Live-R: Financial Services Industry
Live-R brings data analytics for financial services to the cloud
Live-R’s packages for financial services solutions comprises an intelligent mix of R packages to deliver advanced data analytics & BI capabilities.
Effectively analyze financial data.
Live-R’s scalable, secure & reliable data analytics infrastructure enables financial services institutions to meet advanced data analytics & BI needs.
Key Segments
- Clustering
- Computational Finance
- Forecasting
- Microeconometrics
- Multivariate analysis
- Optimization
- Regression models
- Risk management
- Solvers
- Time series analysis etc.
Financial Services Packages (a small sampling)
Clustering – General Cluster Analysis (click for more info)
Clustering is a method of unsupervised learning, and is a common technique for statistical data analysis used in many domains, including computational finance, microeconomics, risk management etc.
| R Package | Package Description |
|---|---|
| cluster | ClusterAnalysis Exteneded-A collection of functions that supports different forms of clustering, such as hierarchial clustering, partition based clustering, model-based clustering.Read More |
| flexclust | Flexible Cluster Algorithms – The main function kcca implements a general framework for k-centroids cluster analysis supporting arbitrary distance measures and centroid computation. Further cluster methods include hard competitive learning, neural gas, and QT clustering. There are numerous visualization methods for cluster results (neighborhood graphs, convex cluster hulls, barcharts of centroids, …), and bootstrap methods for the analysis of cluster stability. Read More |
| flexmix | Flexible Mixture Modeling- FlexMix implements a general framework for finite mixtures of regression models using the EM algorithm. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering. Read More |
| mclust | Model-Based Clustering / Normal Mixture Modeling-Model-based clustering and normal mixture modeling including Bayesian regularization.Read More |
Computational Finance
Computational Finance explores the intersection of computational power and economics. It deals with estimation and calculation for a wide variety of issues dealing with econometrics, including bootstrapping, optimization, estimation, VAR analysis, linear & non-linear methods etc.
| R Package | Package Description |
|---|---|
| fArma | ARMA Time Series Modelling -A collection and description of simple to use functions to model univariate autoregressive moving average time series processes, including time series simulation, parameter estimation, diagnostic analysis of the fit, and predictions of future values. Read More |
| fAsianOptions | EBM and Asian Option Valuation – A collection and description of special mathematical functions which compute the modified Bessel functions of integer order of the first and second kind as well as their derivatives. Read More |
| fAssets | Assets Selection and Modelling -The Rmetrics “fAssets” package is a very powerful collection of functions to investigate and analyze data sets of financial assets from different points ov view.. Read More |
| fBasics | Markets and Basic Statistics – The Rmetrics “fbasics” package is a collection of functions to explore and to investigate basic properties of financial returns and related quantities.The covered fields include techniques of explorative data analysis and the investigation of distributional properties including parameter estimation and hypothesis testing. Read More |
| fBonds | Bonds and Interest Rate Models – A collection and description of functions for term structure modelling. Read More |
| fCopulae | Rmetrics – Dependence Structures with Copulas – A collection of functions for bivariate copulae, bivariate elliptical copulae, bivariate empirical copulae, and extreme value copulae. Also provides description of functions to compute multivariate densities and probabilities from skew normal and skew Student-t distribution functions. Read More |
| fEcofin | Economic and Financial Data Sets -A collection and description of functions to extract financial and economic market statistics from the data resources.Read More |
| fExoticOptions | Exotic Option Valuation – This package provides a collection of functions to evaluate asian options, basic options, binary or digital options, currency translated options, lookback options, multipleasset options, and multiple excercise options. Read More |
| fGarch | Autoregressive Conditional Heteroskedastic Modelling- Package of econometric functions for modelling Generalized Autoregressive Conditional Heteroskedastic process. The models have become important in the analysis of time series data, particularly in financial applications when the goal is to analyze and forecast volatility. Read More |
| fImport | Economic and Financial Data Import – The package makes functions available to download financial market data from the internet. The data for analysis is downloaded from research.stlouisfed.org, www.oanda.com, and chart.yahoo.com.Read More |
| fMultivar | Multivariate Market Analysis- The package provides wide range of functions for generation of bivariate bining, bivariate gridding, bivariate cauchy distribution, bivariate elliptical distribution, bivariate t-distirbution, and multivariate distribuion. Read More |
| fNonlinear | Nonlinear and Chaotic Time Series Modelling – A collection and description of functions for testing various aspects of time series process Read More |
| fOptions | Basics of Option Valuation- A collection and description of functions to valuate basic American options, binomial tree option, Heston-Nandi Garch modelling, Halton’s and Sobol’s low discrepancy sequence, Monte Carlo methods, and vanilla options”. Read More |
| fPortfolio | Rmetrics – Portfolio Selection and Optimization- The Rmetrics “fPortfolio” package is a very powerful collection of functions to optimize portfolios and to analyze them from different points of view.The implemented portfolio models include the traditional mean–variance Markowitz portfolio, robust variants of the Markowitz portfoio, and the mean-CVaR conditional value-at-Risk portfolio. Read More |
| fRegression | Regression Based Decision and Prediction- Package of functions for regression modelling.Read More |
Forecasting
Forecasting involves analyzing time series data in order to extract meaningful statistics and other characteristics of the data, which help to model and predict future data & events based on past data & events.
