Real estate costs and profits change in cycles, sometimes anticipated in type but not in severity or
length. It is known that there are two forms of cycles: ‘capital markets’ cycle which refers to stages of investor
request for commercial real estate assets and the costs and value at which they trade and there is ‘space
market’ cycle which express the demand for and source of real estate space. For a long-term investor, it is
recommended to buy and maintain the stocks event if the estate assets pass through their unavoidable ups and
downs. Real estate investors should be
Movement of Share Prices and Sectoral Analysis: A Reflection Through Interact...Waqas Tariq
Interaction in graphs gives the user with an advantage to analyze the data in greater depth. With the help of interactive graphics users can get better insight of the data in comparison to the static graphical tools. This paper introduces an interactive graphical tool consisting of two graphs, a line diagram complemented by a boxplot. The line diagram helps to understand how successive values of a variable are related to time and box plot can help the visual comparison of several such variables. Here the line diagram is used to visualize share prices of a company corresponding to a number of days and the boxplot displays the position of the Share price of all companies in a particular sector. An investor in share market needs to consider a number of factors before making any decision about investment. Some of the factors influencing the decision are the performance of the particular security in recent past, its position in terms of share price in its own sector. The graphical technique used in this software tool shall be helpful while making investment decision.
Coursework- Soton (Single Index Model and CAPM)Ece Akbulut
This document provides an introduction to portfolio management and the single-index model (SIM) and capital asset pricing model (CAPM). It first describes SIM and tests it on eight companies, analyzing the results. It finds SIM reduces the workload of estimating variables but oversimplifies risk. The document then introduces CAPM, tests it on the same companies, and analyzes the results and merits and demerits of CAPM. It concludes by discussing the sensitivity of the models to sector characteristics.
Using binary logistic regression, the author analyzes factors that influence the probability of company bankruptcy. The regression uses data on 6820 companies to predict bankruptcy based on return on assets, operating profit rate, total asset turnover, and return on equity. The model accurately predicts bankruptcy status for 96.8% of companies. Return on assets, operating profit rate, and total asset turnover are statistically significant predictors of bankruptcy. The author concludes that bankruptcy prediction is important because it can impact connected industries, and financial performance factors like profitability and asset use influence bankruptcy risk.
SAPM - Portfolio Construction and Comparison for Securities on BSEBishnu Kumar
This document presents a study on portfolio construction and comparison of securities listed on the Bombay Stock Exchange (BSE). It begins with an introduction to modern portfolio theory and the single index model. It then discusses factors that impact company performance such as economic, industry and company-specific analyses. The document outlines the study's objectives, methodology and data analysis approach. It describes calculating beta and constructing optimal portfolios using the Sharpe single index model. The document compares the resulting portfolios and provides observations. Tables with portfolio construction worksheets are also included.
IRJET - Forecasting Stock Market Movement Direction using Sentiment Analysis ...IRJET Journal
This document describes a study that uses sentiment analysis and support vector machines to forecast the direction of stock market movement in China. It constructs sentiment indexes from financial news and social media texts, accounting for the day-of-week effect. Support vector machines are used to predict movement of the SSE 50 Index, achieving up to 89.93% accuracy when combining sentiment features with market data. The findings imply that sentiment contains valuable information for predicting stock prices and market trends.
The economic valuation of the negative externalities associated with tourism could help manage emerging social conflicts in major European travel destinations. Citizens' associations in places like Venice, Barcelona, and Mallorca have criticized the environmental and social costs of large tourist crowds on local residents' quality of life. Quantifying the negative externalities of tourism through economic valuation techniques may provide useful information for balancing the interests of locals and the tourism industry. This could play a role in more sustainable management of tourism in these areas.
DATACTIF®, is a Business Intelligence Platform
that generates concept-applications tailor made
for each enterprise needs, enriching in same time
each specific case, with a 15 year experience of
learning processes and accumulating knowledge.
DATACTIF® uses machine learning methodology
and algorithms such as neural network, fuzzy
systems, genetic algorithms, Support Vector
Machines, etc… and contains visualization methods
that allows a global view on the domain that is
under analysis, and an analytical view to all details
offered by the existing data.
This document provides an overview of fundamental analysis techniques used in security analysis and portfolio management. It outlines 5 course outcomes related to macro/industry analysis, equity valuation, financial statement analysis, bond/fixed income strategies, and options/futures. It then discusses various fundamental analysis approaches including economic analysis, industry analysis, and company analysis. Several analysis models and techniques are described such as DuPont analysis, P/E ratios, and portfolio construction approaches. Technical analysis indicators like support/resistance levels, point and figure charts, and gaps are also introduced.
