“Man vs machine” has been a popular discussion in the investment management industry in the last few years. The proponents of the cutting edge technology application to portfolio management claim that algorithms are more efficient and less prone to emotional biases than human investors. They even believe that some day in the not so distant future, artificial intelligence will completely take over the entire industry of active asset management leaving intuition based fundamental investors behind.

在过去的几年中，“人与机器”已成为投资管理行业的热门话题。 支持投资组合管理的最前沿技术的支持者认为，与人类投资者相比，算法更有效且不易产生情感偏见。 他们甚至认为，在不远的将来的某一天，人工智能将完全取代主动资产管理的整个行业，而将基于直觉的基本投资者抛在身后。

Their opponents, equally smart and experienced financial experts, argue that AI no matter how advanced, will never be able to figure out the ever changing markets. Their key argument is that markets are very different from the game of chess, that mere actions of market participants constantly change rules of the game and thus patterns found in history are useless.

他们的对手，同样精明和经验丰富的金融专家，认为AI不管多么先进，都永远无法弄清不断变化的市场。 他们的主要论点是市场与国际象棋游戏有很大的不同，仅市场参与者的行为会不断改变游戏规则，因此历史上发现的模式是无用的。

To make things even more confusing, media coverage on emotionless algorithms is ironically often very emotional and lacks important technical details. Because the headline “Robots beat humans in the Wall Street game” will probably attract a bigger audience than “Statistical analysis of multidimensional big data sets helps identify short term market price dislocations”.

更令人困惑的是，具有讽刺意味的是，媒体对无情感算法的报道往往令人非常激动，并且缺乏重要的技术细节。 因为标题“机器人在华尔街游戏中击败了人类”可能会比“多维大数据集的统计分析有助于识别短期市场价格错位”吸引更多的受众。

Machine learning by itself is not a trading strategy. There are multiple different investment strategies across various asset classes that can benefit from machine learning capabilities. Generalizing based merely on use of the technology is rather meaningless. What matters is the market inefficiency the strategy aims to explore and whether machine learning adds value to that specific process.

机器学习本身不是交易策略。 可以从机器学习功能中受益的各种资产类别有多种不同的投资策略。 仅基于技术的使用进行概括是没有意义的。 重要的是该策略旨在探索市场效率低下以及机器学习是否为该特定过程增加了价值。

Statistical arbitrage — a short term trading strategy that employs mean reversion models — became one of the earliest practical applications of machine learning in investment management. The strategy doesn’t imply directional bets or exposure to the broader market moves, focusing instead on relationships dynamics between factors and prices.

统计套利(一种采用均值回归模型的短期交易策略)已成为机器学习在投资管理中最早的实际应用之一。 该策略并不意味着方向性押注或对大盘走势的关注，而是关注因素与价格之间的关系动态。

The idea of mean reversion assumes that stock prices regardless of fluctuations eventually get back to normal. The opportunity is in finding the “errors” when stock price behavior is different from “normal” explained by historical relations, as such “errors” are supposed to disappear over time. The tricky part is to identify factors or benchmarks that represents the market equilibrium.

均值回归的思想假设股价无论波动如何最终都能恢复正常。 当股票价格行为不同于历史关系所解释的“正常”时，机会就是找到“错误”，因为这种“错误”应该随着时间而消失。 棘手的部分是确定代表市场均衡的因素或基准。

But why would such opportunities even exist in seemingly efficient markets? The reason lies in the actions of the largest market participants — mutual funds, ETFs, large fundamental hedge funds. They dominate the markets in terms of the amounts of capital under management and even in diversified portfolios, their individual positions are very large. When such giants rebalance their portfolios to express their long-term view they don’t care that much about market impact and the precise execution price. With a long investment horizon they operate on a big picture level relying on fundamental considerations in their research. Large player’s trades move the market away from the equilibrium, and that is something shorter term traders can benefit from by exploiting temporary imbalance between what certain correlations are supposed to be and what they are at the moment. In other words, arbitrage opportunities come from other people’s actions and reactions to price moves. With so many active market participants, this source of opportunities is here to stay. Profit margin on each individual arbitrage trade is slim, especially after accounting for trading costs, but even a tiny edge can be just enough to build a profitable strategy.

