The Stock Market Might Be Rigged — In Your Favour

It may surprise you how many traders and fund managers have a history or hobby involving games of chance—blackjack, poker, even roulette.
Some have done exceptionally well. But let’s be clear: there is a vast difference between gambling and professional fund management.

Still, what draws many quantitatively-minded investors to both is the common thread: statistics, probabilities, and identifying inefficiencies.

From the Casino Floor to Wall Street

Names like Ed Thorp, Bill Benter, Blair Hull, David E. Shaw, Jim Simons and Nassim Nicholas Taleb all illustrate the fascinating overlap between gambling strategy and quantitative finance. Many of these figures either came from professional gambling backgrounds or applied gambling-related statistical models to financial markets.

Imagine walking into a casino where the blackjack shoe has an unusually high number of face cards—or discovering a roulette wheel biased just enough to favour a specific number sector. These small statistical edges can be exploited over time.

That is precisely the mindset we apply at Alpha-Elite. While our system is far more sophisticated than a game of cards, our approach is driven by stacking the odds relentlessly in our favour—before we even deploy our core quantitative model.

This thinking isn't just theoretical; it’s something any serious investor can apply.

1. Stocks Already Offer a Statistical Edge

We begin with a basic but powerful observation: equities have outperformed every other major asset class over the long term—be it gold, silver, commodities, real estate, currencies or even bonds.

According to historical data, global equities have delivered real returns in the range of 6–7% annually over the last century. Simply choosing to invest in stocks already places the investor on the statistically favourable side of the ledger.

2. Equities Have a Built-In Bull Market Bias

Unlike commodity or currency markets—where supply and demand can push prices in either direction—the stock market benefits from a long-term structural bias to the upside.

Why? Because equities represent ownership in companies with a mission to grow: revenues, profits, customer bases, product lines. More importantly, the market is underpinned by mandatory, recurring buying from:

  • Pension and retirement funds

  • Endowments

  • Sovereign wealth funds

  • Insurance companies

  • Index-tracking ETFs and mutual funds

  • Trusts and fiduciary managers

These massive, non-speculative inflows contribute to a consistent upward drift over time—despite short-term volatility.

3. Developed Markets: The Historical Outperformers

While emerging markets may offer the illusion of diversification and growth potential, historical data shows that developed markets—especially the US and Europe—have consistently outperformed on both return and risk-adjusted bases.

Moreover, globalisation has led to higher correlation among global markets. Diversifying into emerging markets no longer delivers the uncorrelated benefits it once did.

Thus, focusing our exposure on the most stable, proven equity markets further tilts the odds in our favour.

4. Size Matters: Why Large Caps Win More Often

While it’s tempting to chase meteoric returns from small-cap and mid-cap stocks, long-term evidence favours large-cap stocks.
These companies are generally more stable, liquid, and resilient—especially during downturns.

Large Caps tend to benefit from scale, access to capital, strong corporate governance, and brand strength. Over time, they provide a superior risk-adjusted return with lower volatility.

At Alpha-Elite, we deliberately invest only in Large Caps to reduce fragility and increase reliability of returns.

5. Then We Add Momentum and Conviction

Even before we apply our core strategy—monthly momentum-based selection—we’ve already stacked a series of statistical edges in our favour:

  • Asset class (equities)

  • Structural market inflows (pension and institutional demand)

  • Market selection (developed)

  • Capitalisation bias (Large Cap)

To this, we add:

  • Momentum: Riding trends backed by real money flows and investor behaviour

  • Concentration: Investing in a select group of high-conviction stocks rather than diluting our edge across a sprawling portfolio

The result: a repeatable, robust system that behaves like a probability machine.

The Caveat: It’s not that easy

Now, let’s not be seduced by the metaphor. Just because the market might be rigged in your favour, doesn’t mean success is easy or guaranteed.

To turn odds into results, you still need:

  • A long-term time horizon

  • Sound risk management and capital allocations

  • Psychological resilience, especially during drawdowns

  • Discipline and consistency

Without these core principles, even the most statistically favourable system can fail.
The house edge only works when you stay in the game long enough.

References

Carlson, Ben. "Historical Returns for Stocks, Bonds, Cash, Real Estate and Gold." A Wealth of Common Sense, 2 January 2025.

Silverhall Wealth. "Long-Run Asset Returns: A Deep Dive into Historical Real and Nominal Returns." Silverhall Wealth.

Cambridge Judge Business School. "Report: Stocks Have Far Outperformed Over the Past 125 Years." Cambridge Judge Business School, 2025.