Movement of Share Prices and Sectoral Analysis: A Reflection Through Interact...Waqas Tariq
Interaction in graphs gives the user with an advantage to analyze the data in greater depth. With the help of interactive graphics users can get better insight of the data in comparison to the static graphical tools. This paper introduces an interactive graphical tool consisting of two graphs, a line diagram complemented by a boxplot. The line diagram helps to understand how successive values of a variable are related to time and box plot can help the visual comparison of several such variables. Here the line diagram is used to visualize share prices of a company corresponding to a number of days and the boxplot displays the position of the Share price of all companies in a particular sector. An investor in share market needs to consider a number of factors before making any decision about investment. Some of the factors influencing the decision are the performance of the particular security in recent past, its position in terms of share price in its own sector. The graphical technique used in this software tool shall be helpful while making investment decision.
Coursework- Soton (Single Index Model and CAPM)Ece Akbulut
This document provides an introduction to portfolio management and the single-index model (SIM) and capital asset pricing model (CAPM). It first describes SIM and tests it on eight companies, analyzing the results. It finds SIM reduces the workload of estimating variables but oversimplifies risk. The document then introduces CAPM, tests it on the same companies, and analyzes the results and merits and demerits of CAPM. It concludes by discussing the sensitivity of the models to sector characteristics.
Using binary logistic regression, the author analyzes factors that influence the probability of company bankruptcy. The regression uses data on 6820 companies to predict bankruptcy based on return on assets, operating profit rate, total asset turnover, and return on equity. The model accurately predicts bankruptcy status for 96.8% of companies. Return on assets, operating profit rate, and total asset turnover are statistically significant predictors of bankruptcy. The author concludes that bankruptcy prediction is important because it can impact connected industries, and financial performance factors like profitability and asset use influence bankruptcy risk.
SAPM - Portfolio Construction and Comparison for Securities on BSEBishnu Kumar
This document presents a study on portfolio construction and comparison of securities listed on the Bombay Stock Exchange (BSE). It begins with an introduction to modern portfolio theory and the single index model. It then discusses factors that impact company performance such as economic, industry and company-specific analyses. The document outlines the study's objectives, methodology and data analysis approach. It describes calculating beta and constructing optimal portfolios using the Sharpe single index model. The document compares the resulting portfolios and provides observations. Tables with portfolio construction worksheets are also included.
IRJET - Forecasting Stock Market Movement Direction using Sentiment Analysis ...IRJET Journal
This document describes a study that uses sentiment analysis and support vector machines to forecast the direction of stock market movement in China. It constructs sentiment indexes from financial news and social media texts, accounting for the day-of-week effect. Support vector machines are used to predict movement of the SSE 50 Index, achieving up to 89.93% accuracy when combining sentiment features with market data. The findings imply that sentiment contains valuable information for predicting stock prices and market trends.
The economic valuation of the negative externalities associated with tourism could help manage emerging social conflicts in major European travel destinations. Citizens' associations in places like Venice, Barcelona, and Mallorca have criticized the environmental and social costs of large tourist crowds on local residents' quality of life. Quantifying the negative externalities of tourism through economic valuation techniques may provide useful information for balancing the interests of locals and the tourism industry. This could play a role in more sustainable management of tourism in these areas.
DATACTIF®, is a Business Intelligence Platform
that generates concept-applications tailor made
for each enterprise needs, enriching in same time
each specific case, with a 15 year experience of
learning processes and accumulating knowledge.
DATACTIF® uses machine learning methodology
and algorithms such as neural network, fuzzy
systems, genetic algorithms, Support Vector
Machines, etc… and contains visualization methods
that allows a global view on the domain that is
under analysis, and an analytical view to all details
offered by the existing data.
This document provides an overview of fundamental analysis techniques used in security analysis and portfolio management. It outlines 5 course outcomes related to macro/industry analysis, equity valuation, financial statement analysis, bond/fixed income strategies, and options/futures. It then discusses various fundamental analysis approaches including economic analysis, industry analysis, and company analysis. Several analysis models and techniques are described such as DuPont analysis, P/E ratios, and portfolio construction approaches. Technical analysis indicators like support/resistance levels, point and figure charts, and gaps are also introduced.
Fundamental analysis examines a security's intrinsic value by studying economic and financial factors that may impact its value. It focuses on a company's financials, the economy, and industry conditions. Technical analysis evaluates investments by analyzing statistical trends in trading data like price movement and volume. Fundamental analysis involves determining a stock's intrinsic value by forecasting earnings, dividends, and choosing a discount rate. It consists of economic, industry, and company analysis. Technical analysis is based on historical price patterns and stock trends.
The dividend discount model (DDM) is commonly used to value stocks. It calculates a stock's intrinsic value based on expected future dividends, discounted back to the present. The DDM has advantages like simplicity and relying on theoretical foundations, but it also has disadvantages like not accounting for intangible assets and being dependent on assumptions about stable dividend growth rates. Whether the DDM or multiples approach is more accurate depends on the specific company and analysts must consider various valuation techniques and compare to industry averages to determine if a stock is undervalued, overvalued, or fairly valued.
