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Capital Asset Pricing Model (CAPM) describes rsik free rate of return and risk premium. But in IFM, there is no risk free rate of return. So, CAPM under IFM will be different than the traditional CAPM. what do you think??
Again , in the WACC there will not be the interest under IFM. SO, WACC under IFM equals to cost of equity only. What do you think??
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It would be relatively easy to determine a company's cost of capital if it only received funding from one source, such common stock. The firm's cost of capital would be equal to its cost of equity, or 10%, if investors were anticipating a rate of return of 10% on their investment in shares.
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What will the book value of equity constitute? Should it be just "Equity Share Capital" or "Total Shareholders fund"?
when I am taking "total shareholders fund" as denominator my tobin's Q is almost like "0.0085255183" is it normal?
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The book value of equity is the total shareholder fund.
Tobin Q = (Market value of equity (no of shares* Market price) + Debt)/ Total assets
Remember that there are several versions of Tobin Q used in the literature. The debt value could be zero, and that is fine.
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When I embarked on this journey of exploring more on exploring the condition and status of gender equity and equality in architecture, or public spaces, or urban design, I found very little literature is available. Would be great to discuss on this topic. I have started to get an impression perhaps this topic is irrelevant or not trending at all. But I have strong feeling that has an importance, but very little research has been done on this. Would be great if you know of some documents or share some light on this.
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Designing Gender Sensitive Public Spaces – Cidco Smartcity (niua.org)
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Dear community,
What is the code to perform a Fama-MacBeth regression in Stata? I understand how this works theoretically, but I do not understand how this is implemented in Stata. My variables are the 5 factors of the Fama French 5 factor model and 25 portfolios double sorted on size and book-to-market value of equity.
Additionally I have another question as well. That is, in order to test the Fama French 5 factor model, you just regress the factors on one of the portfolios right? In other words, is the correct code to test the 5 factor model:
- tsset date (in order to declare dataset to be time-series data with date as the time variable)
- reg me1bm1 markt smb hml rmw cma (where me1bm1 is the portfolio with lowest marketcap and lowest B/M and the other 5 variables are the 5 factors).
When I use this code I get very strange results, namely that almost all intercepts are significant (which is in contradiction with the Fama French papers). Hence, I am wondering whether there is something wrong with this code. I hope you all could help me with these 2 questions!
Yours truly,
Niek
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Aarthur Schlitz please can you share those satata codes that you have developed to perform FF-3,5
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I’m doing a research right now and I’m trying ti find how and to what extent corruption impacts the interest of retail investors in a particular country. After a brief lit review, I tought to correlate and do a regression between the corruption perceptions index and equity shares ownership by households in a given country. Now, I think that If I pop in other control variables it will dilute the significance of this one and I won’t be able to distinguish how high of significance is just corruption. Should I be adding things such as GDP, interest rates, inflation or some other even?
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One important reason for including control variables is to "eliminate threats to validity." In particular, you want to show that your key independent variables have effects that are "above and beyond" the effects of other relevant variables. If you don't do this, then you are open to counter-arguments that effects you present were "really due to..."
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Hi All,
I have had lots of experience with computing RV and covariance matrices with equity data. I wanted to compute RV for US Treasury bonds and realized covariance/correlation with the S&P500. I downloaded 5 min continuous TY futures from Reuters Datascope. There are lots of missing observations around the rolling of contracts at maturity. This makes the data nearly impossible to use to construct RV.
Any suggestions on intraday data/different series to compute bond RV and covariance with equities.
Thanks
Adam
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Hello, I would personally like to recommend API Fred because of the very useful database and engine for analyzing bond and futures quotes. Our brokerage houses in Poland very often analyze the volatility of financial instruments on the basis of FRED. Best regards
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Question is
1- if loss occur instead of retaind earning then we calculate the loss in ratio X2? Or earning will be zero?
2- you public company we calculate the X4 as MVOE÷book value of long term debt OR MVOE ÷Totak liabilities?
3- Actually i want to compare the z- score of last 3 year of a company. When i calculate the Market value of equity for past 3 years . (Current price of share) in MVOE formula will be the past market price of every year OR i will use today current share price for MV of past 3 years?
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i suggest you to take help of YouTube....their are many videos for this purpose
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Hedging is a risk management strategy employed to offset losses in investments by taking an opposite position in a related asset. Hedging strategies typically involve derivatives. Firms use derivatives to help mitigate various financial risk exposures. Common uses of derivatives for hedging include foreign exchange rate risk, interest rate risk, commodity price risk, and equity price risk. The reduction in risk provided by hedging also typically results in a reduction in potential profits. Hedging can increase borrowing capacity by reducing the volatility of the enterprise value. According to the Purchasing Power Parity, movements in exchange rates offset price level changes. So, can hedging using derivatives increase the value of the firm? If the answer is yes, how this increase in value is created?
