Beyond Classification and Ranking: Constrained Optimization of the ROI
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This document summarizes a research paper that proposes a new learning algorithm to maximize return on investment (ROI) under budget constraints. The algorithm finds the subset of accounts that will have the highest total collection amount within the allowed pull rate. It does this by learning a differentiable objective function to approximate the ratio of monetary value to pull rate. On a credit card debt collection problem, the new algorithm achieved 11% higher average collection amount than weighted classification and ranking models.
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Mutual funds issue units to investors in proportion to funds contributed, with the goal of providing better returns at minimum risk. There are two main types of mutual funds - open ended and close ended. Open ended funds offer continuous sale and repurchase of units at net asset value, while close ended funds specify a limited redemption period and only sell a set number of units during an offering period. Mutual funds also differ based on whether they focus on income, through regular dividend payouts, or growth, through capital appreciation and some dividend income.
Mutual fund for your portfolio
1. Kotak 50 (G) is a large cap open-ended fund that ranks 3rd with assets of Rs. 719.67 cr as of Jun-30-2013. It has an inception date of 22-Dec-98 and benchmarks against S&P CNX Nifty with a minimum investment of Rs. 5000.
2. ICICI Prudential Top 100 Fund (G) is a large cap fund with an alpha of 0.01 and beta of 0.82 for 1 year.
3. SBI Magnum Midcap Fund (G) had an alpha of 0.6 and beta of
Equlibrium, mutual funds and sharpe ratio
The document discusses concepts related to optimal portfolios, mutual funds, and risk-adjusted performance measures. It defines optimal portfolios, two-fund separation, measures of return including time-weighted and dollar-weighted returns. It also discusses benchmarks, risk-adjusted measures including the Sharpe Ratio, Treynor Ratio, and Jensen's Alpha. It compares the Sharpe and Treynor measures and critiques risk-adjusted performance measures.
Mutual fund
A mutual fund is a trust that pools money from many investors who share a common financial goal. Professional fund managers invest this pooled money in stocks, bonds, and other securities to achieve the fund's stated objectives. The main benefits of mutual funds include diversification of investments, professional management, low costs due to economies of scale, and convenience for small investors. However, mutual funds also have some disadvantages such as fees, inability to build a tailored portfolio, and delays in redeeming investments.
Research methology
The document summarizes research on mutual funds, term plans, and health insurance plans. It analyzed the compounded annual growth of equity, debt, and hybrid mutual funds from seven companies over different time periods. Specifically, it found that over the last year, most equity linked saving schemes had negative returns, with the lowest being -1.88%, while over three and five years some funds had positive returns up to 17.8% and 8.46% respectively. For large and mid cap funds, the highest one year return was 5.68%, and the highest three year return came from the ICICI Prudential dynamic plan.
Mutual fund - Marketing Perspective
Mutual funds allow small investors access to a diversified portfolio of securities through a single investment. They provide expertise and risk management through professional fund management. The mutual fund industry in India began in 1963 with the formation of the Unit Trust of India. Major reasons why some people do not invest include ignorance of mutual funds, risk aversion, and limited promotion outside major cities. Fidelity Investments is one of the largest mutual fund companies in the US, known for its highly rated funds and guidance tools.
Mutual fund in pakistan
This document provides an overview of mutual funds in Pakistan. It discusses key topics such as:
- What a mutual fund is and how it pools investments from many investors.
- The various parties involved in mutual funds, including asset management companies, fund managers, trustees, custodians, and the regulator (SECP).
- The different types of mutual funds based on maturity (open-ended and closed-ended) and investment strategies (equity, balanced, income, money market funds).
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So
Performance evaluation of selected mutual fund schemes in india
This document analyzes the performance of various mutual fund schemes in India using different risk-adjusted return ratios. It summarizes the findings for various types of mutual fund schemes:
- For liquid funds, Hdfc Liquid Fund performed best on most metrics, though SBI Magnum Insta Cash Fund performed best according to Treynor Ratio.
- For equity-linked savings schemes, SBI Magnum Tax Gain was least risky but SBI Magnum Tax Gain and Icici Prudential Tax Saver performed best overall.
- For gilt funds, L&T Gilt Fund performed best on all metrics while Templeton India Government Securities underperformed.
