Algorithms are a sequence of steps or rules designed to achieve a goal. They can take many forms and facilitate optimization throughout the investment process, from idea generation to asset allocation, trade execution, and risk management. The examples in this book will illustrate how ML algorithms can extract information from data to support or automate key investment activities. Ultimately, the goal of active investment management is to generate alpha, defined as portfolio returns in excess of the benchmark used for evaluation.
The fundamental law of active management postulates that the key to generating alpha is having accurate return forecasts combined with the ability to act on these forecasts Grinold ; Grinold and Kahn binex option This law defines the information ratio IR to express the value of active management as the ratio of the return difference between the portfolio and a benchmark to the volatility of those returns.
This is where ML comes in: applications of ML for trading ML4T typically aim to make more efficient use of a rapidly diversifying range of data to produce both better and more actionable forecasts, thus improving the quality of investment decisions and results.
Historically, algorithmic trading used to be more narrowly defined as the automation of trade execution to minimize the costs offered by the sell-side. This book takes a more comprehensive perspective since the use of algorithms in general and ML in particular has come to impact a broader range of activities, from generating ideas and extracting signals from data to asset allocation, position-sizing, and testing and evaluating strategies.
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This chapter looks at industry trends that have led to the emergence of ML as a source of competitive advantage in the investment industry. We will also look at where ML fits into the trading is one hundred and learning process to enable algorithmic trading strategies. The rise of ML in the investment industry The investment industry has evolved dramatically over the last several decades and continues to do so amid increased competition, technological advances, and a challenging economic environment.
This section reviews key trends that have shaped the overall investment environment and the context for algorithmic trading and the use of ML more specifically. One outcome is the rise in low-cost passive investment vehicles in the form of exchange-traded funds ETFs. From electronic to high-frequency trading Electronic trading has advanced dramatically in terms of capabilities, volume, coverage of asset classes, and geographies since networks started routing prices to computer terminals in the s.
Equity markets have been at the forefront of this trend worldwide. See Harris and Strumeyer for comprehensive coverage of relevant changes in financial markets; we will return to this topic when we cover how to work with market and fundamental data in the next chapter. The order-handling rules by the SEC introduced competition to exchanges through electronic communication networks ECNs. ECNs are automated alternative trading systems ATS that match buy-and-sell orders at specified prices, primarily for equities and currencies, and are registered as broker-dealers.
It allows significant brokerages and individual traders in different geographic locations to trade directly without intermediaries, both on exchanges and after hours.
Dark pools are another type of private ATS that allows institutional investors to trade large orders without publicly revealing their information, contrary to how exchanges managed their order books prior to competition from ECNs. Dark pools do not publish pre-trade bids and offers, and trade prices only become public some time after execution. They have grown substantially since the mids to account for 40 percent of equities traded in the US due to concerns about adverse price movements of large orders and order front-running by high-frequency traders.
With the rise of electronic trading, algorithms for cost-effective execution developed rapidly and adoption spread quickly from the sell-side to the buy-side and across asset classes. Automated trading emerged around as a sell-side tool aimed at cost-effective execution that broke down orders into smaller, sequenced chunks to limit their market impact.
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These tools spread to the buy side and became increasingly sophisticated by taking into account, for example, transaction costs and liquidity, as well as short-term price and volume forecasts. Direct market access DMA gives a trader greater control over execution by allowing them to send orders directly to the exchange using the infrastructure and market participant identification of a broker who is a member of an exchange.
Sponsored access removes pre-trade risk controls by the brokers and forms the basis for high-frequency trading HFT. HFT refers to automated trades in financial instruments that are executed with extremely low latency in the microsecond range and where participants hold positions for very short periods.
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The goal is to detect and exploit inefficiencies in the market microstructure, the institutional infrastructure of trading venues. HFT has also grown in futures markets to roughly 80 percent of foreign-exchange futures volumes and two-thirds of both interest rate and Treasury year futures volumes Miller HFT strategies aim to earn small profits per trade using passive or aggressive strategies.
Passive strategies include arbitrage trading to profit from very small price differentials for the same asset, or its derivatives, traded on different venues. Aggressive strategies include order anticipation or momentum ignition. Order anticipation, also known as liquidity detection, involves algorithms that submit small exploratory orders to detect hidden liquidity from large institutional investors and trade ahead of a large order to benefit from subsequent price extreme in trading. Regulators have expressed concern over the potential link between certain aggressive HFT strategies and increased market fragility and volatility, such as that experienced during the May Flash Crash, the October Treasury market volatility, and trading is one hundred and learning sudden crash by over 1, points of the Dow Jones Industrial Average on August 24, At the same time, market liquidity has increased with trading volumes due to the presence of HFT, which has lowered overall transaction costs.