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Interest rates risk is
If the company borrows money to finance its activities, it will use derivatives contracts on interest-bearing instruments to hedge against interest rates increase. It’s applicable, in the first place, to companies that borrow at floating rates based on Libor or Euribor.
If the company deposits funds, for example, an insurance company or a financial company that makes loans or credits, it will enter into derivatives contracts to hedge against interest rates decline.
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Capital Structure is the particular mix of debt and equity used by a company to finance its overall operations and growth. There is an optimal capital structure for a given firm, that is the mix of debt and equity financing matters. The optimal capital structure is the optimal mix of debt and equity financing that lowers the firm's overall weighted average cost of capital and maximizes its value. Corporate Strategy is a portfolio approach to srategic decision making. It takes a look across all the firm's businesses to assess how to create the most value. To develop a corporate strategy, a firm must investigate how the various pieces of the business fit together, how they impact each other, and how the parent company is structured, in order to optimize human capital, processes, and governance. Corporate strategy builds on top of business strategy which is concerned with the strategic decision making for an individual business.
Is there a connection between Corporate Strategy and Capital Structure?
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The optimal capital structure for a given firm exists only in theory. It is an illusion in modern finance theory. The debt to equity ratio is without any logic in practice, because the most important is the differentiation between short and long-term debt. Therefore, long term engaged assets (inventories and short-term receivables) should be long term financed. To achieve this principle, the difference between actual net working capital and net working capital that is needed, is crucial. This is called “capital adequacy” of the company. This difference means a surplus or a deficit of net working capital. Its movement shows the movement of the solvency risk of a company. Maintaining liquidity and solvency of the company is a prerequisite for a sound based corporate strategy. More about capital adequacy, you can find on:
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Shadow banks, which played a key role in triggering the recent global financial crisis, and venture capital companies that followed, have partly displaced traditional banks from corporate financing. Participation in the company as a shareholder increases the equity of these companies. At the same time, venture capital firms are pushing them to submit business plans with extreme growth assumptions that are intended to tempt other venture capital firms to provide additional equity for these companies. Unicorns are to be created that will eventually end up on the stock exchange. The whole thing looks suspiciously like a pyramid scheme. Where can one find empirical studies on this problem, which is capable of triggering the next global financial crisis?
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I thank you for your enquiry; sorry for the delay in my response. I agree with you that a second GFC is on the horizon. The Global Financial System is highly complex, as you know, and depends on which jurisdiction we need to look at. The shadow banking systems differ enormously from nation to nation. The Chinese control their Commercial and Shadow Banking systems assiduously; they are managed with great precision with the two sectors in lockstep. They came to the rescue in 2008 with massive liquidity infusions into the global markets.
I suppose we need to look at the USA as they effectively control the global financial system. Over the last 10 years, US companies have primarily funded themselves via corporate bond issuance owing to artificially low interest rates under QE. This allows them to pay off any bank loan debt and switch to very low interest rates commercial bonds with long maturities. The majority of bonds, about 80 %, are rated 'Investment Grade' but we know, from 2008, that the ratings agencies are not necessarily correct. Junk Bond issuance is 20 % or less. Many "junk" companies can actually be quite viable with good positive cashflows, so we need to look at that category and analyse it carefully. To set the scene, let's look at the numbers:
Total Corporate Bond issuance is currently about $10 Trillion and Current Total Issuance is around $2 Trillion pa. Total US Bond Market is approximately $50 - $52 Trillion (including Corporate Bonds). Source: https://www.sifma.org/resources/research/fixed-income-chart/
Now, compare the above Total Commercial Bank Loans at $10 Trillion and add Total Mortgages at $17.6 Trillion plus above Bond market and we get an estimated Total Debt at around $80 Trillion
Now compare this to these liquid assets:
Total Housing Market Valuation = $44 Trillion (semi liquid)
Total Stock Market Valuation = $53 Trillion
Total Bond Market Valuation = $50 Trillion
We can now deduce a Total Wealth estimate of $150 Trillion, deduct Total Debt and we have in USA a Net Wealth of around $ 70 Trillion.