- For diversified equity funds
Mutual fund ppt
Mutual funds pool money from investors and invest it in stocks, bonds, and other securities. The money earned through investments and any capital appreciation is shared by unit holders proportionate to how many units they own. The document discusses the history of mutual funds in India from 1964 to present. It describes open-ended and closed-ended funds, as well as growth, income, balanced, and money market funds. The advantages of mutual funds include diversification, professional management, convenience, and tax benefits, while the disadvantages include costs and lack of control. Systematic investment plans allow regular investing of small amounts to achieve long-term goals through rupee cost averaging and the power of compounding.
Mutual Funds, Mutual Fund Basics, Types of Mutual Funds, Mutual Fund Investm...
Learn more on Mutual Fund Basics. Understand what is a Mutual Fund, types of Mutual Funds & the advantages of investing in Mutual Funds.
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Mutual funds pool money from investors and invest in a portfolio of securities like stocks, bonds and other assets. The presentation discusses the history, growth and regulations of the Indian mutual fund industry. It covers key concepts like the flow cycle, organizational structure, expense ratios and types of mutual fund schemes. The goal is to educate investors about mutual funds and how they can provide diversification and professional management.
Mutual fund-ppt
Mutual funds pool money from investors and invest it in a portfolio of securities like stocks, bonds, and other assets. Investors share the income and capital gains or losses proportionate to their investment. Mutual funds offer diversification, professional management, affordability, and liquidity. Some risks include potential underperformance, costs, and taxes on capital gains. In India, the Association of Mutual Funds promotes and protects the interests of mutual funds and investors. Long-term capital gains from mutual funds are tax exempt.
Mutual funds
This document provides an overview of mutual funds, including their concept, types, advantages, organization, investment strategies, and growth in India. It discusses key mutual fund topics such as open-ended and closed-ended schemes, growth, income, balanced, and money market funds. The document also summarizes the history and growth of the mutual fund industry in India, from its beginnings in 1964 to recent growth and future prospects, with the industry expected to reach $800 billion by 2022 based on past growth rates.
Presentation On Mutual funds and its types
The document summarizes a seminar presentation on mutual funds and their types. It defines a mutual fund as a trust that pools investor savings and invests in stocks, bonds, and other securities. It outlines the history of mutual funds in India in four phases from 1964 to the present. It also describes the different types of mutual funds based on maturity period (open-ended or closed-ended) and investment objectives (growth, income, balanced, money market, gilt, and index funds). Finally, it lists some major Indian mutual fund companies and the advantages of investing in mutual funds.
Portfolio Management Services in Mutual Funds
A detailed study Portfolio Management services in Mutual Funds which give special emphasis on creation of Portfolio’s as different types of Investors, Portfolio Revision as per various plans, Calculation of returns and Comparison of Mutual Funds with various Performance measure
Mutual fund ppt
This document provides information about mutual funds including their structure, types, history in India, advantages and disadvantages. It discusses that a mutual fund is a trust that collects money from investors and invests in stocks, bonds, money market instruments and other securities. The document outlines the key entities involved in mutual funds like sponsors, trustees, asset management companies, custodians and various distribution channels. It also summarizes the different types of mutual fund schemes and provides a brief history of mutual funds in India from 1964 to the present.
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The document discusses various types of ratios used in ratio analysis for evaluating the financial performance and position of a business. It provides definitions and interpretations for liquidity ratios like current ratio and quick ratio, solvency ratios like debt-equity ratio and proprietary ratio, activity ratios like stock turnover ratio and debtor turnover ratio, and profitability ratios like gross profit ratio, net profit ratio, and return on capital employed. Formulas and ideal ratios are given for each type of financial ratio.
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Performance evaluation of selected mutual fund schemes in india
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This document discusses supervised learning. Supervised learning uses labeled training data to train models to predict outputs for new data. Examples given include weather prediction apps, spam filters, and Netflix recommendations. Supervised learning algorithms are selected based on whether the target variable is categorical or continuous. Classification algorithms are used when the target is categorical while regression is used for continuous targets. Common regression algorithms discussed include linear regression, logistic regression, ridge regression, lasso regression, and elastic net. Metrics for evaluating supervised learning models include accuracy, R-squared, adjusted R-squared, mean squared error, and coefficients/p-values. The document also covers challenges like overfitting and regularization techniques to address it.