Bessembinder, H. (2018). "Do Stocks Outperform Treasury Bills?" Journal of Financial Economics, 129(3), 440–457.

Siegel, J. J. (2014). Stocks for the Long Run: The Definitive Guide to Financial Market Returns & Long-Term Investment Strategies (5th ed.). McGraw-Hill.

Gompers, P. A., & Metrick, A. (2001). "Institutional Investors and Equity Prices." The Quarterly Journal of Economics, 116(1), 229–259.

Sushko, V., & Turner, G. (2018). "The Implications of Passive Investing for Securities Markets." BIS Quarterly Review, March 2018.

Hirshleifer, D. (2001). "Investor Psychology and Asset Pricing." The Journal of Finance, 56(4), 1533–1597.

Malkiel, B. G. (2003). "The Efficient Market Hypothesis and Its Critics." Journal of Economic Perspectives, 17(1), 59–82.

Dimson, E., Marsh, P., & Staunton, M. (2023). "Global Investment Returns Yearbook 2023." Credit Suisse Research Institute.

Fama, E. F., & French, K. R. (2012). "Size, Value, and Momentum in International Stock Returns." Journal of Financial Economics, 105(3), 457–472.

Bekaert, G., Hodrick, R. J., & Zhang, X. (2009). "International Stock Return Comovements." The Journal of Finance, 64(6), 2591–2626.

Forbes, K. J., & Rigobon, R. (2002). "No Contagion, Only Interdependence: Measuring Stock Market Comovements." The Journal of Finance, 57(5), 2223–2261.

Goetzmann, W. N., Li, L., & Rouwenhorst, K. G. (2005). "Long-Term Global Market Correlations." The Journal of Business, 78(1), 1–38.

Ang, A., Hodrick, R. J., Xing, Y., & Zhang, X. (2006). "The Cross-Section of Volatility and Expected Returns." The Journal of Finance, 61(1), 259–299.

Ibbotson, R. G., & Idzorek, T. M. (2014). "Dimensions of Popularity." The Journal of Portfolio Management, 40(5), 68–77.

Chordia, T., Roll, R., & Subrahmanyam, A. (2001). "Market Liquidity and Trading Activity." The Journal of Finance, 56(2), 501–530.

Crypto: The Cult of Digital Nothingness

It might seem strange — even contrarian — to publish a piece critiquing cryptocurrencies in the very week that Bitcoin has reached all-time highs. To many, this would seem like the ultimate market vindication.

However, tulips once traded for mansions.
Manias don’t die because sceptics speak up—they die because reality intrudes.

But this is not a market note. This is a reflection on value, belief, and what we, as investors and as human beings, choose to place our faith in.

As a free-market capitalist, I believe in the right of consenting adults to engage in speculation, commerce, and even folly.
A market, after all, exists whenever a willing buyer and seller meet. And crypto is undeniably a market — an extraordinarily popular one for speculation.

I’ve seen that first-hand through the use of the Vortex Indicator, the technical analysis tool I created, which is widely employed on crypto trading platforms around the world.
I respect the freedom of free people to engage in that. But this freedom must be coupled with clear-eyed awareness of what exactly we are participating in.

Because for all the hype, all the price action, and all the passion — crypto remains a belief system, not a financial system.

Invented Value, Worshipped Worthlessness


Crypto offers no intrinsic value. It generates no income. It has no cash flows, no utility, and no fundamental economic underpinning.
It is not a claim on any productive enterprise. It is not legal tender. It does not pay interest or rent. It is not a commodity in any classical sense.

It is, instead, a pure speculative instrument whose price is determined solely by collective sentiment — the Greater Fool Theory: the fervent conviction that someone, someday, will pay more for it than you did. It is pure abstraction, a digital token with no tether to reality beyond the belief that it is "the future."

This is not new. Humanity has always had a remarkable ability to construct belief systems around unseen, unproven forces. Religion, mythology, ideology — all are testaments to the human capacity for invention and faith. And like these, crypto is a construct. It exists because people believe it does.

No Use, Just Abuse

In the 17 years since the invention of Bitcoin — which makes it nearly as old as the iPhone, and older than Apple Pay — crypto has consistently promised, and failed, to deliver mainstream legal use cases. It is still not a widely used medium of exchange.
It has not meaningfully displaced fiat currencies. And it has not revolutionised payments or banking.

Instead, crypto has flourished where the law fails.