"The central banks that have purchased the most assets over time are the US Federal Reserve, the Bank of Japan and the European Central Bank. Together, they hold around US$ 24.6 Trillion worth of assets (expressed in US Dollar Terms) on their balance sheets. That sounds like a huge amount but it is important to understand that these assets have been accumulated over long periods of time.
It is also important to understand that the assets purchased represent a fairly small portion of total assets available for purchase in their respective economies and they also represent a fairly small part of their cumulative economic activity (GDP) over the time taken in acquisition.
Let’s look at the US, for example. The total assets value available for purchase in the US is estimated to be around US$ 125 Trillion. The Federal Reserve now owns approximately $ 8.5 Trillion of those. This is just 6.8 % of the total. However, if we look at the Fed’s asset holdings compared to cumulative GDP over the last 12 years, then it is half that — (about) 3.4 %.
So, the US central bank has spent funds equivalent to just 3.4 % of cumulative GDP in order to support the US economy during a period of great economic uncertainty and great challenge — firstly, following the Global Financial Crisis of 2008 (when many US banks were on the brink of insolvency) and then following the Global Covid Fear and Panic Crisis of 2020. In BOOM’s opinion, the 3.4 % expended is a very modest sum. So, if the central banks slow or stop their purchases of assets, will it really have much impact? The answer is Yes and No.
Yes, it will have impact in nations where the economy is fragile and unable to generate sufficient fresh new money supply via new bank loan creation. And No, it will not matter in nations where the economy is on a strong pathway to stable growth generated by steady bank loan demand and therefore a growing supply of fresh new money.
When looked at this way, you can see that the central banks have acted fairly prudently over the last 12 years. Many commentators have continually talked about “massive money printing” and “risks of high CPI inflation, even Hyperinflation”. These comments are exaggerated to generate sensation as far as BOOM can see.
Granted, the sums of money involved seem monstrous, even extreme, to most people who are not accustomed to thinking in Billions and Trillions. But when looked at over time and in relation to total cumulative GDP and the total asset situation, they are not so outrageous.
While the QE arguments rage, BOOM concentrates on the shape of the sovereign bond yield curves for each nation. This is a significant indicator of financial stability for the present and for the future."
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I'm currently doing my undergraduate essay about American stock option implied volatility. Our school do not have the access to OptionMetrics. So I find the data in Nasdaq but couldn't subscribe since I can't apply for a credit card in China.
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Sorry, I don't have a subscription
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I have dataset only with number of adults and their incomes, and total health OOP expenditures for whole household. I used IC2 package in Rstudio to calculate concentration index for a income(C), and gini index for a OOP (G). I calculated it without weights and with default parametrs. Kakwani = C-G.
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Yes, of course.
The weights of the surveys are present when there is not a simple random sampling (among others). I meant that you should weights if they are provided.
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If we look at Altman Z-score, we will find that using this equation has a different ranges and each range indicates to a particular status of the company's financial future. For example: The company is predicted to go bankrupt if the score is below 1.81. A score between 1.81 and 2.99 belongs to a grey area. If the score below 2.675, there are 95% chances that the company will go bankrupt. A score of 3.0 or more indicates a low risk of bankruptcy.
On the other hand, when applying z-score (The z-score is defined as z ≡ (k+µ)/σ, where k is equity capital as percent of assets, µ is return as percent of assets, and σ is standard deviation of return on assets as a proxy for return volatility) to measure banking stability, we can not find any range of the results come out from this formula by almost all the studies (for example 1= is instable, 3= is stable). I went through many researches and studies but I have not found any clear explanation of the z-score's range. That is why I am asking this question, hopefully I will get an answer from you.
Thanks for your reading to my question
Regards
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The problem you have is the one of model calibration. The Altman Z-score model, as it is right now, is outdated because its parameters are no longer relevant to the realities of today's banks or financial institutions. To be able to effectively use the model today, you must first recalibrate it, that is re-estimate all the parameters of the model with today's real data. To have stable parameter estimates, you should use out of sample data in your estimation. Once the Altman Z-score model is properly calibrated, it can be used to measure the stability of a large sample of banks and come out with a new stability metric or rule for banks.
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My initial intention is to analyze stock prices after firms have successfully emerged from bankruptcy (post-emergence date), as relevant literature report abnormal high returns after emergence. The complication is that there is a period of time where stocks are often suspended from trading during the chapter 11 process.
The average time between the bankruptcy filing and the official emergence date is around 300 days, meaning there is a gap in stock returns for firms under Chapter 11 process. Furthermore, firms may cancel the old stock and issue new stock on the emergence day.
