Aditya_pro_ppt.pptx
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This document presents a time series analysis project on stock market data from the S&P 500 index. The project involves analyzing the daily returns of stocks over a week, comparing price changes of different stocks, and using libraries like NumPy, Pandas, and Matplotlib to analyze trends like moving averages and correlations between closing prices of companies like Apple. The conclusion finds some stock prices are correlated, so a decrease in one likely means a decrease in another. Future work could include comparing more stocks and adding more visualizations.
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HEHEH
This document compares different methods for forecasting stock returns and prices in the S&P 500 universe. It presents four models: 1) an autoregression (AR) model that forecasts individual stock returns based on past returns, 2) a pair trading model that forecasts returns of cointegrated stock pairs based on their statistical relationship, 3) a principal component analysis (PCA) model that extracts common market factors and forecasts returns based on these factors, and 4) a market neutral model that forms portfolios with returns unrelated to market fundamentals. The models are tested on S&P 500 stock data from 1989-2012 and their forecasting results are compared to real returns.
HEHEH
This document compares different methods for forecasting stock returns and prices in the S&P 500 universe. Four models are presented: 1) an autoregressive (AR) model that assumes stocks behave autocorrelatedly; 2) a pair trading model that exploits statistical relationships between correlated stocks; 3) a principal component analysis (PCA) model that identifies common market factors influencing stocks; and 4) a market neutral model based on mean-reversion. The forecasting results from each method are compared to real values to evaluate their performance, with the statistical arbitrage methods of pair trading and market neutral performing better than the traditional time series AR and VAR models.
CAPM 1.1
The document summarizes a study that uses the Capital Asset Pricing Model (CAPM) to analyze the risk and returns of 5 stocks from 2013-2015. It calculates daily returns, beta, alpha, and the correlation of individual stock returns with market returns. The results show most stocks had a slight negative excess return and negative Sharpe ratio, indicating average risk-adjusted performance. Betas were all statistically significant, with GE closest to the market. R-squared values ranged from 20-48%, explaining some but not all variation in returns. The analysis supports that CAPM provides useful but imperfect insights into the relationship between a stock's risk and return.
Data Samples & Data AnalysesNYU SCPSDataba
Data Samples & Data Analyses
NYU | SCPS
Database Management & Modeling
Edward Colet
[email protected]
Asynchronous Session 3, week of June 7 2021
Class material and homework so far
You should be through text chapters 1-3 (introduction), 4-5 (database fundamentals), and the supplemental readings on RDMBS’s and BigData;
HW submissions were a short write-up about yourselves (hw1), a relational database design exercise, and a BigData discussion (hw2)
Questions from the material?
Please feel free to also use the discussion section on our NYU Discussion site to ask, answer, comment on material from this week (for this week, this will be part of hw3)
Content for this week
Chapter 6: The Analysis Sample
Chapter 7: Analyzing and Manipulating Customer Data
Online Khan Academy content to Introduce SQL
Week3 Overview
‹#›
‹#›
Key themes for this week (and the course)
Databases are important for storing data (obviously), but you have to analyze the data as well otherwise you just have a “data tomb”. The analysis of data to gain insights is what gives the data it’s power and makes it really valuable.
This week we’ll learn about some fundamental analytic concepts operations associated with analysis; We’ll review Correlation, a foundational basis for analytics and modeling; We’ll learn some of the fundamental operations to slice and dice data, and we’ll write basic SQL (Structured Query Language) code to create a table, populate it with records, and query the table to extract and summarize information.
Week3 Overview
‹#›
‹#›
The Analysis Sample
Chapter 6
Key Point of the Chapter:
Data analyses are usually (almost always) done on subsets of the data in the database. As such, the following are the key concepts and points to understand about working with subsets of data
Representative samples
Random samples
Frozen files
Test and validation data sets
Chapter 6: The Analysis Sample
‹#›
‹#›
Know some common marketing scenarios that would be suitable to use a sample and test…
To gauge new product offering/response
Price elasticity
Impact of a creative / change
Identify target market for new test
Gain insights on specific groups/segments
. . . Any decision about your product in the market can be tested and analyzed to minimize/gauge risk
Chapter 6: The Analysis Sample
‹#›
‹#›
What is a representative sample?
A sample accurately reflecting the population of interest from which the marketer wants to draw inferences.
Can not extend or apply results from one population to another
Can not purposely exclude names except for “permission opt-outs”, or other recently promoted per rules/regulations
What is a random sample?
When every member equally likely to be chosen
Nth selects is one approach (select every nth name)
Chapter 6: The Analysis Sample
‹#›
‹#›
What is a frozen file?
