Mean reversion is a financial term for the assumption that an asset's price will tend to converge to the average price over time.
Using mean reversion as a timing strategy involves both the identification of the trading range for a security and the computation of the average price using quantitative methods. Mean reversion is a phenomenon that can be exhibited in a host of financial time-series data, from price data, earnings data, and book value.
When the current market price is less than the average past price, the security is considered attractive for purchase, with the expectation that the price will rise. When the current market price is above the average past price, the market price is expected to fall. In other words, deviations from the average price are expected to revert to the average. This knowledge serves as the cornerstone of multiple trading strategies.
Stock reporting services commonly offer moving averages for periods such as 50 and 100 days. While reporting services provide the averages, identifying the high and low prices for the study period is still necessary.
Mean reversion has the appearance of a more scientific method of choosing stock buy and sell points than charting, because precise numerical values are derived from historical data to identify the buy/sell values, rather than trying to interpret price movements using charts (charting, also known as technical analysis) although the RSI indicator and Average True Range (ATR) are nascent attempts to capture such systematic pattern.
Many asset classes, even exchange rates, are observed to be mean reverting; however, this process may last for years and thus is not of value to a short-term investor.
Mean reversion should demonstrate a form of symmetry since a stock may be above its historical average approximately as often as below.
A historical mean reversion model will not fully incorporate the actual behavior of a security's price. For example, new information may become available that permanently affects the long-term valuation of an underlying stock.
Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.
Explore les stratégies de trading en finance mathématique, en testant les modèles scientifiques et les défis des marchés non stationnaires.
Le trading algorithmique, aussi appelé trading automatisé ou trading automatique, boîte noire de négociation (en anglais : black-box trading), effectué par des robots de trading ou robots traders, est une forme de trading avec utilisation de plates-formes électroniques pour la saisie des ordres de bourse en laissant un algorithme décider des différents aspects de l'ordre, tel que l'instant d'ouverture ou de clôture (le timing), le prix ou le volume de l'ordre et ceci, dans de nombreux cas, sans la moindre in