Given this, I would like to know if this is a correct way of performing an event study: to use pre-bankruptcy stock prices as estimation window and post-emergence stock prices as event window?
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Excellent Topic of Study. Best Wishes!
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Please suggest such topics whose research is not yet done.
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I think these links will help you to come up with good and recent topic in finance
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What research data is available/known (e.g. papers, surveys, simulations, data sets, etc.) on modelling a Business Enterprise finances (Microeconomics) based on mapping its Financial Elements (like Asset, Debt, Equity, Income, Expenses, etc.) to the concepts of natural laws of Physics (like Mass, Energy, Force, Momentum etc.)?
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Thank you Mr. Gruenwald, there have already been developed several methods on applying statistical physics on stock market. But that´s not what I am inquiring. The question here aims at researches on modeling and managing the financials of a Corporate Enterprise (focusing on microecomics company, not on macroeconomical financial markets) based on laws and concepts from natural Physics (e.g. by defining rules corresponding to those, setting-up contracts accordingly, measuring KPIs referenced to those, establishing governance appropriately, etc. ...).
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As part of my research, I have to analyse 10 years time series financial data of four public limited companies through Multiple Linear Regression. First I analysed each company separately using Regression. The adjusted R square value is above 95% and VIF is well within limits but Durbin-watson measure shows either 2.4, 2.6 or 1.1 etc which signifies either positive or negative auto correlation. Then I tried the model with the combined data of all the four companies. This results in very less adjusted R squared value (35%) and again a positive auto correlation of 0.94 Durbin-Watson . As I am trying DuPont Analysis where the dependent variable is Return on Equity and independent variables are Net Profit Margin, Total Asset Turnover and Equity Multiplier, which are fixed, I cannot change the independent variables to reduce the effect of auto correlation. Please suggest me what to do.
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Time series analysis allows you to have various models like auto regressive etc.which would explain the behavior of your data based on which you can project the d.v. into the future.
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I have data of 182 Firms for a period of(2017-2019). I want to know the financial performance of SMEs with reference to Conventional & Islamic Finance.
I have three categories, i.e.
1. Firms with financing from Islamic Finance
2. Firms with financing from Conventional Finance
3. Firms with financing Own-manger own sources/loans and not any bank financing
4. Firms with financing from Conventional + Islamic Finance
ROA, ROE as uses as a measures of financial performance Whereas, Short term debt, Long term debt and Debt to equity uses for Capital Structure.
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Interesting
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Income inequality is increasing and if we look at absolute growth numbers, they look horrifying. One way to gauge the situation is to look at year-on changes, however, is there any 'optimal' range/ratio of income distribution amongst different income groups?
Ideally there should equity, however, in the world we live in, are there studies/yardsticks that one can plan towards?
Thanks!
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Thanks Mario Francisco Giani Monteiro - this can help with data sorting.
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the relationship between decentralization ,gender equity and participatory planning
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You may find some interesting issues pointed out in the work of Lakwo, Alfred (2009): Making decentralization work for women in Uganda published by the African Studies Center of the University of Leiden (African Studies Collection Vol. 16).
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Hi everyone,
I am carrying out a research on mutual fund performance, but I am a little bit stuck at the moment.
The goal of my analysis is: demonstrate that 'value' mutual funds outperform 'growth' mutual funds
Up to now, the workflow has been as follows:
- Select US equity funds in the CRSP Mutual Fund Holdings database, with complete quarterly portfolio holdings reports in the period from 2008 to 2018. Resulting sample size = 50
- Classify funds on the basis of their investment orientation. To this end, I have applied an adapted methodology from Morningstar (2002), thus carrying out a holdings-based analysis. Therefore I have identified the portfolio centroids for all the 50 funds on a quarterly basis, using a value-growth score as the x variable and a market cap score as the y variable.
- Retrieve quarterly returns for all the funds. After this, the funds have been classified, for every quarter, into 10 deciles. The deciles have been taken out of the x variable, so the value/gorwth score. Then, this exercise has been repeated along three market cap dimensions: small, medium, large. Therefore, for every quarter, there are 30 deciles, with at least 1 observation.
- Graph the scatter plot (see file) of the average return for each decile, for the entire sample period. The time variable has been treated both as a discrete and as a continous variable.
Now I would like to know how can I analyze these returns. I was thinking about using a CAPM, but I don't know which kind of parameters to specify, if any. Does someone have a suggestion/reference paper that can assiste me?
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Hello Luigi,
I have my research project of Investment Analysis And portfolio management course when I was student. What I did was choosing 10 funds; 4 Mutual funds, 4 ETFs, 1 index and Bitcoin. The benchmark was MSCI Europe Index.
