Ben Prytherch People who analyze data are usually interested in something other than the data they analyze. A financial analyst might use patterns and anomalies in market data to create an investment strategy for the upcoming year. A physician might reference data from a randomized controlled trial when deciding what drug to prescribe to a patient. A basketball coach might plan player rotations after looking at data collected from their next opponent’s recent matches. Members of a local board of education might look at data from state standardized tests to decide whether to approve a proposed … Read more →

Placeholder for interesting knowledge in marketing […] No prerequisites required to read this … Read more →

This is an Excel computer lab manual to be used in an Introduction to Statistics course at QCC. […] Statistics is present in many ways in our lives. Statistical methodology can be found in surveys, sampling, clinical trials, studies of biomedical treatments, digital marketing, finance, etc. In recent years, Statistics has undergone changes in its techniques and approaches because of the need to analyze exceptionally large and complex data sets that arise all around us but cannot be done by hand. Since we have more powerful computers available to us at the present time, we can employ them to … Read more →

This is a short introduction to some methods used in market research. […] This short book is a practical guide to using R for market research. It provides an overview of some market research tools and methods, with a focus on their practical implementation in R. Each chapter includes step-by-step instructions on how to use R to implement these methods, along with sample code. For suggestions please write to rossialessio095@gmail.com … Read more →

Investment databook containing business financials, industry and market environments, and global economy […] When I first stepped into the investment world, there was just too much information all over the newspapers, websites, books, YouTube, and even lunch table gossips which all seemed to be of the utmost importance to me. Some believed it was the perfect time to invest in the stock market, while others insisted that the whole market was significantly overheated by repeated fiscal stimulus packages from the government and we all should prepare for the impending market crash. I was so … Read more →

R tutorial for marketing students […] In this tutorial, we will explore R as a tool to analyse and visualise data. R is a statistical programming language that has rapidly gained popularity in many scientific fields. The main difference between R and other statistical software like SPSS is that R has no graphical user interface. There are no buttons to click. R is run entirely by typing commands into a text interface. This may seem daunting, but hopefully by the end of this tutorial you will see how R can help you to do better statistical analysis. So why are we using R and not one of the … Read more →

Teaching notes for the MMMEF master […] The following are draft notes related to the lecture “Introduction to Finance”, given at [MMMEF master](http://www.mmmef.fr/ “visit”, year 2022. This is an incomplete work in progress which is neither supposed to be quoted nor … Read more →

Matured Big Data Analytics provides new product, consumer, market, and competitor insights in a real-time fashion. […] This is an early draft for my Marketing Research course (MKTG4000) at CSUB. The free mannual (outline) is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. “I do not care which tool or language you use for assignments, as long as you do your own work.” You can access the script on the course website. You don’t need to actually know to code in R for this course although you will be able to pick up this skill slowly when we … Read more →

KULeuven R tutorial for marketing students […] In this tutorial, we will explore R as a tool to analyse and visualise data. R is a statistical programming language that has rapidly gained popularity in many scientific fields. The main difference between R and other statistical software like SPSS is that R has no graphical user interface. There are no buttons to click. R is run entirely by typing commands into a text interface. This may seem daunting, but hopefully by the end of this tutorial you will see how R can help you to do better statistical analysis. So why are we using R and not one … Read more →

En este bookdown se hace un registro de los datos a los cuáles se tiene acceso y documentación […] En proceso En proceso This API offers real-time quotes for equities trading on U.S. and international exchanges. In addition to last price and stock quote (bid/ask) data, the API also provides intraday tick data, volume and time weighted average prices and other market statistics including open, high, low, close, opening/closing auction prices and other data for active equities, depository receipts and ETFs. Haz el request aquí This API offers real time stock quotes for securities trading on … Read more →

This is the class note for lecture 11. […] The idea behind the “no-arbitrage” principle can be summarized in a few words: there is no such thing as a free lunch. In other words, the financial markets will not allow you to get a risk-free profit. A simple example of arbitrage is when a foreign currency is traded on two markets with different exchange rates. Suppose that you can exchange euros to pounds in London at a rate of e1.15 per pound, and in Paris at e1.16 per pound. Then it would be possible to get a guaranteed profit by exchanging one pound for e1.16 in Paris and then exchanging … Read more →

This is the class note for lecture 7. […] Basic Definition: Annualized standard deviation of the change in price or value of a financial security. Volatility is a statistical measure of the dispersion1 of returns for a given security or market index. In most cases, the higher the volatility, the riskier the security. Volatility is often measured as either the standard deviation or variance between returns from that same security or market index. In the securities markets, volatility is often associated with big swings in either direction. For example, when the stock market rises and falls … Read more →

