Finance refers to monetary resources and to the study and discipline of money, currency, assets and liabilities. As a subject of study, it is related to but distinct from economics, which is the study of the production, distribution, and consumption of goods and services. Based on the scope of financial activities in financial systems, the discipline can be divided into personal, corporate, and public finance.
Financial economics is the branch of economics characterized by a "concentration on monetary activities", in which "money of one type or another is likely to appear on both sides of a trade". Its concern is thus the interrelation of financial variables, such as share prices, interest rates and exchange rates, as opposed to those concerning the real economy. It has two main areas of focus: asset pricing and corporate finance; the first being the perspective of providers of capital, i.e. investors, and the second of users of capital. It thus provides the theoretical underpinning for much of finance.
In financial markets, market impact is the effect that a market participant has when it buys or sells an asset. It is the extent to which the buying or selling moves the price against the buyer or seller, i.e., upward when buying and downward when selling. It is closely related to market liquidity; in many cases "liquidity" and "market impact" are synonymous.
In finance, statistical arbitrage is a class of short-term financial trading strategies that employ mean reversion models involving broadly diversified portfolios of securities held for short periods of time. These strategies are supported by substantial mathematical, computational, and trading platforms.
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. A study in 2019 showed that around 92% of trading in the Forex market was performed by trading algorithms rather than humans.
Computational finance is a branch of applied computer science that deals with problems of practical interest in finance. Some slightly different definitions are the study of data and algorithms currently used in finance and the mathematics of computer programs that realize financial models or systems.
The following outline is provided as an overview of and topical guide to finance:
Market microstructure is a branch of finance concerned with the details of how exchange occurs in markets. While the theory of market microstructure applies to the exchange of real or financial assets, more evidence is available on the microstructure in the financial field due to the availability of transactions data from them. The major thrust of market microstructure research examines the ways in which the working processes of a market affect determinants of transaction costs, prices, quotes, volume, and trading behavior. In the twenty-first century, innovations have allowed an expansion into the study of the impact of market microstructure on the incidence of market abuse, such as insider trading, market manipulation and broker-client conflict.
Frederick Justin Almgren Jr. was an American mathematician working in geometric measure theory. He was born in Birmingham, Alabama.
Neil A. Chriss is a mathematician, academic, hedge fund manager, philanthropist and a founding board member of the charity organization "Math for America" which seeks to improve math education in the United States. Chriss also serves on the board of trustees of the Institute for Advanced Study.
High-frequency trading (HFT) is a type of algorithmic trading in finance characterized by high speeds, high turnover rates, and high order-to-trade ratios that leverages high-frequency financial data and electronic trading tools. While there is no single definition of HFT, among its key attributes are highly sophisticated algorithms, co-location, and very short-term investment horizons in trading securities. HFT uses proprietary trading strategies carried out by computers to move in and out of positions in seconds or fractions of a second.
The scenario approach or scenario optimization approach is a technique for obtaining solutions to robust optimization and chance-constrained optimization problems based on a sample of the constraints. It also relates to inductive reasoning in modeling and decision-making. The technique has existed for decades as a heuristic approach and has more recently been given a systematic theoretical foundation.
Portfolio optimization is the process of selecting an optimal portfolio, out of a set of considered portfolios, according to some objective. The objective typically maximizes factors such as expected return, and minimizes costs like financial risk, resulting in a multi-objective optimization problem. Factors being considered may range from tangible to intangible.
Quantitative analysis is the use of mathematical and statistical methods in finance and investment management. Those working in the field are quantitative analysts (quants). Quants tend to specialize in specific areas which may include derivative structuring or pricing, risk management, investment management and other related finance occupations. The occupation is similar to those in industrial mathematics in other industries. The process usually consists of searching vast databases for patterns, such as correlations among liquid assets or price-movement patterns.
David Alan Easley is an American economist. Easley is the Henry Scarborough Professor of Social Science and is a professor of information science at Cornell University.
Mathematical finance, also known as quantitative finance and financial mathematics, is a field of applied mathematics, concerned with mathematical modeling in the financial field.
Smart order routing (SOR) is an automated process of handling orders, aimed at taking the best available opportunity throughout a range of different trading venues.
Transaction cost analysis (TCA), as used by institutional investors, is defined by the Financial Times as "the study of trade prices to determine whether the trades were arranged at favourable prices – low prices for purchases and high prices for sales". It is often split into two parts – pre-trade and post-trade. Recent regulations, such as the European Markets in Financial Instruments Directive, have required institutions to achieve best execution.
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 already begun to rise in which case it should best be handled with urgency; this example illustrates the fact that Alpha Profiling must combine public information such as market data with private information including as the identity of the portfolio manager and the size and origin of the order, to identify the optimal execution schedule.