A file containing a snapshot view of the customer(s) at the time of the promotion, updated with response data to the promotion
Why is a fr ...
Text
This document provides an introduction to a study on using technical analysis to make investment decisions regarding blue-chip automobile companies listed on the National Stock Exchange (NSE) in India. The study aims to analyze stock indices and the role of technical analysis in decision making. Specifically, it will use tools like RSI, MFI, MACD, and beta to analyze trends and risk factors of selected automobile companies over a one year period. The document outlines the objectives, methodology, sources of data, tools for analysis including correlation, RSI, and beta that will be used in the study. It is limited to three automobile companies on the NSE for a short time period from January to December 2014.
Analysis of Microsoft, Google and Apple Stock Prices
The document analyzes stock price data for Microsoft, Google, and Apple from 2008 to 2012. Time series plots show overall increases in price for each company, with a large dip during the 2008 market crash. Autocorrelation and partial autocorrelation functions show no evidence that the raw stock price data follows ARMA models. Taking the logged difference of prices produces stationary series for Google and potentially Microsoft, but not Apple. Normality tests show the differenced returns are not normally distributed for any of the companies. Overall, no ARIMA models are found to fit the stock price data.
The t Test for Related.docx
The t Test for Related Samples
The t Test for Related Samples
Program Transcript
MATT JONES: As its name implies, the independent samples t-test has the
assumption of the independence of observations. But that's not always the case.
Sometimes we take multiple observations of the same unit of analysis, such as a
person, over time. In this case, we'll use a paired sample t-test, sometimes
referred to as the dependent sample t-test. Let's go to SPSS to see how we do
this.
To perform the paired sample t-test in SPSS, we once again go to Analyze,
Compare Means, and down to the Paired Sample T-test. SPSS doesn't require
much information here;; only the pair of variables of which we would like to test.
We have a simulated data set here for statistical anxiety of students. Students
were provided with an instrument that measures their anxiety around statistical
topics on a number of different constructs-- teachers, interpretation, asking for
help, worth, and self-conceptualization.
They were given the test at the beginning of the class and at the conclusion of a
class. Hence, why in the value labels we see pre-test and post-test. As a teacher,
I might have some interest in determining whether students felt more comfortable
with me or had lowering anxiety over time. This is perfect for a paired sample t-
test. To perform this paired sample t-test, we'll go to Analyze, Compare Means,
the Paired Sample T-test.
SPSS doesn't ask for much information;; only the pair of variables of which I
would like to test. In this case, teacher pre-test and teacher post-test. So this is a
classic before and after. The first piece of output I obtain from the paired sample
t-test are some descriptive statistics, specifically around the pairwise comparison
I'm looking at, which is the teacher subscale pre-test and post-test.
I see that there is mean on the pre-test of 17.32 and on the post-test, an 18.44.
So it appears, at least from a descriptive sense, that there is a higher mean on
the post-test than the pre-test. On the instrument, higher scores on an item or the
subscale indicate higher levels of anxiety for that specific attitude. Except for this
specific subscale, fear of statistics teachers, where higher scores actually
indicate lower levels of anxiety.
So if post scores are higher than pre scores, that means on average, students
feel lower levels of anxiety and more positive attitude about their statistics
teacher. I can see here, at least from a descriptive sense, that that appears to be
the case. But from the sample, I am performing a test of statistical significance.
Next to the mean, I'm provided with the sample size 25-- 25 observations pre-test
and 25 observations post-test, all the same person-- the standard deviation for
the pre-test and the post-test, and the standard error of the mean. ...
