Investing has changed a lot over the years. Humans have been the ones making investment choices for centuries, but now, AI stock-market algorithms are shaking things up. They’re raising a big question: Will machines replace humans in the stock market, ending a 400-year old legacy?
The Context
Artificial intelligence (AI) in the context of stock markets is receiving a lot of attention these days, but can it finally replace human investing efforts and destroy the 400-year-old legacy?
With disruptive innovations like Chat GPT, it would be meaningless to discuss whether technology, by introducing new tools for market research and decision-making, has the potential to alter the way stock markets operate. The capacity of AI to process terabytes of data quickly is one of the technology’s primary advantages in the stock market.
Although it may sound too futuristic to be true, most new-age investors have been up against machines for more than a decade.
Numerous projects involving financial data and decision-making already uses AI.
This includes Algo Trading, Speed Trading, AI ETFs, Robo Advisors, Sentiment Analysis, and a wide variety of Risk Management Models.
The AI Warzone
Artificial intelligence (AI) and machine learning (ML) applications are being used in new ways that are assisting professional institutions and retail investors in spotting patterns and trends that would be hard or impossible to spot using conventional methods. AI-powered algorithms, for instance, can evaluate a significant amount of historical stock market data to spot trends and forecast future stock prices. This enables traders and investors to make more informed choices about when to buy and sell stocks.
AI in stock markets offers a plethora of advantages, one of which is the automation of labor-intensive tasks that are currently performed manually. Cutting-edge AI-powered algorithms now can automatically execute trades based on predefined conditions, such as specific stock price levels or trading trends. Additionally, AI can significantly enhance the accuracy and efficiency of risk management strategies in stock markets.
Moreover, AI is widely utilized to identify and monitor potential risks, such as shifts in market conditions or political developments that could impact the value of equities. This empowers traders with improved decision-making abilities in terms of when to buy or sell stocks.
In addition to aiding traders, AI also serves as a valuable tool for stock market regulators. By employing advanced analytics, AI can swiftly detect unusual trading behaviors, such as insider trading, by identifying patterns and anomalies that may signify criminal activity.
As the possibilities of AI in stock markets are boundless, traditional methods such as manual data crunching using outdated Excel spreadsheets may seem antiquated in comparison. Embracing the power of innovative AI technologies can streamline processes and enhance outcomes, rendering traditional methods obsolete in today’s rapidly evolving stock market landscape.
AI Screw-Ups Are Real
While nuclear power holds promise as a green energy source for the future, the destructive potential of a nuclear bomb remains a haunting reality, capable of devastating life as we know it. As a species, humans often succumb to self-indulgence, driven by ingrained tendencies like greed and competition, leading us to frequently overstep our limits. The beginning of any new idea may seem like a fairy tale ending, but turmoil often follows as reality sets in.
The concept of automated trading systems, or algo trading as it is known today, can be traced back to Richard Donchian who introduced the idea in 1949 by using rules to guide buying and selling of funds. Rule-based trading gained popularity in the 1980s with renowned traders like John Henry adopting similar strategies.
However, our example takes us back to 2011 when algorithms were vying against each other with hefty funding from financial institutions, attempting to “correlate everything against everything for most profitable results.”
During this time, Dan Mirvish, a blogger for the Huffington Post, noticed an intriguing trend. The shares of Warren Buffett’s Berkshire Hathaway saw an unusual price increase coinciding with news about Hollywood actress Anne Hathaway.
The explanation proved to be just as absurd as it sounds.
Robotic, automated trading algorithms were interpreting the buzz surrounding “Hathaway” as measured by the IMDb’s StarMeter and applying it to the stock market.
The automation was obliteration.
The likelihood that algorithms based on AI may be used to manipulate the market is one of the key concerns with AI. For instance, a stock value might be artificially inflated by an algorithm by purchasing it up in big quantities, which could lead to the bursting of a bubble.
This is also known as Flash Crash.
