For building the model, we will divide our data into two different data sets, namely training and testing datasets. The model will be built using the training set and then we will test it on the testing set to evaluate how our model is performing. There are many ways in which we can split the […]
Risk Management
German Credit Data : Data Preprocessing and Feature Selection in R
The purpose of preprocessing is to make your raw data suitable for the data science algorithms. For example, we may want to remove the outliers, remove or change imputations (missing values, and so on). The dataset that we have selected does not have any missing data. But, in real time there is possibility that the […]
Import Credit Data Set in R
We are using the German Credit Scoring Data Set in numeric format which contains information about 21 attributes of 1000 loans. First, setup a working directory and place this data file in that directory. Then, import the data into your R session using the following command: Attribute Details 20 attributes are used in judging a […]
Case Study – German Credit – Steps to Build a Predictive Model
We will preform various steps in building our predictive model. These steps are explained below: Step 1 – Data Selection The first step is to get the dataset that we will use for building the model. For this case study, we are using the German Credit Scoring Data Set in the numeric format which contains […]
Classification vs. Regression Models
While building any predictive model, it is important to first understand whether it is a classification or a regression problem. Let’s understand the difference between the two: 1. Classification In a classification problem, we are trying to predict the class of a data point (discreet number of values). The Y variable that we are trying […]
Credit Risk Modelling – Case Studies
In this tutorial, we will learn credit risk modeling in R using case studies. Specifically, we will use two case studies starting with a simpler one using which we will learn the methodology and important concepts and techniques. Note: As a pre-requisite, it will be helpful to go through this tutorial first – Foundations of […]
Role of Data Science in Risk Management
Risk management is an integral part of any financial institution. All businesses face a variety of risks and the risk management practice works towards maximizing the businesses’ return on investment and reducing their losses. Anyone who works in risk management is not new to data analytics. The field of risk management is highly dependent on […]
Calmar Ratio: Definition, Calculation, and Importance
Calmar ratio is a popular risk-adjusted measure used by investors in their investment selection process. The Calmar ratio is calculated by dividing the compounded annual rate of return for period and dividing it by the maximum drawdown for the same period. The calculations are done using absolute values. Calmar ratios are generally calculated using 36 […]
Backtesting Quantitative Trading Strategies
Backtesting is one of the most important steps in building a successful quantitative trading strategy. It is in fact a key step that differentiates algorithmic trading from discretionary trading. Quants use their computational finance and programming skills to build complex trading strategies. However, before these strategies are executed in the live market, they are tested […]
YIELD MANAGEMENT INC. TOKYO, JAPAN REVIEW
March 2018: Rising Interest Rates The word inflation is regular topic now in the business news. Economic indicators show it’s gradually rising in the United States which will have ripple effects throughout global markets. Inflation refers to an economic climate where prices are increasing and the value of money is falling. Low but positive rates […]