{"id":14217,"date":"2026-01-21T05:00:21","date_gmt":"2026-01-21T05:00:21","guid":{"rendered":"https:\/\/microvibenews.com\/?p=14217"},"modified":"2026-01-21T05:00:21","modified_gmt":"2026-01-21T05:00:21","slug":"ai-trading-tools-can-hallucinate-and-outright-lie","status":"publish","type":"post","link":"https:\/\/microvibenews.com\/?p=14217","title":{"rendered":"AI trading tools can hallucinate and outright lie"},"content":{"rendered":"<p><\/p>\n<div>\n<p><iframe loading=\"lazy\" src=\"https:\/\/iframe.iono.fm\/e\/1636891?layout=modern\" width=\"100%\" height=\"170\" frameborder=\"0\" data-mce-fragment=\"1\"><\/iframe><\/p>\n<p>You can also listen to this podcast on iono.fm here.<\/p>\n<p>More and more crypto traders are turning to artificial intelligence (AI) to give them a jump on the market. But what happens when AI goes rogue and gives you completely wrong signals?<\/p>\n<p>Statistics from online brokers show nearly 80% of traders lose money, and even those using quant strategies are prone to losses. Many believed AI would help them over this hump, but the research here is less than encouraging.<\/p>\n<blockquote>\n<p>It turns out that AI hallucinations are all too common.<\/p>\n<\/blockquote>\n<p>Abhishek Saxena, head of strategy and growth at Sentient, delves into the complexities of AI in trading.<\/p>\n<p>\u201cAI has been used across different knowledge work to improve productivity,\u201d he explains. However, the stakes are high in trading, where \u201ca small mistake can cost you a lot of money\u201d.<\/p>\n<p>Traders use AI to build strategies, leveraging it for research and backtesting before deployment. Yet fully autonomous trading remains fraught with issues like hallucinations.<\/p>\n<blockquote>\n<p>\u201cHallucination is not the only issue,\u201d Abhishek notes. AI engines often steer traders in the wrong direction due to data inaccuracies.<\/p>\n<\/blockquote>\n<p>\u201cWhen Large Language Model (LLM) is building a strategy, it is reasoning a lot,\u201d he says, which can lead to \u201ccontext rot\u201d where the AI forgets its initial context. This, combined with a lack of real-time data access, results in flawed strategies.<\/p>\n<p>One of the great underreported skeletons of fund management is the huge cost of developing quantitative systems that don\u2019t work.<\/p>\n<p data-start=\"901\" data-end=\"1044\" data-is-last-node=\"\" data-is-only-node=\"\">Read: Making the case for simply following the trend<\/p>\n<p>A case in point is the 1998 collapse of Long-Term Capital Management (LTCM), a hedge fund led by Nobel laureates Robert Merton and Myron Scholes.<\/p>\n<p>Another is the 2012 wipeout of $440 million at Knight Capital in just 30 minutes due to a faulty trading algorithm. To be fair, there are algorithms that perform as expected, but success has often come at a huge cost. AI is supposed to fix these problems, but we\u2019re still a long way from that, says Saxena.<\/p>\n<p>Read:<br \/>How to create high probability trades with AI<br \/>It turns out AI trading bots behave just like humans \u2026<\/p>\n<p>Another little-known feature of AI is the shift to \u201cengagement optimised black boxes\u201d, which are wired into financial infrastructure.<\/p>\n<blockquote>\n<p>These systems aim to keep users happy, sometimes at the expense of accuracy.<\/p>\n<\/blockquote>\n<p>\u201cThey\u2019ll tell you whatever it is you want to hear, even if you\u2019re going to lose money,\u201d Saxena warns. This bias towards user satisfaction can lead to poor trading outcomes.<\/p>\n<p>Despite these challenges, Saxena remains optimistic about AI\u2019s potential. \u201cIt\u2019s not 100% solved,\u201d he admits, but improvements are ongoing. The key lies in enhancing AI\u2019s reasoning capabilities and ensuring access to accurate data.<\/p>\n<blockquote>\n<p>\u201cAs AI reasoning becomes stronger, more accurate, with very low hallucinations\u201d the potential for successful trading increases.<\/p>\n<\/blockquote>\n<p>Saxena advises traders to use AI as a tool rather than relying on it entirely.<\/p>\n<p>\u201cI would caution you to not give all your money to an AI LLM and trade for them,\u201d he suggests. Instead, traders should focus on research and strategy development, with a human in the loop to verify AI-generated insights.<\/p>\n<p>Sentient is at the forefront of this research, developing frameworks to improve AI reasoning. Its goal is to create systems that provide accurate, unbiased outputs.<\/p>\n<p>\u201cWe are optimising for reasoning,\u201d says Saxena, aiming to solve issues related to data analysis and prompt understanding.<\/p>\n<p>Read: Artificial Intelligence: The future of finance? [2017]<\/p>\n<p>In conclusion, while AI offers promising tools for traders, it is not yet a foolproof solution.<\/p>\n<p>Human oversight remains crucial to mitigate risks and ensure successful trading outcomes.<\/p>\n<p>As Saxena puts it: \u201cAlpha (outperformance against a benchmark) is still what humans would generate. LLMs can help you get there.\u201d<\/p>\n<p>For previous Moneyweb Crypto Pod episodes,\u00a0click\u00a0here.<\/p>\n<p>You can also sign up for our\u00a0crypto newsletter.<\/p>\n<\/p><\/div>\n<p><script data-cfasync=\"false\">\n            !function(f,b,e,v,n,t,s)\n            {if(f.fbq)return;n=f.fbq=function(){n.callMethod?\n                n.callMethod.apply(n,arguments):n.queue.push(arguments)};\n                if(!f._fbq)f._fbq=n;n.push=n;n.loaded=!0;n.version='2.0';\n                n.queue=[];t=b.createElement(e);t.async=!0;\n                t.src=v;s=b.getElementsByTagName(e)[0];\n                s.parentNode.insertBefore(t,s)}(window, document,'script',\n                'https:\/\/connect.facebook.net\/en_US\/fbevents.js');\n            fbq('init', '779812924991616');\n            fbq('track', 'PageView');\n        <\/script>#trading #tools #hallucinate #outright #lie<\/p>\n","protected":false},"excerpt":{"rendered":"<p>You can also listen to this po&hellip; <\/p>\n","protected":false},"author":1,"featured_media":9604,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[4],"tags":[9763,5646,9764,5283,2573],"_links":{"self":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/posts\/14217"}],"collection":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=14217"}],"version-history":[{"count":0,"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/posts\/14217\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=\/wp\/v2\/media\/9604"}],"wp:attachment":[{"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14217"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=14217"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/microvibenews.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=14217"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}