2023年经济学人 推特帖子是有力的经济衡量指标(1)

（单词翻译）

    Finance and economics

    财经版块

    Fed-X

    美联储与X

    The site formerly¹ known as Twitter is not lucrative², but it is a powerful gauge³ of the economy.

    之前名为推特的网站并不赚钱，但它是有力的经济衡量标准。

    Elon musk⁴ is no fan of the Federal Reserve.

    埃隆·马斯克不是美联储的粉丝。

    At least a dozen times after the past year the owner of X (a firm until recently known as Twitter) has savaged⁵ America’s central bank for raising interest rates.

    在过去的一年里，这位X(之前叫推特，最近才改名)公司的老板至少有十几次猛烈抨击了美国中央银行提高利率的行为。

    Last December, for instance, he tweeted that its hikes might go down as the “most damaging ever”.

    例如，去年12月，他在推特上表示，美联储加息可能成为“最具破坏性的”举措。

    But Mr Musk’s disdain⁶ for the Fed is not mirrored by the Fed’s attitude towards X.

    但马斯克对美联储的蔑视并没有反映在美联储对X的态度上。

    On the contrary, the central bank’s researchers rather like the site, treating it as a compelling barometer⁷ of the economy.

    相反，美联储的研究人员很喜欢这个网站，将其视为令人信服的经济晴雨表。

    This puts X in a peculiar⁸ position.

    这使X处于一个特殊的位置。

    Its value as a business remains⁹ dubious¹⁰, which is why Mr Musk has been scrambling¹¹ to remake it, with changes including (but not limited to) the company’s name.

    它的商业价值仍然值得怀疑，正因如此，马斯克一直急着重塑它，包括(但不限于)更改该公司的名称。

    Yet its value to the economy is a different story altogether.

    但它对经济的价值则完全是另一回事。

    The firm can serve as a timely indicator¹² of both fundamental trends and market sentiment.

    它可以作为衡量基本趋势和市场情绪的及时指标。

    There is a large, growing literature on how to decode¹³ economic signals from social-media sites, ranging from Facebook to Reddit.

    关于如何破译Facebook和Reddit等社交媒体网站的经济信号的文献有很多，而且数量还在不断增加。

    But even in the sea of online information and commentary, Mr Musk’s site stands out.

    然而，即使在网络信息和评论的茫茫海洋之中，马斯克的网站也是脱颖而出的。

    Others simply cannot match its volume and frequency.

    其他公司根本无法与其规模和频率相匹敌。

    By 2013 Twitter users were already producing more than 5,700 posts a second.

    到2013年，推特用户在一秒钟内发布的帖子数量已经超过了5700条。

    By 2016 Instagram’s larger user base was producing only 1,000.

    到2016年，Instagram规模更大的用户群在一秒钟内仅发布1000条帖子。

    Three papers recently published by the Fed explore the platform’s economic contributions.

    美联储最近发表的三篇论文探讨了推特的经济贡献。

    The first is as a predictor of markets.

    首先是作为市场的预测者。

    Sentiment gleaned¹⁴ from tweets seems to be rather good at presaging¹⁵ short-term movements in both share prices and bond yields.

    从推文中收集的情绪似乎能相当准确地预测股价和债券收益率的短期走势。

    In one paper a group of economists¹⁶ including Francisco Vazquez-Grande sifted¹⁷ 4.4m finance-related tweets posted between 2007 and April 2023 to create a Twitter Financial Sentiment Index.

    在一篇论文中，包括弗朗西斯科·巴斯克斯-格兰德在内的一群经济学家筛选了从2007年至2023年4月发布的440万条与金融相关的推文，创建了一个推特金融情绪指数。

    They used a machine-learning model to measure each tweet’s sentiments: a message about stocks going to the Moon would be positive; Mr Musk’s quips about the Fed would presumably count as negative.

    他们使用机器学习模型来衡量每条推文的情绪：关于股票上涨的推文是积极的，马斯克对美联储的调侃大概会被算作消极的。

    The index, they find, correlates tightly with corporate¹⁸-bond spreads (the difference between yields on corporate and government bonds, which usually widens as investors¹⁹ turn pessimistic).

    他们发现，该指数与公司债券利差(公司债券和政府债券收益率之间的差距，通常随着投资者变得悲观而扩大)密切相关。

    More than merely shadowing financial movements, posts can even foreshadow them.

    推特帖子不只是跟踪金融动向，甚至可以预示金融动向。

    The overnight index before the stockmarket’s open dovetails with the coming day’s equity²⁰ returns.

    股市开盘前的隔夜指数与第二天的股票回报相吻合。

    A separate paper by Clara Vega and colleagues finds that the site’s sentiment also closely tracks Treasury²¹ yields.

    克拉拉·维加及其同事的另一篇论文发现，推特情绪也密切跟踪着美国国债收益率。

    Indeed, the correlation²² is stronger with tweets than with sentiment measures gleaned from the Fed’s own official communications.

    事实上，与美联储从自己的官方沟通中收集的情绪指标相比，推特情绪与国债收益率的相关性更强。

    A second use of tweets is as a gauge of economic conditions.

    推特帖子的第二个用途是作为经济状况的衡量标准。

    Posts about job losses in particular seem to offer timely information about the labour market.

    尤其是关于失业的帖子似乎提供了有关劳动力市场的及时信息。

    Tomaz Cajner and co-authors construct a separate machine-learning model to digest posts with keywords such as “lost job” or “pink slip”.

    托马兹·卡纳和其合著者构建了一个不同的机器学习模型，用于处理带有“失业”或“解雇通知”等关键词的帖子。

    Their measure of job losses mirrors official data on employment levels from 2015 to 2023.

    他们对失业的衡量反映了2015年至2023年官方数据显示的就业水平。

    This correlation is potentially powerful because most government statistics appear with a lag, whereas the tweets are available immediately.

    这种相关性可能很强，因为大多数政府统计数据有滞后性，而推特帖子立刻就能看到。

    Twitter, for example, would have provided a ten-day advantage in detecting the collapse²³ in employment at the height of the covid-19 pandemic in 2020.

    例如，在2020年新冠肺炎疫情最严重的时候，推特提前10天检测到了就业的崩溃。