09 Chapter modelChapter 9. Stocks and Their Valuation (Models)Thi.docx
09 Chapter modelChapter 9. Stocks and Their Valuation (Models)This model is similar to the bond valuation models developed in Chapter 7 in that we employ discounted cash flow analysis to find the value of a firm's stock.THE DISCOUNTED DIVIDEND MODEL (Section 9-4)The value of any financial asset is equal to the present value of future cash flows provided by the asset. Stocks can be evaluated in two ways: (1) by finding the present value of the expected future dividends, or (2) by finding the present value of the firm's expected future free cash flows, subtracting the market value of the debt and preferred stock to find the total value of the common equity, and then dividing that total value by the number of shares outstanding to find the value per share. Both approaches are examined in this spreadsheet.When an investor buys a share of stock, he/she typically expects to receive cash in the form of dividends and then, eventually, to sell the stock and to receive cash from the sale. Moreover, the price any investor receives is dependent upon the dividends the next investor expects to earn, and so on for different generations of investors. The basic dividend valuation equation is:P0 =D1+D2+. . . .Dn( 1 + rs )( 1 + rs ) 2( 1 + rs ) nThe dividend stream theoretically extends on out forever, i.e., n = infinity. It would not be feasible to deal with an infinite stream of dividends, but if dividends are expected to grow at a constant rate, we can use the constant growth equation as developed in the text to find the value.CONSTANT GROWTH STOCKS (Section 9-5)In the constant growth model, we assume that the dividend will grow forever at a constant growth rate. This is a very strong assumption, but for stable, mature firms, it can be reasonable to assume that the firm will experience some ups and downs throughout its life but those ups and downs balance each other out and result in a long-term constant rate. In addition, we assume that the required return for the stock is a constant. With these assumptions, the price equation for a common stock simplifies to the following expression:P 0 =D 1( r s − g )The long-run growth rate (g) is especially difficult to measure, but one approximates this rate by multiplying the firm's return on equity by the fraction of earnings retained, ROE x
(1 – Payout ratio). Generally speaking, the long-run growth rate is likely to fall between 5% and 8%.EXAMPLEAllied Food Products just paid a dividend of $1.15, and the dividend is expected to grow at a constant rate of 8.3%. What stock price is consistent with these numbers, assuming a 13.7% required return?D0$2.15g8.3%rs13.7%P0 =D1=D0 (1+g)=$2.33( rs − g )( rs − g )0.054P0 =$43.12STOCK PRICE SENSITIVITYOne of the keys to understanding stock valuation is knowing how various factors affect the stock price. We construct below a series of data tables and a graph to show how the stock price is affected by changes in the dividend, the growth rate, and rs. R ...
Recommended
HEHEH
This document compares different methods for forecasting stock returns and prices in the S&P 500 universe. It presents four models: 1) an autoregression (AR) model that forecasts individual stock returns based on past returns, 2) a pair trading model that forecasts returns of cointegrated stock pairs based on their statistical relationship, 3) a principal component analysis (PCA) model that extracts common market factors and forecasts returns based on these factors, and 4) a market neutral model that forms portfolios with returns unrelated to market fundamentals. The models are tested on S&P 500 stock data from 1989-2012 and their forecasting results are compared to real returns.
HEHEH
This document compares different methods for forecasting stock returns and prices in the S&P 500 universe. Four models are presented: 1) an autoregressive (AR) model that assumes stocks behave autocorrelatedly; 2) a pair trading model that exploits statistical relationships between correlated stocks; 3) a principal component analysis (PCA) model that identifies common market factors influencing stocks; and 4) a market neutral model based on mean-reversion. The forecasting results from each method are compared to real values to evaluate their performance, with the statistical arbitrage methods of pair trading and market neutral performing better than the traditional time series AR and VAR models.
CAPM 1.1
The document summarizes a study that uses the Capital Asset Pricing Model (CAPM) to analyze the risk and returns of 5 stocks from 2013-2015. It calculates daily returns, beta, alpha, and the correlation of individual stock returns with market returns. The results show most stocks had a slight negative excess return and negative Sharpe ratio, indicating average risk-adjusted performance. Betas were all statistically significant, with GE closest to the market. R-squared values ranged from 20-48%, explaining some but not all variation in returns. The analysis supports that CAPM provides useful but imperfect insights into the relationship between a stock's risk and return.
Data Samples & Data AnalysesNYU SCPSDataba
Data Samples & Data Analyses
NYU | SCPS
Database Management & Modeling
Edward Colet
[email protected]
Asynchronous Session 3, week of June 7 2021
Class material and homework so far
You should be through text chapters 1-3 (introduction), 4-5 (database fundamentals), and the supplemental readings on RDMBS’s and BigData;
HW submissions were a short write-up about yourselves (hw1), a relational database design exercise, and a BigData discussion (hw2)
Questions from the material?
Please feel free to also use the discussion section on our NYU Discussion site to ask, answer, comment on material from this week (for this week, this will be part of hw3)
Content for this week
Chapter 6: The Analysis Sample
Chapter 7: Analyzing and Manipulating Customer Data
Online Khan Academy content to Introduce SQL
Week3 Overview
‹#›
‹#›
Key themes for this week (and the course)
Databases are important for storing data (obviously), but you have to analyze the data as well otherwise you just have a “data tomb”. The analysis of data to gain insights is what gives the data it’s power and makes it really valuable.
This week we’ll learn about some fundamental analytic concepts operations associated with analysis; We’ll review Correlation, a foundational basis for analytics and modeling; We’ll learn some of the fundamental operations to slice and dice data, and we’ll write basic SQL (Structured Query Language) code to create a table, populate it with records, and query the table to extract and summarize information.