Multiple Regression.ppt
This document discusses multiple regression analysis. It begins by introducing multiple regression as an extension of simple linear regression that allows for modeling relationships between a response variable and multiple explanatory variables. It then covers topics such as examining variable distributions, building regression models, estimating model parameters, and assessing overall model fit and significance of individual predictors. An example demonstrates using multiple regression to build a model for predicting cable television subscribers based on advertising rates, station power, number of local families, and number of competing stations.
Churn Analysis in Telecom Industry
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Detection of credit card fraud
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Study on Evaluation of Venture Capital Based onInteractive Projection Algorithm
International Journal of Business and Management Invention (IJBMI) is an international journal intended for professionals and researchers in all fields of Business and Management. IJBMI publishes research articles and reviews within the whole field Business and Management, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
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The document describes a regression analysis conducted to determine the relationship between advertising costs and number of orders for a new diabetes drug. A strong positive correlation was found between the two variables (r=0.88093). The regression equation derived to predict advertising costs based on orders was y = 0.00971950x + 47895, with R^2 = 0.776. This indicates that 77.6% of the variation in advertising costs is explained by number of orders. Based on this strong correlation and the small standard error, the regression results provide sufficient evidence for the company to use in making decisions about next year's advertising budget.
lanen_5e_ch05_student.ppt
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1 resource optimization 2
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Management Science
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German credit score shivaram prakash
The document analyzes models for predicting loan default using a German credit dataset. It fits generalized linear models, generalized additive models, linear discriminant analysis, and classification trees to the data. Based on out-of-sample testing, the linear discriminant analysis model provided the best results with a minimum misclassification rate of 0.40 and maximum area under the ROC curve of 0.867. However, the performance of all the models was quite similar.
Course Project AJ DAVIS DEPARTMENT STORESIntroduction.docx
Course Project: AJ DAVIS DEPARTMENT STORES
Introduction
AJ DAVIS is a department store chain, which has many credit customers and wants to find out more information about these customers. A sample of 50 credit customers is selected with data collected on the following five variables.
1. Location (rural, urban, suburban)
2. Income (in $1,000's—be careful with this)
3. Size (household size, meaning number of people living in the household)
4. Years (the number of years that the customer has lived in the current location)
5. Credit balance (the customers current credit card balance on the store's credit card, in $).
The data is available in Doc Sharing Course Project Data Set as an Excel file. You are to copy and paste the data set into a minitab worksheet.
PROJECT PART A: Exploratory Data Analysis
· Open the file MATH533 Project Consumer.xls from the Course Project Data Set folder in Doc Sharing.
· For each of the five variables, process, organize, present, and summarize the data. Analyze each variable by itself using graphical and numerical techniques of summarization. Use minitab as much as possible, explaining what the printout tells you. You may wish to use some of the following graphs: stem-leaf diagram, frequency or relative frequency table, histogram, boxplot, dotplot, pie chart, bar graph. Caution: Not all of these are appropriate for each of these variables, nor are they all necessary. More is not necessarily better. In addition, be sure to find the appropriate measures of central tendency and measures of dispersion for the above data. Where appropriate use the five number summary (the Min, Q1, Median, Q3, Max). Once again, use minitab as appropriate, and explain what the results mean.
· Analyze the connections or relationships between the variables. There are 10 pairings here (location and income, location and size, location and years, location and credit balance, income and size, income and years, income and balance, size and years, size and credit balance, years and Credit Balance). Use graphical as well as numerical summary measures. Explain what you see. Be sure to consider all 10 pairings. Some variables show clear relationships, while others do not.
· Prepare your report in Microsoft Word (or some other word processing package), integrating your graphs and tables with text explanations and interpretations.Be sure that you have graphical and numerical back up for your explanations and interpretations. Be selective in what you include in the report. I'm not looking for a 20-page report on every variable and every possible relationship (that's 15 things to do). Rather, what I want you do is to highlight what you see for three individual variables(no more than one graph for each, one or two measures of central tendency and variability (as appropriate), and two or three sentences of interpretation). For the 10 pairings, identify and report only on three of the pairings, again using graphical and numerical summary (as.