The combination of reduced trading volumes amid lower volatility and rising costs of technology and access to both data and trading venues has led to financial pressure.
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This trend has led to industry consolidation, with various acquisitions by, for example, the largest listed proprietary trading firm, Virtu Financial, and shared infrastructure investments, such as the new Go West ultra-low latency route between Chicago and Tokyo. Factor investing and smart beta funds The return provided by an asset is a function of the uncertainty binary options robot program risk associated with the investment.
An equity investment implies, for example, assuming a company's business risk, and a bond investment entails default risk. To the extent that specific risk characteristics predict returns, identifying and forecasting the behavior of these risk factors becomes a primary focus when designing an investment strategy.
It yields valuable trading signals and is the key to superior active-management results. The industry's understanding of risk factors has evolved very substantially over time and has impacted how ML is used for trading.
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Modern portfolio theory MPT introduced the distinction between idiosyncratic and systematic sources of risk for a given asset. Idiosyncratic risk can be eliminated through diversification, but systematic risk cannot. In the early s, the capital asset pricing model CAPM identified a trading is one hundred and learning factor driving all asset returns: the return on trading is one hundred and learning market portfolio in excess of T-bills.
The market portfolio consisted of all tradable securities, weighted by their market value. The recognition that the risk of an asset does not depend on the asset in isolation, but rather how it moves relative to other assets and the market as a whole, was a major conceptual breakthrough. In other words, assets earn a risk premium based on their exposure to underlying, common risks experienced by all assets, not due to their specific, idiosyncratic characteristics.
Subsequently, academic research and industry experience have raised numerous critical questions regarding the CAPM prediction that an asset's risk premium depends only on its exposure to a single factor measured by the asset's beta. Instead, numerous additional risk factors have since been discovered.
These risk factors were labeled anomalies since they contradicted the efficient market hypothesis EMH. The EMH maintains that market equilibrium would always price securities according to the CAPM so that no other factors should have predictive power Malkiel Well-known anomalies include the value, size, and momentum effects that help predict returns while controlling for the CAPM market factor.
The size effect rests on small firms systematically outperforming large firms Banz ; Reinganum The value effect Basu et. It suggests that firms with low price multiples, such as the price-to-earnings or the price-to-book ratios, perform better than their more expensive peers as suggested by the inventors of value investing, Benjamin Graham and David Dodd, and popularized by Warren Buffet.
The momentum effect, discovered in the late s by, among others, Clifford Asness, the founding partner of AQR, states that stocks with good momentum, in terms of recent month returns, have higher returns going forward than poor momentum stocks with similar market risk.
In fixed income, the value strategy is called riding the yield curve and is a form of the duration premium. In commodities, it is called the roll return, with a positive return for an upward-sloping futures curve and a negative return otherwise.
There is also an illiquidity premium. Securities that are more illiquid trade at low prices and have high average excess returns, relative to their more liquid counterparts. Bonds with a higher default risk tend to have higher returns on average, reflecting a credit risk premium. Multifactor models define risks in broader and more diverse terms than just the market portfolio.
InStephen Ross proposed the arbitrage pricing theory, false signals on binary options asserted that investors are compensated for multiple systematic sources of risk that cannot be diversified away Roll and Ross The three most important macro factors are growth, inflation, and volatility, in addition to productivity, demographic, and political risk.
InEugene Fama and Kenneth French combined the equity risk factors' size and value with a market factor into a single three-factor model that better explained cross-sectional stock returns.
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They later added a model that also included bond risk factors to simultaneously explain returns for both asset classes Fama and French ; A particularly attractive aspect of risk factors is their low or negative correlation.
Value and momentum risk factors, for instance, are negatively correlated, reducing the risk and increasing risk-adjusted returns above and beyond the benefit implied by the risk factors.
Furthermore, using leverage and long-short strategies, factor strategies can be combined into market-neutral approaches. The combination of long positions in securities exposed to positive risks with underweight or short positions in the securities exposed to negative risks allows for the collection of dynamic risk premiums.
