After more than $2 trillion was wiped off the Nasdaq in a single month, Wall Street is starting to look for buying opportunities! Deutsche Bank upgrades its tech rating, while Goldman Sachs' "seven signals" outline the bullish narrative.

Title context: After more than $2 trillion was wiped off the Nasdaq in a single month, Wall Street is starting to look for buying opportunities! Deutsche Bank upgrades its tech rating, while Goldman Sachs' "seven signals" outline the bullish narrative. Text: After the sharp selloff in tech stocks at the end of October, Wall Street analysts' outlook reports for the market are beginning to show noteworthy shifts in judgment: pressure from AI capital expenditure and AI infrastructure financing remains, but the sharp recent declines across the broader tech sectordominated by AI computing themes and broad semiconductor conceptsthe pullback in relative valuations, and investor de-risking, combined with the still-strong long-term fundamental growth trajectories of most AI computing leaders, have made the potential returns on some tech assets more attractive. Deutsche Bank has turned back bullish on the U.S. equity market's tech-stock theme, focusing on adjusted prices; Morgan Stanley's chief equity strategist Mike Wilson remains bullish on a rebound after risks are released, focusing on the overpricing and absorption path of AI financing cost pressure, midterm elections, and liquidity pressure. Another Wall Street financial giant, Goldman Sachs, bases its "seven repair signals" mainly on signals from the S&P 500, Nasdaq 100, U.S. equity options, and hedge fund positioning and fund flows in the U.S. market, focusing on the long-term bullish narrative for U.S. tech stocks and AI-related stocks. Deutsche Bank's latest U.S. equity market research report shows that the institution has suddenly turned bullish on U.S. tech stocks, completely overturning its decision three weeks ago to cut its rating on the entire U.S. tech sector to "Neutral"a cautious stance. The institution's strategist team said the sharp selloff has created attractive buying opportunities, and a considerable portion of AI-related risks has already been reflected in prices. As investors reassess whether AI-related infrastructure investment may fail to recover its full costs and AI-driven economic growth expectations, the Nasdaq 100dubbed the "global tech-stock bellwether"saw its total market capitalization evaporate by more than $2 trillion last month. Deutsche Bank's research report shows that over the past three weeks, tech stocks and mega-cap growth stocks have underperformed the rest of the S&P 500 constituents by more than 16 percentage points: the former fell about 11%, while the rest of the index's constituents rose nearly 6%, bringing the sector close to the low end of its historical trading range. The "rest of the S&P 500 constituents" cited in Deutsche Bank's research report refers to the comparison group made up of the remaining S&P 500 constituents after Deutsche Bank excludes the group of "tech stocks and mega-cap growth stocks" in its calculations, including stocks in finance, industrials, energy, healthcare and other sectors. Therefore, this sentence compares the performance of two groups of assets"tech and mega-cap growth stocks" versus "the rest of the S&P 500 stocks"and is not a comparison with the entire broad S&P 500 index, nor can it be simply understood as excluding only the "Magnificent Seven." After the sharp tech selloff, Deutsche Bank turns bullish: the market is re-pricing "the price of growth" The performance of the "Magnificent Seven" U.S. tech giants reinforced investors' concerns about the pace at which returns on massive AI spending will materialize, becoming the most core factor behind the recent sharp weakness in large-cap tech stocks and even the core DRIVE of the U.S. stock bull market in recent yearsnamely AI computing and broad semiconductor stocks. Deutsche Bank strategists said the relative performance of tech stocks has fallen to the bottom of its long-term range, a level that in recent years has marked attractive entry points. Deutsche Bank's proprietary statistics show that previously, when capital rotated back into tech stocks, the sector rose by an average of about 32%, outperforming the rest of the S&P 500 constituents by about 30 percentage points. Although the long-term earnings growth outlook for the U.S. tech sector remains solid, overall equity positioning remains relatively light; Deutsche Bank's strategist team said that if the profits of these AI-related framework companies remain resilient, investors still have ample room to increase exposure, and therefore Deutsche Bank strategists upgraded U.S. tech stocks from "Neutral" to "Overweight"their most optimistic long-term bullish rating. The severity of this correction 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, for a cumulative two-day decline of about 4.79%; the Philadelphia Semiconductor Index fell 1.42% and 7.20% respectively, for a cumulative two-day decline of about 8.52%. The recovery gains from the semiconductor sector's consecutive counterattacks were almost entirely given back within two trading days. The Nasdaq 100 fell about 6.65% in October, with tech growth stocks once again at the center of market risk release. The historically