Week3 Overview
‹#›
‹#›
The Analysis Sample
Chapter 6
Key Point of the Chapter:
Data analyses are usually (almost always) done on subsets of the data in the database. As such, the following are the key concepts and points to understand about working with subsets of data
Representative samples
Random samples
Frozen files
Test and validation data sets
Chapter 6: The Analysis Sample
‹#›
‹#›
Know some common marketing scenarios that would be suitable to use a sample and test…
To gauge new product offering/response
Price elasticity
Impact of a creative / change
Identify target market for new test
Gain insights on specific groups/segments
. . . Any decision about your product in the market can be tested and analyzed to minimize/gauge risk
Chapter 6: The Analysis Sample
‹#›
‹#›
What is a representative sample?
A sample accurately reflecting the population of interest from which the marketer wants to draw inferences.
Can not extend or apply results from one population to another
Can not purposely exclude names except for “permission opt-outs”, or other recently promoted per rules/regulations
What is a random sample?
When every member equally likely to be chosen
Nth selects is one approach (select every nth name)
Chapter 6: The Analysis Sample
‹#›
‹#›
What is a frozen file?
A file containing a snapshot view of the customer(s) at the time of the promotion, updated with response data to the promotion
Why is a fr ...
Text
This document provides an introduction to a study on using technical analysis to make investment decisions regarding blue-chip automobile companies listed on the National Stock Exchange (NSE) in India. The study aims to analyze stock indices and the role of technical analysis in decision making. Specifically, it will use tools like RSI, MFI, MACD, and beta to analyze trends and risk factors of selected automobile companies over a one year period. The document outlines the objectives, methodology, sources of data, tools for analysis including correlation, RSI, and beta that will be used in the study. It is limited to three automobile companies on the NSE for a short time period from January to December 2014.
Analysis of Microsoft, Google and Apple Stock Prices
The document analyzes stock price data for Microsoft, Google, and Apple from 2008 to 2012. Time series plots show overall increases in price for each company, with a large dip during the 2008 market crash. Autocorrelation and partial autocorrelation functions show no evidence that the raw stock price data follows ARMA models. Taking the logged difference of prices produces stationary series for Google and potentially Microsoft, but not Apple. Normality tests show the differenced returns are not normally distributed for any of the companies. Overall, no ARIMA models are found to fit the stock price data.
The t Test for Related.docx
The t Test for Related Samples
The t Test for Related Samples
Program Transcript
MATT JONES: As its name implies, the independent samples t-test has the
assumption of the independence of observations. But that's not always the case.
Sometimes we take multiple observations of the same unit of analysis, such as a
person, over time. In this case, we'll use a paired sample t-test, sometimes
referred to as the dependent sample t-test. Let's go to SPSS to see how we do
this.
To perform the paired sample t-test in SPSS, we once again go to Analyze,
Compare Means, and down to the Paired Sample T-test. SPSS doesn't require
much information here;; only the pair of variables of which we would like to test.
We have a simulated data set here for statistical anxiety of students. Students
were provided with an instrument that measures their anxiety around statistical
topics on a number of different constructs-- teachers, interpretation, asking for
help, worth, and self-conceptualization.
They were given the test at the beginning of the class and at the conclusion of a
class. Hence, why in the value labels we see pre-test and post-test. As a teacher,
I might have some interest in determining whether students felt more comfortable
with me or had lowering anxiety over time. This is perfect for a paired sample t-
test. To perform this paired sample t-test, we'll go to Analyze, Compare Means,
the Paired Sample T-test.
SPSS doesn't ask for much information;; only the pair of variables of which I
would like to test. In this case, teacher pre-test and teacher post-test. So this is a
classic before and after. The first piece of output I obtain from the paired sample
t-test are some descriptive statistics, specifically around the pairwise comparison
I'm looking at, which is the teacher subscale pre-test and post-test.
I see that there is mean on the pre-test of 17.32 and on the post-test, an 18.44.
So it appears, at least from a descriptive sense, that there is a higher mean on
the post-test than the pre-test. On the instrument, higher scores on an item or the
subscale indicate higher levels of anxiety for that specific attitude. Except for this
specific subscale, fear of statistics teachers, where higher scores actually
indicate lower levels of anxiety.
So if post scores are higher than pre scores, that means on average, students
feel lower levels of anxiety and more positive attitude about their statistics
teacher. I can see here, at least from a descriptive sense, that that appears to be
the case. But from the sample, I am performing a test of statistical significance.
Next to the mean, I'm provided with the sample size 25-- 25 observations pre-test
and 25 observations post-test, all the same person-- the standard deviation for
the pre-test and the post-test, and the standard error of the mean. ...