This document discusses fundamental analysis techniques for evaluating stocks. It covers analyzing macroeconomic factors, industries, and individual companies. Fundamental analysis involves forecasting earnings, cash flows, and dividends to determine a stock's intrinsic value. It breaks down as 30-35% economic analysis, 15-20% industry analysis, and 30-35% company analysis. Key aspects covered include Porter's five forces model, industry life cycles, quantitative ratios like P/E, and qualitative factors. The overall aim is to understand factors that affect stock prices and identify undervalued stocks.
A predictive system called "INSIGHT" was built using structured and unstructured data from various sources to identify potential buyers and sellers for large block trades. The system analyzed data like daily block trades, shareholder patterns, holdings, and market news to predict fund behavior. Additionally, the author learned quantitative research including technical analysis, building pair trading strategies, and value-at-risk models to analyze stock predictions and portfolio risk. The internship provided valuable experience in institutional equities trading and quantitative analysis techniques.
The document discusses investment regulations and foreign investment. It notes that foreign investment provides needed capital for modernizing factories and remaining competitive internationally. There are benefits to allowing foreign investment in countries. The screening process for foreign investment involves determining demand, available resources, analyzing the business environment, measuring market potential, and selecting sites.
Many countries have seen the importance of financial education by making financial
education a national strategy. In Vietnam, although the National Strategies for Inclusive Financial
Education has been proposed since 2017 and officially included in the National Financial Inclusion
Strategy in 2020, however, financial education is still quite new, and many people are not aware of
the necessity of financial l
Today, in the rapidly emerging globalization process, increasing the competitiveness of enterprises
depends on increasing of their firm performance. Although there are many methods and techniques affecting
firm performance, Information technology (IT) capabilities has become one of the most widely used method,
especially in dealing with supply chain matters of a firm. The aim of our study is to express whether innovation
and organization learning is effective as intermediate variable to the effects of IT capabilities at firm’s
performance. The opinion which claim
Globally, the number of startup companies has rapidly expanded during the last 5-8 years. Offering
products and/or services that greatly enhance the lives of its clients is a major focus for these firms. In India,
local and federal government initiatives have provided new enterprises and entrepreneurs with much
momentum and assistance, helping India become the world's top startup location. The Government of India
(GOI) launched the "Startup India" campaign in 2015 to promote entrepreneurship and support businesses to
achieve this goal (Babu, S., Sridevi, K.,2019). An IBM Center for Business Value and Oxford Economics study
in 2018 found that 90% of Indian companies fail within the first five years of operation. Potential difficulties
that startups may run across, both generally and specifically in the Indian market, have been described by
several authors.
Behaviour finance is the study of how psychological phenomena affect financial behaviour. This
financial science is used in making financial decisions. Amid the development of the digital economy, paylater
innovation has emerged. It is feared that the ease of use of paylater can have a negative impact, one of which is
the attitude of impulsive buying. This research will analyze the effect of financial literacy, self-control, risk
perception, and percieved ease of use on impulsive buying behaviour. This research is based on Decision Affect
Theory, which is a theory that discusses financial decision behaviour that is influenced by self-emotion. This
research is uses purposive sampling wi
Improving the business environment is one of the key strategies to promote local and regional
economic development. However, which factors affect the business environment of the provinces is still
controversial. Using survey data from 400 investors and managers and a multivariate regression analysis
method, this study has identified the factors affecting the business environment of Hai Phong province. The
analysis results show that there are 09 factors affecting the business environment of Hai Phong City, including
entry costs, land access and tenure, transparent, informal charges, time cost, pro-activeness, business support
services, labor training and legal institutions. In
The effect of work attitude and innovation ability on employee innovation performance is of great
significance for improving the innovation ability of manufacturing enterprises and building an "Innovative
Country" in China.This article theoretical analysis was conducted on the mechanism by which the work attitude
of employees in manufacturing enterprises affects innovation performance and the mediating mechanism of
innovation ability. Based on data from Chinese manufacturing enterprises, empirical analysis was conducted
using SEM models. Resear
The concept of organizational resilience continues to grow in focus and importance, but there
has yet to be an agreed upon measure of organizational resilience. Organizational resilience can be seen as a
corporation’s ability to adapt to change and maintain flexibility within their supply chain. Resilience and
flexibility at all organizational levels is necessary, in a proactive manner, to turn resilience into a competitive
advantage
The document summarizes research on nonlinear correlation coefficients on manifolds. It defines a new nonlinear correlation coefficient called SEVP, proves some of its basic properties including that it ranges from 0 to 1. It discusses how to measure nonlinear correlation between variables on a manifold and reviews common dimensionality reduction methods for manifolds. The goal is to preserve nonlinear structure as much as possible by projecting onto the orthogonal complement of tangent spaces. An optimization problem is formulated to find the linear space with the largest angle to all tangent spaces, transforming it into an eigenvalue problem to solve.
This study aims to analyze and prove whether there is a positive and significant influence
between product quality and poki prices on purchasing decisions for Kobba brand coffee. The survey was
conducted using 53 respondents who were buyers who had purchased Kobba brand coffee more than once.
