleasure of gaining additional profits. Likewise, the failure to cut losses early is not factored into the analysis. Secondly, the existence of trading fees slightly disadvantages traders in each trade, leading to a predicted outcome slightly in loss for each transaction.
As mentioned earlier, currently we exclusively consider behaviors that were detectable at the point of trade entry. These include the following:
- Revenge trading
- Over trading
- Fighting the trend
- Fail to call it a day
- Emotional trading
- Catch a falling knife
- Extensive doubling down
- News trading
- and many more…
The ultimate dataset encompasses 2.75 million trades of in total 6.5 thousand traders trading real funds (not demo funds). Our data collection criteria stipulated that traders must have a minimum total of 50 trades, including at least 10 trades identified as having an issue. The determination of whether a trade was categorized as having an issue or not hinged on surpassing a specified threshold of detrimental behavioral issues within a singular trade. These behavioral issues were discerned exclusively from the trade history of the user up to the point of deciding the subsequent trade issues.
Furthermore, it’s crucial to highlight that our AI hasn’t yet optimized for identifying pattern correlations, structuring persona groups, or evaluating the strength of different behaviors. We anticipate that future optimization efforts and the inclusion of behaviors not currently detectable at trade entry will bring significant improvements in results. The analysis was conducted using CFD trades, predominantly focusing on FX, commodities, metals, and indices trades. However, cognitive psychology biases are applicable across diverse trading markets, encompassing stocks, options, Forex, cryptocurrencies, and other financial markets.
Conclusion & Outlook:
Trading psychology and its effect on individual trading performance has become much more apparent. Hoc-trade AI can identify trading behaviors that lead to underperformance for a trader and is capable of forecasting such underperformance in upcoming trades.
By leveraging AI, we enter a new era of retail trading and investments. We believe the Hoc-trade AI presents the solution to one of the biggest obstacles retail traders & investors are currently experiencing, causing underperformance to tens of millions of traders and investors. As a smart analytics trading tool, Hoc-trade is a pioneer in simplifying the extremely complex topic of trading psychology, providing personalized insights, establishing causality, and quantifying issues that were previously purely qualitative for traders. While this may initially seem somewhat abstract, it carries significant implications for retail traders in the future:
- Every individual trader will have the capability to have AI analyze their personal trading behaviors and their impact on their own performance, offering unprecedented opportunities for learning and improvement.
- AI will possess the capacity to identify and filter out ‘negative trading behaviors’ among trades in the future.
- The upcoming generation of copy/social trading will enable traders to filter out ‘undesirable behaviors’ of those they follow, providing unique customization opportunities, increased potential, and enhanced protection.
- AI will be capable of alerting traders who exhibit emotional decisions, biases, or gambling behavior, opening revolutionary opportunities for user protection.
With Hoc-trade AI, brokers, exchanges, prop firms, and other entities within the trading industry now have a distinctive opportunity to integrate groundbreaking technology that delivers truly powerful and tangible benefits to their traders.
If this sparked your interest, feel free to visit us at https://hoc-trade.com
As a broker, exchange, or prop trading firm, please visit our partners page at https://hoc-trade.com/become-partner to get into contact.