It is a tool of extortion, money laundering, and increasingly, political bribery. The most prominent and consistent applications of cryptocurrencies have been in ransomware attacks, anonymous transfers for criminal enterprises, and schemes designed to deceive and defraud the uninformed.

This isn’t incidental. This is the core of the industry. 

Money-laundering and investor scams are not unfortunate behaviours that taint an otherwise promising innovation. They are the enterprise. Crypto has become the mechanism by which bad actors transfer wealth, hide funds, and lure in speculative capital from retail dreamers chasing outsized returns on thin air.

Whatever language future legislation uses to describe or regulate crypto, the result will still be the same: it will be enabling an enterprise that is, at its root, a vessel for deception.

Even as crypto’s criminal uses proliferate, it is currently gaining a surreal political¹ legitimacy.
The same U.S. administration whose First Family² reportedly profited from crypto promotions now floats the idea of a "crypto reserve"—an attempt to launder crypto’s reputation through state endorsement. This isn’t innovation; it’s regulatory capture by speculators, dressing up a speculative cult as national policy. 

A Digital Religion, Built on Code

Crypto has all the characteristics of religion: prophets (crypto “thought leaders”), rituals (halvings, forks), temples (blockchain conferences), and a devout, often uncritical congregation.

It promises salvation — from centralised banks, from inflation, from the state — and offers freedom through belief. But unlike traditional religions, which at least claim moral or metaphysical guidance, crypto offers no such philosophy. It offers only price movement. And belief in price, unanchored to value, is a dangerous foundation for capital.

Yes, the blockchain may have utility in select areas — record-keeping, supply chains, digital verification.
But blockchain is a tool. Tokens are not. The conflation of the two is the magic trick that keeps the illusion alive.


Faith is not a strategy

At Alpha-Elite, we invest in reality.
Our capital is allocated exclusively to the two largest and most advanced economies in the world: the United States and Europe.

We focus on the world’s leading companies — those that build, heal, entertain, defend, and drive civilisation forward.
From life-saving biotechnology and global entertainment, to cement, infrastructure, aerospace, and defence systems used in the fight against global aggressors.

These are not promises; they are products. In a world seduced by digital illusions, we remain committed to investing in what is tangible, enduring, and real.


The Silver Lining: Human Ingenuity Unleashed

And yet — I do not end this criticism in despair.

Because, paradoxically, the very ability that allows mankind to construct myths, religions, and speculative manias… is the same brilliance that has birthed science, art, mathematics, medicine, and markets. The same imagination that dreams up false gods is also responsible for real breakthroughs.

Crypto is, in its own way, a testament to human creativity — just not one rooted in value. And while this particular belief system may collapse under its own weight, we should still admire the extraordinary capacity of the human brain to invent, organise, and convince.

That ingenuity is not always used wisely. But it is always awe-inspiring.

References:

1. The Crypto Industry Got What It Paid For - The Verge
2. The Real Trump Family Business Is Crypto - The Atlantic

Artificial Intelligence and Hedge Funds – A False Promise?

In the year AD 2025, the first question we’re often asked as a quantitative momentum fund is:

Is Alpha-Elite an AI fund?

Before we answer that, it’s worth stepping back to ask a broader question:

Is artificial intelligence an existential threat to us—and to the hedge fund industry as a whole?

Revolution or Hype?

First, we need to distinguish how AI is actually used in finance.

Financial advisors, as one example, use it to sift through huge amounts of data and tailor solutions for clients. Some hedge funds, on the other hand, leverage AI to analyse company fundamentals, earnings calls, or masses of other financial information.

In all these cases, AI excels as a data processing assistant—not as the core of an investment strategy.

Unfortunately, the AI hype has also led to the phenomenon of “AI Washing” – where some unscrupulous participants in the industry simply pretend to be using AI, when in reality they are simply using basic quantitative techniques. 

Then there are hedge funds built entirely around AI-driven strategies.
These use machine learning (ML) for algorithmic trading, pattern recognition, and predictive analytics. Techniques like random forests, ensemble learning, and neural networks attempt to uncover nonlinear relationships between data and price movements. Some even deploy reinforcement learning to evolve their strategies over time.

Sounds unbeatable, right? Not so fast.

The Reality: Mixed Results and Missed Expectations

Academic research and industry data paint a more sobering picture.

Yes, AI can help with risk management, trade execution, operational efficiency and processing vast information flows. Its speed may also give short-term traders an edge.¹ ² ³ ⁴  

But when it comes to actual large-scale fund management returns, AI-driven funds have struggled to beat traditional benchmarks.⁵ ⁶ ⁷

A 2021 review in the International Journal of Data Science and Analytics ⁸ analysed 27 peer-reviewed studies on AI in equity investing. It found that the Eurekahedge AI Hedge Fund Index ⁹ significantly underperformed the S&P 500 and MSCI World Indexes from 2011 to 2020.