Information from respondents was obtained through a list of questions that were sent and returned by
respondents
In this paper, we introduce a universal framework for mean-distortion robust risk measurement and
portfolio optimization. We take accounts for the uncertainty based on Gelbrich distance and another uncertainty
set proposed by Delage & Ye. We also establish the model under the constraints of probabilistic safety
criteria and compare the different frontiers and the investment ratio to each asset. The empirical analysis in the
final part explores the impact of different parameters on the model results.
Despite the attainment of the famous Millennium Development Goals (MDGs) of reducing the number
of poor people across the globe a significant number still live below the poverty line. The problem of poverty is
more endemic in developing countries like Nigeria. Several intervention efforts have been in place to address
the poverty question which persists partly due to serious financial exclusion and unethical activities of informal
finance providers.
The focus of this research was to establish the effect of entrepreneurship Ecosystem in inculcating
entrepreneurial propensity for community development. Promotion of entrepreneurship in Kenya has existed
ever since independence. The Government has shown tremendous support to entrepreneurship growth. The
Government have channelled financial support through funding such as Women Enterprise fund, Youth
Enterprise Fund and Uwezo Fund
In this paper, we consider an AAI with two types of insurance business with p-thinning dependent
claims risk, diversify claims risk by purchasing proportional reinsurance, and invest in a stock with Heston
model price process, a risk-free bond, and a credit bond in the financial market with the objective of maximizing
the expectation of the terminal wealth index effect, and construct the wealth process of AAI as well as the the
model of robust optimal reinsurance-investment problem is obtained, using dynamic programming, the HJB
equation to obtain the pre-default and post-default reinsurance-investment strategies and the display expression
of the value function, respectively, and the sensitivity of the model parameters is analyzed through numerical
experiments to obtain a realistic economic interpretation. The model as well as the results in this paper are a
generalization and extension of the results of existing studies.
:Textiles and clothing are a fundamental part of everyday life and an important sector in the global
economy. It is hard to imagine a world without textiles. Clothes are worn by almost everyone, almost all the time
and it also becomes an important expression for an individuality. In 2015, emission from textiles production
totaled 1.2 billion tons of CO2 equivalent throughout its lifecycle. The fashion industry is a large consumer of
water, high volumes of water containing
In this paper, we construct a Credit Default Swap pricing model for default recovery rates under
distributional uncertainty based on a structured pricing model and distributional uncertainty theory. The model
is algorithmically transformed into a solvable semi-definite programming problem using the Lagrangian dual
method, and the solution of the model is given using the projection interior point method. Finally, an empirical
analysis is conducted, and the results show that the model constructed in this paper is reasonable and efficient
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Fundamental analysis examines a security's intrinsic value by studying economic and financial factors that may impact its value. It focuses on a company's financials, the economy, and industry conditions. Technical analysis evaluates investments by analyzing statistical trends in trading data like price movement and volume. Fundamental analysis involves determining a stock's intrinsic value by forecasting earnings, dividends, and choosing a discount rate. It consists of economic, industry, and company analysis. Technical analysis is based on historical price patterns and stock trends.
The dividend discount model (DDM) is commonly used to value stocks. It calculates a stock's intrinsic value based on expected future dividends, discounted back to the present. The DDM has advantages like simplicity and relying on theoretical foundations, but it also has disadvantages like not accounting for intangible assets and being dependent on assumptions about stable dividend growth rates. Whether the DDM or multiples approach is more accurate depends on the specific company and analysts must consider various valuation techniques and compare to industry averages to determine if a stock is undervalued, overvalued, or fairly valued.
This document discusses fundamental analysis techniques for evaluating stocks. It covers analyzing macroeconomic factors, industries, and individual companies. Fundamental analysis involves forecasting earnings, cash flows, and dividends to determine a stock's intrinsic value. It breaks down as 30-35% economic analysis, 15-20% industry analysis, and 30-35% company analysis. Key aspects covered include Porter's five forces model, industry life cycles, quantitative ratios like P/E, and qualitative factors. The overall aim is to understand factors that affect stock prices and identify undervalued stocks.
A predictive system called "INSIGHT" was built using structured and unstructured data from various sources to identify potential buyers and sellers for large block trades. The system analyzed data like daily block trades, shareholder patterns, holdings, and market news to predict fund behavior. Additionally, the author learned quantitative research including technical analysis, building pair trading strategies, and value-at-risk models to analyze stock predictions and portfolio risk. The internship provided valuable experience in institutional equities trading and quantitative analysis techniques.
The document discusses investment regulations and foreign investment. It notes that foreign investment provides needed capital for modernizing factories and remaining competitive internationally. There are benefits to allowing foreign investment in countries. The screening process for foreign investment involves determining demand, available resources, analyzing the business environment, measuring market potential, and selecting sites.