significant shock was concentrated in individual stocks and trend structure: Intel plunged about 28.58% at the close on October 30, its largest single-day drop since 1974; according to Goldman Sachs trading desk statistics, the Philadelphia Semiconductor Index fell below its 200-day moving average, ending a streak of 355 consecutive trading days above that line. Intel's data center business growth of nearly 80% year-over-year still failed to offset the valuation reset caused by insufficient forward guidance, which is enough to show that the market's requirements for AI beneficiary companies have risen from "whether AI computing demand is strong" to "whether they can deliver on schedule, remain profitable, and deliver optimistic cash returns." It is precisely in this pessimistic pricing that Deutsche Bank sees an opportunity to reallocate to tech stocks. The institution noted that tech and mega-cap growth stocks fell about 11% over the past three weeks, while the rest of the S&P 500 constituents rose nearly 6%, with the former underperforming by more than 16 percentage points and having fallen back to the bottom of its long-term relative performance range. For tech stocks, the most investment-relevant change is that the price relationship between tech stocks and other sectors has adjusted significantly: the premium investors were previously willing to pay for AI growth has been rapidly compressed, while safe-haven rotation has pushed up the relative prices of other assets. Deutsche Bank therefore believes that a considerable portion of AI-related risks has been overpriced by the market. This divergence also explains why tech stocks were hit hard while the broader market did not simultaneously fall into a full-blown bear market. According to AI versus non-AI stock portfolio statistics cited by Kobeissi, in October the two performed roughly -13% and +8% respectively; Goldman Sachs calculated that on Thursday, 406 S&P 500 constituents rose while the market-cap-weighted index closed lower. Capital is reducing exposure to highly concentrated, capital-intensive AI positions while shifting toward other sources of earnings. The core of Deutsche Bank's renewed bullishness is the judgment that this rotation has made tech stocks' relative prices attractive; the historical experience it cites shows that previously, when capital rotated back into tech stocks, the sector rose by an average of about 32% and outperformed the rest of the S&P 500 constituents by about 30 percentage pointsbut note that these figures describe historical recovery magnitudes, not the target gain for this round. Morgan Stanley's Wilson bets on a counterattack after risk release: 7,100 points is a potential entry zone, 8,000 points is the recovery direction In a high-profile media interview on November 1 U.S. Eastern Time, Wilson further clarified his long-term bullish judgment on U.S. stocks, especially AI-related tech stocks: midterm elections, AI investment and financing disputes, and liquidity constraints may still constitute three types of events that could push the market briefly lower; but after these pressures are released in a concentrated manner, they may instead create opportunities for investors to re-establish equity risk exposure. He believes the S&P 500 could fall to 7,100 points, about 9% below its recent all-time high, and then accelerate toward a recovery to 8,000 points. Based on the S&P 500's October 30 close of 7,580.07 points, 7,100 points implies about 6.3% of further downside, and 8,000 points corresponds to about 5.5% of upside; if the index first falls to 7,100 points and then recovers to 8,000 points, the gain would be about 12.7%. This price path reveals his investment thinking: remain bullish on medium-term returns while accepting that short-term risk release may provide better buying prices. However, his previous official investment strategy commentary also emphasized that lower-quality AI participants may still face widening credit spreads and further stock price declines. On the midterm elections, Wilson focuses more on the dissipation of uncertainty than on betting on a particular party winning. Statistics he cited show that since 1950, the S&P 500 has risen in the six and twelve months after every midterm election; but he also explicitly acknowledged that the period from the end of the election to the end of that year does not necessarily see gains, with 2018 being a counterexample. The political variables that truly affect AI investment now lie in data center approvals, community opposition, electricity cost allocation, and regulatory policy: if election results significantly exceed scenarios already priced in by the market, project construction could still be affected; if opposition during the campaign subsequently turns into negotiations with local governments seeking investment and jobs, construction resistance could also ease marginally. Regarding the AI financing pressure that the market is increasingly worried about, Wilson's attitude is more nuanced than the market's panic narrative. He does not yet believe that equity issuance has grown large enough to overwhelm funding demand, because corporate fundraising involves not only AI but also M&A and broader physical investment. He judges that a large amount of financing has already been