分析师要当！一台机器对你的工作有目光

大数据突然是一个大问题。5月下旬，在Michael Milken在洛杉矶的金融标题的年度聚会中，一个中心舞台主题是越来越强大的计算机和软件的影响及其从巨型数据集中提取意义的能力。该会议以奥巴马总统2012年的2012年博马纳竞技经理，吉姆·墨西拿主管，专注于大数据，专注于一个小组。梅娜讨论了大量信息有助于获得胜利的信息。但它是一个交易小组，对血腥，机器人和大数据的看似无可救药3月的血肉血资和投资分析师界定了挑战。

LOUIS SALKIND，HDEDE基金公司的长期总经理D.E.Shaw＆Co Co，最清楚地诬陷大数据的挑战：Salkind，谁有博士学位。在纽约大学的计算机科学和机器人中，描述了分析机和证券分析师之间的安装对抗。在Hourlong Panel的尽头附近，他描述了一个增加自动化的世界，其中供应商创建产品“以切碎Twitter和Facebook”并汇总交易信号。“想象一下，当他们开始使用大数据技术来看看基本数据时会发生什么，”他说，讲述了一个关于一个经纪人的故事，他们使用了沃尔玛的卫星图像的经纪人，商店停车场预测季度收益。“当人们开始整合这些形式的数据时，它只是在那里成为一个不同的世界。”

Salkind和D.E.Shaw长期以来一直是在使用计算机进行交易和投资的创新者。但有些人认为，自动化和人力从业者之间的斗争，这席卷了交易所，交易地板甚至监管和遵守，已达到新的前线：证券分析。

Computers and software clearly have advantages in tracking complex market patterns or monitoring and analyzing data points in news reports, social media and other digital sources. Computers famously never have to use the bathroom or ask for a raise, though they do break down. They are growing increasingly fast and more powerful, and they have access to far more data. Apostles of big data predict a rout of rank-and-file analysts by computers that can interpret the market with superior results. They even believe that big data will allow machines to discern the future — for an election, a stock price or a corporation — from the noise of the moment. Big data will not only reshape trading, they say, but long-term investment practices.

其他人是持怀疑态度的。无论在人们参加市场和经济，无论汇总和分析多少数据，将来都是不可预测的。到目前为止，实际的性能结果已经薄：我​​们遭受闪存崩溃，不快乐的金融冲击和隐藏在平原视线中的气泡。人类分析师无法处理机器可以的数据泛滥，但它们具有算法缺乏：如果易于恐怖，判断，判断。人体分析师可以称重可能无法降低量化的模糊值;平衡长期和短期视角;关于公司及其期货直接的利润;预测技术，品牌或时尚的演变;并应对歧义和复杂性。

这人与机器的冲突是最娱乐ent chapter in a centuries-old struggle that heated up when mechanical looms replaced home-based spinning wheels. Although the outcome is not clear, what is obvious is that the world of securities analysis will change under the impact of these powerful new tools. The technology may well transform the already precarious economics of securities analysis and further cull the ranks of analysts, dividing them into those who can effectively use the new techniques and those who cannot. Big data is probably here to stay. The larger question is, how do we live with it?

At the heart of this trend is the algorithm, a series of steps or instructions that tells computers how to search for and interpret data. It’s a simple but powerful concept when allied with a computer. Consumers encounter algorithms every day. In addition to routine tasks, from spell-checking to GPS route guidance to online shopping, algorithms help fly passenger jets and perform medical diagnoses, even surgeries. Soon they may drive cars.

Algorithms are also ubiquitous in finance, playing a role in everything from high frequency trading to complex valuation calculations to economic forecasting. They feed off information that washes over global finance daily — data now measured in petabytes, or billions of megabytes. No army of humans could outprocess these algorithms in weeks, much less in the fractions of a second they need to churn through data.