Similar to Statistical Analysis of Real Estate Stocks (7)
Many countries have seen the importance of financial education by making financial
education a national strategy. In Vietnam, although the National Strategies for Inclusive Financial
Education has been proposed since 2017 and officially included in the National Financial Inclusion
Strategy in 2020, however, financial education is still quite new, and many people are not aware of
the necessity of financial l
Today, in the rapidly emerging globalization process, increasing the competitiveness of enterprises
depends on increasing of their firm performance. Although there are many methods and techniques affecting
firm performance, Information technology (IT) capabilities has become one of the most widely used method,
especially in dealing with supply chain matters of a firm. The aim of our study is to express whether innovation
and organization learning is effective as intermediate variable to the effects of IT capabilities at firm’s
performance. The opinion which claim
Globally, the number of startup companies has rapidly expanded during the last 5-8 years. Offering
products and/or services that greatly enhance the lives of its clients is a major focus for these firms. In India,
local and federal government initiatives have provided new enterprises and entrepreneurs with much
momentum and assistance, helping India become the world's top startup location. The Government of India
(GOI) launched the "Startup India" campaign in 2015 to promote entrepreneurship and support businesses to
achieve this goal (Babu, S., Sridevi, K.,2019). An IBM Center for Business Value and Oxford Economics study
in 2018 found that 90% of Indian companies fail within the first five years of operation. Potential difficulties
that startups may run across, both generally and specifically in the Indian market, have been described by
several authors.
Behaviour finance is the study of how psychological phenomena affect financial behaviour. This
financial science is used in making financial decisions. Amid the development of the digital economy, paylater
innovation has emerged. It is feared that the ease of use of paylater can have a negative impact, one of which is
the attitude of impulsive buying. This research will analyze the effect of financial literacy, self-control, risk
perception, and percieved ease of use on impulsive buying behaviour. This research is based on Decision Affect
Theory, which is a theory that discusses financial decision behaviour that is influenced by self-emotion. This
research is uses purposive sampling wi
Improving the business environment is one of the key strategies to promote local and regional
economic development. However, which factors affect the business environment of the provinces is still
controversial. Using survey data from 400 investors and managers and a multivariate regression analysis
method, this study has identified the factors affecting the business environment of Hai Phong province. The
analysis results show that there are 09 factors affecting the business environment of Hai Phong City, including
entry costs, land access and tenure, transparent, informal charges, time cost, pro-activeness, business support
services, labor training and legal institutions. In
The effect of work attitude and innovation ability on employee innovation performance is of great
significance for improving the innovation ability of manufacturing enterprises and building an "Innovative
Country" in China.This article theoretical analysis was conducted on the mechanism by which the work attitude
of employees in manufacturing enterprises affects innovation performance and the mediating mechanism of
innovation ability. Based on data from Chinese manufacturing enterprises, empirical analysis was conducted
using SEM models. Resear
The concept of organizational resilience continues to grow in focus and importance, but there
has yet to be an agreed upon measure of organizational resilience. Organizational resilience can be seen as a
corporation’s ability to adapt to change and maintain flexibility within their supply chain. Resilience and
flexibility at all organizational levels is necessary, in a proactive manner, to turn resilience into a competitive
advantage
The document summarizes research on nonlinear correlation coefficients on manifolds. It defines a new nonlinear correlation coefficient called SEVP, proves some of its basic properties including that it ranges from 0 to 1. It discusses how to measure nonlinear correlation between variables on a manifold and reviews common dimensionality reduction methods for manifolds. The goal is to preserve nonlinear structure as much as possible by projecting onto the orthogonal complement of tangent spaces. An optimization problem is formulated to find the linear space with the largest angle to all tangent spaces, transforming it into an eigenvalue problem to solve.
This study aims to analyze and prove whether there is a positive and significant influence
between product quality and poki prices on purchasing decisions for Kobba brand coffee. The survey was
conducted using 53 respondents who were buyers who had purchased Kobba brand coffee more than once.
Information from respondents was obtained through a list of questions that were sent and returned by
respondents
In this paper, we introduce a universal framework for mean-distortion robust risk measurement and
portfolio optimization. We take accounts for the uncertainty based on Gelbrich distance and another uncertainty
set proposed by Delage & Ye. We also establish the model under the constraints of probabilistic safety
criteria and compare the different frontiers and the investment ratio to each asset. The empirical analysis in the
final part explores the impact of different parameters on the model results.
Despite the attainment of the famous Millennium Development Goals (MDGs) of reducing the number
of poor people across the globe a significant number still live below the poverty line. The problem of poverty is
more endemic in developing countries like Nigeria. Several intervention efforts have been in place to address
the poverty question which persists partly due to serious financial exclusion and unethical activities of informal
finance providers.
The focus of this research was to establish the effect of entrepreneurship Ecosystem in inculcating
entrepreneurial propensity for community development. Promotion of entrepreneurship in Kenya has existed
ever since independence. The Government has shown tremendous support to entrepreneurship growth. The
Government have channelled financial support through funding such as Women Enterprise fund, Youth
Enterprise Fund and Uwezo Fund
In this paper, we consider an AAI with two types of insurance business with p-thinning dependent
claims risk, diversify claims risk by purchasing proportional reinsurance, and invest in a stock with Heston
model price process, a risk-free bond, and a credit bond in the financial market with the objective of maximizing
the expectation of the terminal wealth index effect, and construct the wealth process of AAI as well as the the
model of robust optimal reinsurance-investment problem is obtained, using dynamic programming, the HJB
equation to obtain the pre-default and post-default reinsurance-investment strategies and the display expression
of the value function, respectively, and the sensitivity of the model parameters is analyzed through numerical
experiments to obtain a realistic economic interpretation. The model as well as the results in this paper are a
generalization and extension of the results of existing studies.