As a result, the factors that explained returns above and beyond the CAPM were incorporated into investment styles that tilt portfolios in favor of one or more factors, and assets began to migrate into factor-based portfolios. The financial crisis underlined how asset-class labels could be highly misleading and create a false sense of diversification when investors do not look at the underlying factor risks, as asset classes came crashing down together. Over the past several decades, quantitative factor investing has evolved from a simple approach based on two or three styles to multifactor smart or exotic beta products.
Smart beta funds take a passive strategy but modify it according to one or more factors, such as cheaper stocks or screening them according to dividend payouts, to generate better returns.
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Trading is one hundred and learning ongoing discovery and successful forecasting of risk factors that, either individually or in combination with other risk factors, significantly impact future asset returns across asset classes is a key driver of the surge in ML in the investment industry and will be a key theme throughout this book.
Algorithmic pioneers outperform humans The track record and growth of assets under management AUM of firms that spearheaded algorithmic trading has played a key role in generating investor interest and subsequent industry efforts to replicate their success.
Systematic strategies that mostly or exclusively rely on algorithmic decision-making were most famously introduced by mathematician James Simons, who founded Renaissance Technologies in buy bitcoin commission built it into the premier quant firm. Two Sigma, started only in by D.
Similarly, on the Institutional Investors Hedge Fund list, the four largest firms, and five of the top six firms, rely largely or completely on computers and trading algorithms to make investment decisions—and all of them have been growing their assets in an otherwise challenging environment.
Several quantitatively focused firms climbed the ranks and, in some cases, grew their assets by double-digit percentages.
As a result, algorithmic approaches are not only finding wider application in the hedge-fund industry that pioneered these strategies but across a broader range of asset managers and even passively managed vehicles such as ETFs.
In particular, predictive analytics using ML and algorithmic automation play an increasingly prominent role in all steps of the investment process across asset classes, from idea generation and research to strategy formulation binary options robot reviews portfolio construction, trade execution, and risk management.
Estimates of industry size vary because there is no objective definition of a quantitative or algorithmic fund. Many traditional hedge funds or even mutual funds and ETFs are introducing computer-driven trading is one hundred and learning or integrating them into a discretionary environment in a human-plus-machine approach. Quick extra income to the Economist, insystematic funds became the largest driver of institutional trading in the US stock market ignoring HFT, which mainly acts as a middleman.
The three types of computer-managed funds—index funds, ETFs, and quant funds—run around 35 percent, whereas human managers at traditional hedge funds and other mutual funds manage just 24 percent. The market research firm Preqin estimates that almost 1, hedge funds make a majority of their trades with help from computer models. Quantitative hedge funds are now responsible for 27 percent of all US stock trades by investors, up from 14 percent trading is one hundred and learning In recent years, however, funds have moved toward true ML, where artificially intelligent systems can analyze large amounts of data at speed and improve themselves through such analyses.
Recent examples include Rebellion Research, Sentient, and Aidyia, which rely on evolutionary algorithms and deep learning to devise fully automatic artificial intelligence AI -driven investment platforms.
From the core hedge fund industry, the adoption of algorithmic strategies has spread to mutual funds and even passively managed EFTs in the form of smart beta funds, and to discretionary funds in the form of quantamental approaches. The emergence of quantamental funds Two distinct approaches have evolved in active investment management: systematic or quant and discretionary investing.
Systematic approaches rely on algorithms for a repeatable and data-driven approach to identify investment opportunities across many securities.
These two approaches are becoming more similar as fundamental managers take more data science-driven approaches. Agnostic to specific companies, quantitative funds trade based on patterns and dynamics across a wide swath of securities. Such quants accounted for about 17 percent of total hedge fund assets, as data compiled by Barclays in showed. Point72 is also investing tens of millions of dollars into a group that analyzes large amounts of alternative data and passes the results on to traders.
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Morgan Stanley noted internet earnings formula only 23 percent of its quant clients say they are not considering using or not already using ML, down from 44 percent in AQR is a quantitative investment group that relies on academic research to identify and systematically trade factors that have, over time, proven to beat the broader market. The firm used to eschew the purely computer-powered strategies of quant peers such as Renaissance Technologies or DE Shaw.
Franklin Templeton bought Random Forest Capital, a debt-focused, data-led investment company, for an undisclosed amount, hoping that its technology can support the wider asset manager. ML and alternative data Hedge funds have long looked for alpha through informational advantage and the ability to uncover new uncorrelated signals. Historically, this included things such as proprietary surveys of shoppers, or of voters ahead of elections or referendums. Conventional data includes economic statistics, trading data, or corporate reports.