Algorithms excel at performing rapid, nearly limitless computations. But they require data as raw material. That data is increasingly available in large quantities, much of it a world apart from the traditional grist of financial analysis: prices, valuations, ratios. Increasingly powerful computer systems squeeze market patterns from news items, financial statements, blogs and other digital texts where insights may lurk — a developing field known as news analytics. Massive digital memories keep tabs in real time on thousands of companies, along with their competitors, customers, vendors and investors.

新闻分析来自财务比率阵列与首席执行官转型给中国农村股票，行业，同行团体或市场的当地工作停止转型的因素。程序扫描视野和标志事件，具有市场影响。一些算法甚至泵出了媒体消耗的新闻更新。

新数据可以通过市场情绪，供应链关系或新闻事件产生趋势和新的同行组。对于投资者来说，更好的是，新的同伴团体往往表现出独特的交易模式。

“这个信息来源是否会改变投资行业？毫无疑问，潜力就在那里，“威斯利陈说，金融博士。高盛＆恤师傅的高盛;波士顿Acadian资产管理股票选择研究总监，是一家总基于基于新闻分析的投资经理。“几十年来，会计和市场数据对投资业变得非常重要。没有理由认为新闻分析在更短的时间范围内不会产生相同的影响。“

这对证券分析师的意思是什么？一些大数据的倡导者认为计算能力和预测算法将扫除传统分析师，并通过延伸，传统的投资方法。结果取决于这些基于算法的一些预测系统的有效性以及传统投资研究和活跃组合管理的有效性如何以及传统的投资研究和活跃的组合管理可以为生存有效。

然而，人类判决的持续中心的倡导者认为，算法核心的二元决策不一定使世界更加可预测，长期投资的优点将持续存在。毕竟，每种算法都有一套由人设计的指令;像经济模型一样，算法将世界简化为可管理的输入和输出。人类专家指挥比算法更广泛的知识，并以算法，加州大学逻辑助理教授詹姆斯·欧文·沃尔德（Chinualia），Irvine和Author的作者提供了算法的方式华尔街的物理学：预测不可预测的简要历史。

Medical research furnishes a classic example. A 1972 study asked oncologists to predict, on the basis of biopsies, the survival time of 193 Hodgkin’s lymphoma patients. The correlation between expert predictions and actual survival time was zero. But the coding of the biopsies generated by those physicians and run through a multiple regression model accurately predicted the survival time. To Weatherall this illustrates a key difference between human and automated roles. Researchers are better at identifying the variables that computers need to function. Computers can more effectively synthesize information in a systematic way to make predictions.

In finance, algorithms have played a growing role since the 1980s, principally in trading. But as D.E. Shaw’s Salkind suggested, companies with proprietary algorithms have turned their ambitions to buy-and-hold investing and to fundamentals. Vendors such as Bloomberg, Dow Jones & Co. and Thomson Reuters have joined an ever-expanding collection of fledgling firms with names like亚历山大投资研究和技术那alphanius技术那DataMinr.那数字抹子那卢博纳研究那MarketyCh.那Narrative Science那Quantopian.那ravenpack.和记录未来。

These companies sell various twists on news analytics. The Big Three firms aggregate suites of interactive products. Eikon, marketed by Thomson Reuters, deploys news analytics in a user-friendly window on market trends, securities prices and even the exact location of oceangoing freighters on a digital map. In isolation a freighter’s coordinates may not alter the outlook for a company, but in conjunction with news, weather conditions and market demand, a severe storm might affect market value.

