Goldman Sachs trader: The volatility at this time each year is a "normal phenomenon," nothing "abnormal."

Goldman Sachs believes that the recent 5% pullback in the US stock market is a typical year-end seasonal fluctuation within the AI cycle, not an abnormal signal indicating the end of the rally.

Goldman traders point out that despite the market correction, there is still room for upside before the end of the year. With the combined effect of seasonal factors, an AI investment cycle still in its early stages, and relatively light institutional positioning, the index still has potential to move higher.

According to Goldman’s fixed income, FX, and commodities trader Shreeti Kapa, a 5% drop at this time of year is a normal phenomenon in the current cycle. She believes that despite a strong rebound since the April low, “overall, it’s not excessive.”

Kapa’s optimistic outlook is based on favorable year-end seasonality, and she expects the market to have another 5-10% upside before the year ends, with this rally accompanied by broad market participation. She points out that many institutional investors remain skeptical about the market’s future, believing the peak has already arrived this year and adjusting their positions in response. In her view, this prevalent caution actually creates the possibility for the index to “rise sharply” in the remaining 35 trading days of the year.

Regarding macro uncertainties such as the risk of a federal government shutdown, Kapa considers these only temporary issues. As for concerns over AI potentially replacing white-collar jobs, she acknowledges this is a pillar of retail demand, but says “it is not a problem today.”

Year-End Upside Still Exists

The core logic supporting further market gains before year-end is based on the judgment that the AI revolution is still in its early stages.

Kapa thinks that institutional portfolios have not been fully allocated to AI themes yet. At the same time, capital flows will become more favorable before year-end, and the market expects the Fed’s monetary policy next year to be more dovish than last year.

On the corporate fundamentals side, while large tech companies are investing massive capital in AI, they possess solid balance sheets, reasonable P/E ratios, and compound EPS growth exceeding 20%. Kapa emphasizes that the market’s major concern is not in investment returns, but rather “solving spending problems and investing in what could become a historic technology revolution.”

Is It 1998 or 2000 Now?

There is relentless debate over whether the current market is replaying the tech bubble, but the key difference lies in today’s valuation basis and profitability.

Goldman Sachs Head of Technology, Media & Telecom Research Eric Sheridan notes that compared to periods of internet and real estate bubbles, there is much more discussion today about an “AI bubble.” He admits that private market valuations are far above public market levels, and focus more on revenue growth than profits, similar to historic risk signals.

However, he emphasizes that the valuations of today’s listed companies are still based on free cash flow, capital return, and profit margins, which is completely different from 1999, when companies with no revenue could fetch the highest valuations. He states that today’s tech giants mostly generate massive free cash flow and conduct share buybacks and dividends, which was “virtually unheard of” in 1999. Additionally, current capital market activity is far below levels seen during historical bubbles, and the IPO market is “much more selective.”

Trillion-Dollar Investment Still Within Controllable Range

The AI race has spurred huge speculation about future capital expenditure, but from a macro perspective, this investment boom may still be within a manageable range.

Sequoia Capital partner David Cahn proposed a thought-provoking conversion: he converted the energy demand for constructing AI data centers from “gigawatts” into “US dollars.” He estimates that building 100 to 250 gigawatts of AI energy facilities implies $4 trillion to $10 trillion in data center expenditures. He believes that such massive investment can only be justified by a breakthrough in artificial general intelligence (AGI).

However, Goldman Sachs macroeconomist Joe Briggs provides another perspective. He notes that while current AI investment appears enormous in nominal dollar terms, its impact is “quite mild” if measured as a proportion of GDP. In the past 12 months, US investment in AI has accounted for less than 1% of GDP, whereas historical peaks for infrastructure investment have typically reached 2% to 5% of GDP. The Briggs team estimates that generative AI will ultimately create $20 trillion in US GDP economic value.

Valuation and Positioning Provide Support

Multiple data points show that current market valuations and investor positioning are still at a distance from historical highs, providing potential support for a future rally.

According to Goldman Sachs Global Investment Research (GIR), the Nasdaq 100 is trading at about a 46% discount compared to the internet bubble era, with lower P/E ratios indicating that earnings are underpinning valuations. Although current Treasury yields are below those of 1999-2000, market returns in the past 12 months have also been relatively low. The report finds this suggests market upside remains for the next 6-12 months.

Additionally, investor positioning data supports Shreeti Kapa’s view. Data shows that after remaining neutral for most of Q3, current market positioning has actually entered “light positioning” territory, meaning that once sentiment turns positive, there is substantial capital waiting to come in.

Risk Warning and DisclaimerThe market has risks; investment needs caution. This article does not constitute personal investment advice, nor does it take into account the specific investment objectives, financial situation, or needs of individual users. Users should consider whether any opinions, views, or conclusions in this article are suitable for their particular situation. Investment based on this is at your own risk.