This module is part of the Minor Data Driven Decision Making in Business (3DMiB, for short). The module carries 2.5 credits (ECTS) […] This module makes use of R. To replicate, understand and adapt the code used in this module, download R and RStudio. In this module, you will use several R packages. You have to install these packages on your computer, using install.packages(“packagename”). You only have to install packages once on your computer. Only after installing new versions of R, you have to install packages again. For invoking the functions and accessing the data sets embedded in … Read more →

This is a textbook for use in undergraduate critical thinking courses. […] This is a textbook written primarily for my students in PHIL 1502: Critical Thinking, at Oklahoma Baptist University in Shawnee, Oklahoma. There are many good textbooks for critical thinking on the market today, so why write another one? First, all of those books were written for someone else’s course. None cover all of the topics that I would like to cover in class. Second, as I’m sure any student can attest to, textbooks are remarkably expensive, to the point that most of the world’s population cannot afford access … Read more →

This module is part of the Minor Data Driven Decision Making in Business (3DMiB, for short). The module carries 2.5 credits (ECTS) […] This module makes use of R. To replicate, understand and adapt the code used in this module, download R and RStudio. In this module, you will use several R packages. You have to install these packages on your computer, using install.packages(“packagename”). You only have to install packages once on your computer. Only after installing new versions of R, you have to install packages again. For invoking the functions and accessing the data sets embedded in … Read more →

ECO 397 Book Review […] Cathy O’Neill’s book “Weapons of Math Destruction” is an analysis of big data and its use of machine learning programs that aim to maximize market efficiency. In the process of doing so, she coins the initialism WMDs as logical flaws in the models that skew results in one way or another. Her argument focuses on the fact that more often than not, these failures result in the worsening of ongoing structural violence and only add fuel to the fire for recidivism rates, bankruptcies, mortgage defaults, college dropouts, and health-related deaths. While there is absolutely … Read more →

Este libro es el resultado de más de una década de publicar en el blog de Market Variance®, temas relacionadas a la investigación de mercados; principalmente de estadística y de marcas. Y de una selección de sus artículos, que a nuestro juicio son los más útiles, amenos y divertidos… […] … Read more →

Everything you need (and nothing more) to begin to learn R for fundamental data analysis in Market Research […] … Read more →

An overview of the ideas, methods, and institutions that permit human society to manage risks and foster enterprise. Description of practices today and analysis of prospects for the future. Introduction to risk management and behavioral finance principles to understand the functioning of securities, insurance, and banking industries. Fabozzi, Frank J., Franco Modigliani, Frank J. Jones, and Michael G. Ferri. Foundations of Financial Markets and Institutions, 4th ed. Prentice Hall, 2010. Shiller, Robert J. Finance and the Good Society. Princeton University Press, 2012. Brandeis, Louis D. Other … Read more →

Data Visualization Project […] This study aims at investigating how the change of information dissemination process would affect the window-dressing behaviors of mutual fund managers. By convention, window-dressing is defined as the portfolio manipulations right before the quarter-end date, when all the fund managers are required to disclosure their holding firms of that date. Over the past decades, technological progresses largely change the way how information disseminates, and these further influence the information flow of capital markets. For example, the implementation of “Electronic … Read more →

是郭耀仁，資料科學與推廣教育的愛好者，喜歡使用 R 語言與 Python 做資料科學應用，在台大資工系統訓練班開設多門 R 語言與 Python 的相關課程，亦與企業合作提供客製化的內訓課程；同時也是一個超棒的中文資料科學專欄 DataInPoint 的主編；這個專欄與波士頓的資料科學教學團隊 DataCamp 有行銷合作（Affiliate Marketing）。 如果您有 R 語言、Python、資料科學、教學、專案或顧問的需求，可以 email 與我聯絡：tonykuoyj@gmail.com R 語言是一個高階的統計程式語言，她在 2017 IEEE 調查中排名位於第 6 名，1是以資料分析為主要目的程式語言中的最高位。其他熟為人知的像是 Matlab 排名在第 15 名、SQL 排名在第 23 名、 Julia … Read more →

Backtesting strategies with R […] This book is designed to not only produce statistics on many of the most common technical patterns in the stock market, but to show actual trades in such scenarios. Test a strategy; reject if results are not promising Apply a range of parameters to strategies for optimization Attempt to kill any strategy that looks promising. Let me explain that last one a bit. Just because you may find a strategy that seems to outperform the market, have good profit and low drawdown this doesn’t mean you’ve found a strategy to put to work. On the contrary, you must work to … Read more →