同时，启动alphanius矿山推特式市场信号的流量。记录的未来扫除主要新闻出版物，贸易出版物，政府网站和财务数据库，以获得未来事件的明确和隐含的迹象。Ravenpack提供数据产品和先进的可视化工具，识别情绪或媒体关注的国家或公司的高低。寻找市场模式，Alexandria在人类基因组研究中采用计算机技术。Lucena Research，由前F-15飞行员建立，拥有佐治亚州理工学院博士学位。在机器人学中，将人工智能与投资策略相结合。Quandopian用科学家配备了一种发展和反向他们自己的金融算法的手段。

今天的分析技术远远超出了早期呼吸的简单词数，进入新闻分析并试图跟踪市场情绪。那些努力标记为正面或负面的话，锚定在简单的假设中，消极的词总是意味着坏消息，积极的话总是意味着好。背景，长期以来的人类判断力，已经变得至高无上。

Windows for leveraging news analytics and market sentiment do not just flash by like an equity trade in a dark pool; they can, practitioners assert, last days, weeks, months or longer, thus becoming relevant to investment analysis. “I’ve seen clients trading on our information on investment horizons for up to three years,” says Peter Hafez, director of quantitative research at RavenPack and a former portfolio manager. His customers seek quantitative frameworks for news that does not require interpretation by analysts. “As you get stronger news analytics and more information, you can bypass analysts and find value in the data itself,” he says.

2月份哈佛商业评论published a case study在矩形借给情感探测验证orded Future, an analytics start-up based in Cambridge, Massachusetts. The study reported on a Recorded Future strategy that categorized 500 stocks according to sentiment, then bought the top 10 percent and shorted the bottom 10 percent. “RF’s own analyses suggested that if investors had followed its predictions and investing recommendations about equities in the S&P 500 over the past year, they would have substantially outperformed the market,” according to the case study.

几十年来的学术研究往往是为了证实爆炸新闻的策略。“资本化的股票，小，无利可图，高波动，非股息支付增长公司或财务困境的公司股票，可能对广泛的投资者情绪敏感，”哈佛大学和杰弗里·贝弗雷迪纽约大学的Wurgler报道了2007年的纸张，“股票市场的投资者情绪”。其他研究同意，虽然意见对影响的持续时间和程度不同，但情绪信号可以产生市场变化的预先警告。

为了测试新闻分析的价值和寿命，Deutsche Bank的一系列最近的研究论文侧重于最先进的情绪分析。量化策略演员罗切斯特·凯汉及其同事们在术语中以非结构化数据探索了新闻 - 在股票选择中。他们的结论：新闻和互联网数据中的真正价值超出了简单的漫长的积极情绪战略。

Deutsche团队的总结说，绝对术语的情绪与市场预期相比的情绪较少。成功的财务模型通过捕获价格和卷等情感和市场数据变量之间的复杂相互作用来提取新闻的alpha。“如果一家公司有很多良好的情绪，人​​们在博客上写好东西，发推文好事，有一种自动假设这是一个积极的故事，你应该买，”卡恩说。“市场不像那样工作。重要的是预期。“

情绪为期望提供细微差别的代理。Vale，Bias和Contult的价值，偏见和背景，Tabb集团的高级分析师E. Paul Rowady Jr表示，其中监视新闻分析。根据其背景，裁员可能是良好的或不好的情绪。有些是购买信号;其他，卖信号。福特汽车公司供应商可能会为通用汽车有限公司提供良好的新闻。市场领导者的强劲收入可能会强迫争夺争夺以维持市场份额或相反地冲浪潮流。正确编程，新闻分析算法可以识别上下文中的影响和情绪。

最终，Rowady预见到普遍访问的数据和计算火力足够强大，以吸收实时发生的一切，然后表达市场情绪。“这基本上是我们所在的地方，”他说。

CAN NEWS ANALYTICS PRODUCTS predict the market future any better than traditional analysts? That’s a question that resists an unequivocal answer. For one thing, news analytics firms sign agreements not to divulge the names of customers, which understandably don’t want to expose investment strategies. (A number of larger firms reportedly have been using such algorithmic tools for a while.) Moreover, those analytic systems are diverse and changing fast. And apart from backtesting by vendors and rare testimonials, evidence of success or failure is largely circumstantial and sketchy.

To flourish, news analytics must boost quantifiable returns above the cost of installing and operating sophisticated systems. Leasing a news analytics system plus data feeds can cost $5,000 to $20,000 a month, depending on the features, frequency of data refresh and number of seats. That’s particularly a challenge for newer, smaller firms.

