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Hierarchical Risk Parity
Hierarchical Risk Parity (HRP) is an advanced investment portfolio optimization framework developed in 2016 by Marcos López de Prado at Guggenheim Partners and Cornell University. HRP is a probabilistic graph-based alternative to the prevailing Convex optimization, mean-variance optimization (MVO) framework developed by Harry Markowitz in 1952, and for which he received the Nobel Prize in economic sciences. HRP algorithms apply discrete mathematics and machine learning techniques to create diversified and robust Portfolio (finance), investment portfolios that outperform MVO methods Training, validation, and test data sets, out-of-sample. HRP aims to address the limitations of traditional Modern portfolio theory, portfolio construction methods, particularly when dealing with highly correlated assets. Following its publication, HRP has been implemented in numerous open-source libraries, and received multiple extensions. Key Features HRP portfolios have been proposed as a robust al ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Portfolio Optimization
Portfolio optimization is the process of selecting an optimal portfolio (asset distribution), 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 (such as assets, liabilities, earnings or other fundamentals) to intangible (such as selective divestment). Modern portfolio theory Modern portfolio theory was introduced in a 1952 doctoral thesis by Harry Markowitz, where the Markowitz model was first defined. The model assumes that an investor aims to maximize a portfolio's expected return contingent on a prescribed amount of risk. Portfolios that meet this criterion, i.e., maximize the expected return given a prescribed amount of risk, are known as efficient portfolios. By definition, any other portfolio yielding a higher amount of expected return must also h ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Modern Portfolio Theory
Modern portfolio theory (MPT), or mean-variance analysis, is a mathematical framework for assembling a portfolio of assets such that the expected return is maximized for a given level of risk. It is a formalization and extension of Diversification (finance), diversification in investing, the idea that owning different kinds of financial assets is less risky than owning only one type. Its key insight is that an asset's risk and return should not be assessed by itself, but by how it contributes to a portfolio's overall risk and return. The variance of return (or its transformation, the standard deviation) is used as a measure of risk, because it is tractable when assets are combined into portfolios. Often, the historical variance and covariance of returns is used as a proxy for the forward-looking versions of these quantities, but other, more sophisticated methods are available. Economist Harry Markowitz introduced MPT in a 1952 paper, for which he was later awarded a Nobel Memorial ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Machine Learning Algorithms
The following outline is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory.http://www.britannica.com/EBchecked/topic/1116194/machine-learning In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". ML involves the study and construction of algorithms that can learn from and make predictions on data. These algorithms operate by building a model from a training set of example observations to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions. How can machine learning be categorized? * An academic discipline * A branch of science ** An applied science *** A subfield of computer science **** A branch of artificial intelligenc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Risk Parity
Risk parity (or risk premia parity) is an approach to investment management which focuses on allocation of risk, usually defined as volatility, rather than allocation of capital. The risk parity approach asserts that when asset allocations are adjusted (leveraged or deleveraged) to the same risk level, the risk parity portfolio can achieve a higher Sharpe ratio and can be more resistant to market downturns than the traditional portfolio. Risk parity is vulnerable to significant shifts in correlation regimes, such as observed in Q1 2020, which led to the significant underperformance of risk-parity funds in the COVID-19 sell-off. Roughly speaking, the approach of building a risk parity portfolio is similar to creating a minimum-variance portfolio subject to the constraint that each asset (or asset class, such as bonds, stocks, real estate, etc.) contributes equally to the portfolio overall volatility. Some of its theoretical components were developed in the 1950s and 1960s but the ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
HRP Vs CLA Vs IVP
HRP may refer to: Political parties * Happiness Realization Party, a Japanese political party * Haryana Republican Party, a political party in Haryana, India * Human Rights Party (other) Science, technology, and medicine * Haptoglobin-related protein, a serum protein involved in immunity to parasites * Horseradish peroxidase, an enzyme used as a marker * IEEE 802.15.4 HRP (High-Rate Pulse) Ultra-Wideband (UWB) Radio Standard * HRP, a Rockwell scale of materials' hardness * HRP Rescuer, an American helicopter * Human Research Program, a NASA program * Humanoid Robotics Project * Special Programme on Human Reproduction (HRP), a World Health Organization endeavor Other uses * Halifax Regional Police, in Halifax, Nova Scotia, Canada * Hard Rock Park, now the Freestyle Music Park in Myrtle Beach, South Carolina, United States * '' The Harvard Review of Philosophy'' * Haute Randonnée Pyrénéenne, a hiking trail in France * Historic Royal Palaces * Human remains po ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Dendrogram Of Clustered Distance Matrix
