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Higgenbotham wrote: Fri Nov 06, 2026 2:36 pm There will be a new set of winners who can be identified later. Kind of like how Joe Granville was the man in the 1970s.

What Happened: On this day in 1981, Joe Granville warned his newsletter readers to "Sell everything!"

Where The Market Was: The Dow closed at 1,004.69 and the S&P 500 traded at 138.12.

Granville's Bold Prediction: On Jan. 6, 1981, in his weekly newsletter, Granville warned investors that a stock market crash was on the horizon and they should dump all their stocks. The following day, the Dow tanked 4.2% on record trading volume of 92.9 million shares, making Granville look like a genius.

A year after Granville's call, the Dow was still down 14%, and Granville had become famous for calling the drop. At the time, Granville has 11,000 newsletter subscribers paying $250 per year for his insight. Others paid $500 for urgent phone alerts.

Granville's followers praised him for his 1981 call, his prediction of the bear market in the late 1970s and his bearish call ahead of the bursting of the dot-com bubble in 2000. Critics point out that he completely missed the bull market that started in 1982.

While Granville gained notoriety for his 1981 call, his overall performance was far from impressive. From 1980 through January 2005, Granville's stock picks for investors lost 0.5% on an annualized basis compared to an 11.9% average yearly gain from the market as a whole.

aedens wrote: Fri Nov 06, 2026 12:05 am Every five percent down one percent sell...

Higgenbotham wrote: Sat Oct 03, 2026 2:18 pm

Higgenbotham wrote: Thu Sep 24, 2026 3:10 pm I did the last 7% last week and am 100% in triple inverse. The 10 year has been up to multi year highs the past 2 days, climbing more today, but not over 5.3%. Meantime, some are explaining why that is not a problem. If the stock market collapses, then some will explain why it is a problem after the fact.

Now I'm all in with the investment accounts. As the market holds or climbs I will be adding some fresh cash and putting it directly into triple inverse. I added some yesterday. Due to the interest rate rise, the triple inverse is returning about 5 percent per year with average volatility at a constant market.

Now let's do some math. This is ideal and just for illustration. If the S&P loses 98 percent of its value at a constant 1 percent daily loss, this is 0.99 raised to the 389th power. Therefore, the triple inverse would increase by 1.03 to the 389th power, or about 98,000 fold.

Higgenbotham wrote: Thu Nov 05, 2026 2:35 pm The algos will do what they do also which is to mimic the irrational emotional actions of traders, just in a more convoluted way.

Do algos mimic the irrational emotional actions of traders but just in a more convoluted way?

AI Overview

Yes, algorithmic trading often mimics human irrationality and emotional volatility, but it does so through systemic, mathematical loops rather than actual feelings. While an individual line of code cannot feel "fear" or "greed," algorithms are built by humans, trained on human historical data, and adjusted by stressed human operators. When market panic hits, these factors combine to make algorithms behave exactly like a hyper-rationalized, highly convoluted version of a panicked crowd.

How "Emotionless" Code Becomes Emotionally Volatile

The tech industry sells algorithmic trading as the ultimate cure for human psychological flaws. In reality, human psychology is simply pushed further up the chain of production.

1. Overfitting (The Math Version of "Recency Bias")
The Human Flaw: A trader loses money on three tech stocks, gets spooked, and vows never to trade tech again (recency bias).
The Algo Equivalent: A developer forces an algorithm to match historical data perfectly to ensure it looks flawlessly profitable on paper. This is called overfitting. When the live market changes even slightly, the "perfect" algorithm panics, triggers a cascade of hardcoded stop-losses, and crashes because it cannot handle unpredicted reality.

2. Momentum-Chasing & Cascades (The Math Version of "FOMO")
The Human Flaw: Retail traders see a stock skyrocketing, get Fear Of Missing Out (FOMO), and pile in late.
The Algo Equivalent: Trend-following and high-frequency algorithms are programmed to spot micro-momentum. When a trend triggers their entry parameters, hundreds of massive algorithmic funds simultaneously buy or short the exact same asset. This creates an artificial, feedback-loop snowball that leads to sudden "flash crashes" or massive asset bubbles.

3. Strict Stop-Losses (The Math Version of "Loss Aversion")
The Human Flaw: A trader sees their portfolio drop 5%, panics, and sells everything at the absolute bottom.
The Algo Equivalent: An algorithm has a strict, unyielding risk threshold. If a sudden liquidity blip drops the price past a specific decimal point, the algorithm automatically dumps millions of shares into a falling market. This isn't "emotional panic"—it is a cold calculation that yields the exact same, destructive market result.

The Human "Off-Switch" Problem

The most convoluted aspect of algorithmic trading is that humans still control the master switches.

Scenario The Human Discretionary Trader The Algorithmic Trader
Market Drawdown Panics and manually sells positions at a steep loss out of fear. Panics, loses faith in the system, and manually turns the algorithm off mid-trade.
Winning Streak Becomes overconfident, doubles their risk size, and overleverages. Becomes overconfident, tweak the code to maximize profits, and breaks the risk safety parameters.

Ultimately, algorithms execute orders without a heartbeat, but they are trapped inside a framework dictated by human biases. They do not eliminate irrationality; they just automate it at the speed of light.