And yet there are fans. Kevin Shea is confident that the payoff exceeds the cost. “If I can’t get at least 3 to 5 percentage points of alpha from a factor, I’m not interested in looking at it,” says Shea, a veteran of Cadence Capital Management, Batterymarch Financial Management and Invesco who launched Boston hedge fund Disciplined Alpha this year. As its name suggests, the firm adheres to a systematic investment strategy. Its algorithmic approach, developed by Los Angeles–based Alexandria, is rooted in bioinformatics, an information technology that emerged from genomics. Conceptually, analyzing the genome sounds pretty mechanical; after all, DNA may be long, but it only has a four-letter code. But that code features deletions, mutations and “junk” sequences along its 3 billion base pairs, and its interactions with RNA and the assembly of proteins has proved to be extremely complex. Context and relationships matter, just as with financial information.

There are contextual elements in the way Alexandria “trains” algorithms to generate market insights. Rather than assigning positive, neutral or negative meanings to words in advance and imposing rules to classify sentiment, Alexandria analyzed 55,000 documents in one study deemed positive, neutral or negative by outside investment professionals and searched for deeper commonalities that supported assessments. When fed 5,000 new documents, the algorithm matched human assessments 91 percent of the time, says Shea.

Still, a 91 percent hit rate to one observer looks like a 9 percent miss rate to another. At high volumes that’s a lot of misses, though human assessments aren’t necessarily better. Moreover, the program processes tens of thousands of documents that otherwise might escape notice.

没有广泛的股票筛查被反垄断的肯定，正式风险模型和情绪信号，谢伊说，纪律alpha会发现难以在一致的基础上产生令人满意的风险控制性能。至少在纪律处于纪律的alpha中，该算法是仍然展开的实验。

Daniel Sandberg is also exploring the potential for algorithm-driven investment. Sandberg earned a Ph.D. in computational physical chemistry in 2012 from the University of Connecticut, then, like several of his peers, headed to finance. He joined the Legacy Foundation, an investment advisory firm in Charlottesville, Virginia. If algorithms can extract meaningful signals from scientific research, Sandberg saw no reason that they couldn’t work in finance. So he began to develop his own algorithms, working with venture-backed Boston start-up Quantopian, which provides tools like backtesters, data feeds, algorithm writing and a community of users. His first project: an algorithmic tool for implementing a sector rotation strategy.

就其部分而言，兰拓正试图为遗产等较小资产管理人员制定算法开发，甚至是消费者。它声称在浏览器中拥有第一个算法交易平台。公司正计划发布折扣交易平台，这意味着算法交易和算法开发，可能会来零售投资。

SOME SKEPTICS CONTINUE TO FIND news analytics less than breathtaking. “Perhaps we’re behind the curve, but this isn’t a topic that we utilize much here,” says an equity research director at a global investment firm. New York–based Investment Technology Group provides many services and third-party research to hedge funds; in fact, one of its marketing slogans, “Decoding signal from noise,” could come from a big-data vendor. But ITG does not “traffic in news analytics,” says a spokesman. In late 2011, London’s Derwent Capital Markets launched a fund based on Twitter with great fanfare and celebrated a hefty 1.85 percent gain in the first month of operation. A year later, with returns far short of expectations, Derwent closed its Twitter-based fund.

人类判决和监督似乎有弹性，这是纽约安全分析师德国替代投资委员会的前主席克里斯托弗·德珀。“这可能是一个大型游戏更换器吗？”他问。“我不会高估它。投资只有人类的基本一侧的东西也可以看看。“通过说明，Cudler引用了分析师和公司行政之间的对话，从而达到了对竞争对手的错误思考。

