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Alpha Profiling
Alpha profiling is an application of machine learning to optimize the execution of large orders in financial markets by means of algorithmic trading. The purpose is to select an execution schedule that minimizes the expected implementation shortfall, or more generally, ensures compliance with a best execution mandate. Alpha profiling models learn statistically-significant patterns in the execution of orders from a particular trading strategy or portfolio manager and leverages these patterns to associate an optimal execution schedule to new orders. In this sense, it is an application of statistical arbitrage to best execution. For example, a portfolio manager specialized in value investing may have a behavioral bias to place orders to buy while an asset is still declining in value. In this case, a slow or back-loaded execution schedule would provide better execution results than an urgent one. But this same portfolio manager will occasionally place an order after the asset price has ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning
Machine learning (ML) is a field of inquiry devoted to understanding and building methods that 'learn', that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, speech recognition, agriculture, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.Hu, J.; Niu, H.; Carrasco, J.; Lennox, B.; Arvin, F.,Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning IEEE Transactions on Vehicular Technology, 2020. A subset of machine learning is closely related to computational statistics, which focuses on making predicti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Algorithmic Trading
Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. This type of trading attempts to leverage the speed and computational resources of computers relative to human traders. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders. It is widely used by investment banks, pension funds, mutual funds, and hedge funds that may need to spread out the execution of a larger order or perform trades too fast for human traders to react to. A study in 2019 showed that around 92% of trading in the Forex market was performed by trading algorithms rather than humans. The term algorithmic trading is often used synonymously with automated trading system. These encompass a variety of trading strategies, some of which are based on formulas and results from mathematical finance, and often rely on specialized software. Examples o ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Implementation Shortfall
In financial markets, implementation shortfall is the difference between the decision price and the final execution price (including commissions, taxes, etc.) for a trade. This is also known as the " slippage". Agency trading is largely concerned with minimizing implementation shortfall and finding liquidity. Decision price The decision price is the price of the stock that prompted the decision to buy or sell. The most common decision price is the close price or the arrival price. If we split the decision to buy a stock from the actual trading of the stock, as is often the case with fund managers (decision makers) and brokers (trade executors), you can see why both are used. From the fund manager's point of view, his decision to trade is often based on the closing price of the day's trading (along with the entire history of the stock and other signals/indicators). When he decides to buy a particular stock the next day, it is because he believes that the price will go up from tha ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Best Execution
Best execution refers to the duty of an investment services firm (such as a stock broker) executing orders on behalf of customers to ensure the best execution possible for their customers' orders. Some of the factors the broker must consider when seeking best execution of their customers' orders include: the opportunity to get a better price than what Is currently quoted, and the likelihood and speed of execution. In Europe, there has been an attempt to define "best execution" within the Markets in Financial Instruments Directive (MiFID), which introduces the principle that, when carrying out transactions on their clients' behalf, "investment firms halltake all sufficient steps to obtain, when executing orders, the best possible result for their clients taking into account price, costs, speed, likelihood of execution and settlement, size, nature or any other consideration relevant to the execution of the order. Nevertheless, where there is a specific instruction from the client the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Arbitrage
In finance, statistical arbitrage (often abbreviated as ''Stat Arb'' or ''StatArb'') is a class of short-term financial trading strategies that employ mean reversion models involving broadly diversified portfolios of securities (hundreds to thousands) held for short periods of time (generally seconds to days). These strategies are supported by substantial mathematical, computational, and trading platforms. Trading strategy Broadly speaking, StatArb is actually any strategy that is bottom-up, beta-neutral in approach and uses statistical/econometric techniques in order to provide signals for execution. Signals are often generated through a contrarian mean reversion principle but can also be designed using such factors as lead/lag effects, corporate activity, short-term momentum, etc. This is usually referred to as a multi-factor approach to StatArb. Because of the large number of stocks involved, the high portfolio turnover and the fairly small size of the effects one is trying to ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Portfolio Manager
A portfolio manager (PM) is a professional responsible for making investment decisions and carrying out investment activities on behalf of vested individuals or institutions. Clients invest their money into the PM's investment policy for future growth, such as a retirement fund, endowment fund, or education fund. PMs work with a team of analysts and researchers and are responsible for establishing an investment strategy, selecting appropriate investments, and allocating each investment properly towards an investment fund or asset management vehicle. Model In the 1950s, Harry Markowitz, an American economist, developed the modern portfolio theory. Jack Treynor (1961, 1962), William F. Sharpe (1964), John Lintner (1965) and Jan Mossin (1966) later build the Capital Asset Pricing Model (CAPM) on the theory of Markowitz. Nowadays, the CAPM is one of the primary portfolio management tools. The formula calculates the potential return percentage of an investment vehicle based on its vest ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Value Investing