09 Chapter modelChapter 9. Stocks and Their Valuation (Models)Thi.docx
09 Chapter modelChapter 9. Stocks and Their Valuation (Models)This model is similar to the bond valuation models developed in Chapter 7 in that we employ discounted cash flow analysis to find the value of a firm's stock.THE DISCOUNTED DIVIDEND MODEL (Section 9-4)The value of any financial asset is equal to the present value of future cash flows provided by the asset. Stocks can be evaluated in two ways: (1) by finding the present value of the expected future dividends, or (2) by finding the present value of the firm's expected future free cash flows, subtracting the market value of the debt and preferred stock to find the total value of the common equity, and then dividing that total value by the number of shares outstanding to find the value per share. Both approaches are examined in this spreadsheet.When an investor buys a share of stock, he/she typically expects to receive cash in the form of dividends and then, eventually, to sell the stock and to receive cash from the sale. Moreover, the price any investor receives is dependent upon the dividends the next investor expects to earn, and so on for different generations of investors. The basic dividend valuation equation is:P0 =D1+D2+. . . .Dn( 1 + rs )( 1 + rs ) 2( 1 + rs ) nThe dividend stream theoretically extends on out forever, i.e., n = infinity. It would not be feasible to deal with an infinite stream of dividends, but if dividends are expected to grow at a constant rate, we can use the constant growth equation as developed in the text to find the value.CONSTANT GROWTH STOCKS (Section 9-5)In the constant growth model, we assume that the dividend will grow forever at a constant growth rate. This is a very strong assumption, but for stable, mature firms, it can be reasonable to assume that the firm will experience some ups and downs throughout its life but those ups and downs balance each other out and result in a long-term constant rate. In addition, we assume that the required return for the stock is a constant. With these assumptions, the price equation for a common stock simplifies to the following expression:P 0 =D 1( r s − g )The long-run growth rate (g) is especially difficult to measure, but one approximates this rate by multiplying the firm's return on equity by the fraction of earnings retained, ROE x
(1 – Payout ratio). Generally speaking, the long-run growth rate is likely to fall between 5% and 8%.EXAMPLEAllied Food Products just paid a dividend of $1.15, and the dividend is expected to grow at a constant rate of 8.3%. What stock price is consistent with these numbers, assuming a 13.7% required return?D0$2.15g8.3%rs13.7%P0 =D1=D0 (1+g)=$2.33( rs − g )( rs − g )0.054P0 =$43.12STOCK PRICE SENSITIVITYOne of the keys to understanding stock valuation is knowing how various factors affect the stock price. We construct below a series of data tables and a graph to show how the stock price is affected by changes in the dividend, the growth rate, and rs. R ...
Company segmentation - an approach with R
We classify companies based on how their stocks trade using their daily stock returns (percentage movement from one day to the next). This analysis will help your organization determine which companies are related to each other (competitors and have similar attributes).
The Case Against Mid Cap Stock Funds
- Mid cap stocks do not provide a clear performance premium over large or small cap stocks based on long-term historical data. There is evidence of a small cap premium but not a mid cap premium.
- Mid cap stocks also do not provide strong diversification benefits compared to other asset classes. Mid cap stocks are highly correlated to both large and small cap stocks.
- Overall, the paper argues that mid cap stocks are not necessary in a defined contribution plan's investment lineup since they do not offer a clear performance or diversification advantage over other asset classes like large and small cap stocks.
Deep Reinforcement Learning for Financial Portfolio Management
How Risk-Sensitive Deep Reinforcement Learning discovers Large-Scale Market-Neutral Pair-Trading for Financial Portfolio Management?
This presentation demonstrates the fascinating ability of (Deep Reinforcement Learning) AI to evolve market-neutral, risk-sensitive, commission-avoidant and market-agnostic Financial Portfolio Management strategies, which are able to avoid transaction costs and generalize out-of-sample periods on both FX and US-stock markets, by acting very similar to the industry-standard pair-trading and performing that strategy efficiently on a very large-scale via learning and exploiting correlations among complex hierarchical clusters of financial assets.
Stock Selection Models
The document compares different stock valuation models and stock selection measures. It finds that the Bakshi-Chen-Dong (BCD) stock valuation model produces mispricing estimates that are highly mean-reverting, with the most and least overvalued stocks reversing roles about every 1.5 years. Combining the BCD mispricing measure with stock momentum and firm size results in the most profitable investment strategy.