After testing Efficient market hypothesis and measuring the performance of the selected funds, I did my evaluation using frontier curve, Skewnes, R squar, ratios (CV, sharp ratio, Treynor ratio) and CAPM.
This is the link, I hope you find it useful:
All the best.
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Could anyone please help me to acquire quarterly data on banks between 2011-19. I am specifically interested in: net interest margins, ROA, ROE, Equity/Assets, total assets, deposits to total assets, NPL, loan loss provisions, net interest income, operating revenues, total interest income, total liabilities, total common equity / core tier 1 capital, cost-to-income (efficiency) ratio, and equity. In USD if available. Any help is appreciated, thank you.
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I would like to help you, but he is not in my specialty. I wish you success
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In Graham and Harvey (2001) paper, CEOs consider equity dillusion to be the most concerning factor when considering an equity offering, and therefore I am wondering is there a proxy for this controlling variable.
Thanks.
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I have four independent variables that are advance to deposit, return on capital employed, EPS, dividend yield ratios, one dependent variable return on equity, and three moderating variables GDP growth, Unemployment, and Inflation . which model should I chose please guide me
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can you update us the type of data you are using so we can help you with concrete answer. This depends:
1. If your data is Panel : check for fixed and random effect models. Robustness using IV should be done
2. If your data is time series: proceed with cointegration, ARDL, NARDL models
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Hi for all,
Excellent opportunity to submit research proposal about Digital Governance. New track about Digital Transformation in Subnational Governments led by me (Prof. Thiago Ávila) and Profª Drª Beatriz Lanza and Profª Drª Maria Alexandra Cunha.
----------------------------------------------------------------------------------------------------------------------------   
Call for Papers/Proposals dg.o 2021: 22nd Annual International Conference on Digital Government Research
Digital Innovations for Public Values: Inclusive Collaboration and Community
College of Public Affairs and Community Service, University of Nebraska at Omaha, June 9-11, 2021
Conference Website: http://dgsociety.org/dgo-2021/
IMPORTANT DATES
  • January 20, 2021: Papers, workshops, tutorials, and panels are due
  • March 1, 2021: Application deadline for doctoral colloquium
  • March 31, 2021: Author notifications (papers, workshops, tutorials, panels)
  • April 1, 2021: Doctoral colloquium notification
  • April 15, 2021: Posters and demo proposals due
  • April 24, 2021: Poster/demo author notifications
  • April 25, 2021: Final version of manuscripts due in EasyChair
  • May 1, 2021: Early registration begins
  • May 20, 2021: Early registration closes
TRACK 1. Artificial Intelligence and Algorithms for Future Governments
Track Chairs: Sehl Mellouli, Marijn Janssen, Adegboyega Ojo
TRACK 2. Social Media and Government
Track chairs: Andrea Kavanaugh, Rodrigo Sandoval-Almazan, and J. Ignacio Criado
TRACK 3. Digital Sovereignty in the Era of Smart Cities
Track chairs: Bettina Distel, Robert Krimmer, and Hendrik Scholta
TRACK 4. Opening Government: Open Data-driven Innovation and Collaboration for a better Public Value
Track chairs: Fatemeh Ahmadi Zeleti and Grace Walsh
TRACK 5. Security and AI Ethics for the Next Wave of Data-driven Society
Track chairs: Kwon Hun-Yeong, Kim Mi-Ryang, Ko Yoon-Seok
TRACK 6. Beyond Bureaucracy: Participatory Online Politics and the Future of E-democracy
Track chairs: Zach Bastick and Alois Paulin
TRACK 7. Inclusive and Resilient Smart Cities
Track chairs: Leonidas Anthopoulos, Dongwook Kim, and Soon Ae Chun
TRACK 8. Collaborative Intelligence: Humans, Crowds, and Machines
Track chairs: Helen K. Liu, Benjamin Clark, and Lisa Schmidthuber
TRACK 9. Digital Transformation in Subnational Governments (NEW TRACK)
Track chairs: Beatriz Barreto Brasileiro Lanza, Thiago José Tavares Ávila, and Maria Alexandra Cunha  
TRACK 10. Organizational Factors, Adoption Issues and Digital Government Impacts
Track Chairs: Jing Zhang, Chris Hinnant, and Lei Zheng
TRACK 11. Cyber-physical Innovations for Public Policy and Service
Track chairs: Sukumar Ganapati, Michael Ahn, and Chengyu (Victor) Huang
TRACK 12. Automation of Public Services – Concepts, Practice, Implications and Emerging Perspectives
Track chairs: Ida Lindgren, Christian Østergaard Madsen, and Ulf Melin
TRACK 13. Digital Government and Sustainable Development Goals
Track Chairs: Rony Medaglia and Gianluca Misuraca
TRACK 14. Blockchain-based applications for e-Government
Track Chairs: Jolien Ubacht, Svein Ølnes, Lemuria Carter, and Ramzi El-Haddadeh
TRACK 15. Legal Informatics
Track Chairs: Peter Parycek, Charalabidis Yannis, and Anna-Sophie Novak