Marketing Research Approaches .docx
Marketing Research Approaches to Demand EstimationConsumer Surveysdata from survey questionsObservational Researchdata from observed behaviorConsumer Clinicsdata from laboratory experimentsMarket Experimentsdata from real market tests
Regression Analysis
Scatter Diagram
Regression AnalysisRegression Line: Line of Best Fit
Regression Line: Minimizes the sum of the squared vertical deviations (et) of each point from the regression line.
Ordinary Least Squares (OLS) Method
Ordinary Least Squares (OLS)
Model:
Ordinary Least Squares (OLS)
Objective: Determine the slope and intercept that minimize the sum of the squared errors.
Ordinary Least Squares (OLS)
Estimation Procedure
Ordinary Least Squares (OLS)
Estimation Example
Ordinary Least Squares (OLS)
Estimation Example
Tests of Significance
Standard Error of the Slope Estimate
Tests of Significance
Example Calculation
Tests of Significance
Example Calculation
Tests of Significance
Calculation of the t Statistic
Degrees of Freedom = (n-k) = (10-2) = 8
Critical Value at 5% level =2.306
Tests of Significance
Decomposition of Sum of Squares
Total Variation = Explained Variation + Unexplained Variation
Tests of Significance
Coefficient of Determination
Tests of Significance
Coefficient of Correlation
Multiple Regression Analysis
Model:
Multiple Regression Analysis
Adjusted Coefficient of Determination
Multiple Regression Analysis
Analysis of Variance and F Statistic
Problems in Regression AnalysisMulticollinearity: Two or more explanatory variables are highly correlated.Heteroskedasticity: Variance of error term is not independent of the Y variable.Autocorrelation: Consecutive error terms are correlated.
Durbin-Watson Statistic
Test for Autocorrelation
If d = 2, autocorrelation is absent.
Steps in Demand EstimationModel Specification: Identify VariablesCollect DataSpecify Functional FormEstimate FunctionTest the Results
Functional Form Specifications
Linear Function:
Power Function:
Estimation Format:
Chapter 5 Appendix
Getting StartedInstall the Analysis ToolPak add-in from the Excel installation media if it has not already been installedAttach the Analysis ToolPak add-inFrom the menu, select Tools and then Add-Ins...When the Add-Ins dialog appears, select Analysis ToolPak and then click OK.
Entering DataData on each variable must be entered in a separate columnLabel the top of each column with a symbol or brief description to identify the variableMultiple regression analysis requires that all data on independent variables be in adjacent columns
Example Data
Running the RegressionSelect the Regression tool from the Analysis ToolPak dialogFrom the menu, select Tools and then Data Analysis...On the Data Anal.
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Logistic regression is a machine learning classification algorithm used to predict the probability of a categorical dependent variable given one or more independent variables. It uses a logit link function to transform the probability values into odds ratios between 0 and infinity. The model is trained by minimizing a cost function called logistic loss using gradient descent optimization. Model performance is evaluated using metrics like accuracy, precision, recall, and the confusion matrix, and can be optimized by adjusting the probability threshold for classifications.
WEKA:Credibility Evaluating Whats Been Learned
- Training and test sets are used to measure classification success rates, with the test set being independent of the training set. The error rate on the training set is optimistic. Cross validation techniques like 10-fold stratified cross validation are used when data is limited.
- True success rates are predicted using properties of statistics and normal distributions. Confidence levels determine the range within which the true rate is expected to lie.
- Techniques like paired t-tests are used to statistically compare the performance of different algorithms or data mining methods. They determine if performance differences are statistically significant.
WEKA: Credibility Evaluating Whats Been Learned
This document discusses various techniques for evaluating machine learning models and comparing their performance, including:
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- Using techniques like cross-validation, bootstrapping, and holdout validation when data is limited
- Comparing algorithms using statistical tests like paired t-tests
- Accounting for costs of different prediction outcomes in evaluation and model training
- Visualizing performance using lift charts and ROC curves to compare models
- The Minimum Description Length principle for selecting the model that best compresses the data
Machine learning in credit risk modeling : a James white paper
In Machine Learning in Credit Risk Modeling, we provide an explanation of the main Machine Learning models used in James so that Efficiency does not come at the expense of Explainability.
