From its inception, in , Alibaba experienced great growth on its e-commerce platform. Over the course of the meeting, our disjointed observations and ideas about e-commerce trends began to coalesce into a larger view of the future, and by the end, we had agreed on a vision. Our strategic imperative was to make sure that the platform provided all the resources, or access to the resources, that an online business would need to succeed, and hence supported the evolution of the ecosystem.
The ecosystem we built was simple at first: We linked buyers and sellers of goods. As technology advanced, more business functions moved online—including established ones, such as advertising, marketing, logistics, and finance, and emerging ones, such as affiliate marketing, product recommenders, and social media influencers. Alibaba today is not just an online commerce company. It is what you get if you take all functions associated with retail and coordinate them online into a sprawling, data-driven network of sellers, marketers, service providers, logistics companies, and manufacturers.
In other words, Alibaba does what Amazon, eBay, PayPal, Google, FedEx, wholesalers, and a good portion of manufacturers do in the United States, with a healthy helping of financial services for garnish. Why has so much value and market power emerged so quickly? Because of new capabilities in network coordination and data intelligence that all these companies put to use. The ecosystems they steward are vastly more economically efficient and customer-centric than traditional industries.
These firms follow an approach I call smart business, and I believe it represents the dominant business logic of the future. Smart business emerges when all players involved in achieving a common business goal—retailing, for example, or ride sharing—are coordinated in an online network and use machine-learning technology to efficiently leverage data in real time. This tech-enabled model, in which most operational decisions are made by machines, allows companies to adapt dynamically and rapidly to changing market conditions and customer preferences, gaining tremendous competitive advantage over traditional businesses.
Ample computing power and digital data are the fuel for machine learning, of course. The more data and the more iterations the algorithmic engine goes through, the better its output gets. Data scientists come up with probabilistic prediction models for specific actions, and then the algorithm churns through loads of data to produce better decisions in real time with every iteration. These prediction models become the basis for most business decisions. Thus machine learning is more than a technological innovation; it will transform the way business is conducted as human decision making is increasingly replaced by algorithmic output.
Ant Microloans provides a striking example of what this future will look like. When Alibaba launched Ant, in , the typical loan given by large banks in China was in the millions of dollars. Banks were reluctant to service companies that lacked any kind of credit history or even adequate documentation of their business activities.
As a consequence, tens of millions of businesses in China were having real difficulties securing the money necessary to grow their operations. At Alibaba, we realized we had the ingredient for creating a high-functioning, scalable, and profitable SME lending business: the huge amount of transaction data generated by the many small businesses using our platform.
In , we bundled this lending operation together with Alipay, our very successful payments business, to create Ant Financial Services. We gave the new venture that name to capture the idea that we were empowering all the little but industrious, antlike companies. How is this possible?
Algorithmic trading - Wikipedia
When faced with potential borrowers, lending institutions need answer only three basic questions: Should we lend to them, how much should we lend, and at what interest rate? Once sellers on our platforms gave us authorization to analyze their data, we were well positioned to answer those questions.
Our algorithms can look at transaction data to assess how well a business is doing, how competitive its offerings are in the market, whether its partners have high credit ratings, and so on. Ant uses that data to compare good borrowers those who repay on time with bad ones those who do not to isolate traits common in both groups. Those traits are then used to calculate credit scores. All lending institutions do this in some fashion, of course, but at Ant the analysis is done automatically on all borrowers and on all their behavioral data in real time.
At the same time, the algorithms that calculate the scores are themselves evolving in real time, improving the quality of decision making with each iteration. The algorithms might, for example, analyze the frequency, length, and type of communications instant messaging, e-mail, or other methods common in China to assess relationship quality. This work requires both a deep understanding of the business and expertise in machine-learning algorithms.
Consider again Ant Financial. If a seller deemed to have poor credit pays back its loan on time or a seller with excellent credit catastrophically defaults, the algorithm clearly needs tweaking. Engineers can quickly and easily check their assumptions. Which parameters should be added or removed? Which kinds of user behavior should be given more weight?
To become a smart business, your firm must enable as many operating decisions as possible to be made by machines fueled by live data rather than by humans supported by their own data analysis. Transforming decision making in this way is a four-step process. Ant was fortunate to have access to plenty of data on potential borrowers to answer the questions inherent in its lending business. For many businesses, the data capture process will be more challenging. But live data is essential to creating the feedback loops that are the basis of machine learning. Market timing algorithms will typically use technical indicators such as moving averages but can also include pattern recognition logic implemented using Finite State Machines.
