TipRanks

Wed, Jun 25, 2025, 6:18 PM5 min read

Disappointment remains an overriding theme for mobile technology specialist AppLovin (APP). Since early June, APP stock has declined by more than 22%, with Friday’s price action being particularly severe. Fundamentally, investors have been rushing for the exits when it became clear that the underlying enterprise would not be joining the S&P 500.

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Inclusion in the S&P 500 is more than a badge of honor—it drives institutional buying as index funds rebalance. Many expected AppLovin (APP) to join the index, but when S&P Global opted not to make changes this quarter, the stock dropped about 8% on the news.

The stock has clawed back some of its losses since last week. Despite this disappointment, analyst Stone Fox Capital views the exclusion as a temporary hiccup and remains focused on AppLovin’s strong fundamentals, particularly the robust growth of its ad platform.

For options traders, this dip has created a potentially attractive setup. With implied volatility elevated and sentiment temporarily shaken, long debit strategies could be relatively inexpensive. I remain short-term bullish on APP as a contrarian high-risk, high-reward opportunity.

Although the fundamental and technical frameworks for APP stock are arguably bullish for contrarians, both methodologies lack specificity. While financial publications typically utilize probabilistic language, they often lack an empirical basis for their lexicon. However, the problem ultimately stems from the architecture of these standard methodologies.

In technical analysis, practitioners often discuss patterns such as head-and-shoulders, cups, and handles. Unfortunately, none of the patterns in the technical realm are falsifiable. As such, empirical peer review is practically impossible. Fundamental analysis (along with technical analysis) suffers from the so-called “non-stationarity dilemma” — a complex way of saying that the core measurement metric, such as share price or valuation ratios, changes temporally and contextually.

Due to non-stationarity, any probabilistic analyses are derivative in nature, calculating outcomes across the entire distribution of the underlying dataset. This is akin to calculating a baseball player’s batting average for the entire season. It’s a factoid, which is a nice piece of information, but it doesn’t tell you the batting average for the situation you’re in right now. In other words, looking at how batters perform over the past 5-10 games is a better indicator of how they will perform in the next match (as opposed to looking at the seasonal average.