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totaled 1.2 billion tons of CO2 equivalent throughout its lifecycle. The fashion industry is a large consumer of
water, high volumes of water containing
In this paper, we construct a Credit Default Swap pricing model for default recovery rates under
distributional uncertainty based on a structured pricing model and distributional uncertainty theory. The model
is algorithmically transformed into a solvable semi-definite programming problem using the Lagrangian dual
method, and the solution of the model is given using the projection interior point method. Finally, an empirical
analysis is conducted, and the results show that the model constructed in this paper is reasonable and efficient
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Introduction
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Frameworks/Models included:
Microsoft’s Digital Transformation Framework
McKinsey’s Ten Guiding Principles of Digital Transformation
Forrester’s Digital Transformation Framework
IDC’s Digital Transformation MaturityScape
MIT’s Digital Transformation Framework
Gartner’s Digital Transformation Framework
Accenture’s Digital Strategy & Enterprise Frameworks
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Capgemini’s Digital Transformation Framework
PwC’s Digital Transformation Framework
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Customer Journey Map
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1. International Journal of Business Marketing and Management (IJBMM)
Volume 7 Issue 2 Mar-Apr 2022, P.P. 35-39
ISSN: 2456-4559
www.ijbmm.com
International Journal of Business Marketing and Management (IJBMM) Page 35
Statistical Analysis of Real Estate Stocks
Chiriac Andreea Ioana1
1
(Economic Cybernetics and Statistics Doctoral School, The Bucharest University of Economic
Studies, Bucharest, Romania, chiriacandreea15@stud.ase.ro)
Abstract: Real estate costs and profits change in cycles, sometimes anticipated in type but not in severity or
length. It is known that there are two forms of cycles: ‘capital markets’ cycle which refers to stages of investor
request for commercial real estate assets and the costs and value at which they trade and there is ‘space
market’ cycle which express the demand for and source of real estate space. For a long-term investor, it is
recommended to buy and maintain the stocks event if the estate assets pass through their unavoidable ups and
downs. Real estate investors should be conscious of these cycles which can impact dividend growth and cash
flow, and also stock prices. The main purpose of this paper is to analyze if potential investors should buy or not
stock prices. In order to summarize this analysis, I use a binary logistic regression because it is important to
verify if some financial ratios could have an impact on real estate stocks. Binary logistic regression estimates
the probability of an event occurring, in this case the probability of potential traders to buy or not real estate
stocks. The software that I have used in order to clean and arrange the data is Tableau Prep Builder and the
software that I have used in order to express my findings is IBM SPSS Statistics.
Keywords: Stocks, Real Estate, Binary regression, Statistics.
I. Introduction
Everyone thinks that knows real estate, and it is true that we all have been implicated with this sector
in one way or another. All the buildings around us, our houses, our apartments, the mall and centers where we
all shop, the hotels where we go on vacation, all of them represent real estate. So, we are surrounded by real
estate. The question is: do we really understand it?
Years and years, we all had a weird relationship with this sector. Even with the housing market issues
of last year, we still buy houses and completely assume that over the years will appreciate. Moreover, we think
real estate to be an insecure investment at how presumably complex institutional investors have spent a plenty
amount of money on office buildings, hotels and other properties just to see their values addition in real estate
recessions, most from 2007 to 2009. During the last 20 years, we all faced some drastic changes in property
valuations.
Is real estate a good investment? Indeed, real estate investments have provided extraordinary returns to
their investors. The question is: can the investors make money even if there are unavoidable ups and downs of
real estate cycles? Real estate investors have been capable to earn returns close to an average equal to 12%
annually, over the last 40 years, along with, throughout most market periods, stable income, low market price
instability and investment safety.
Real estate investors now have a larger option of investment properties than before and can select from
some of the most skilled and qualified management teams that have ever invested in.
Stocks and real estate are a very valuable resource for many investors. It is well known that economic
conditions (financial crisis, inflation, economic growth, etc.) can powerfully determine and condition stocks and
real estate.
2. Patterns of Expenditure of Older People in Rural Indonesia
International Journal of Business Marketing and Management (IJBMM) Page 36
II. Review of the scientific literature
According to Liu et al. (1990), that has a study which investigate the relationship between stock
market and real estate, he defined terms “segmentation” and “integration”. Segmentation appears if the risk
priced for real estate is methodical risk corresponding to the real estate market, so real estate investors do not
automatically gain the same anticipated return on stocks and real estate. As soon as it is defined an integration
relationship between investments, then the assets are replaceable. (Liow and Yang, 2005). Integration appears
only it the risk price for real estate and stocks methodical risk corresponding to the overall market. Investors
will gain the same risk-adjusted return on real estate and stock. So, if the investments are segmented, then the
assets could be retained together in order to have portfolio variability.
