After the Nasdaq evaporated over $2 trillion in a single month, Wall Street is starting to look for buying opportunities! Deutsche Bank upgrades its technology rating, and Goldman Sachs’s “seven signals” outline the bull narrative.

According to Zhitong Finance APP, after the sharp sell-off of tech stocks at the end of October, Wall Street analysts’ forecasts for the market have begun to show noteworthy changes: Capex and financing pressure for AI infrastructure remain, but as stocks in the tech sector led by AI computing power and the broader semiconductor concept have recently plummeted, relative valuations have fallen, and investors have reduced holdings. Coupled with the consistently strong, long-term fundamental growth trajectory of many leaders in AI computing, the potential returns for some tech assets have now become more attractive.

Deutsche Bank has turned bullish again on the theme of US tech stocks, focusing on adjusted prices; while Mike Wilson, Chief Equity Strategist at Morgan Stanley, continues to favor a rebound after risk release, emphasizing that the cost of AI financing, the overpricing of midterm elections and liquidity pressure, and their digestion paths, are primary. Another Wall Street giant, Goldman Sachs, bases its “Seven Major Recovery Signals” mainly on signals from the S&P 500, Nasdaq 100, US stock options, and hedge fund positions and capital flows in the US, with a focus on the long-term bullish narrative of US tech and AI-related stocks.

Deutsche Bank’s latest US stock market research report reveals that the firm suddenly turned bullish on US tech stocks, completely overturning its prudent move to downgrade the rating of the entire US tech sector to “Neutral” just three weeks ago. Their strategist team states that the severe sell-off has created attractive buying opportunities, and a considerable portion of AI-related risks have already been reflected in the prices.

As investors reassess the possibility that AI-related infrastructure investments may not recoup their comprehensive costs and reconsider AI-driven economic growth expectations, the Nasdaq 100—the so-called "barometer of global tech stocks"—lost over $2 trillion in total market cap last month.

Deutsche Bank's research shows that over the past three weeks, tech stocks and ultra-large-cap growth stocks underperformed the rest of the S&P 500 components by more than 16 percentage points: the former dropped around 11%, while the rest of the index’s components rose nearly 6%, bringing the sector close to the lower end of its historical trading range.

The term “rest of the S&P 500 components” cited in the Deutsche Bank report refers to the comparison group in which Deutsche Bank excluded “tech stocks and ultra-large-cap growth stocks” and used the remaining S&P 500 stocks, including those from finance, industrials, energy, healthcare, and other sectors. Thus, this statement compares the performance of “tech and ultra-large-cap growth stocks” to “the rest of the S&P 500,” not to the entire S&P 500 index, and should not be simply understood as excluding only the “Magnificent Seven.”

Deutsche Bank Turns Bullish After Tech Stock Plunge: The Market is Reassessing “the Price of Growth”

The performance of the “Magnificent Seven” in the US tech market has intensified investor concerns about the speed at which massive AI spending returns materialize, becoming the key factor behind the recent sharp declines in large tech stocks and the core driver of the US bull market in recent years—i.e., the significant weakening of AI computing power and broad semiconductor stocks.

Deutsche Bank strategists stated that the relative performance of tech stocks has dropped to the bottom of its long-term range, a level that in recent years has marked attractive entry points. According to Deutsche Bank’s proprietary data, in previous cycles when capital rotated back into tech stocks, the sector saw an average surge of about 32%, outperforming the rest of the S&P 500 components by about 30 percentage points.

Although the long-term profit growth outlook for the US tech sector remains robust and overall stock positions are still relatively light, Deutsche Bank’s team says that if these AI-linked framework companies keep their profits resilient, investors still have ample room to add exposure. Thus, Deutsche Bank strategists have upgraded US tech stocks from “Neutral” to the most optimistic long-term “Overweight.”