Lesson 1 07 measures of variation
This document describes a lesson on measures of variation. The lesson introduces concepts like standard deviation and variance as measures of risk. Students will analyze stock return data for two stocks (A and B) and calculate summary statistics. They will discover that investing half in each stock reduces risk compared to investing fully in one stock, as the standard deviation is lower for a mixed portfolio. The lesson aims to show students that variation measures provide important information beyond just averages.
Confirmations and ContradictionsThe Leontief Paradox, Reco.docx
Confirmations and Contradictions
The Leontief Paradox, Reconsidered:
Correction
Iraj Heravi
University of California, Los Arigetes
Leontief (1954) incorrectly inferred that the United States is revealed
by trade to be relatively more abundant in labor than in capital from
the fact that the capital per man in imports exceeds the capital per
man in exports. The counterexample in Leamer (1980) is in error,
however, since it uses commodity prices at which the first commodity
is not economically produced. This is easily remedied by setting the
price vector to (2, 1, 1) instead of (1, 1, 1). Then, by the series of
calculations outlined in Leamer (1980), it is possible to compute the
ratio of capital per man in exports to capital per man in imports to be
0.97, even though capital is the relatively abundant factor.
Incidentally, the error in the choice of prices is revealed by the
computation of the returns to factors, which for the original numbers
contains a negative return to capital.
References
Heravi, Iraj. "Determination of Patterns of Trade." Ph.D. dissertation, Univ.
California at Los Angeles, 1984.
Leamer, Edward E. "The Leontief Paradox, Reconsidered."/.P.£:. 88 (June
1980): 495-503.
Leontief, Wassily W. "Domestic Production and Foreign Trade: The Ameri-
can Capital Position Re-examined." Econ. Internazionate 7 (February 1954):
9—38. Reprinted in Readings in International Economics, edited bv Richard E.
Caves and Harry G.Johnson. Homewood, III.: Irwin (for American Econ.
Assoc), 1968.
Postdoctoral scholar. Graduate School of Management, UCLA. I am indebted to
Edward Leamer for his advice and encouragement. See my dissertation (1984) for
further work.
[Jounuil (i/ Piiliiiral Ecimomy. 19R6, vol. 94, no. 5]
© 198(3 by l h e L nivfisuy of Chicago. All rights reserved. 0022-3808/86/9405-0010$01.50
1 12O
#1. DUE Thursday 1/ 22 /2015 NOON…. Continue working with the company you originally chose from the Fortune’s 500 Best List ( Johnson & Johnson) http://archive.fortune.com/magazines/fortune/mostadmired/2006/best_worst/worst8.html
Detail Directions (See Below) – CLASSROOM FORUM DISCUSSION
1. You have been working with each environment individually; now it's time to integrate and synthesize.
2. Perform research (minimum of 3 sources in APA format).
3. Analyze each environment collectively by incorporating theories learned.
4. How are the individual environments working together for your chosen company?
5. How is each working with the other environments?
6. Is an environment causing limitations on another? If so, which environments are involved and what are the limitations?
7. What could you change to create synergy? Your answer cannot be that you wouldn’t change a thing. You must incorporate critical thinking.
8. Minimum 6 complete paragraphs; a paragraph is a minimum of 100 words.
*****Use this as one of the References: Use at least one of the sources provided below in “Reading / Resour.
Operations research
This document provides an introduction to operations research. It discusses how operations research uses quantitative methods and mathematical modeling to help solve complex problems in engineering and management. Some key points covered include:
- Operations research originated from efforts during World War II to apply scientific methods to military problems.
- It involves constructing mathematical models of real-world systems and using analytical techniques like linear programming to derive optimal solutions.
- Common applications include production and distribution planning, investment analysis, and facility layout.
- The modeling process involves formulating the problem, developing and testing a model, and implementing the results.
- Operations research has helped many organizations save millions of dollars by optimizing resource allocation and other business decisions.
Operations research
This document provides an introduction to operations research. It discusses how operations research uses quantitative methods and mathematical modeling to help solve complex problems in engineering and management. Some key points covered include:
- Operations research originated from efforts during World War II to apply scientific methods to military problems.
- It involves constructing mathematical models of real-world systems and using analytical techniques like linear programming to derive optimal solutions.
- Common applications include production and distribution planning, investment analysis, and facility layout.
- The modeling process involves formulating the problem, developing and testing a model, and implementing the results.
- Operations research has helped many organizations save millions of dollars by optimizing resource allocation and other business decisions.
Rational Expectations and The Firm’s Dividend Behavior
This study aimed to test a rational expectations hypothesis for firms' dividend behavior by extending Lintner's model of dividends. The researchers specified an econometric model based on Lintner's work but with the addition of lagged earnings as an explanatory variable. Using panel data from US and Japanese firms, they found the rational expectations model yielded somewhat better predictions of dividend payouts in 11 of 12 industry groups based on higher R-squared values. The study concluded the rational expectations hypothesis provides an empirically testable specification of firms' dividend behavior.