III. Research Methodology
The database that I used in my research paper contains 200+ financial indicators, that are commonly
found in the 10-K filings each publicly traded company releases yearly, for a plethora of US stocks. The data
was cleaned and arranged using the Tableau Prep Builder software. The field of interest that is at the center of
the analysis is real estate, and the data correspond to the period 2014-2018. The dataset was extracted from
kaggle.com. The dataset consists of approximately 3700 records, filtered for Real Estate sector only.
In order to establish from a trading perspective, if a hypothetical trader should buy or should not buy
stocks, I used a binary logistic regression. The dataset was imported in IBM SPSS Statistics and all the variables
were coded accordingly.
The independent variables that were in used in my analysis are: Revenue, Revenue Growth, EBITDA,
Profit Margin, and the binary dichotomous dependent variable is Class. The binary variables “Class” describes
if a trader should buy or not stocks and was coded with 0 and 1. (1-should buy, 0-should not buy). For each
stock, if the PRICE VAR [%] value is positive then class = 1. From a trading perspective, the 1 identifies those
stocks that a hypothetical trader should buy at the start of the year and sell at the end of the year for a profit. for
each stock, if the PRICE VAR [%] value is negative, class = 0. From a trading perspective, the 0 identifies those
stocks that a hypothetical trader should not buy, since their value will decrease, meaning a loss of capital.
Revenue represents the sales value of a company in a period. So, revenue is the sales of goods or
services.
Revenue formula is written:
Revenue = Number of Units Sold * Average Price or Revenue = Number of Customers + Average
Price of Services.
(1)
Revenue Growth indicates a rise in revenue over a period of time.
EBITDA is a financial performance indicator, the acronym for “Earnings Before Interest, Taxes,
Depreciation and Amortization”. EBITDA is measure of a company’s operational profitability over time but
taking out the potentially distorting effects of changes in interest, taxes, depreciation and amortization, which
can all be manipulated by financing and accounting decisions. The argument for using EBITDA is that it
allows us to better compare companies and their operational profitability without considering their capital
structure.
Formula:
EBITDA = Revenue − Expenses (excluding interest, tax, depreciation, and amortization)
(2)
Profit Margin describes the profitability of a service, product or business and it is expressed as a
percentage.
IV. Results and discussion
Binary logistic regression estimates the probability of an event occurring, in this case the probability
that a trader will buy or not stocks.
Table no. 1. Classification Table
Observed Predicted
Step 0
National Standards Class
Percentage
correct
Class Should not buy Should buy
Should not buy 0 705 .0
Should buy 0 1388 100.0
Overall Percentage 66.3
Source: Author own research results
3. Patterns of Expenditure of Older People in Rural Indonesia
International Journal of Business Marketing and Management (IJBMM) Page 37
According to the classification table (table no. 1), the model always assumes "should buy" because
there are more hypothetical traders who should buy real estate stocks from different companies compared to
those who should not. (1388 compared with 705). The overall percentage tells us that this approach to
prediction is correct with 66.3%, which is a good approximation.
Table no. 2. Variables in Equation
B S.E. Wald
df Sig. Exp (B)
Step 0 Constant
.677 .046 214.549
1 .000 1.969
Source: Author own research results
The variables in the table of the equation show us the coefficient for the constant (). According to the
table, the model with this constant has a statistically significant predictor of the result, because Sig = 0.000. The
model has a high accuracy of almost 70%.
Table no. 3. Omnibus Test of Model Coefficients
Chi-Square df Sig.
Step 1 Step 11.588 4 .021
Block 11.588 4 .021
Model 11.588 4 .021
Source: Author own research results
Omnibus tests of the model coefficients (table no. 3) are used to check if the new model (with
explanatory variables included) is an improvement of the basic model. The Chi-Square test was used to see if
there was a significant difference between the -2Log likelihood of the base model and the new model. In this
case, Chi-Square=155.038 and Sig=000, which means that the null hypothesis is rejected. Because Chi-Square
is significant, means that the new model is significantly better. The “Model” row always compares the new
model with the original one. The Step and Block rows are important only if the explanatory variables are added
to the model in a gradual or hierarchical manner. If the model was built in stages, then these rows would
compare -2 Log likelihood of the newest model with the previous version to determine if each new set of
explanatory variables determined improvements or not. In this case, I added all five explanatory variables in a
single block and therefore there is only one step. This means that the Chi-square values are the same for step,
block and model. Sig values are equal to 0.021 which indicates improved model accuracy when the explanatory
variables are added.
However, the most important of all the results is the table Variables in the table of equations. This table
needs to be studied very closely, as it is at the heart of the answer to our questions about the common
association of Revenue, Revenue Growth, EBITDA and Profit Margin.