| R Package | Package Description |
|---|---|
| forecast | Forecasting functions for time series- Methods and tools for displaying and analysing univariate time series forecasts including exponential smoothing via state space models and automatic ARIMA modelling. Read More |
Microeconometrics
Microeconometrics provides various functions and models to analyze micro-econometric data.
| R Package | Package Description |
|---|---|
| bayesm | Bayesian Inference for Marketing/Micro-econometrics – bayesm covers many important models used in marketing and micro-econometrics applications. The package includes: Bayes Regression (univariate or multivariate dep var), Bayes Seemingly Unrelated Regression (SUR), Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial Probit (MNP), Multivariate Probit, Negative Binomial (Poisson) Regression, Multivariate Mixtures of Normals (including clustering), Dirichlet Process Prior Density Estimation with normal base, Hierarchical Linear Models with normal prior and covariates, Hierarchical Linear Models with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a mixture of normals prior and covariates, Hierarchical Multinomial Logits with a Dirichlet Process prior and covariates, Hierarchical Negative Binomial Regression Models, Bayesian analysis of choice-based conjoint data, Bayesian treatment of linear instrumental variables models, and Analysis of Multivariate Ordinal survey data with scale usage heterogeneity (as in Rossi et al, JASA (01)). For further reference, consult our book, Bayesian Statistics and Marketing by Rossi, Allenby and McCulloch.. Read More |
Multivariate analysis
Multivariate Analysis involves the observation and analysis of more than one statistical variable at a time. In design and analysis, the technique is used to perform trade studies across multiple dimensions while taking into account the effects of all variables on the responses of interest.
| R Package | Package Description |
|---|---|
| ade4 | Exploratory and Euclidean methods in Environmental sciences-Multivariate data analysis and graphical display. Read More |
| cluster | ClusterAnalysis Exteneded-Cluster Analysis, extended original from Peter Rousseeuw et al. Read More |
| MASS | Support Functions and Datasets for Venables and Ripley’s MASS- Functions and datasets to support Venables and Ripley, ‘Modern Applied Statistics with S. Read More |
| vegan | Community Ecology Package- Ordination methods, diversity analysis and other functions for community and vegetation ecologists. Read More |
Optimization
Optimization forms the fundamental step in problem solving, be it non-linear variables calculating the flow of gas in a turbine, or variables associated to share values of different industry sectors for a given day’s stock trades.
| R Package | Package Description |
|---|---|
| Rglpk | R/GNU Linear Programming Kit Interface – Lp_solve is freely available (under LGPL 2) software for solving linear, integer and mixed integer programs. In this implementation we supply a “wrapper” function in C and some R functions that solve general linear/integer problems, assignment problems, and transportation problems. This version calls lp_solve version 5.5. Read More |
| Rsolnp | General Non-linear Optimization- General Non-linear Optimization Using Augmented Lagrange Multiplier Method. Read More |
| Rsymphpony | Symphony in R- An R interface to the SYMPHONY MILP solver (version 5.2.4).. Read More |
| TSP | Travelling Salesperson Problem- Basic infrastructure and some algorithms for the traveling salesperson problem (TSP). The package provides some simple algorithms and an interface to Concorde, the currently fastest TSP solver. Concorde itself is not included in the package and has to be obtained separately. Read More |
Risk management
Risk modeling, a critical aspect of risk management, is a technique to determine aggregate risk in finance. Risk modeling uses various techniques such as Value Added Risk and Extreme Value Theory to analyze a portfolio and make forecasts of the likely profits & losses due to a variety of risk senarios. This type of analysis is widely used for credit, liquidity & operational risks.