Value investing is an investment paradigm that involves buying securities that appear underpriced by some form of fundamental analysis. The various forms of value investing derive from the investment philosophy first taught by Benjamin Graham and David Dodd at Columbia Business School in 1928, and subsequently developed in their 1934 text ''Security Analysis''. The early value opportunities identified by Graham and Dodd included stock in public companies trading at discounts to book value or tangible book value, those with high dividend yields, and those having low price-to-earning multiples, or low price-to-book ratios. High-profile proponents of value investing, including Berkshire Hathaway chairman Warren Buffett, have argued that the essence of value investing is buying stocks at less than their intrinsic value. The discount of the market price to the intrinsic value is what Benjamin Graham called the " margin of safety". For the last 25 years, under the influence of ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Behavioral Economics
Behavioral economics studies the effects of psychological, cognitive, emotional, cultural and social factors on the decisions of individuals or institutions, such as how those decisions vary from those implied by classical economic theory. Behavioral economics is primarily concerned with the bounds of rationality of economic agents. Behavioral models typically integrate insights from psychology, neuroscience and microeconomic theory. The study of behavioral economics includes how market decisions are made and the mechanisms that drive public opinion. The concepts used in behavioral economics today can be traced back to 18th-century economists, such as Adam Smith, who deliberated how the economic behavior of individuals could be influenced by their desires. The status of behavioral economics as a subfield of economics is a fairly recent development; the breakthroughs that laid the foundation for it were published through the last three decades of the 20th century. Behavio ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Statistical Classification
In statistics, classification is the problem of identifying which of a set of categories (sub-populations) an observation (or observations) belongs to. Examples are assigning a given email to the "spam" or "non-spam" class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (sex, blood pressure, presence or absence of certain symptoms, etc.). Often, the individual observations are analyzed into a set of quantifiable properties, known variously as explanatory variables or ''features''. These properties may variously be categorical (e.g. "A", "B", "AB" or "O", for blood type), ordinal (e.g. "large", "medium" or "small"), integer-valued (e.g. the number of occurrences of a particular word in an email) or real-valued (e.g. a measurement of blood pressure). Other classifiers work by comparing observations to previous observations by means of a similarity or distance function. An algorithm that implements classification, especially in a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Naive Bayes
In statistics, naive Bayes classifiers are a family of simple "Probabilistic classification, probabilistic classifiers" based on applying Bayes' theorem with strong (naive) statistical independence, independence assumptions between the features (see Bayes classifier). They are among the simplest Bayesian network models, but coupled with kernel density estimation, they can achieve high accuracy levels. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood estimation, Maximum-likelihood training can be done by evaluating a closed-form expression, which takes linear time, rather than by expensive Iterative method, iterative approximation as used for many other types of classifiers. In the statistics literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. All these names reference the use of Bayes' theorem i ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Robust Statistics
Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal. Robust statistical methods have been developed for many common problems, such as estimating location, scale, and regression parameters. One motivation is to produce statistical methods that are not unduly affected by outliers. Another motivation is to provide methods with good performance when there are small departures from a parametric distribution. For example, robust methods work well for mixtures of two normal distributions with different standard deviations; under this model, non-robust methods like a t-test work poorly. Introduction Robust statistics seek to provide methods that emulate popular statistical methods, but which are not unduly affected by outliers or other small departures from Statistical assumption, model assumptions. In statistics, classical estimation methods rely heavily on assumpti ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Generalization (learning)
Generalization is the concept that humans and other animals use past learning in present situations of learning if the conditions in the situations are regarded as similar. The learner uses generalized patterns, principles, and other similarities between past experiences and novel experiences to more efficiently navigate the world.Banich, M. T., Dukes, P., & Caccamise, D. (2010). Generalization of knowledge: Multidisciplinary perspectives. Psychology Press. For example, if a person has learned in the past that every time they eat an apple, their throat becomes itchy and swollen, they might assume they are allergic to all fruit. When this person is offered a banana to eat, they reject it upon assuming they are also allergic to it through generalizing that all fruits cause the same reaction. Although this generalization about being allergic to all fruit based on experiences with one fruit could be correct in some cases, it may not be correct in all. Both positive and negative effects h ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