Table no. 4. Variables in the Equation
Source: Author own research results
This table provides the regression coefficient (B), Wald statistics (to test statistical significance), and
(Exp (B)) for each variable category.
The variable Revenue Growth statistically significant because Sig is less than 0.05. Wald for revenue
growth is 3.630.
The classification chart answers a similar question as the classification table which is “How accurate is
our model in classifying individual cases”? However, the rating chart provides some finer details. This graph
shows the frequency of categorizations for different predicted probabilities and whether there were "should buy"
or "should not buy" categorizations. This provides a useful visual guide to how accurate the model is by
B S.E. Wald
df Sig. Exp (B)
Step 1 Revenue .000 .000 1.098 1 .295 1.000
Revenue Growth -.065 .034 3630 1 .047 937
EBITDA .000 .000 .797 1 .375 1.000
Profit Margin -.003 .012 .044 1 .835 .997
Constant .701 .049 203.168 1 .000 2.015
4. Patterns of Expenditure of Older People in Rural Indonesia
International Journal of Business Marketing and Management (IJBMM) Page 38
showing how many times the model would predict a "should buy" result based on the predicted calculated
probability when in fact the participant's result was "should not buy".
Figure no. 1. Observed Groups and Predicted Probabilities
Observed Groups and Predicted Probabilities
1600 +
+
I
Y I
I
Y I
F I
Y I
R 1200 +
Y +
E I
Y I
Q I
Y I
U I
Y I
E 800 +
Y +
N I
Y I
C I
Y I
Y I
N I
400 +
N +
I
NY I
I
NY I
I
YNN I
Predicted ---------+---------+---------+---------+---------+---------+-----
----+---------+---------+----------
Prob: 0 .1 .2 .3 .4 .5 .6
.7 .8 .9 1
Group:
NNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNNYYYYYYYYYYYYYYYYYYYYYYYYY
YYYYYYYYYYYYYYYYYYYYYYYYY
Predicted Probability is of Membership for Yes
The Cut Value is .50
Symbols: N – No (should not buy)
Y – Yes (should buy)
Each Symbol Represents 100 Cases.
Source: Author own research results
It is observed according to figure 1 that the model is good for predicting the result for individual cases,
because there are a lot of observations towards the left and right ends of the graph. Such a diagram shows that
where the event took place (on the 0.7th place - indicated by the letter Y on the graph), the predicted probability
was also high and that where the event did not occur (letter N on the graph) the predicted probability was low.
The graph shows that there are quite a few cases with the letter Y on the graph, which means that the model
predicts a 70% probability that the event will happen.
The regression equation is written:
Log(p/1-p) = 0.701 -0.065 * Revenue Growth - 0.003 * Profit Margin
5. Patterns of Expenditure of Older People in Rural Indonesia
International Journal of Business Marketing and Management (IJBMM) Page 39
V. Conclusion
There are three fundamental factors of long-run real estate risk: bad management of properties, debt
leverage and deficiency of diversification. Despite the fact that real estate investments have usually been greatly
leveraged, it is the strong debt leverage, instead of real estate itself, and it is the substantial risk. Real estate
possessors could take advantage of inflation, but inflation is not the main reason for having real estate. Interest
rates, supply and demand, expected returns and the actual and future power of the economy are market factors
thar are more important than inflation in deciding real estate values. In order to establish from a trading
perspective, if a hypothetical trader should buy or should not buy stocks, I used a binary logistic regression.
Binary logistic regression estimates the probability of an event occurring, in this case the probability that a
trader will buy or not stocks. There are more hypothetical traders who should buy real estate stocks from
different companies compared to those who should not. (1388 compared with 705). The overall percentage tells
us that this approach to prediction is correct with 66.3%, which is a good approximation. The variables in the
table of the equation show us the coefficient for the constant (). According to the table, the model with this
constant has a statistically significant predictor of the result, because Sig = 0.000. The model has a high
accuracy of almost 70%.
References
[1] Tsoyu Calvin Lin and Zong-Han Lin, Are stock and real estate markets integrated? An empirical
study of six Asian economies. Pacific-Basin Finance Journal, 2011.
[2] Liow K.H. and Yang H., Long-term co-memories and short-run adjustment: securitized real estate
and stock markets. Journal of Real Estate Finance and Economics, 2005.
[3] Liu C.H., Hartzell D.J., Greig W. and Grissom T.V, The integration of the real estate market and the
stock market: some preliminary evidence. Journal of Real Estate Finance and Economics, 1990.
[4] Chiriac Andreea Ioana, Combining Regression and Clustering for Financial Analysis. Proceedings of
the International Conference on Applied Statistics, Online ISSN: 2668-6309
[5] Chiriac Andreea Ioana, Churn Prediction of Bank Customers. 7th BASIQ International Conference
on New Trends in Sustainable Business and Consumption. Foggia, Italy, 3-5 June 2021.
[6] Chiriac Andreea Ioana, A Detailed Analysis of the Profitability of Chinese Banks from 2016 to 2019.
Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(2), pages 55-
63, December 2020.