The biggest one yet, also known as the crash of 2:45 occurred on May 6, 2010. It was a trillion-dollar flash crash in the United States that began at 2:32 p.m. EDT and lasted approximately 36 minutes. Despite the market indexes’ partial recovery later that day, the flash crash destroyed over $1 trillion in market value in just a few minutes. Another issue is that AI-driven algorithms may increase the stock market’s susceptibility to hacking and online attacks.
AI algorithms could easily become a target for hackers attempting to acquire private financial data or disrupt trade as they develop and are integrated into stock market systems.
Limitations Of Artificial Intelligence
Thankfully, we won’t be facing any threats such as Skynet from the classic Hollywood film Terminator anytime soon. When it comes to the stock market, artificial intelligence is still synthetic and has several limitations, including:
⦾ Data Quality— The caliber of the data that AI models are trained on has a significant impact on their accuracy. Incomplete or inaccurate data can result in shaky predictions and judgment calls.
⦾ Lack of Explainability— Deep learning models in particular can be challenging to interpret and comprehend. This makes it difficult to go back and determine and address the factors that led to a model’s conclusions and projections.
⦾ Overfitting— Overfitting is a bad machine learning behavior that occurs when a machine learning model predicts correctly for training data but not for new data.
AI models can overfit the training data, leading to poor performance on unseen data.
⦾ Market Dynamics— The stock market is constantly changing, and AI models have not evolved sufficiently to adapt to new market conditions promptly.
⦾ Ethical Concerns— AI-based stock market decision-making can raise concerns about bias, transparency, and accountability.
⦾ High Computational Cost— Training and running AI models necessitate a large number of computational resources, which can be prohibitively expensive for smaller financial firms and retail investors.
⦾ Limited to Historical Data— AI models can only be as good as the data on which they are trained. They can only forecast based on historical data and may be unable to forecast unexpected events like COVID-19 or World War 3 and other market fluctuations.
⦾ Market plurality— While this may seem strange to some, the vast majority of algorithms are much simpler than most humans think. “Market Plularity” is our unique concept for explaining the composite outcomes of multiple independent ideas that drive the stock market’s unpredictable behavior.
Consider the stock market to be a large neural network guided by numerous disconnected neurons (impromptu investor decisions) firing at the same time, making predictions almost inconceivable.
Conclusion
AI has the potential to play a significant role in the stock market through the use of trading and analysis algorithms. However, it is unlikely that AI will completely replace human oversight and decision-making in the stock market. Stock markets are highly volatile and can fluctuate dramatically in response to a variety of factors such as Political events, Economic conditions, Ann Hathaway’s, and Company news.
Because of this unpredictability, AI algorithms may struggle to accurately predict market trends or identify profitable trades. Furthermore, the performance of AI in the stock market would almost certainly be limited by the quality of the data and algorithms used, as well as market conditions. In theory, however, most of these limitations can be overcome when smart algorithms are capable of solving the Turing test (1950).
Imagine a future where a machine passes the elusive Turing test, demonstrating truly independent intelligence, akin to the realm of science fiction. However, if such a scenario were to become a reality within our lifetime, it would be a “game over” moment for humanity, dwarfing any discussions about investing and stock markets on finance blogs, as the implications would be profound and far-reaching.
Until that hypothetical day arrives, AI-based solutions may be seen as fascinating toys to be explored and experimented with, but they are still far from surpassing the capabilities of human decision-making. Even with all the advancements in artificial intelligence, that trusty old Excel spreadsheet, with its ability to humanly crunch numbers and spot potential multi-bagger investment opportunities, still holds its relevance in the world of finance. As we ponder the possibilities and uncertainties of AI, the notion of a machine achieving true independent intelligence remains a distant concept, stirring both excitement and trepidation.
The impact of such a breakthrough would undoubtedly reshape the landscape of investing and stock markets, but for now, we continue to navigate the ever-evolving world of finance with a combination of human judgment and technological tools, recognizing the limitations and potential of both.
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