Core Viewpoint - Goldman Sachs' brokerage division indicates that U.S. software stocks are expected to continue rebounding after previous market volatility due to concerns that AI tools could disrupt the software industry [1] Group 1: Market Strategy - Goldman Sachs has introduced a customized stock basket to address the volatility in software stocks, betting on companies with attributes resistant to AI replacement [1] - The basket employs a pair trading strategy, going long on companies that require physical execution, are under regulatory protection, or involve human accountability, as well as suppliers directly benefiting from the proliferation of AI in computing services, data infrastructure, and cybersecurity [1] - Conversely, the strategy involves shorting companies whose workflows are easily automated or replicated internally by AI [1]

格隆汇2月8日｜高盛交易员Louis Miller认为：本周，做多内存受益股对做空内存冲击股(图中蓝线)的配 对交易被市场波动影响，目前与内存价格(图中黑线)的差距扩大超7%。尽管软件及其他市场板块的市场 规模受到关注，但内存市场的基本面并未发生任何变化，而且，除非资本支出大幅放缓，否则人工智能 的日益普及应该会进一步推高需求。虽然该策略目前呈现动能倾向，但其基本面依然强劲。值得注意的 是，内存相关股的预期收益已增长超过两倍，而内存输入成本相关股的收益则落后于大盘。 ...

尽管现货价格剧烈震荡，但部分贵金属投资产品的价格却未能同步跟上。管理规模达100亿美元的封闭 式实物贵金属基金——斯普罗特实物金银信托（Sprott Physical Gold and Silver Trust，CEF），上周 五的交易价格较其资产净值（NAV）折让高达9.5%。此前在1月30日，黄金和白银单日跌幅分别创下逾 11%和31%的历史纪录；不过周二两大贵金属价格均大幅反弹，凸显市场波动性已升至极高水平。 事实上，在1月28日贵金属价格接近峰值时，该基金的折价幅度甚至更大，当日收盘较资产净值折价达 到11.4%的历史极值。该基金的持仓全部为存放于加拿大皇家铸币厂（Royal Canadian Mint）的实物黄 金和白银，外加少量现金，其资产净值会根据市场价格每日更新。 解读这一异象的一种方式是：当日，投资者实质上是以89美分的价格，买入了价值1美元的黄金和白 银。当然，考虑到随后金银价格的暴跌，当时的买入时点堪称糟糕。但对于那些倾向于投资贵金属、并 希望在波动中抄底的逢低买入者而言，此类封闭式实物贵金属基金或构成买入机会——至少在大幅折价 状态持续期间是如此。 贵金属市场的剧烈波动正为黄金和白银投 ...

Core Viewpoint - Investors are caught in a dilemma between missing out on the AI boom and fearing a potential bubble burst, leading to expected market volatility in the U.S. stock market in 2026 [1] Group 1: Market Trends and Predictions - The stock market has experienced alternating phases of significant sell-offs and rapid rebounds over the past 18 months, a trend likely to continue into 2026 [1] - Some strategists predict that the AI sector may follow a historical pattern of "boom-bust" cycles seen in previous technological revolutions [1] - The performance of tech companies, which have a disproportionate market influence, is expected to diverge from other components of the S&P 500 in 2025, potentially stabilizing overall market volatility [1] Group 2: Volatility and Trading Strategies - Strategists anticipate that market volatility will be supported in 2026 due to the instability often associated with expanding asset bubbles, with potential for over 10% declines followed by rapid rebounds [2] - UBS strategists suggest that regardless of whether the AI trend continues or collapses, profiting from increased volatility in the Nasdaq 100 index is key, recommending strategies like straddles or OTC swaps [2] - The VIX index is expected to maintain a median range of 16 to 17 in 2026, but could spike significantly during risk-averse market conditions [2] Group 3: Options Pricing and Trading Strategies - The imbalance of investment funds is projected to influence options pricing, potentially steepening the volatility curve [3] - Pair trading strategies, betting on individual stock volatility rising while index volatility narrows, are expected to gain popularity, although some funds are taking contrarian positions due to overcrowding [3][4] - Investors are encouraged to explore various forms of pair trading to extract profits, as the traditional strategy has become widely known and its excess return potential diminished [4] Group 4: Market Dynamics and Risk Management - A model proposed by Societe Generale suggests that a flattening yield curve signals a buy in volatility, while a steepening curve indicates a sell, with the model having successfully avoided major downturns in the past [5] - The current low leverage levels in U.S. corporate sectors may be the beginning of a new leverage cycle driven by AI, which could elevate credit spreads and stock market volatility [5] - Investors are advised to prepare for extreme market conditions in 2026, driven by fears of missing out, conflicting narratives around AI, and uncertainties from U.S. government policies [5]