completed this year, and that this round of concentrated fundraising may come to a temporary pause over the next roughly 30 days. The positive implication of his latest judgment is that the new securities supply the market needs to absorb may temporarily decrease, thereby easing capital competition pressure; this is a different repair path from expecting the Federal Reserve to cut rates immediately. He particularly emphasizes companies actively adjusting between bond and equity financing: when equity issuance meets market resistance, companies can assess debt financing; when debt burdens become too heavy, they can supplement capital through equity. Rising credit spreads will also force projects with insufficient economics to slow expansion, forming the market's own constraint mechanism. However, this process will intensify corporate divergence. According to Wilson's framework, an environment with nominal economic growth of about 7%8% and a 10-year U.S. Treasury yield of about 5% may still support high-quality corporate growth; for companies with borrowing costs of 8%10% and weaker earnings and cash reserves, the same environment may be difficult to withstand. This is also why Wilson's long-standing bullish logic for the U.S. equity market has always included screening for balance sheet and earnings quality. Undoubtedly, the strong cloud computing business growth reported last week by the two major cloud giants, Google parent Alphabet and Microsoft, is providing revenue-side validation for AI infrastructure investment. And from the series of positive growth dynamics recently seen among leaders in the AI computing supply chain, from memory that carries models and inference tasks to power supply and liquid cooling systems that ensure high-density racks operate, supply chain earnings reports are providing mutually corroborating demand signals, with memory makers' profits and long-term supply arrangements being particularly representative. AI application-side companies including Google parent Alphabet as well as Microsoft, Meta, and Oracle, while bearing the pressure of rising AI financing costs, have also continued to actively provide positive signals about AI computing demand. Social advertising and AI application giant Meta reported third-quarter revenue of $64.601 billion, up 26% year-over-year, with ad impressions up 10% and average price per ad up 14%; daily active people across its apps reached 3.64 billion. Muse registered more than 10 million accounts in the U.S. and Canada, about a quarter of which use it daily; enterprise database and cloud infrastructure giant Oracle explicitly stated that free cash flow will turn positive in the second half of fiscal 2028, exceed $30 billion in fiscal 2029, and revenue will be at least $225 billion in fiscal 2030. The former shows application growth coexisting with cash burn, while the latter provides a testable timetable for cash recovery after the peak in capital expenditure. As Meta's Muse and OpenAI's GPT-6 Astra fully ignite the wave of AI agents, the "AI FOMO trade" (referring to investors' fear of missing out on gains in AI-related assets, leading them to chase or rebuild previously reduced positions) is expected to return. From the underlying architecture of AI computing infrastructure, Muse and Astra are expected to further expand the scope of AI agent use; the massive expansion of AI inference workloads they bring will simultaneously increase demand for model inference, tool execution, and state management, while also deepening competitive pressure on some software companies. JPMorgan's strategist team's AI capital expenditure forecasts and outlook show that hyperscale cloud providers' capital expenditure will be about $950 billion in 2026, about $1.4 trillion in 2027, and about $3 trillion in 2030. However, the next stage of a more solidly supported strong upward trajectory for the global AI computing theme and the semiconductor sector should be driven jointly by upward earnings forecast revisions, procurement payment collection materializing, and credit spread improvement, especially in scarce capacity and scarce technology segments. From a technical mechanism perspective, AI agents expand a single answer into repeated reasoning, retrieval, tool execution, and state preservation. GPUs and TPUs handle model computation, CPUs handle tool and task orchestration, HBM holds data for high-bandwidth access, server DRAM carries the runtime environment, and SSDs and HDDs serve high-performance access and large-capacity retention respectively. At the same time, network infrastructure, high-speed optical interconnects in data centers, power supply, and cooling determine the actual utilization of expensive accelerators. Therefore, accelerating cloud revenue can support multiple hardware segments simultaneously; how much profit a specific company captures still depends on performance advantages, supply bottlenecks, and bargaining power. Nvidia, with a market capitalization of about $5 trillion, remains an important bullish pricing anchor on Wall Street amid the sharp bull-bear divergence in this market. As of the October 30 close, Nvidia's stock price was $204.64 and its market capitalization was about $4.94 trillion; the $300, $315, and $400 target prices recently given by Morgan Stanley, Citi, and