AI versus the basic stock market…

…and what happens when Alpha-Elite Quantitative Momentum is added.

Their conclusion?

“There is no conclusive evidence of any ML-driven investment funds delivering spectacular returns at scale. All market data indicates substantial underperformance compared to benchmark indices.”

More revealing, Boczynski, Cuzzolin, and Sahakian wrote:

“The picture of real-world AI-driven investments is ambiguous and conspicuously lacking in high-profile success cases (while it is not lacking in high-profile failures).”

Our own observations mirror these findings.

AI is an invaluable tool in the investment process—especially for fundamental research, portfolio design, and operational support. But as a standalone investment strategy? The evidence is thin.

Our Experience with AI

So, have we tried AI ourselves in our momentum stock selection process?

Yes we have.

We experimented extensively with AI/ML to explore novel stock selection metrics. Using tools like XGBoost and SMOTE in Python / Jupyter notebooks on Google Colab, we analysed price and volume data from the Nasdaq 100 and S&P 100 to identify potential improvements.

The result? AI didn’t deliver. Despite rigorous testing, we found that AI could not generate any significantly new momentum signals. At best, it produced slight tweaks on already well-known metrics.

Why? Because AI lacks what still sets humans apart: creativity.

Intelligence does not equate to creativity.
And sometimes, creativity = weirdness.
Fortunately, we have weirdness in abundance.

Why Alpha-Elite Stays Human-Powered

We don’t see AI as a threat to our strategy, nor do we believe it offers a consistent edge in large-scale fund management.

Here’s why: AI models rely on historical data. If multiple funds use AI for momentum strategies, they’ll likely pick the same stocks, leading to overcrowded trades and reduced returns. The market becomes a hall of mirrors, reflecting the same signals.

Our quantitative momentum approach is grounded in rigorous research and the scientific method. Yet, human creativity remains our core. It’s what sets us apart in a world of algorithms.

The Twist: AI Could Fuel Our Wins

Here’s the fun part. Even if AI-driven funds proliferate, Alpha-Elite can still come out on top. If countless investors use AI to select stocks, their buying will create price movements. Our Allocation Engine, designed to detect momentum, will spot these trends and ride the wave. AI’s momentum behaviour becomes our opportunity.

The Human Element Still Wins

AI is a phenomenal tool, but it’s not a silver bullet for hedge fund success.
Our quantitative momentum strategy is rooted in the scientific method: observation, hypothesis, testing, iteration.

But at the core of Alpha-Elite is human ingenuity—the bold questions, the creative leaps, and yes, the occasional weird idea that works.

That’s not something you can automate.

References:

1. Lopez de Prado, M. (2019) ‘Can Machines “Learn” Finance?’, The Journal of Financial Data Science, 1(1), pp. 10–21.
2. Zhang, Z., Zohren, S. and Roberts, S. (2020) ‘Deep Learning for Portfolio Optimization’, The Journal of Financial Data Science, 2(4), pp. 8–20.
3. Gu, S., Kelly, B. and Xiu, D. (2021) ‘Artificial Intelligence and Systematic Trading’, The Journal of Financial Economics, 141(2), pp. 641–666.
4. Agarwal, V. and Ren, H. (2023) ‘Hedge Funds: Performance, Risk Management, and Impact on Asset Markets’, Oxford Research Encyclopedia of Economics and Finance. Oxford University Press.
5. Bachelier, L. and Sornette, D. (2020) ‘Do Hedge Funds Use AI Effectively?’, The Journal of Portfolio Management, 46(4), pp. 56–70.
6. Harvey, C.R. and Liu, Y. (2021) ‘The Limits of Machine Learning in Hedge Fund Performance’, The Journal of Financial Data Science, 3(2), pp. 30–46.
7. Fabozzi, F.J. and López de Prado, M. (2022) ‘Artificial Intelligence in Asset Management: Hype or Reality?’, The Journal of Financial Data Science, 4(1), pp. 10–29.
8. Buczynski, W., Cuzzolin, F. and Sahakian, B. (2021) ‘A review of machine learning experiments in equity investment decision-making: why most published research findings do not live up to their promise in real life’, International Journal of Data Science and Analytics, 11(3), pp. 221–242.
9. https://platform.withintelligence.com/performance/indices/11793