Backtesting the algorithm is typically the first stage and involves simulating the hypothetical trades through an in-sample data period. Optimization is performed in order to determine the most optimal inputs. Forward testing the algorithm is the next stage and involves running the algorithm through an out of sample data set to ensure the algorithm performs within backtested expectations. Live testing is the final stage of development and requires the developer to compare actual live trades with both the backtested and forward tested models.
Metrics compared include percent profitable, profit factor, maximum drawdown and average gain per trade. As noted above, high-frequency trading HFT is a form of algorithmic trading characterized by high turnover and high order-to-trade ratios. Although there is no single definition of HFT, among its key attributes are highly sophisticated algorithms, specialized order types, co-location, very short-term investment horizons, and high cancellation rates for orders.
High-frequency funds started to become especially popular in and Among the major U. There are four key categories of HFT strategies: market-making based on order flow, market-making based on tick data information, event arbitrage and statistical arbitrage. All portfolio-allocation decisions are made by computerized quantitative models. The success of computerized strategies is largely driven by their ability to simultaneously process volumes of information, something ordinary human traders cannot do.
Market making involves placing a limit order to sell or offer above the current market price or a buy limit order or bid below the current price on a regular and continuous basis to capture the bid-ask spread. Another set of HFT strategies in classical arbitrage strategy might involve several securities such as covered interest rate parity in the foreign exchange market which gives a relation between the prices of a domestic bond, a bond denominated in a foreign currency, the spot price of the currency, and the price of a forward contract on the currency.
If the market prices are sufficiently different from those implied in the model to cover transaction cost then four transactions can be made to guarantee a risk-free profit. HFT allows similar arbitrages using models of greater complexity involving many more than 4 securities.
A wide range of statistical arbitrage strategies have been developed whereby trading decisions are made on the basis of deviations from statistically significant relationships. Like market-making strategies, statistical arbitrage can be applied in all asset classes. A subset of risk, merger, convertible, or distressed securities arbitrage that counts on a specific event, such as a contract signing, regulatory approval, judicial decision, etc. Merger arbitrage also called risk arbitrage would be an example of this.
Merger arbitrage generally consists of buying the stock of a company that is the target of a takeover while shorting the stock of the acquiring company. Usually the market price of the target company is less than the price offered by the acquiring company. The spread between these two prices depends mainly on the probability and the timing of the takeover being completed as well as the prevailing level of interest rates.
The bet in a merger arbitrage is that such a spread will eventually be zero, if and when the takeover is completed. The risk is that the deal "breaks" and the spread massively widens. One strategy that some traders have employed, which has been proscribed yet likely continues, is called spoofing.
It is the act of placing orders to give the impression of wanting to buy or sell shares, without ever having the intention of letting the order execute to temporarily manipulate the market to buy or sell shares at a more favorable price. This is done by creating limit orders outside the current bid or ask price to change the reported price to other market participants. The trader can subsequently place trades based on the artificial change in price, then canceling the limit orders before they are executed.
The trader then executes a market order for the sale of the shares they wished to sell. The trader subsequently cancels their limit order on the purchase he never had the intention of completing. Quote stuffing is a tactic employed by malicious traders that involves quickly entering and withdrawing large quantities of orders in an attempt to flood the market, thereby gaining an advantage over slower market participants.
HFT firms benefit from proprietary, higher-capacity feeds and the most capable, lowest latency infrastructure. Researchers showed high-frequency traders are able to profit by the artificially induced latencies and arbitrage opportunities that result from quote stuffing. Network-induced latency, a synonym for delay, measured in one-way delay or round-trip time, is normally defined as how much time it takes for a data packet to travel from one point to another. Low-latency traders depend on ultra-low latency networks.
They profit by providing information, such as competing bids and offers, to their algorithms microseconds faster than their competitors.
This is due to the evolutionary nature of algorithmic trading strategies — they must be able to adapt and trade intelligently, regardless of market conditions, which involves being flexible enough to withstand a vast array of market scenarios. Most of the algorithmic strategies are implemented using modern programming languages, although some still implement strategies designed in spreadsheets. Increasingly, the algorithms used by large brokerages and asset managers are written to the FIX Protocol's Algorithmic Trading Definition Language FIXatdl , which allows firms receiving orders to specify exactly how their electronic orders should be expressed.