The ferocity of this adjustment was first reflected in the rapid reversal of the early-week rebound. On October 29 and 30, the Nasdaq 100 fell 1.38% and 3.46%, respectively, a combined loss of around 4.79% in two days; the Philadelphia Semiconductor Index fell 1.42% and 7.20%, for a cumulative two-day loss of about 8.52%. The previous recovery in the semiconductor segment was almost entirely erased in just two trading days. The Nasdaq 100 declined about 6.65% in October, with tech growth stocks once again at the center of market risk release.

Historically significant impacts also appeared in individual stock and trend structures: Intel plummeted about 28.58% at the close on October 30, its largest single-day drop since 1974; according to Goldman Sachs’ trading desk, the Philadelphia Semiconductor Index fell below its 200-day moving average, ending a streak of 355 consecutive trading days above it. Even with Intel’s data center business growing nearly 80% year-over-year, it could not offset the valuation reset driven by insufficient forward guidance, proof that market demands on AI-beneficiary companies have shifted from “Is there strong AI computing demand?” to “Can it deliver on time, maintain profitability, and provide optimistic cash returns?”

It is precisely amid such pessimistic pricing that Deutsche Bank sees an opportunity to reallocate to tech stocks. The firm points out that over the past three weeks, technology and ultra-large-cap growth stocks have declined about 11%, while the remaining S&P 500 components have risen nearly 6%. The former’s relative underperformance—over 16 percentage points—now approaches the bottom of its long-term range. For tech stocks, the most meaningful change is the significant adjustment in the price relationship between tech and other sectors: the premium investors were previously willing to pay for AI growth was quickly compressed, while risk-off rotations pushed up the relative price of other assets. Deutsche Bank therefore believes that a considerable share of AI-related risks has now been over-discounted by the market.

This divergence also explains why tech stocks took a hit but the overall market did not fall into a full-on bear. According to Kobeissi’s AI vs. non-AI stock composition, in October they returned about -13% and +8%, respectively; Goldman Sachs calculates 406 S&P 500 constituents rose on Thursday, yet the market cap-weighted index still closed lower. Capital is cutting high-concentration, capital-intensive AI exposure while shifting to other profit sources.

The essence of Deutsche Bank’s renewed bullishness is the assessment that this rotation has made tech stocks’ relative prices attractive; their historical data shows that when capital previously rotated back into tech, the sector outperformed the rest of the S&P 500 by about 30 percentage points, with an average increase of around 32%—but note that these figures reflect historical repairs, not targets for this round.

Morgan Stanley’s Wilson Bets on a Rebound After Risk Release: 7,100 Points as Potential Entry, 8,000 Points as Recovery Target

In a major media interview on November 1st Eastern Time, Wilson further clarified his long-term bullish view on US stocks, especially AI-linked tech: Midterm elections, AI investment and financing debate, as well as liquidity constraints, may still provoke short-term market dips; however, once these pressures are released, they may instead create opportunities to rebuild equity exposure. He believes the S&P 500 could fall toward 7,100, representing a roughly 9% drop from the recent high, followed by a rapid recovery toward 8,000.

Based on the S&P 500’s closing value of 7,580.07 on October 30, 7,100 implies about another 6.3% downside, with 8,000 offering about 5.5% upside; should the index retreat to 7,100 and then recover to 8,000, this would represent a gain of about 12.7%. This price path reveals his investment logic: he remains bullish on the intermediate-term returns but is willing to accept that short-term risk release may allow better entry prices. Previously, his official investment strategy review also emphasized that low-quality AI players may still face wider credit spreads and further stock declines.

On midterm elections, Wilson is more focused on the easing of uncertainty rather than betting on any specific party’s win. He cites data showing that since 1950, the S&P 500 in the six and twelve months after every midterm election has posted gains; however, he also points out that the index does not always rise from the election through year-end, as illustrated by 2018. The real political variables currently impacting AI investments involve data center approvals, community opposition, electricity cost sharing, and regulatory policies: If election results differ markedly from market expectations, project development could still be affected; conversely, local government negotiations for investment and jobs could ease construction opposition after the campaign period ends.

