Are tech and AI stocks falling in 2026?

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1. 1

   The Impact of Nvidia’s AI Hardware Dominance on Valuations across AIrelated Semiconductor Stocks

   International Journal of Social Science and Economic Research (Ikezawa)Published Jan 1, 2026Checked Oct 15, 2026

   “A quantitative analysis was conducted using recent stock data from January 2023 to December 2025 for five AI-exposed firms (Nvidia, AMD, Broadcom, TSMC, ASML) and a control group of non-AI semiconductor companies. Valuation metrics including price-to-earnings ratios, market capitalization, and revenue figures were analyzed alongside correlation analysis of daily closing prices. The results reveal that AI-exposed semiconductor firms trade at significantly higher valuation multiples compared to non-AI-exposed firms, with a mean P/E ratio of 55.80 compared to 43.54. Correlation coefficients between Nvidia and its AI semiconductor firms ranged from 0.5 to 0.69, indicating a co-movement of pricing within the industry. These findings provide empirical evidence that Nvidia acts as a central valuation anchor for the AI semiconductor sector, suggesting that AI-related firms’ valuations are inflated through exposure to the trend rather than independent performance. This study contributes to the literature on market behavior regarding AI exposure and highlights the existing systematic risks in AI focused technology markets.”

2. 2

   Understanding Sectoral Responses to Tariff Uncertainty via a High-Dimensional Mediation Analysis

   arXiv (Cornell University) (Hong & Li)Published Sep 27, 2026Checked Oct 15, 2026

   “To accommodate a large number of candidate mediators, we use a partially penalized least-squares procedure that regularizes financial-variable coefficients while unpenalizes coefficients of all sector indicators. Wald and F-type tests are used to examine whether indirect and direct effects are significant or not, respectively. The results show that tariff news did not produce one uniform market response. Sector effects vary across five economically distinct tariff-policy windows. The clearest findings are in the direct sector effects. Technology was the sector most negatively affected during the initial decline and escalation collapse, but it also rebounded most strongly during the April 9 relief rally. The results indicate that in addition to Technology, Energy and other trade-exposed sectors were particularly affected during the initial decline and escalation phases. The selected mediators provide evidence that growth, investment, liquidity, profitability, and cash-flow characteristics helped characterize firms associated with sector-level return differences.”

3. 3

   Is there an AI bubble? Robust date-stamping for periods of exuberance

   Frontiers of Mathematical Finance (Sarkar & Wells)Published Jan 1, 2026Checked Oct 15, 2026

   “Motivated by this episode, we develop a stochastic-volatility-robust ADF (SV-ADF) framework for date-stamping asset-price bubbles when prices exhibit persistent, time-varying volatility. Standard Dickey–Fuller procedures can be distorted in heteroskedastic environments, leading to unstable inference and spurious bubble classifications. We extend recursive ADF testing to autoregressive models with highly persistent mean and volatility components and derive nuisance-parameter-free asymmetric calibration rules for bubble origination and collapse. The resulting procedure delivers consistent date-stamping and remains implementable for real-time monitoring. Monte Carlo evidence shows improved power and dating accuracy relative to homoskedastic PWY tests. Applying the method to AI-exposed equities, including the 'Magnificent Seven' and major semiconductor stocks, we find substantial cross-sectional heterogeneity in exuberance, with current bubble dynamics concentrated in Alphabet and TSMC, while Tesla and Nvidia exhibited earlier explosive episodes.”

4. 4

   Dissecting AI Trading: Behavioral Finance and Market Bubbles

   arXiv (Cornell University) (Ouyang & Sui)Published Apr 20, 2026Checked Oct 15, 2026

   “We study how AI agents form expectations and trade in experimental asset markets. Using a simulated open-call auction populated by autonomous Large Language Model (LLM) agents, we document three main findings. First, AI agents exhibit classic behavioral patterns: a pronounced disposition effect and recency-weighted extrapolative beliefs. Second, these individual-level patterns aggregate into equilibrium dynamics that replicate classic experimental findings (Smith et al., 1988), including the predictive power of excess demand for future prices and the positive relationship between disagreement and trading volume. Third, by analyzing the agents' reasoning text through a twenty-mechanism scoring framework, we show that targeted prompt interventions causally amplify or suppress specific behavioral mechanisms, significantly altering the magnitude of market bubbles.”

5. 5

   The AI Private-Valuation Marks-vs-Reality Ledger (v1): a pre-registered, reproducible scoreboard for how private AI valuations survive a real public price

   Zenodo (CERN European Organization for Nuclear Research) (Research)Published Jun 12, 2026Checked Oct 15, 2026

   “The open prediction, publicly time-stamped here, is that any real correction lands at lock-up expiry, not on debut (Morningstar fair value about $780B, roughly 56% below the $1.77T price). Changes in v1.1 (standards refresh, not the lock-up version). Corrects one isolated stray figure (day-one intraday high $165 to the web-confirmed $176.52; the rest of the document already carried $176.52); standardises the Morningstar gap (about 56% below the $1.77T price, about 55% below the $1.75T target); tidies the Anthropic Series H note ($65B round, $965B post-money). Repackaged to portfolio standard: Markdown source plus a house-style PDF,replacing the v1 .docx. The substantive v2 follows at SpaceX lock-up expiry. Author: N. Milton (ORCID 0009-0003-4213-7769), independent. The author holds no position in, and receives no compensation from, any entity named. Produced with AI assistance (Claude Opus 4.8, Anthropic), which is disclosed for transparency as the model's maker is within the sector analysed; the analysis relies on cited primary and public sources. Open-science documentation only, not investment advice. CC BY 4.0.”