More complex methods such as Markov chain Monte Carlo have been used to create these models. Algorithmic trading has been shown to substantially improve market liquidity  among other benefits.
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However, improvements in productivity brought by algorithmic trading have been opposed by human brokers and traders facing stiff competition from computers. Technological advances in finance, particularly those relating to algorithmic trading, has increased financial speed, connectivity, reach, and complexity while simultaneously reducing its humanity. Computers running software based on complex algorithms have replaced humans in many functions in the financial industry. While many experts laud the benefits of innovation in computerized algorithmic trading, other analysts have expressed concern with specific aspects of computerized trading.
Williams said. In its annual report the regulator remarked on the great benefits of efficiency that new technology is bringing to the market. But it also pointed out that 'greater reliance on sophisticated technology and modelling brings with it a greater risk that systems failure can result in business interruption'. UK Treasury minister Lord Myners has warned that companies could become the "playthings" of speculators because of automatic high-frequency trading. Lord Myners said the process risked destroying the relationship between an investor and a company.
Other issues include the technical problem of latency or the delay in getting quotes to traders,  security and the possibility of a complete system breakdown leading to a market crash. They have more people working in their technology area than people on the trading desk The nature of the markets has changed dramatically. This issue was related to Knight's installation of trading software and resulted in Knight sending numerous erroneous orders in NYSE-listed securities into the market.
This software has been removed from the company's systems. Algorithmic and high-frequency trading were shown to have contributed to volatility during the May 6, Flash Crash,   when the Dow Jones Industrial Average plunged about points only to recover those losses within minutes. At the time, it was the second largest point swing, 1, And this almost instantaneous information forms a direct feed into other computers which trade on the news. The algorithms do not simply trade on simple news stories but also interpret more difficult to understand news.
Some firms are also attempting to automatically assign sentiment deciding if the news is good or bad to news stories so that automated trading can work directly on the news story. His firm provides both a low latency news feed and news analytics for traders. Passarella also pointed to new academic research being conducted on the degree to which frequent Google searches on various stocks can serve as trading indicators, the potential impact of various phrases and words that may appear in Securities and Exchange Commission statements and the latest wave of online communities devoted to stock trading topics.
So the way conversations get created in a digital society will be used to convert news into trades, as well, Passarella said. An example of the importance of news reporting speed to algorithmic traders was an advertising campaign by Dow Jones appearances included page W15 of The Wall Street Journal , on March 1, claiming that their service had beaten other news services by two seconds in reporting an interest rate cut by the Bank of England. In late , The UK Government Office for Science initiated a Foresight project investigating the future of computer trading in the financial markets,  led by Dame Clara Furse , ex-CEO of the London Stock Exchange and in September the project published its initial findings in the form of a three-chapter working paper available in three languages, along with 16 additional papers that provide supporting evidence.
Released in , the Foresight study acknowledged issues related to periodic illiquidity, new forms of manipulation and potential threats to market stability due to errant algorithms or excessive message traffic. However, the report was also criticized for adopting "standard pro-HFT arguments" and advisory panel members being linked to the HFT industry. A traditional trading system consists primarily of two blocks — one that receives the market data while the other that sends the order request to the exchange. However, an algorithmic trading system can be broken down into three parts:.
Exchange s provide data to the system, which typically consists of the latest order book, traded volumes, and last traded price LTP of scrip. The server in turn receives the data simultaneously acting as a store for historical database. The data is analyzed at the application side, where trading strategies are fed from the user and can be viewed on the GUI.
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Once the order is generated, it is sent to the order management system OMS , which in turn transmits it to the exchange. Gradually, old-school, high latency architecture of algorithmic systems is being replaced by newer, state-of-the-art, high infrastructure, low-latency networks. The complex event processing engine CEP , which is the heart of decision making in algo-based trading systems, is used for order routing and risk management. With the emergence of the FIX Financial Information Exchange protocol, the connection to different destinations has become easier and the go-to market time has reduced, when it comes to connecting with a new destination.
With the standard protocol in place, integration of third-party vendors for data feeds is not cumbersome anymore. Though its development may have been prompted by decreasing trade sizes caused by decimalization, algorithmic trading has reduced trade sizes further. Jobs once done by human traders are being switched to computers. The speeds of computer connections, measured in milliseconds and even microseconds , have become very important.