T. Rowe Price Group已长期以来，其传统的买入和持有投资策略繁荣。“自动驾驶的过程可能会赚一些钱，但在我的脑海里，这不是魔药，”美国美国股票交易公司负责人安德鲁布鲁克斯说。“有40,000件新闻发布将让您进入LinkedIn $ 6，并保持200美元？我不认为通过阅读大数据来发生这种情况。“投资组合保险在20世纪80年代中期炎热，布鲁克斯召回。然后是1987年的崩溃。是什么派出市场旋转？“投资组合保险，”他说。

News analytics can go only so far without humans, says Burke Lau, a Hong Kong–based market analyst at Macquarie Group, which endorses news analytics and, in partnership with RavenPack, sells tools to global banking customers. Humans are needed to spot and correct false confidence in algorithmic models (and, perhaps, vice versa). A popular case in point: When new software at electronic trader Knight Capital Group malfunctioned in August 2012, unleashing a flood of buy orders on the exchanges, millions of dollars essentially vanished before a human — or humans — had to intervene.

Computers are suited to finding a specific data set and predicting an outcome. But when the unanticipated comes along, computers are ill equipped to respond without human intervention. For example, IBM Corp.’s chess-playing computer, Deep Blue, defeated Russian champion Garry Kasparov but was still unable to bluff or spot a bluff in poker — a game that resembles trading more than chess.

“在国际象棋中，你可以在需要了解的一切周围画一个圆圈，而谁知道什么会影响汽车行业？”Leslie Valiant说，T.Jefferson Cocidge Im哈佛大学计算机科学和应用数学教授，2010年上午A.M.图灵计算卓越研究奖。电脑看到棋牌游戏中的一个壮观的举动，但截至目前，勇敢说：“我们不知道如何使它们复制常识。”

算法有更多的学习才能给investment advice with minimal human interference. But analysts who hope that judgment will always trump algorithms could face a rude surprise. Natural language processing, which allows algorithms to analyze everyday English, has made progress, as smartphone owners can testify. Reading a poker bluff requires sensitivity to semantic nuance. An IBM successor to Deep Blue named Watson, using multiple algorithms, famously defeated human champions on危险！部分是准确地解析游戏显示的WordPlay。

“在金融市场中的语义搜索的出现是强迫金融专业人士消费和分析信息的方式的转变，最重要的是赚钱，”Haris Husain说，汤森路透社努力开发植物的智能搜索工具语。

这可能证明是证券分析师的摩擦，他们已经遭受了超过十年的痛苦变化。通过投资资本加油初创公司和大量的精明工程思想从事设计更直观的算法，证券分析师可能面临重大的破坏。最好的将生存和繁荣;其余的可能落到机器上。

Christopher Steiner进一步走了很多。施泰纳，作者Automate This: How Algorithms Came to Rule Our World，是前者Forbestechnology editor and a current Internet entrepreneur; he is bearish on the outlook not only for securities analysts but for professional portfolio managers. “In ten years I don’t see a whole lot of room for active managers of money,” he declares.

这可能已接触到透明的世界末日，但在整个白领经济中都有明显的力量。诺贝尔劳特埃和普林斯顿大学经济学家保罗克鲁格曼呼应了一个警告最近的纽约时报columnaimed at workers who think advanced degrees mean job security. “A much darker picture of the effects of technology on labor is emerging,” he wrote. “In this picture, highly educated workers are as likely as less educated workers to find themselves displaced and devalued.”

在这种情况下，投资研究可能需要较少数量的META-或ÜberAnalysts，说明在5月份会议的大会上召开会议的量级分析师Joseph Mezrich表示，这是一个专门的小组。

尽管如此，这些是新闻分析中的早期日，MIT Sloan Management Monance教授Andrew Lo教授，他指导学校的金融​​工程实验室。LO看到可以从新闻中提取数据的算法看涨前景。他说，把新闻分析工作到工作，或者在市场搬家时投降无数的机会。“无论你试图预测市场行为多少，”罗说，“新闻量将始终是这种情况会产生波动性的尖峰。”

我们需要知道的是，新闻驱动的波动率是否只是经过的花哨或实质性发展，夏季风暴或气候变化。如果我们有一个算法告诉我们。•

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