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Course Description In this course, we will introduce a number of financial analytic techniques. You will learn why, when, and how to apply financial analytics in real-world situations. We will explore techniques to analyze time series data and how to evaluate the risk-reward trade off expounded in modern portfolio theory. While most of the focus will be on the prices, returns, and risks of corporate stocks, the analytical techniques can be leveraged in other domains. Finally, a short introduction to algorithmic trading concludes the course. After completing this course, you should be able to understand time series data, create forecasts, and determine the efficacy of the estimates. Also, you will be able to create a portfolio of assets using actual stock price data while optimizing risk and reward. Understanding financial data is an important skill as an analyst, manager, or consultant. Course Goals and Objectives Upon completion of this course, you should be able to: Understand the forecasting process. Evaluate a forecast. Describe time series data. Perform moving average analysis. Perform exponential smoothing. Develop a Holt-Winters model. Develop an ARIMA model. Understand how to create a portfolio of assets. Understand a basic trading algorithm.
This repository is used by the developer site training content, Orlando release. It is used for the Build the NeedIt App, Scripting in ServiceNow, Application Security, Importing Data, Automating Application Logic, Flow Designer, REST Integrations, Reporting and Analytics, Domain Separation, Mobile Applications, and Context-sensitive Help courses.
This repository is used by the developer site training content, Paris release. It is used for the Build the NeedIt App, Scripting in ServiceNow, Application Security, Importing Data, Automating Application Logic, Flow Designer, REST Integrations, Reporting and Analytics, Domain Separation, Mobile Applications, and Context-sensitive Help courses.
Winter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices Class intro: Forecasting and Finance The random walk hypothesis Stationarity Time-varying volatility and General Least Squares Robust standard errors and OLS Topic 2: Time-dependence and predictability ARMA models The likelihood function, exact and conditional likelihood estimation Predictive regressions, autocorrelation robust standard errors The Campbell-Shiller decomposition Present value restrictions Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter Topic 3: Heteroscedasticity Time-varying volatility in the data Realized Variance ARCH and GARCH models, application to Value-at-Risk Topic 4: Time series properties of the cross-section of stock returns Single- and multifactor models Economic factors: Models and data exploration Statistical factors: Principal Components Analysis Fama-MacBeth regressions and characteristics-based factors
Term Paper for BM60112-CFFA
This repository contains the Tutorials for the NPTEL MOOC on Machine Learning.
A React tiling window manager
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Analyse the stock market using yahoo finance data and the tidyquant package.
This project studies the possibilities of forecasting stock market prices of firms using the sentiments captured via web scrapping. We have experimented with the stock market price of Tesla and Moderna using sentiment analysis and the ARIMA model. An accuracy analysis was also carried out with an R- squared value of each of the models to evaluate how they faired in the forecasting. The aim is to help reduce participants in the loss while investing using Twitter data. The stock data was pulled from the Yahoo Finance API. The sentiments were obtained from the sentences of tweets from Twitter. Results have proved that the ARIMA model has an excellent R-Squared value for short-term prediction.
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