(Contact Yvan De Munck for more info or to receive other and future updates on the subject @yvandemunck or yvan@james.finance)
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IRJET- Supervised Learning Classification Algorithms Comparison
This document compares several supervised machine learning classification algorithms on a Titanic dataset: Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, Support Vector Machine, and Naive Bayes. It finds that Random Forest achieves the highest accuracy. It preprocesses the dataset, trains models on a training set, and evaluates them using metrics like precision, recall, and F1-score calculated from the confusion matrix on a test set.
Operations Management VTU BE Mechanical 2015 Solved paper
The document provides information about operations management concepts including scientific management, productivity, ABC analysis, economic order quantity, and materials requirements planning. It defines each concept and provides examples to illustrate how they are applied. Scientific management aims to improve efficiency through systematic analysis of work processes. Productivity is a measure of output per unit of input. ABC analysis categorizes inventory items based on their value and usage to determine appropriate control methods. Economic order quantity and ordering cycle determine optimal replenishment amounts and frequencies to minimize total inventory costs. Materials requirements planning is a technique to plan material needs at different production levels based on a product structure tree.
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In a tight labour market, job-seekers gain bargaining power and leverage it into greater job quality—at least, that’s the conventional wisdom.
Michael, LMIC Economist, presented findings that reveal a weakened relationship between labour market tightness and job quality indicators following the pandemic. Labour market tightness coincided with growth in real wages for only a portion of workers: those in low-wage jobs requiring little education. Several factors—including labour market composition, worker and employer behaviour, and labour market practices—have contributed to the absence of worker benefits. These will be investigated further in future work.Who Is Abhay Bhutada, MD of Poonawalla Fincorp
Abhay Bhutada, the Managing Director of Poonawalla Fincorp Limited, is an accomplished leader with over 15 years of experience in commercial and retail lending. A Qualified Chartered Accountant, he has been pivotal in leveraging technology to enhance financial services. Starting his career at Bank of India, he later founded TAB Capital Limited and co-founded Poonawalla Finance Private Limited, emphasizing digital lending. Under his leadership, Poonawalla Fincorp achieved a 'AAA' credit rating, integrating acquisitions and emphasizing corporate governance. Actively involved in industry forums and CSR initiatives, Abhay has been recognized with awards like "Young Entrepreneur of India 2017" and "40 under 40 Most Influential Leader for 2020-21." Personally, he values mindfulness, enjoys gardening, yoga, and sees every day as an opportunity for growth and improvement.
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在线办理(GU毕业证书)美国贡萨加大学毕业证学历证书一模一样
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Independent Study - College of Wooster Research (2023-2024) FDI, Culture, Glo...
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"Does Foreign Direct Investment Negatively Affect Preservation of Culture in the Global South? Case Studies in Thailand and Cambodia."
Do elements of globalization, such as Foreign Direct Investment (FDI), negatively affect the ability of countries in the Global South to preserve their culture? This research aims to answer this question by employing a cross-sectional comparative case study analysis utilizing methods of difference. Thailand and Cambodia are compared as they are in the same region and have a similar culture. The metric of difference between Thailand and Cambodia is their ability to preserve their culture. This ability is operationalized by their respective attitudes towards FDI; Thailand imposes stringent regulations and limitations on FDI while Cambodia does not hesitate to accept most FDI and imposes fewer limitations. The evidence from this study suggests that FDI from globally influential countries with high gross domestic products (GDPs) (e.g. China, U.S.) challenges the ability of countries with lower GDPs (e.g. Cambodia) to protect their culture. Furthermore, the ability, or lack thereof, of the receiving countries to protect their culture is amplified by the existence and implementation of restrictive FDI policies imposed by their governments.
My study abroad in Bali, Indonesia, inspired this research topic as I noticed how globalization is changing the culture of its people. I learned their language and way of life which helped me understand the beauty and importance of cultural preservation. I believe we could all benefit from learning new perspectives as they could help us ideate solutions to contemporary issues and empathize with others.一比一原版(UoB毕业证)伯明翰大学毕业证如何办理
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