Bernstein imply potential upside of about 46.6%, 53.9%, and 95.5%, respectively, reflecting that Wall Street's most core sell-side institutions still maintain positive judgments on its earnings prospects. Seven repair signals emerge in the equity market: rebound capital is getting ready, but cash returns still determine who can lead the market The seven types of repair signs compiled by Brian Garrett, a senior derivatives strategist at another Wall Street financial giant, Goldman Sachs, supplement the trading-level evidence in Deutsche Bank's and Wilson's judgments: index options are beginning to price in panic, fund positioning has clearly contracted, downside volatility has regained dominance, non-AI stocks are showing more resilience, defensive assets have become more expensive, AI financing demand may be lower than previously expected, and retail investors have shifted from unconditional dip-buying to waiting and watching first. The latest array of views from this Goldman Sachs derivatives strategist collectively indicates that the market has gradually abandoned its previous one-way pricing of "growth can cover everything." Among them, the most significant are positioning and market breadth. Goldman Sachs statistics show that U.S. fundamental long/short funds' net leverage has fallen to 47%, in the 7th percentile of the past year; in historical regressions, such positioning is usually accompanied by index drawdowns of more than 20%, while the S&P 500 is currently only about 3% below its all-time high. This also largely means that some institutions have already carried out considerable risk reduction in advance, and once earnings remain resilient and event shocks ease, the room to re-add exposure could become rebound momentum. However, the latest data flow compiled by Goldman Sachs' strategist team shows that 73% of net fund buying that week came from macro-type fund products and was mainly reflected in short covering, indicating that current buying is more about reducing bearish exposure, and evidence of sustained active increases in tech stock holdings still needs to form further. The severe negative catalysts brought by the heavy pressure of AI financing costs and free cash flow turning negative are precisely the key to whether such active allocation can be strong and sustained over the long term. Amazon's cloud computing unit AWS revenue grew 39.3% in the third quarter, and management expects growth rates of more than 40% in both 2027 and 2028; at the same time, the group's free cash flow over the past twelve months was a net outflow of about $42.4 billion, mainly affected by increased AI-related capital expenditure. Google parent Alphabet's third-quarter free cash flow was negative $9.065 billion, but remained positive at $19.747 billion over the past twelve months. The coexistence of demand growth and cash burn means investors are reassessing when construction spending occurs, when fee revenue materializes, and how much external capital is needed in between. The group's free cash flow turning negative itself does not prove that the cloud business is losing money or that project long-term returns are insufficient. Credit pressure on new cloud companies such as CoreWeave makes this time gap more sensitive: tighter financing conditions will first affect companies' ability to expand capital expenditure, then transmit to the supply chain through project starts, equipment delivery, and procurement pace. Anthropic's plan to issue up to $3.5 billion of stock to Japanese investors in its U.S. IPO precisely reflects corporate efforts to expand funding sources; regulatory filings show the funds will be used for working capital, operating expenses, and capital expenditure. This is the portion of the U.S. IPO offered to Japanese investors, not a separate listing in Japan. Its potential role is to use equity capital to absorb part of the long-term expansion risk and reduce pressure from relying solely on debt, while also requiring investors to accept equity dilution and endorse the strong cash return expectations from a future AI computing resource buildout on the scale of hundreds of billions of dollars. According to the latest tech stock outlooks from the three major financial giants, the next recovery trajectory for the AI computing and semiconductor theme, and even for tech stocks broadly, should be most strongly supported by three changes occurring simultaneously: adjusted prices can compensate for financing and execution risks, customers' procurement budgets have reliable funding sources, and suppliers can convert orders into deliveries, profits, and cash. The expansion of AI agent applications can continue to increase computing demand, but for the return outlook of equity market investment strategies, construction plans with financing already secured, verifiable equipment utilization, and stable payment collection are more convincing than simply expanding capital expenditure commitments. Deutsche Bank's strategist team's latest research report provides the basis that "relative prices have already improved," Morgan Stanley's senior strategist Wilson provides the path of "preparing to add exposure after risk release," and Goldman Sachs reveals the reality that "positioning has room to repair, while the nature of buying is still changing."