Regarding market concerns about AI financing pressure, Wilson’s attitude is more nuanced than the panic narrative. He does not believe that equity issuance has grown so large as to overwhelm capital demand, as corporate fundraising involves not just AI but also mergers and wider physical investments. He estimates that a significant wave of fundraising has already occurred this year and that this round may largely taper off within the next 30 days. The positive implication of his latest view is that incremental new securities supply for the market to absorb may temporarily ease, reducing capital competition pressures—this is a different recovery path from hoping for an immediate Fed rate cut.

He pays particular attention to companies’ proactive shifts between debt and equity financing: when markets resist equity issuance, firms can tap debt; when leverage gets heavy, they can top up capital via stock issuance. Wider credit spreads also force weaker projects to slow expansion, introducing a market discipline mechanism. However, this process intensifies corporate divergence. In Wilson’s framework, nominal economic growth of about 7%–8% with a 10-year US Treasury yield near 5% can still support growth for high-quality firms; for those borrowing at 8%–10% with weak profits and little cash, the same setting may be unsustainable. This is why balance sheet and earnings quality screening have always been part of Wilson’s long-standing bullish logic for US equities.

Undoubtedly, the robust cloud growth reported by Alphabet and Microsoft last week is providing revenue-side validation for AI infrastructure investment. As seen from the recent string of positive growth metrics from AI computing industry leaders, earnings reports throughout the supply chain are confirming demand signals—from memory supporting models and inference tasks, to power and liquid cooling for high-density racks; the profits and long-term supply contracts of storage vendors are especially notable.

The AI demand outlook remains bright at the application end for Alphabet (Google’s parent), Microsoft, Meta, and Oracle, even as they absorb climbing AI financing costs. Meta (social advertising and AI giant) posted Q3 revenues of $64.601 billion, up 26% year-on-year, with ad impressions up 10% and average price up 14%; its apps had 3.64 billion daily active users. Muse boasts over 10 million registered accounts in the US and Canada, around a quarter of whom use it daily; Oracle, the enterprise database/cloud infrastructure giant, projects positive free cash flow in the second half of fiscal 2028, over $30 billion in fiscal 2029, and at least $225 billion in revenues in fiscal 2030. Meta demonstrates the coexistence of application growth and cash consumption; Oracle provides a checkable timetable for cash returns after peak capex.

With Meta’s Muse and OpenAI’s GPT-6 Astra triggering a surge in AI agents, “AI FOMO trading” (i.e., investors chasing or replenishing AI-linked assets out of fear of missing out) may return. From the perspective of AI computing infrastructure, Muse and Astra could further expand the use of AI agents. Their enormous inference workloads will ramp up requirements for model inference, tool execution, and state management, intensifying competition for some software firms. JPMorgan’s AI capex outlook projects hyperscale cloud operators will spend around $950 billion in 2026, $1.4 trillion in 2027, and $3 trillion in 2030.

However, for the next stage of the global AI computing and semiconductor sectors to witness strong, well-supported uptrends, earnings estimate upgrades, realization of procurement payments, and improved credit spreads must work together, especially in scarce capacity and key technology links.

From a technical perspective, AI agents turn a single answer into repeated reasoning, retrieval, tool execution, and state retention. GPUs and TPUs handle model computation, CPUs manage tools and orchestrate tasks, HBM provides high-bandwidth data access, server DRAM supports the operating environment, and SSDs/HDDs serve high performance and large capacity storage, respectively. Meanwhile, network infrastructure, data center optical interconnects, power supply, and cooling are critical for high-value accelerators’ utilization rates. Thus, accelerating cloud revs benefits multiple hardware links simultaneously; how much profit a company takes depends on performance advantages, supply constraints, and bargaining power.