A dendrogram is a diagram A diagram is a symbolic Depiction, representation of information using Visualization (graphics), visualization techniques. Diagrams have been used since prehistoric times on Cave painting, walls of caves, but became more prevalent during the Age o ... representing a Tree (graph theory), tree graph. This diagrammatic representation is frequently used in different contexts: * in hierarchical clustering, it illustrates the arrangement of the clusters produced by the corresponding analyses. * in computational biology, it shows the clustering of genes or samples, sometimes in the margins of heat map, heatmaps. * in phylogenetics, it displays the evolutionary relationships among various biological taxa. In this case, the dendrogram is also called a phylogenetic tree. The name ''dendrogram'' derives from the two ancient greek words (), meaning "tree", and (), meaning "drawing, mathematical figure". Clustering example For a clustering example, sup ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Heatmap Of Original Covariance Matrix
A heat map (or heatmap) is a 2-dimensional data visualization technique that represents the magnitude of individual values within a dataset as a color. The variation in color may be by hue or intensity. In some applications such as crime analytics or website click-tracking, color is used to represent the ''density'' of data points rather than a value associated with each point. "Heat map" is a relatively new term, but the practice of shading matrices has existed for over a century. History Heat maps originated in 2D displays of the values in a data matrix. Larger values were represented by small dark gray or black squares (pixels) and smaller values by lighter squares. The earliest known example dates to 1873, when Toussaint Loua used a hand-drawn and colored shaded matrix to visualize social statistics across the districts of Paris. The idea of reordering rows and columns to reveal structure in a data matrix, known as seriation, was introduced by Flinders Petrie in 1899. In ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Eigenvalues Of Ill-conditioned Matrices
In linear algebra, an eigenvector ( ) or characteristic vector is a vector that has its direction unchanged (or reversed) by a given linear transformation. More precisely, an eigenvector \mathbf v of a linear transformation T is scaled by a constant factor \lambda when the linear transformation is applied to it: T\mathbf v=\lambda \mathbf v. The corresponding eigenvalue, characteristic value, or characteristic root is the multiplying factor \lambda (possibly a negative or complex number). Geometrically, vectors are multi-dimensional quantities with magnitude and direction, often pictured as arrows. A linear transformation rotates, stretches, or shears the vectors upon which it acts. A linear transformation's eigenvectors are those vectors that are only stretched or shrunk, with neither rotation nor shear. The corresponding eigenvalue is the factor by which an eigenvector is stretched or shrunk. If the eigenvalue is negative, the eigenvector's direction is reversed. The ei ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Training, Validation, And Test Data Sets
In machine learning, a common task is the study and construction of algorithms that can learn from and make predictions on data. Such algorithms function by making data-driven predictions or decisions, through building a mathematical model from input data. These input data used to build the model are usually divided into multiple data sets. In particular, three data sets are commonly used in different stages of the creation of the model: training, validation, and test sets. The model is initially fit on a training data set, which is a set of examples used to fit the parameters (e.g. weights of connections between neurons in artificial neural networks) of the model. The model (e.g. a naive Bayes classifier) is trained on the training data set using a supervised learning method, for example using optimization methods such as gradient descent or stochastic gradient descent. In practice, the training data set often consists of pairs of an input vector (or scalar) and the corresp ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Marcos López De Prado
Marcos may refer to: People with the given name ''Marcos'' *Marcos (given name) *Marcos family Sports ;Surnamed * Dayton Marcos, Negro league baseball team from Dayton, Ohio (early twentieth-century) * Dimitris Markos, Greek footballer * Nélson Marcos, Portuguese footballer * Randa Markos, Iraqi-Canadian female mixed martial artist ;Nicknamed simply as ''Marcos'' * Marcos Pereira Martins (born 1943), Brazilian football winger * Marcos Roberto Silveira Reis (born 1973), Brazilian football goalkeeper * Marcos Joaquim dos Santos (born 1975), Brazilian footballer * Marcos de Paula (born 1983), Brazilian footballer * Marcos Alonso Peña (born 1959), Spanish footballer ;Named * Marcos Ambrose, Australian racing driver currently competing in ''NASCAR'' * Marcos Baghdatis, Cypriot tennis player * Marcos Bristow, Indian badminton player * Marcos Hernández (swimmer), Cuban freestyle swimmer * Marcos Pizzelli, Brazilian-Armenian footballer * Marcos García Barreno, Spanish foot ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Portfolio (finance)
In finance, a portfolio is a collection of investments. Definition The term "portfolio" refers to any combination of financial assets such as stocks, bonds and cash. Portfolios may be held by individual investors or managed by financial professionals, hedge funds, banks and other financial institutions. It is a generally accepted principle that a portfolio is designed according to the investor's risk tolerance, time frame and investment objectives. The monetary value of each asset may influence the risk/reward ratio of the portfolio. When determining asset allocation, the aim is to maximise the expected return and minimise the risk. This is an example of a multi-objective optimization problem: many efficient solutions are available and the preferred solution must be selected by considering a tradeoff between risk and return. In particular, a portfolio A is dominated by another portfolio A' if A' has a greater expected gain and a lesser risk than A. If no portfolio dominates A ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