The Digital Government Society (DGS) will hold the 22nd Annual International Conference on Digital Government Research – dg.o 2021, with a special focus on the theme ” Digital Innovations for Public Values: Inclusive Collaboration and Community“.  the Digital Governance and Analytics Lab, the School of Public Administration, the Center for Public Affairs Research, and the College of Public Affairs and Community Service, University of Nebraska at Omaha, Omaha, Nebraska on June 9-11, 2021.  The dg.o conferences are an established forum for presentation, discussion, and demonstration of interdisciplinary research on digital government, political participation, civic engagement, technology innovation, applications, and practice. Each year the conference brings together scholars recognized for the interdisciplinary and innovative nature of their work, their contributions to rigor of theory and relevance of practice, their focus on important and timely topics and the quality of their writing.
THEME & TRACK TOPICS:
The 22nd Annual International Conference on Digital Government Research (dg.o 2021) will feature the main theme of “Digital Innovations for Public Values: Inclusive Collaboration and Community.” Public values – such as efficiency, equity, transparency, privacy, security, trust, etc. — serve as the compass and goals for the development and implementation of digital innovations for public service. Recent developments in digital innovations — such as artificial intelligence, IoT, blockchain, social networking platforms, 5G, etc.— offer strategic opportunities for public value creation. These digital innovations are tools for us to solve monumental challenges facing our society such as pandemics, climate change, and sustainable development. More importantly, there is a return to focus on societal needs and values to guide digital innovations and to move away from technology push only for the sake of innovations.
Specifically, the conference aims to advance research and practice of public value creation via digital innovations by leveraging collaboration and community-oriented solutions in an inclusive manner. Collaboration can span the boundaries of individuals, organizations, sectors (public, private, and voluntary), and national borders in such forms as data and technology collaboratives, public-private partnership, and regional or global technology standards and policies. Communities can take on a virtual, physical, or blended form with a local, national, or global reach such as people’s local communities and our global community of the Digital Government Society (DGS). Community is also about taking a holistic (community-as-a-whole) approach to integrating digital innovations such as smart city and intelligent government. Inclusivity is about bridging socioeconomic and digital divides in governance such as inclusive civic engagement and e-participation. Inclusivity also entails openness, transparency, and leveraging digital means to engage community members for public value creation.
Submit your papers. We want to see you in dg.o 2021!!!
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Interesting, associated with the special issue of any journal?
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I conducted a study to investigate stock market reaction to equity issues announcement and i ended up with 64 announcements and i then used the announcement day returns as my DV and investigated the effect of corporate governance using 8 variables in the model. However, the number of observations came down to 34 firms based on data obtained. How can i justify my regression results given this scenario? Please help me.
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i will update you shorlty
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We all need these vaccines regardless of our pockets and our health system weaknesse. No one should left behind.
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vaccination should be according to the need of individual person. Doctors and geneticists will guide us better in this matter. People should not use this matter for political benefit. Distribution of vaccine needs equity, not equality.
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Mutual Funds helps in creating a very large corpus of funds by collecting in small amount from a number of investors. It helps in further development of an economy, and the benefits so reaped from the improved performance of companies are enjoyed by the small investors, otherwise not willing to enter equity market.
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Mutual funds is a investment vechile to help retail investors to enter the equity market and widen the participation of retailers. But mutual funds charge higher fees and might not fulfil their role of widening participation and diversification if not properly managed. Mutual funds need a portfolio of stocks in the equity market to diversity into to succeed.
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how we can compare distributive effects of debt and equity in economics?
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How do you define distributional effects? Distributional effects from economic or social perspective? If it is the former then spending by govt is prioritized to mininize distributional effects. If it is latter then changes in taxation is employed as appropriate tool to reduce distributional effects.