Nvidia, with a market cap around $5 trillion, remains a crucial anchor in the bull-bear tug of war on Wall Street. After closing at $204.64 on October 30 (market cap around $4.94 trillion), Morgan Stanley, Citi, and Bernstein have offered target prices of $300, $315, and $400, representing potential upsides of about 46.6%, 53.9%, and 95.5%, respectively—reflecting the core sell-side firms’ continued optimistic judgment on its profit outlook.

Seven Major Recovery Signals for the Stock Market Emerging: Rebound Funds Are Preparing, Cash Returns Will Still Decide Who Leads the Market

Goldman Sachs’ senior derivatives strategist Brian Garrett has summarized seven types of recovery signals, adding trading-level evidence to the assessments of Deutsche Bank and Wilson: index options starting to reprice fear, significantly reduced fund positions, downside volatility resuming dominance, higher resilience in non-AI stocks, risk asset prices getting expensive, AI financing demand possibly below previous expectations, and retail investors shifting from unconditional dips to a wait-and-see approach.

The latest positions from this Goldman Sachs derivative strategist collectively suggest that the market is gradually abandoning the previous one-way pricing belief that "growth solves everything.” Of greatest significance are positions and market breadth. Goldman Sachs statistics show that US fundamental long-short fund net leverage has dropped to 47%, the 7th percentile for the past year; historically, such positioning is typically seen with index drawdowns of over 20%, yet at present the S&P 500 is only about 3% off its high. This largely implies that some institutions have already taken considerable preemptive risk reduction measures—if profits remain resilient and event shocks moderate, there is room for reallocation that could power a rebound.

However, Goldman’s latest fund flow data shows that, for the week, 73% of net fund buying came from macro funds, mainly in the form of short covering—indicating that current buying is primarily lowering bearish exposure, and that clear evidence of sustained tech accumulation remains to be seen.

The heavy negative catalyst from AI financing cost pressure and negative free cash flow is the key determinant of whether this active reallocation can be strong and sustained. Amazon’s cloud division AWS saw revenue grow 39.3% in Q3, with management predicting growth over 40% in 2027 and 2028; at the same time, the group had a negative free cash flow of about $42.4 billion over the past twelve months, mainly due to increased AI-related capital spending. Alphabet (Google’s parent) had negative $9.065 billion in Q3 free cash flow, but remained positive $19.747 billion over the past year. Demand growth and cash burn co-exist, meaning investors are reassessing the timing of construction spending, fee revenue realization, and the amount of external capital needed in between. Negative group free cash flow, by itself, does not prove that cloud businesses are loss-making or that projects will lack long-term return.

Credit pressure on newer cloud players like CoreWeave makes this timing gap all the more sensitive: tighter funding first affects a company’s capacity to boost capex, which then transmits through project starts, equipment deliveries, and procurement rhythms throughout the supply chain. Anthropic’s plan to offer up to $3.5 billion in shares to Japanese investors in a US IPO demonstrates efforts to broaden capital sources; the prospectus notes funds will be used for working capital, operations, and capital spending. This is a portion of a US IPO issued to Japanese investors—not a Japan listing. The aim is to use equity capital to absorb part of the long-term expansion risk and lessen sole reliance on debt, but also requires investors to accept equity dilution and recognize strong future cash return expectations from multi-hundred-billion-dollar AI compute buildout.

According to the latest tech stock outlooks from the three financial giants, a synchronized threefold catalyst forms the strongest bullish foundation for the next repair in AI computing, semiconductors, and broader tech: adjusted prices that compensate for financing and execution risk, robust procurement budgets with reliable funding, and suppliers able to convert orders into delivery, profit, and cash. The expansion of AI agent applications will keep boosting compute demand, but for stock market returns, actionable construction funding, verifiable equipment utilization, and stable payments are more persuasive than simply escalating capex commitments.

The latest Deutsche Bank strategist report provides “relative price improvement” justification; Morgan Stanley’s seasoned strategist Wilson has laid out a “post-risk-release reallocation” path; Goldman Sachs reveals the “positioning repair conditions and the still-evolving nature of buying flows.”