PPT On How To Use Statistical Method

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Statistical data is widely used in commerce for informed decision-making. It helps in market research, sales forecasting, financial analysis, and inventory management. Businesses use it to understand consumer behavior, improve product quality, and plan effective marketing strategies. It also aids in budgeting, policy formulation, and performance evaluation, making it essential for business growth and efficiency.

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USES OF STATISTICAL METHOD IN INVESTMENT MANAGEMENT
USES OF STATISTICAL METHOD IN INVESTMENT MANAGEMENT INTRODUCTION Statistics is a set of tools that researchers use to gather, examine, and draw conclusions from data. There are different methods of analyzing data, which are typically grouped into descriptive and inferential statistics. Descriptive statistics helps you learn about the features of an entire data set, such as average and spread, and examine how data points relate to each other. Inferential statistics allows you to make generalizations about a larger population through a smaller sample. Statistical methods are useful to analyze, evaluate, and summarize large volumes of data and also have several applications in financial analysis and investing. For example, the standard deviation R-squared, and the Sharpe ratio are statistical measures that may help you evaluate the performance of individual stocks.
Asset Type Equity index fund LETS ASSUME PORTFOLIO Percentage invested in the asset type 7000 Average return Bond index fund Money market fund Real estate fund
USES OF STATISTICAL METHOD IN INVESTMENT MANAGEMENT STATISTICAL METHODS FOR INVESTMENT & FINANCE Overview of Probability & Statistic 2 Regression Methods 3 Multivariate Methods 4 Time Series
1. OVERVIEW OF PROBABILITY & STATISTIC Randomness, probability, random variables, expectation & moments, variances & covariances, dependence & conditioning, Markowitz mean-variance portfolio theory, mean-square optimal prediction, the multivariate normal distribution, estimation (maximum likelihood approach), hypothesis testing (likelihood ratio tests), some basic tests including randomization and bootstrap ideas, brief intro to Monte Carlo. 2. REGRESSION METHODS Ordinary and generalized least squares, multicollinearity, lagged dependent variables, autocorrelated errors (serial correlation), regression diagnostics, robustness issues. nonlinear methods.
3. MULTIVARIATE METHODS Covariance functions. Principal components. Factor analysis. Discussion of applications, including Litterman- Scheinkman's approach to immunizing bond portfolios, and BARRA's approach to modeling portfolio risk. 4. Time Series Univariate and multivariate time series. Stationarity, ARIMA models, Spectral Analysis (briefly Basic ideas of stochastic volatility modeling (ARCH and GARCH). Scenario generation. (A) More on ARCH/GARCH modeling and (B) Value at Risk and statistical issues of the J.P. Morgan RiskMetrics document.
SUMMARY The most commonly used financial analysis tools are comparative statements, common size statements, trend analysis, ratio analysis, funds flow analysis and cash flow analysis.
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PPT On How To Use Statistical Method

Ruban R / Agra

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