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, longtime managing director of hedge fund firm D.E. Shaw & Co., framed the challenge of big data most clearly: Salkind, who has a Ph.D. in computer science and robotics from New York University, described a mounting confrontation between analytical machines and securities analysts. Near the end of the hourlong panel, he described a world of increasing automation in which vendors create products “to shred apart Twitter and Facebook” and aggregate trading signals. “Imagine what happens when they start using big-data techniques to look at fundamental data,” he said, recounting a story about a broker who used satellite imagery of Wal-Mart Stores parking lots to forecast quarterly earnings. “When people start integrating these forms of data, it’s just going to be a different world out there.”
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.
算法也普遍存在财务中，从高频交易到复杂的估值计算到经济预测中的一切中发挥着作用。他们剥夺了在卑鄙的全球财务日常水中洗涤的信息 - 现在在Petabytes或数十亿兆字节中测量的数据。在几周内，没有人类的人类可以在几周内突出这些算法，在第二个部分中，他们需要通过数据流失。
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。
Fresh data can yield trends and new peer groups tied together by market sentiment, supply-chain relationships or news events. Better yet for investors, novel peer groups often exhibit unique trading patterns.
“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。“
What does this mean for securities analysts? Some advocates of big data believe computing power and predictive algorithms will sweep away traditional analysts and, by extension, a traditional approach to investing. The outcome depends on how effective some of these algorithm-based predictive systems prove to be and whether traditional investment research and active portfolio management can make an effective case for survival.
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The Physics of Wall Street: A Brief History of Predicting the Unpredictable。
医学研究提供了一个经典的例子。1972年的研究要求在生物清单，193年霍奇金淋巴瘤患者的生存期内预测诱发人。专家预测与实际存活时间之间的相关性为零。但是，这些医生产生的活检和通过多元回归模型的编码准确地预测了生存时间。威慑此操作说明了人类和自动角色之间的关键差异。研究人员更好地识别计算机需要运行的变量。计算机可以更有效地综合以系统的方式来制作预测的信息。
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亚历山大投资研究与技术那AlphaGenius Technologies那Dataminr那数字抹子那卢博纳研究那MarketyCh.那叙事科学那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。
用于利用新闻分析和市场情绪的窗口不仅仅是在黑暗池中就像股票贸易一样闪光;他们可以，从业者断言，最后几天，几个月或更长时间，从而与投资分析相关。“我已经看到了客户关于我们有关投资视野的信息，长达三年，”Ravenpack和前投资组合经理的定量研究总监彼得哈菲兹说。他的客户寻求数量框架，以获得不需要分析师解释的新闻。“当您获得更强大的新闻分析和更多信息时，您可以绕过分析师并在数据本身中找到价值，”他说。
2月份哈佛商业评论发表了一个案例研究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。
To test the value and life span of news analytics, a series of recent research papers by Deutsche Bank focuses on state-of-the-art sentiment analysis. Quantitative strategist Rochester Cahan and his colleagues explore using news — known in the jargon as unstructured data — in stock selection. Their conclusion: The real value in news and Internet data lies beyond simple long positive–short negative sentiment strategies.
Sentiment in absolute terms has less meaning than sentiment relative to market expectations, the Deutsche team concludes. Successful financial models extract alpha from news by capturing complex interactions between sentiment and market data variables like price and volume. “If a company has lots of good sentiment, people writing good things on blogs, tweeting good things, there is an automatic assumption that that is a positive story and you should buy,” Cahan says. “Markets don’t work like that. What matters is expectation.”
情绪提供细致入微的代理的期许n. Value, bias and context all color sentiment, says E. Paul Rowady Jr., a senior analyst at TABB Group, which monitors news analytics. A layoff might be good or bad for sentiment, depending on its context. Some are buy signals; others, sell signals. Bad news for a Ford Motor Co. supplier might suggest good news for General Motors Co., or not. Strong earnings by a market leader might force rivals to scramble to maintain market share or, conversely, surf a rising tide. Programmed properly, news analytics algorithms can recognize implications and sentiment in context.
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新闻分析产品预测市场未来，比传统分析师更好吗？这是一个抵抗明确答案的问题。一方面，新闻分析公司签署协议不会泄露客户的名称，可理解地不想暴露投资策略。（据报道，许多更大的公司一直在使用这种算法工具。）此外，这些分析系统是多种多样的，更快地变化。除了供应商和罕见的推荐方面的反向，还有成功或失败的证据在很大程度上是间接和粗略的。
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.
For its part, Quantopian is trying to democratize algorithm development for smaller asset managers like Legacy and even for consumers. It claims to have the first algorithmic trading platform in a browser. And the company is planning to release a discount trading platform, meaning that algorithmic trading, and algorithm development, could be coming to retail investing.
一些怀疑论者继续发现新闻分析少于令人叹为观止。“也许我们在曲线后面，但这不是我们在这里使用的话题，”全球投资公司的股权研究主任说。纽约的投资技术集团为对冲基金提供许多服务和第三方研究;事实上，它的一个营销口号“来自噪音的解码信号”可能来自一个大数据供应商。但ITG没有“新闻分析中的交通”，发言人说。2011年底，伦敦的Derwent Capital Markets在Twitter上推出了一个基于Twitter的基金，在运营的第一个月内庆祝了巨额1.85％的收益。一年后，随着期望的回报，Derwent关闭了基于Twitter的基金。
Human judgment and oversight appear resilient to Christopher Cutler, a former chairman of the Alternative Investments Committee of the New York Society of Security Analysts. “Could this be a big game changer?” he asks. “I wouldn’t overestimate it. Too many things on the fundamental side of investing only humans can take a look at.” By way of illustration, Cutler cites a conversation between an analyst and a corporate executive that yields insight into the mispricing of products by rivals.
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。”
算法在他们可以在最小的人类干扰提供投资建议之前有更多的学习。但是，分析师希望判断总是胜过算法可能面临着一个粗鲁的惊喜。当智能手机所有者可以作证时，自然语言处理允许算法分析日常英语，取得了进步。读扑克诈唬需要对语义细胞的敏感性。使用多种算法的Deew Blue的IBM继任者命名Watson，着名的人类冠军危险！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，是前者福布斯technology 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.
That may verge on the glibly apocalyptic, but there clearly are larger forces at work throughout the white-collar economy. Nobel laureate and Princeton University economist Paul Krugman echoed the warning in a最近的New York Timescolumn旨在思考高级学历的工人意味着工作保障。“他写的技术对劳动力的影响更为黑暗的照片，”他写道。“在这张照片中，受过高等教育的工人可能与受过教育的工人一样可能发现自己流离失所和贬值。”
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。”
What we need to know is whether that news-driven volatility is just a passing fancy or a substantive development, a summer storm or climate change. If only we had an algorithm to tell us that. • •
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