Let us open the discussion with a foray into recent developments in quantum computing. In late April, Cisco introduced Universal Quantum Switch, advancing the Path to a Quantum Network. Cisco posted this in their website:
“Imagine connecting billions of people and tens of billions of devices with direct cables. It would be unmanageable. The internet became possible because classical switches could connect all of those endpoints through a shared, scalable network. The Cisco Universal Quantum Switch does the same thing for quantum. When two quantum computers need to share information, it accepts the signal in whatever modality it arrives, translates it into a common language for routing, and delivers it in the format the receiving system needs, without losing any quantum information along the way. Quantum computers encode information in different ways, and until now, no switch could accept and translate between all major encoding modalities without destroying the quantum information in the process. The Cisco Universal Quantum Switch is designed to address this challenge for the first time, routing quantum information while preserving it at room temperature, on existing telecom fibre, with a Cisco-patented conversion engine that translates between encoding modalities at input and output.”
Does better technology imply better stock market predictions?
Quantum computing is important because it introduces a fundamentally new way of processing information—one that can solve certain problems far more efficiently than even the fastest classical supercomputers. It doesn’t replace classical computing; it extends what is computationally possible. The importance of quantum computing can be understood from the fact that some problems are practically impossible for classical computers. Even with massive computing power, classical machines struggle with problems involving huge combinations, or complex probability spaces, etc. Quantum computing is important at a national and corporate level because it affects Cybersecurity, Defence, Drug discovery, Energy, Financial systems, etc.
However as explained below, stock market prediction does not sit in the computational realm which can be solved by technological advancements, because factors affecting stock price are uncertain, not just unknown.
Stochastic markets
A stochastic system is one that contains inherent randomness. Outcomes are described by probabilities, not certainties. In markets, this means that price movements are not fully deterministic and that the same conditions today can lead to different results tomorrow.
Market prices are influenced by news arrivals (often unpredictable), human decisions, macro events (policy changes, wars, weather), order flow randomness, etc. Even if you knew everything about the current market state, future information does not exist yet. You cannot be right about information that hasn’t happened yet.
Reflexive nature of recommendations
A recommendation changes behaviour. Changed behaviour alters outcomes. The original reasoning becomes invalid jeopardising initial recommendation. For example, when a stock recommendation attracts buyers, the price may rise faster than fundamentals support, and the correction that may follow could make the recommendation “fail” despite good analysis. Success here depends not only on correctness, but on how others react.
Good research can fail for correct reasons
A recommendation can be logically correct and still fail in outcome. For example, valuation may be correct, but timing wrong; reasoning may be correct, but catalyst delayed, or risk has been identified, but tail event dominates.
This breaks the assumption that better research implies perfect success.
Non‑stationarity destroys guarantees
Research relies on historical patterns. But real systems are non‑stationary. Relationships change, regimes shift, incentives evolve, old correlations break etc. A recommendation built on yesterday’s structure may fail tomorrow- even if it was optimal at the time.
Let us address the question: Is 100% research success mathematically possible?
Even if each recommendation has a very high probability of success (say 80%), the probability of never being wrong over many attempts approaches zero. For example, probability of 50 correct recommendations in a row at 80% accuracy is 0.850 ≈ 0.000000014, or near zero.
Conclusion
No research system be it human or machine can be 100% accurate, because the future it seeks to assess is shaped by uncertainty, evolving information, and adaptive human behaviour. Research is therefore not a prediction engine but a framework for reasoning under incomplete knowledge. In this sense, a useful analogy is how models like GPT respond to questions: when the full answer is not known, they generate the most plausible response based on available context, patterns, and probabilities- sometimes correct, sometimes incomplete, and occasionally wrong, yet often still useful. Investors should approach research in the same way: not as a source of certainty, but as informed guidance that improves decision quality, clarifies risks, and tilts probabilities in their favour over time. Success lies not in being right every time, but in consistently making well‑reasoned decisions with a clear understanding of uncertainty, limitations, and downside risk.