With best regards,
Syed Sadaqat Ali Shah
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I'm trying to analyse the impact of sustainability aspect on firm performance. My sample includes firms from the United States (US) as well as non-US firms. The study includes various variables (accounting ratios) such as Return on Assets, Return on Equity, Return on Sales, Debt-to-Equity, Capital Expenditure to Sales, etc. With the difference in the accounting treatment between IFRS and GAAP, are the ratios subjected to differ? If yes, then can the difference adversely affect my study's analysis and its results?
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Hi Athem Mac ! As IFRS and US GAAP differ, I would say that the results of your empirical modeling can also be effected by that. There are already some published papers that talk about these differences and their influence on earnings and financial performance. Please, have a look at:
Barth, M. E., Landsman, W. R., Lang, M., & Williams, C. (2012). Are IFRS-based and US GAAP-based accounting amounts comparable?. Journal of Accounting and Economics, 54(1), 68-93.
Beuren, I. M., Hein, N., & Klann, R. C. (2008). Impact of the IFRS and US‐GAAP on economic‐financial indicators. Managerial Auditing Journal, 23(7), 632-649.
Van der Meulen, S., Gaeremynck, A., & Willekens, M. (2007). Attribute differences between US GAAP and IFRS earnings: An exploratory study. The International Journal of Accounting, 42(2), 123-142.
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Hi Guys,
I am trying to do option pricing using QuantLib in Java, I have downloaded the relevant jar library and also the dll and they work perfectly. I am just looking to find a simple example of option pricing code in Java which I can then extend. There is a C++ example in here:
But I can not figure out how to create the Java version.
Cheers
Pooia
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What a pain. . Looks like this has not been supported for some time. The joys of using open source software
Have not used it but what about Strata opengamma.
have you tried posting a question on the quantlib user group.
you will have to use SWIG. To link the C++ version of quantlib to Java. sounds messy
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Dear community,
for a study I am currently conducting I would like to employ Tobin's Q as a DV. I employ the approximate Q measure suggested by:
Wang Jinqiang Yang (2017). Investment, Tobin’s q, and Interest Rates. Journal of Financial Economics, 1-47.
Here, Equity Value is calculated as: Stock Price * Shares outstanding
My question: If I want to approximate the Tobin's Q for a given year, should I use the closing stock price (fiscal year) or the average stock price?
Many studies have measures Q in yearly terms but did not specify this. Thanks in advance for your help.
Best, Philipp
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In one of my recent project, I took the average of accounts closing month of my accounting data. If the closing month of data is December, may take the average price for this month and if it is October then may take for this month.....so on. It will be more close to book based valuation methods.
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greetings all , i hope this question meet you in a good health ,
am working on my thesis that is about financial structure and its relation with financial performance, am doing an empirical study evidence from Jordan market, furthermore ,after extracting the data and running the Hsiao Test it occur that the panel data is heterogeneous at the both levels, slope and the intercept, means i cannot do fixed or random effect, as am new to panel data , i do not know what model is suitable for this case . and a heterogeneous panel data does it differ from homogeneous panel data only on the model choice ? or there are other test i must run? keeping in mind that my N is 24 and T is 14 , i tried to play with the dimensions but the heterogeneity existed anyway, and my variables are ratios ( ROA , ROE / Debt Ratio , Equity Ratio ) and the capital structure determinants as control variables ( Size , Assets , Tax , Liquidity ),
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Kenza Medjahed Thank you for the answer , i did avoid hsiao test on determinant the model as i read that it is sensitive to a lot of parameters , i used Fisher test and hausman test, the result did support fixed model , i did run covariance robust estimator ( sandwich estimator " arellano method " ) to correct the model from hetero problem and serial correlation , all that with R langauge ( plm Package ) .
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FInance literature suggests that one non behavior source of feedback trading is price manipulation. Any empirical studies that show any linkages between these 2 aspects in any kind of market eg equity markets, derivatives or foreing exchange market?
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You can find such studies in Researchgate as follows:
Many more studies can be found if google them.
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Hi guys,
I'm trying to calculate each factor of Fama and French 5 factors for my country capital market. Related to the data that I need (Market Value of Equity, Book Value of Equity, etc), do I need the data for all the firms in my country stock exchange or I just need the data for all the firms within the same sector that I want to study (for the example banking sector, consumer good sector, etc)?
Really appreciate any help from you guys.
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Szymon Stereńczak : thanks for your answer.