Seminar 2
This document provides an overview of stocks and the stock market. It defines what stocks are, including common stock and different types. It discusses factors that influence stock prices such as company news, earnings, and investor sentiment. It introduces several major stock indices like the Dow Jones Industrial Average and S&P 500. It also explains why stock prices change in both the short-term due to news and long-term due to company fundamentals like earnings. The document is meant as an educational guide for understanding stocks and the stock market.
An Empirical Analysis of the Capital Asset Pricing Model.pdf
The International Journal of Engineering and Science and Research is an online journal in English published. The aim is to publish peer reviewed research and review articles fastly with out delay in the developing field of engineering and science Research.
journal paperjournal paper
The International Journal of Engineering and Science and Research is an online journal in English published. The aim is to publish peer reviewed research and review articles fastly with out delay in the developing field of engineering and science Research.
5.1 Assignment Reading and RemediationGetting Start.docx
5.1 Assignment: Reading and Remediation
Getting Started
Viewing the videos and practicing using the practice Excel file with its video can prepare you for the work needed on the research report.
In order to successfully complete this exercise, you should be able to:
Review videos about statistics fundamentals.
Practice with the Excel file provided.
Resources
File: WS5Practice
File: WS5Homework
Video: Chi-Square
Video: Chi-Square Testing
Textbook:
OpenIntro Statistics
File: Chi-Square_10by10.xlsx
Background Information
Providing background and descriptive statistics is like a literature review section of a dissertation. You review and communicate the analysis on the raw data. You present visual representations of the data to give meaning to the raw data.
Instructions
Watch the following video on chi-square:
Watch the Excel remediation videos on chi-square testing:
Chi-Square Testing
using the
Chi-Square_10by10.xlsx
file.
An optional supplementary textbook is
OpenIntro Statistics
, and you can read the concepts there.
Use the lab file
WS5Practice
to practice the Excel skills (includes an Excel hands-on video inside the spreadsheet as a link).
Use the file
WS5Homework
to demonstrate the Excel skills. Each of the three problems is worth 20 points, for a total of 60 points possible for this assignment.
When you have completed your assignment, save a copy for yourself and submit a copy to your instructor by the end of the workshop.
=================================================
5.2 Assignment: Summarizing Raw Data
Getting Started
Being able to summarize data using pivot tables is a crucial skill to develop in business and research. Excel provides a powerful pivot table tool that is heavily used in finance, accounting, information technology, and other business areas. In this course, the skill is used to support a chi-square analysis.
In order to successfully complete this assignment, you should be able to:
Summarize data into a contingency table from raw data using pivot tables.
Resources
File: WS5-2_PivotTables
File: WS5-2_PivotAssignment
Instructions
Review the rubric to make sure you understand the criteria for earning your grade.
Read the file
WS5-2_PivotTables
.
After reading the Word document, open the file
WS5-2_PivotAssignment
.
Create the appropriate summary tables called contingency tables.
Turn in an Excel file showing the pivot table you have created.
When you have completed your assignment, save a copy for yourself and submit a copy to your instructor by the end of the workshop.
===============================================
5.4 Assignment: Create Categorical Hypothesis
Getting Started
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In order to successfully complete this assignment, you should be able to:
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This document provides a lesson on advanced spreadsheet skills in Microsoft Excel. It discusses using Excel to estimate the product cost of a milk tea by inputting ingredient costs and calculating the total. It then adjusts the estimated sale price to ensure it is profitable. The document also discusses using Excel to analyze survey data on the milk tea by calculating averages of ratings and counts of yes/no responses using formulas like SUM, AVERAGE, and COUNTIF. The goal is to teach how to use Excel for market research and product development.
2.2 Assignment Totaling CostsGetting StartedProdu.docx
2.2 Assignment: Totaling Costs
Getting Started
Production and cost are the fundamental building blocks on the supply side of the market. In this assignment you will learn how to calculate costs and construct cost schedules.
Upon successful completion of this assignment, you will be able to:
Describe the determinants of a firm’s output and costs in the long run.
Use cost relationships to predict a firm’s profit-maximizing decisions.
Resources
Textbook:
Economics for Managers
File: Ch05.ppt
Media: Costs of Production
Background Information
In this assignment you will learn how to calculate costs and construct cost schedules for a small business.
Instructions
Review the rubric to make sure you understand the criteria for earning your grade.
Read Chapter 5, “Production and Cost Analysis in the Short Run,” in
Economics for Managers
. As you read, think about what it costs you to do some basic task.