但是，为什么在看似有效的市场中甚至还会存在这样的机会？ 原因在于最大的市场参与者(共同基金，ETF，大型基本对冲基金)的行动。 它们在管理的资金数量上甚至在分散的投资组合中都主导着市场，它们的个人头寸非常大。 当这些巨头重新平衡其投资组合以表达其长期看法时，他们并不在乎市场影响和精确的执行价格。 由于投资期很长，他们在总体上依靠研究中的基本考虑因素来运作。 大型参与者的交易使市场远离均衡，而短期交易者可以通过利用某些相关假设与当前相关之间的暂时失衡而受益。 换句话说，套利机会来自其他人对价格变动的行动和React。 由于有这么多活跃的市场参与者，这种机会的来源将一直存在。 每项套利交易的利润率都很低，尤其是在考虑了交易成本之后，但是即使是很小的优势也足以建立有利可图的策略。

Years ago stat arb funds exploited simple pair trades, relying on rather obvious correlations (e.g. historically stock A always did better than stock B in a bull market, but in a recent market rally they both added 25%. A trader would then sell stock A and buy B in anticipation that their relative performance will be back to historically normal). But those no longer work. As more people chase the same arbitrage opportunities, already thin profit margins start fading away. That doesn’t mean there are no more inefficiencies and opportunities though. Markets constantly evolve and become not only more efficient but also more complex and interconnected. There are plenty of factors that impact market prices; any information that can be digitized and tested potentially represent a source of market moving signals.

几年前，统计套利基金利用相当明显的相关性来利用简单的配对交易(例如，在牛市中，历史上股票A总是比股票B更好，但是在最近的市场反弹中，它们都增加了25％。然后交易者会卖出股票A并购买B，以期望其相对表现会恢复到历史正常水平)。 但是那些不再起作用。 随着越来越多的人追逐相同的套利机会，本已微薄的利润率开始逐渐消失。 但这并不意味着就没有低效率和机会。 市场在不断发展，不仅变得更有效率，而且变得更加复杂和相互联系。 有很多因素会影响市场价格； 任何可以数字化和测试的信息都可能表示市场动荡的信号源。

Investment strategy focused on exploring short term market inefficiencies thus became a natural application of machine learning. Algorithms can identify subtle multidimensional anomalies an investor can’t see by the unaided eye. Of course, there is a risk of identifying false coincidental correlations (a commonly criticized quant wishful thinking called overfitting), but with the right testing process in place, this can be avoided. For statistical significant signals lack of intuitive explainability is not necessarily a deal breaker. The good thing about using deep factors that are not easily explainable is that those signals are rarely overcrowded.

专注于探索短期市场效率低下的投资策略因此成为机器学习的自然应用。 算法可以识别出肉眼看不到的细微多维异常。 当然，存在识别错误​​的巧合相关性的风险(通常被批评的量化一厢情愿的想法称为过拟合)，但是如果有正确的测试过程，就可以避免这种情况。 对于统计上重要的信号，缺乏直观的解释性并不一定会破坏交易。 使用难以解释的深层因素的好处是，这些信号很少会过分拥挤。

Identifying inefficiencies is just the beginning of the process though. Most of the signals are just not strong enough to support sizable bets. To produce solid results, thousands of strategies need to be combined and assigned carefully calibrated weights. The optimal portfolio with the target risk profile is ultimately a unified system, not a random collection of strategies.

但是，识别低效率只是该过程的开始。 大多数信号不够强大，无法支持可观的下注。 为了产生可靠的结果，需要组合成千上万种策略并仔细分配经过校准的权重。 具有目标风险特征的最佳投资组合最终是一个统一的系统，而不是随机的策略集合。

On top of that, execution limitations should be taken into account. If the model suggestions are impractical (for example, it suggests to sell short a stock that can’t be borrowed) realized profits of such a strategy will be very disappointing. One of the biggest execution limitations is market impact. The larger the trade is the more it will move the market and reduce profit margin. Modeling market impact is yet another important application of machine learning in the quant investing process.

