Despite a recent dip following cautious fourth-quarter guidance, Texas Instruments (TXN) stock appears to be undervalued, according to a quantitative analysis. While the company's third-quarter earnings exceeded revenue expectations, a slight miss on per-share earnings and a conservative outlook for the upcoming quarter led to a negative market reaction. However, an in-depth examination of historical price behavior using the GARCH framework suggests that the probability of a bullish movement in TXN stock is higher than what traditional options pricing models currently indicate.
Detailed Report on Texas Instruments' Stock Performance and Options Strategy
On October 24, 2025, Texas Instruments (NASDAQ: TXN) experienced a notable decline in its stock value, dropping approximately 2% during Friday afternoon trading and over 8% in the preceding month. This downturn followed the release of its third-quarter financial results. The company reported revenues of $4.74 billion, surpassing the analyst consensus of $4.65 billion. However, its earnings per share came in at $1.48, just below the anticipated $1.49. The primary driver of investor concern was the cautious guidance for the fourth quarter, with management projecting revenues between $4.22 billion and $4.58 billion, falling short of the $4.52 billion consensus, and earnings per share ranging from $1.13 to $1.39, below the $1.41 target.
This pessimistic outlook from market participants suggests that options for TXN stock are currently priced with a roughly 49% probability of the stock rising by half a percent above the $170 strike price by the November 28 expiration date, as derived from the Black-Scholes-Merton (BSM) model. However, a different perspective emerges from quantitative analysis, particularly employing the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) framework. This method, which focuses on the clustering and dependency of volatility, indicates that recent market stimuli have a greater impact on future price behavior than distant historical data. In the past ten weeks, TXN stock demonstrated a '4-6-D sequence' – four up weeks and six down weeks, resulting in an overall downward trend. Under such conditions, historical data suggests that median price clustering is most likely around $173, followed by $177, over the next ten weeks, assuming an anchor price of $169.34.
This analysis reveals a 1.74% positive delta in price-clustering dynamics under similar '4-6-D' conditions, implying that investors historically tend to 'buy the dip' when the stock faces downward pressure. Therefore, despite the BSM model's more conservative probability of 36.77% for TXN reaching $175 by November 28, the quantitative approach, based on empirical data, estimates this probability at 50%. This discrepancy highlights a potential mispricing of options, favoring a bullish narrative.
Given this analysis, a compelling options trading strategy is a 170/175 bull call spread expiring on November 28. This strategy would achieve its maximum payout of nearly 113% if TXN stock surpasses $175 by expiration, with a breakeven point at $172.35. The argument posits that actual historical data provides a more reliable forecast than theoretical models based on assumed implied volatility, suggesting an over 13 percentage point favorable mispricing in this particular bull spread.
The disparity between the traditional Black-Scholes-Merton model and the GARCH framework in analyzing Texas Instruments' stock performance offers a compelling insight. It underscores the importance of considering diverse analytical approaches beyond conventional wisdom. For investors, this situation highlights that market sentiment, while influential, may not always accurately reflect underlying probabilities derived from empirical data. It encourages a deeper dive into quantitative analysis, especially when identifying potential mispricings in options, suggesting that sometimes, historical patterns can unveil opportunities that theoretical models might overlook. The takeaway is to maintain a critical perspective and integrate various analytical tools to form a robust investment strategy, particularly in volatile markets where a 'buy the dip' mentality, supported by data, could prove advantageous.