Download and review the
Ch05.ppt
PowerPoint file.
View the media:
Costs of Production
. This lecture presentation covers output and costs in the short run, deriving long-run average cost and firm profit maximization.
Assess data (in the table below) related to a small lawn mowing business.
Angela hires students at $40 a day to mow lawns.
Regardless of the number of students she hires, Angela leases five lawn mowers for $200 a day.
The following table gives the daily output:
Labor
(students per day)Total Product
(lawns cut per day)0012024437049451146120
Write a one- to two-page paper that addresses the following. Be sure to show your calculations.
Construct the average fixed cost, average variable cost, and average total cost schedules.
Construct the marginal cost schedule.
Check that the gap between total cost and total variable cost is the same at all output levels. Explain why this relationship is so important and demonstrate clear insightful and critical thinking.
When you have completed your assignment, save a copy for yourself and submit a copy to our instructor by the end of the workshop.
==========================================================
2.3 Discussion: Economies of Scale
Getting Started
You will examine production and cost issues in the long run, where all inputs in a production process are variable, using a case study.
Upon successful completion of this discussion, you will be able to:
Interpret a long-run production function in which all inputs can be varied and possibly substituted for each other.
Explain how production and cost analysis is used in management decision making.
Resources
Textbook:
Economics for Managers
Video: COVID-19
Background Information
This information and activity build on the short-run production and cost issues covered in the previous workshop.
Instructions
Review the rubric to make sure you understand the criteria for earning your grade.
Read Chapter 6 and review Chapter 5 in
Economics for Managers.
As you read, think a.
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"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
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Leontief (1954) incorrectly inferred that the United States is revealed
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man in exports. The counterexample in Leamer (1980) is in error,
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References
Heravi, Iraj. "Determination of Patterns of Trade." Ph.D. dissertation, Univ.
California at Los Angeles, 1984.
Leamer, Edward E. "The Leontief Paradox, Reconsidered."/.P.£:. 88 (June
1980): 495-503.
Leontief, Wassily W. "Domestic Production and Foreign Trade: The Ameri-
can Capital Position Re-examined." Econ. Internazionate 7 (February 1954):
9—38. Reprinted in Readings in International Economics, edited bv Richard E.
Caves and Harry G.Johnson. Homewood, III.: Irwin (for American Econ.
Assoc), 1968.
Postdoctoral scholar. Graduate School of Management, UCLA. I am indebted to
Edward Leamer for his advice and encouragement. See my dissertation (1984) for
further work.
[Jounuil (i/ Piiliiiral Ecimomy. 19R6, vol. 94, no. 5]
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In order to successfully complete this exercise, you should be able to:
Review videos about statistics fundamentals.
Practice with the Excel file provided.
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File: WS5Practice
File: WS5Homework
Video: Chi-Square
Video: Chi-Square Testing
Textbook:
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File: Chi-Square_10by10.xlsx
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Use the file
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Media: Costs of Production
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Review the rubric to make sure you understand the criteria for earning your grade.
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View the media:
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Assess data (in the table below) related to a small lawn mowing business.
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Construct the average fixed cost, average variable cost, and average total cost schedules.
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ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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How to Add Chatter in the odoo 17 ERP Module
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
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Aditya_pro_ppt.pptx
- 1. Time Series Project (Stock Market case study) ADITYAARUN DESHPANDE ROLL NO : C21027 UNDER THE GUIDANCE OF DR.SULAKSHANA VISPUTE
- 2. Problem Statement : S & P 500 is a stock market index that measures the stock performance of 500 large companies listed on stock exchange in the US ( United States ). What type of analysis ? 1. Analyzing : ( Analyze what is the daily return of each of the stock in a week). 2. Compare prices on each of the stock company data.
- 3. Life cycle / Flow chart :
- 4. Liabraries : Numpy Pandas Matplotlib Plotly Seaborn Warnings
- 5. Change in the price of the stock overtime ? :
- 6. Moving average of the stocks : Moving average :- It is a statistics that computes the average change in a data over some specific interval.
- 7. Analyse of closing price change in a apple stock :
- 8. Resampling analysis of closing price (Apple) :
- 9. Checking closing prices of these companies are correlated or not :
- 11. Conclusion : The idea behind the correlation is as simple as that so if the value is lets say 0.40, it means that if my amazon stock price is decreses or if the percentage change in the amazon then there is a 40% probability that my Microsoft stock price is also decreases or percentage change is also decreases.
- 12. Future Enhancement : Compairing more stocks with each other. Visualization (We can add more different different bar graphs.)
- 13. Thank You