最重要的是，应该考虑执行限制。 如果模型建议不切实际(例如，建议卖空不能借入的股票)，则该策略的实现利润将非常令人失望。 最大的执行限制之一是市场影响力。 交易越大，越会打动市场并降低利润率。 对市场影响进行建模是机器学习在定量投资过程中的另一个重要应用。

Perhaps the biggest misconception about machine learning funds is that all it takes to succeed is to buy a commercialized dataset and an off-the-shelf machine learning algorithm. This myth leads to an unreasonable expectation that high tech will make competitive alpha generation easier. In reality it is quite the opposite, the proliferation of big data and machine learning will further raise the entry barrier and make the hedge fund industry more competitive.

关于机器学习资金的最大误解可能是，要想成功，要做的就是购买商业化的数据集和现成的机器学习算法。 这个神话导致对高科技将使竞争性alpha生成变得更加容易的不合理预期。 实际上，情况恰恰相反，大数据和机器学习的激增将进一步提高准入门槛，并使对冲基金行业更具竞争力。

Even for the most talented quant teams, it may take years of hard work and millions of dollars in investments in R&D to build a highly complex custom-made system to collect and process data, identify factors, test signals, create an optimal portfolio and execute with minimum possible market impact. But once the whole system is combined into an elegant monolithic model, economies of scale become apparent, and it may become a self-improving machine that just requires data as a fuel.

即使对于最有才华的量化团队，建立一个高度复杂的定制系统来收集和处理数据，识别因素，测试信号，创建最佳组合并执行，也可能需要花费多年的辛勤工作和数百万美元的研发投资。尽可能减少对市场的影响。 但是，一旦将整个系统组合成一个优雅的整体模型，规模经济就会变得显而易见，并且它可能会成为一种自我完善的机器，只需要数据作为燃料。

If machine learning works for investment research, then why hasn’t it taken over the industry yet and why investors still work with fundamental researchers and discretionary fund managers? The reason is in the nature of the trading strategies machine learning is most suitable for (at least for now). A high Sharpe strategy exploiting short-term opportunity faces a tradeoff between scale and profitability. Statistical arbitrage is limited in capacity, some of the successful quant teams can’t even reinvest profits to achieve compounding growth without shrinking profit margins. And when they face a dilemma on whether to take 20% of an investor’s profit or retain 100% of return, simply borrowing money through leverage seems like a much cheaper option.

如果机器学习用于投资研究，那么为什么它还没有接管整个行业，为什么投资者仍然与基础研究人员和全权委托基金经理一起工作？ 原因是(至少目前)机器学习最适合交易策略的性质。 利用短期机会的高Sharpe战略面临规模与盈利能力之间的权衡。 统计套利的能力是有限的，一些成功的量化团队甚至不能在不降低利润率的情况下对利润进行再投资以实现复合增长。 当他们面对是要获得投资者利润的20％还是保留100％的回报的困境时，仅仅通过杠杆借钱似乎是一种便宜得多的选择。

One way for successful stat arb teams to accommodate external investors without diluting their high-Sharpe strategy is to offer a separate product with a longer holding period and higher capacity. Of course, the returns of such scalable longer-term programs will not be the same as those of short-term high Sharpe funds, but investors can still get an attractive level of risk adjusted return and benefit from the state-of-the-art infrastructure and research process powered by machine learning.

成功的统计应用rb团队在不削弱其高清晰度策略的情况下容纳外部投资者的一种方法是提供具有更长持有期限和更高容量的单独产品。 当然，此类可扩展的长期计划的收益将与短期高额夏普基金的收益不同，但投资者仍可以获得诱人的风险调整后收益水平，并从最新技术中受益机器学习支持的基础设施和研究过程。

Stat arb funds are an example of early adopters of machine learning, but that is by far not the only investment strategy that can benefit from the technology. A growing number of portfolio managers who seemingly have nothing to do with quant trading, have already started using products of natural language processing and image recognition as inputs into their research process. In the nearest future virtually all asset managers will utilize machine learning techniques either by developing their own tools, or by consuming some sort of information product created by a third party provider using elements of machine learning.

Stat Arb基金是机器学习的早期采用者的一个例子，但到目前为止，这并不是唯一可以从该技术中受益的投资策略。 越来越多的看似与量化交易无关的投资组合经理已经开始使用自然语言处理和图像识别产品作为研究过程的输入。 在不久的将来，几乎所有资产管理者都将通过开发自己的工具或通过使用第三方提供商使用机器学习元素来消费某种信息产品来利用机器学习技术。