6. 6

   Mutual Fund Bubble in India: An Analysis of Market Resilience and Valuation Concerns in 2026

   International Journal For Multidisciplinary Research (Sarkar)Published Feb 5, 2026Checked Oct 15, 2026

   “This research paper investigates the structural shifts in the Indian financial landscape as of early 2026, specifically addressing the widespread concerns regarding a "Mutual Fund Bubble." With the unique investor base exceeding 120 million and monthly SIP inflows crossing Rs. 28,000 crore, the study analyzes whether this growth is driven by fundamental "financialization of savings" or speculative "irrational exuberance." By evaluating the Priceto-Earnings (P/E) dispersion between Large-cap and Small-cap indices and the resilience of Domestic Institutional Investors (DIIs) against global headwinds, the paper concludes that while specific sectors experienced a "valuation froth" that corrected in 2025, the broader mutual fund ecosystem remains robust. The findings suggest that regulatory oversight and systematic retail participation have created a "new normal" for Indian capital markets, distinct from historical speculative bubbles.”

7. 7

   Political uncertainty and multi-scale systemic risk spillovers in the cryptocurrency market: A STVAR-based network topology approach.

   PloS one (Wang et al.)Published May 28, 2026Checked Oct 16, 2026

   “To reveal the risk characteristics at different time scales, we use wavelet packet decomposition to decompose the sequence into short-term, medium-term, and long-term components; We also use forecast error variance decomposition to quantify risk spillovers, in order to study the direction and intensity of risk spillovers between different cryptocurrencies. In terms of the responses of cryptocurrency systemic risks to political uncertainty shocks, we find significant asymmetries. The mid- and long-term risk components indicate that most cryptocurrencies exhibit a stronger response during periods of high political uncertainty than during low periods. Moreover, shocks during high political uncertainty periods enlarge the cross-cryptocurrency risk spillovers. Finally, Bitcoin, Ethereum and Monero are stable risk transmitters, while Peercoin and Namecoin are more vulnerable risk receivers. This paper provides some insights into the differential impact of political uncertainty on the stability and interconnectivity of various cryptocurrencies.”

8. 8

   Narrative Research Study: Market Sentiment As A Trigger For Cryptocurrency Volatility

   SENTRALISASI (Hidayatullah & Juniar)Published Dec 7, 2024Checked Oct 16, 2026

   “The analysis employs Structural Equation Modeling - Partial Least Squares to examine the relationship between market sentiment and price volatility. The latent variable "Market Sentiment" is constructed using the NLP indicator, while "Price Volatility" is measured by daily standard deviations and the Average True Range. Additional factors, such as Layer-1, Layer-2, Decentralized Finance, Specialized Computing, Real World Assets, and Decentralized Infrastructure Projects, are also incorporated for a more comprehensive analysis. The results show that positive sentiment significantly increased price volatility, especially for speculative projects like Layer-2 and DeFi, while negative sentiment significantly reduced volatility. Neutral sentiment had no significant effect on price volatility. These findings highlight the important role of social media and news in driving sharp price movements in the cryptocurrency market, providing valuable insights for investors and policymakers in managing and responding to market sentiment.”

9. 9

   The Impact of Russia–Ukraine Conflict on the Crude Oil Prices

   International Journal of Energy Economics and Policy (Imeri et al.)Published Feb 8, 2026Checked Oct 16, 2026

   “This paper investigates the properties of the crude oil prices in the context of the Ukraine- Russia conflict by using statistical techniques based on long memory and fractional integration. In order to understand the volatility of West Texas Intermediate crude oil prices, data from Federal Reserve Bank of St. Louis for the period September 12, 2012 to September 12, 2022 are used. The results using unit root tests indicate nonstationarity I(1) for the whole sample period but also for the two subsamples created by the war. Additionally, the findings through ARFIMA approach for the whole period as well as pre-Russia-Ukraine war period confirm mean reversion and transitory shock, whereas for the period from the start of Russia-Ukraine war, the shock in crude oil prices is expected to be permanent. There is a need for inter-governmental cooperation in keeping the oil price at its normal value.”

10. 10

    The Price of Isolation: Sanctions, Supply Shocks, and Price Discovery Dynamics in Russia’s Crude Oil Market

    International Journal of Energy Economics and Policy (Wieland)Published Jul 5, 2026Checked Oct 16, 2026

    “The Sanctions Proxy dominates information shares (22.4%), followed by OPEC+ supply (19.8%), RUB/USD exchange rate (17.3%), and CBR Key Rate (14.7%). A stark absorption asymmetry emerges: OPEC+ quota decisions are priced within 3.8 trading days, whereas sanctions enforcement shocks persist for 14.7 days. IV/GMM noise-robust bias correction reveals a +63.7% OLS underestimate for the Sanctions Proxy, confirming that standard models systematically misattribute shadow fleet friction costs as informational absence. Shadow fleet logistics costs reached $773 million (6.7-8.1% of cargo value) in early 2026. The Urals-Brent discount is projected at $22/bbl for Q2 2026, reflecting a structural isolation premium that implies annual revenue losses of $7-12 billion. Policy implications highlight benchmark standardisation, intensified OPEC+ engagement, and accelerated technology import substitution as priority responses.”