Big investors are buying this “unlisted” stock

When the founder who sold his last company to Zillow for $120M starts a new venture, people notice. That’s why the same VCs behind Uber and eBay also backed Pacaso. They made $110M+ in gross profit to date. They even reserved the Nasdaq ticker PCSO. Now, you can join, too.

Invest for $2.90/Share

_{Paid advertisement for Pacaso’s Regulation A offering. Read the offering circular at invest.pacaso.com. Reserving a ticker symbol is not a guarantee that the company will go public. Listing on the NASDAQ is subject to approvals.}

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Moving averages are one of the most widely used tools in technical analysis. Their appeal lies in their simplicity and visual clarity.

They smooth out price fluctuations, reveal general direction, and offer a structured way to view market behavior over time.

However, when moving averages are used as stand-alone signals or as the core of trading systems, their limitations become clear.

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Smoothing Is Not Signal

A moving average is a transformation of past price data. It reduces noise and helps identify trend direction.

But this smoothing process inherently delays information. When a moving average crossover occurs, it reflects a change that has already happened.

It does not anticipate a future move.

In backtesting, this delay can appear tolerable. In live trading, especially in fast-moving or range-bound markets, it introduces significant inefficiency.

Strategies that rely on crossovers for entry and exit signals suffer from this delay. They often react after the initial move has occurred, which means the most favorable price action has already passed.

Adjusting the lookback period to reduce lag increases reactivity, but it also raises the rate of false signals, particularly in sideways markets.

Context-Free Logic Fails in Dynamic Environments

The effectiveness of a moving average depends heavily on market conditions. In strong, clean trends, it may provide a useful framework. In choppy, mean-reverting, or low-volume environments, it often performs poorly.

This creates a core structural problem for systems built around moving averages: they are static rules applied to dynamic systems.

Even adaptive moving averages, which attempt to adjust based on volatility or other factors, operate under rigid frameworks. They do not account for structural shifts in liquidity, narrative, macroeconomic events, or cross-asset correlations. As a result, performance is highly inconsistent across time.

The issue is not that the tool is broken, but that it lacks any built-in awareness of regime change.

A strategy that performs well in one year may underperform or generate drawdowns in another, even with no changes in logic or parameters.

Overfitting and Historical Precision

Backtests involving moving averages often produce attractive performance when parameters are tuned correctly. A 20-period and 50-period crossover, for example, may generate smooth equity curves on historical data.

However, this success frequently depends on specific market conditions and particular instrument behavior during the test period.

Extensive testing across different timeframes, instruments, and economic cycles shows a consistent pattern: strategies built on moving average rules tend to overfit.

They adapt well to a certain window of time but fail to generalize when conditions change. This is especially evident when evaluating results on out-of-sample data or through walk-forward testing.

Even when incorporating stop-losses, filters, and position-sizing rules, the underlying fragility remains. Moving averages alone do not provide a reliable predictive framework.

They describe what has happened, not what is likely to happen next.

Role in Broader Frameworks

While moving averages have significant limitations as primary signals, they still hold value as part of a broader analytical framework.

They can be useful for defining structure, identifying potential areas of interest (such as pullbacks to the average in a trending market), or for measuring general bias over a given period.

Some institutional traders use long-term moving averages (e.g., 200-day) to define high-level trend context. Others use short-term averages to gauge microstructure within a higher timeframe.

In these cases, the moving average is not treated as a signal generator but as a reference point.

Used this way, moving averages help organize information. They can simplify charts and reduce cognitive load, especially in environments with high volatility.

But this is different from relying on them to dictate trade entries or exits.

Moving averages are not inherently flawed, but they are often misused.

Their simplicity can lead to overconfidence, especially when backtests show promising results.

In practice, their delayed reaction, lack of contextual awareness, and tendency to overfit make them unreliable as core components of trading strategies.

Their strength lies in providing structural orientation, not prediction.

Traders who understand their limitations and apply them within larger frameworks, supported by context, filters, and adaptive logic may still find value.

But those seeking reliable, standalone signals will likely find themselves relying on little more than smoothed noise.