| R Package | Package Description |
|---|---|
| fExtremes | Rmetrics – Extreme Financial Market Data – A collection and description of functions for data preprocessing of extreme values. This includes tools to separate data beyond a threshold value, explorative data analysis, computing tail risk under GPD approach, and many more. Read More |
| fPortfolio | Rmetrics – Portfolio Selection and Optimization- The Rmetrics “fPortfolio” package is a very powerful collection of functions to optimize portfolios and to analyze them from different points of view. The implemented portfolio models include the traditional mean–variance Markowitz portfolio, robust variants of the Markowitz portfoio, and the mean-CVaR conditional value-at-Risk portfolio. Read More |
| PerformanceAnalytics | Econometric tools for performance and risk analysis – Collection of econometric functions for performance and risk analysis. This package aims to aid practitioners and researchers in utilizing the latest research in analysis of non-normal return streams. In general, it is most tested on return (rather than price) data on a regular scale, but most functions will work with irregular return data as well, and increasing numbers of functions will work with P&L or price data where possible.Read More |
Solvers
Solvers are a set of computational algorithms used over a set of complex variables to optimize and simplify various financial models, such as portfolio optimizations.
| R Package | Package Description |
|---|---|
| lpSolve | Interface to Lp_solve v. 5.5 to solve linear/integer programs – Lp_solve is freely available (under LGPL 2) software for solving linear, integer and mixed integer programs. In this implementation we supply a “wrapper” function in C and some R functions that solve general linear/integer problems, assignment problems, and transportation problems. Read More |
| minqa | Derivative-free optimization algorithms by quadratic approximation- Derivative-free optimization by quadratic approximation based on an interface to Fortran implementations by M. J. D. Powell Read More |
| optimx | A replacement and extension of the optim() function- Provides a replacement and extension of the optim() function to unify and streamline optimization capabilities in R for smooth, box constrained functions of several or many parameters.Read More |
| quadprog | Functions to solve Quadratic Programming Problems – This package contains routines and documentation for solving quadratic programming problems. Read More |
Time series analysis
Time series analysis is used to analyze patterns that vary with respect to time in order to extract meaningful statistics and other characteristics from the data, which helps to model future data & events based on past data & events. Time series data have a natural temporal ordering; making time series analysis distinct from other data analysis where there is no natural ordering of the observations.
| R Package | Package Description |
|---|---|
| fGarch | Autoregressive Conditional Heteroskedastic Modelling- Package of econometric functions for modelling Generalized Autoregressive Conditional Heteroskedastic process. The models have become important in the analysis of time series data, particularly in financial applications when the goal is to analyze and forecast volatility. Read More |
| fMultivar | Multivariate Market Analysis- The package provides wide range of functions for generation of bivariate bining, bivariate gridding, bivariate cauchy distribution, bivariate elliptical distribution, bivariate t-distirbution, and multivariate distribuion. Read More |
| tseries | Time series analysis and computational finance- Package for time series analysis and computational finance.Read More |
| urca | Unit root and cointegration tests for time series data- Unit root and cointegration tests encountered in applied econometric analysis are implemented. Read More |
| zoo | Z’s ordered observations- An S3 class with methods for totally ordered indexed observations. It is particularly aimed at irregular time series of numeric vectors/matrices and factors. zoo’s key design goals are independence of a particular index/date/time class and consistency with ts and base R by providing methods to extend standard generics.. Read More |
Custom Live-R Deployments
Live-R’s custom deployment builder creates Live-R deployments that are fine-tuned to specific user needs. Please contact Live-R sales for further discussion. sales@live-analytics.com