Economies of scale in electronic trading have contributed to lowering commissions and trade processing fees, and contributed to international mergers and consolidation of financial exchanges. Competition is developing among exchanges for the fastest processing times for completing trades. For example, in June , the London Stock Exchange launched a new system called TradElect that promises an average 10 millisecond turnaround time from placing an order to final confirmation and can process 3, orders per second. This is of great importance to high-frequency traders, because they have to attempt to pinpoint the consistent and probable performance ranges of given financial instruments.
With high volatility in these markets, this becomes a complex and potentially nerve-wracking endeavor, where a small mistake can lead to a large loss. Absolute frequency data play into the development of the trader's pre-programmed instructions. Algorithmic trading has caused a shift in the types of employees working in the financial industry. For example, many physicists have entered the financial industry as quantitative analysts. Some physicists have even begun to do research in economics as part of doctoral research.
Global textile production emits 1. Young, upcoming brands in the fashion space are making moves to align with this shift in consumer sensitivities. The shift to sustainability is particularly noticeable in the shoe industry. Allbirds, for example, produces shoes made from eucalyptus leaves. In response, a number of prominent brands have announced new clothing lines and initiatives focused on sustainable materials:.
A shift to more sustainable fabrics is not the only way the fashion industry is shifting to embrace more sustainable practices. Another company, Poshmark, takes a marketplace approach to fashion, allowing customers to buy and sell used items with other users through the site. AI and advanced technologies may also have a part to play in the push for sustainability in fashion. One area they could improve is returns, which is currently a significant source of waste within the fashion industry and the e-commerce segment in particular.
Even as the slow fashion movement gains traction, the rise of social media and the fast fashion model not to mention e-commerce have transformed fashion as we know it. The costs of starting a fashion brand have gone down significantly, thanks to technology and e-commerce. In years past, fashion labels would have to manufacture hundreds or thousands of items in order to produce them at a reasonable price.
Emerging brands can weave small-batch runs and transparent production standards into their marketing. Large high-end brands are also evolving their approach to production to better compete with fast fashion retailers. Yet plenty of technologies are emerging to make scalable, sustainable production more feasible, at a faster pace. In April , Gucci launched Gucci Art Lab, a 37,square-meter product development and lab testing center with in-house prototyping and sampling activity for leather goods, new materials, metal hardware, and packaging.
Elsewhere, brands are exploring how 3D printing can help them produce goods on-demand and create new avenues for customization. For now, Futurecraft 4D shoes are hard to find, because production is limited. It will be faster, more limited material. Ideally, the vision is to build and print on demand … Right now, most of our products are made out of Asia and we put them on a boat or on a plane so they end up on Fifth Avenue.
This opens up brand new possibilities both for what we can create, and the speed with which we can create it. As with every other industry, automation and robotics are also coming for fashion manufacturing. The Sewbots use specialized cameras and computer vision software to track individual threads at 1, frames per second.
In February , SoftWear announced Sewbots-as-a-Service, which allows manufacturers, brands, and retailers to rent the fully automated sewing workline. The program is intended to enable US-based companies to source and manufacture in the US at a lower cost than outsourcing, with greater predictability and quality. Robots have long been used in shoemaking, but Nike doubled down on robo-manufacturing with its investment Grabit , a robotics startup that uses electroadhesion a form of static electricity to help machines manipulate objects in novel ways.
The upper is highly technical to manufacture, and had long been one area where human intervention was necessary. By embracing manufacturing systems that rely more on machines and less on humans, fashion brands of the future will speed up production and minimize concerns around labor conditions in their facilities. As fashion enters its next era, goods produced using hyper-rapid manufacturing systems will be tracked and distributed using next-gen inventory management tools.
Brands are increasingly deploying a combination of sensors, scanners, and cloud-based software to monitor and maintain inventory. Radio frequency identification technology RFID tagging is one approach likely to see widespread adoption. RFID tags are cheap, battery-free smart stickers that can be used for digital cataloging.
Unlike barcodes, the signals from RFID tags can be read from some distance away , lessening the time it takes to manually log items. Brands like Benetton and Salvatore Ferragamo have pursued similar programs. The granularity allows online window shoppers to check if an item is in stock at a local store before making a purchase.