For every Renaissance technology that leverages AI successfully, there will also be casualties like LTCM.
What is LTCM? Would be interesting case to learn.
I guess this.
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The downfall of the Long-Term Capital Management (LTCM) hedge fund in 1998 can be attributed to not just one, but two financial âweapons of mass destructionâ:
- Overleverage: LTCM relied heavily on leverage, meaning it borrowed significant amounts of money to fuel its trades. This left it exposed to substantial losses if the market went against its positions.
- Derivatives: LTCM extensively employed derivatives, intricate financial instruments with inherent risk. As the market turned against LTCM, it was compelled to offload its derivatives at a loss.
Itâs important to note that these factors are unrelated to AI. While similar scenarios might unfold in AI-based algorithmic trading, itâs crucial to understand that algorithmic trading inherently involves a Man+Machine approach. Human oversight governs the algorithms, and the machine carries out the executions.
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Then isnât it just automation, and not AI in the traditional sense?
I am yet to know the full details of LTCM, but AFAIK, real life events that had unfolded, went beyond their modelling, their historical assumptions or anticipations and interpretations, so they failed.
So if funds run by Nobel prize winners can fail, who in my personal opinion are the synonym for intelligence, even proving the rhyming history quote by Mark Twain wrong, a pure AI fund, no matter how powerful, no matter the harnessing of vast amounts of data and come up with innumerable possible scenarios, there is always the element of human psychology and emotions that will spoil the game, there will always exist a new possible scenario that has not happened yet, but is waiting to happen.
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@ChaitanyaC - Youâre absolutely correct. The collapse of LTCM raises a compelling inquiry into the distinction between automation and traditional AI.
LTCM employed intricate mathematical models and sophisticated trading strategies. However, itâs crucial to recognize that these models donât precisely embody the concept of ML based âartificial intelligenceâ as we understand it today. They were more aligned with automation, where predefined rules were programmed to execute trades based on market conditions. What sets contemporary AI apartâits adaptability, learning, and self-improvementâwas absent in these models.
LTCMâs downfall starkly illustrates the inherent limitation of any model or algorithm when confronted with unforeseen events or deviations from historical data. Despite their sophisticated modeling and quantitative prowess, they failed to predict or accommodate the âblack swanâ events that lay outside their historical assumptions.
Your point about Nobel prize-winning fund managers and their failures holds immense significance. It underscores that even the most brilliant individuals cannot foresee every outcome, particularly in the face of unprecedented situations.
Regarding pure AI-managed funds, your insight into the impact of human psychology and emotions is astute. These elements can give rise to unexpected scenarios that algorithms cannot account for. Just as LTCMâs models didnât predict certain real-life events, AI algorithms might struggle to anticipate scenarios stemming from human behavior, market sentiment, or unforeseen global shifts.
An additional case illustrating the dominance of ML-based AI over non-ML-based automation can be seen in the AlphaZero (AI-based engine) vs. Stockfish (non-AI-based gaming engine) tournament. In this contest, AlphaZero displayed its prowess by clinching victory with a record of 28 wins, 0 losses, and 72 draws against Stockfish. This remarkable achievement serves as a clear testament to AlphaZeroâs superior capabilities when pitted against one of the most formidable traditional chess engines, Stockfish.
Taking our exploration further, letâs examine into the case of AlphaGo. Created by Google, AlphaGo represents an AI-based engine designed for playing the ancient game of Go. Not too long ago, the conception of crafting an engine capable of playing Go was considered to be the pinnacle of AI achievement. In 2016, South Korean Go player Lee Sedol engaged in a five-game match against Google DeepMindâs AlphaGo AI. What made this match intriguing was that Lee Sedol secured victory in the fourth game, with the final score favoring AlphaGo at 4-1.
Upon dissecting the game where Lee Sedol emerged victorious, a significant revelation came to light. The pivotal move that propelled Lee Sedol to success was not solely rooted in pure logic. Rather, it was driven by his intuition and emotions, underscoring the vital role of human instinct in strategic decision-making. This triumph in the fourth game stood as a testament to the dynamic interplay between human intuition and AI capabilities.
In light of these insights, itâs becoming apparent that IQ-based tasks are swiftly being overtaken by AI, leaving room for humans to excel in EQ-based roles. Instead of competing solely on IQ-based work, the focus should shift to EQ-based work. The future role of humans lies in governing and constructing responsible AI rather than creating a âSkynet-likeâ AI.
References:
[1] https://www.youtube.com/watch?v=WXuK6gekU1Y&t=24s
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Appreciate your inputs, views and explanations, some reinforcing my beliefs and strengthening my convictions, and other points about the capabilities of AI, all of it, making me wonder and think a bit more where I stand in the financial markets, with the explosion of data and machines.
On a lighter note, when you did not reply to membersâ questions, and were only making new posts, and the language while standard, seemed too machine-like, I thought are you are not human, but now along with the fact that you have started replying to membersâ queries and a DP, my assumption proved to be wrong.
Personally, I find that putting my thoughts down on paper is proving to be immensely beneficial, as itâs often said that writing can be even more insightful than reading. As a freelancer consultant, Iâm fortunate to have the opportunity to engage in this practice. The LTCM case study has captured my fascination, and delving deep into it to craft an article has been an exciting journey. Iâve published this article on both LinkedIn and Quora, @ChaitanyaC and @StonePitbull contribution to our discussions played its role in shaping its content.
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An interesting read. Pretty much sums up the impact across industries and labour force in developing and developed economies.
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