Just to make sure, these are the data that I need to calculate the 5 factors:
  1. Market return,
  2. Risk-free asset's return,
  3. All firms in the market's data:
  • Total return index / adjusted close price,
  • For big and small: market capitalization (share price x no of shares),
  • For high, medium, and low: book to equity (BE/ME) that comprises of net book value (total assets - total liabilities) and market capitalization,
  • For robust and weak: revenues, COGS, SGA, interest expenses, BE (t-1),
  • For aggressive and conservative: growth of total assets (t-1) and total assets (t-2).
Are these right?
What is your suggestion for the database that I should use to get all of those data in bulk? Can I use Bloomberg or Datastream? Or should I use multiple databases?
Thanks in advance.
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As board remuneration is an expense and its impact on the profit of the same year must be negative. Consequently impact of board remuneration (dependent variable) will be negative on return on equity (ROE) and return on assets (ROA) as well. Would there be situations where board remuneration will positively affect net income/ROE/ROA of the comntemporaneous year?
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Your analysis fails to consider two important aspects. First, the reason a company has a Board is to garner expert guidance on how to increase return on equity and assets, insure legal compliance, etc. Higher Board remuneration may allow the company to enlist a higher quality Board that does a better job at helping to increase its returns. If so, higher Board pay would result in rising returns. Second, Board compensation can be tied to financial performance, which again means higher compensation as returns rise.
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Hello everyone,
I am working on a project to find out how teachers contribute to social justice and equity in schools. Therefore, I am interested in to what extent teachers are prepared to respond the diverse needs of students in the same classroom. How teacher preparation programs should guide beginning teachers so that they will be adaptive teachers to promote equity and justice among all learners?
I appreciate any related suggestions and articles that will facilitate my research about preparing socially just teachers.
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Dear Mehtap Akay I think this is a crucial issue. I can contribute with a reflection, but the text is in Spanish. Hope you are useful.
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I want to analyse the effect of inflation, interest rate, GDP and exchange rate on performance of equity investment.
I intend to use ROA as a measure of performance of equity.
am also thinking to transform ROA data from cross sectional to time series data by getting an average to enable me use time series model.
Kindly advice
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So many time series models are available, some of which are mentioned above - GARCH, VAR, VECM, SVAR, ARIMA, Regression models. In dealing with time series data, test for stationary and causality is standard procedure. Also, we need a sufficiently long time series. Otherwise some of the models mentioned above may not work.
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Hey, everybody, I need some help with my master's dissertation.
I am using a simple regression, often used in literature that establishes a relationship between Price=B1+B2 Equity+ Net Result.
However, in reviewing the results I have the following problem:
-> If we analyse separately the equity variable(X) in relation to price(Y) it is significant for 5%, however in the model with net result it is no longer significant.
-> In the following year separately or together the equity variable is significant.
I am afraid that in the defence of the dissertation the question will be raised because in a model (year 2017) equity is not significant and in the following year it is.
Someone can help me?
p.s: the correlation between variables and own capital is very high but close for the two years 0.8
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You might calculate the partial correlation coefficients between y and old variable( equity) given the new variable ( net result) and it might be significant .
If there are very small and very large outliers in the dependent variable with almost same values of the old independent variables and with extremely different values for new independent variable such a problem can happen. You can also check for multicollinearity.
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How to integrate ESG analysis in infra equity and fixed income analysis?
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Following
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I want to calculate the performance for actively managed equity mutual funds with the Carhart’s (1997) four-factor model.
rit = αiT + βiTRMRF + βiTSMBt + βiTHMLt + βiTPR1YRt + εit
If I go to “Monthly Returns and Fama-French Factors” I obtain small-minus-big return (SMB), high-minus-low return (HML), the one-year momentum factor (PR1YR) and the risk-free return rate (one month Treasury bill rate). I also get the "excess return on the market". My question: is this the alpha? So, in other words, is this return already risk adjusted with the beta? If not, how can I calculate it?
Many thanks for any help!
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I'd like to add to this question. The data I pull from the “Monthly Returns and Fama-French Factors” page is the "Total Returns per Share". Is this the same as the mutual fund returns, Ri, that I subtract the risk free rate from to get the dependent variable?
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Monthly data of oil prices is one of my independent variables. The purpose of doing this analysis is to get the relationship of oil prices with other financial variables (bond spreads, equity prices and others) and not predication. Can the non-stationarity of the independent and in some even the dependent variable still be a problem? and if so, what are the alternatives that I can work with.
At present, I have tried transforming my data by differencing it (which is unfortunately changing the relationship between my variables) or transforming by taking natural log of the data.
Thank you for your assistance in advance!
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Non stationary is a process that does not meet either of the two conditions.
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Have you ever used an intelligent tutor in your classroom? If yes, please share your experiences. How did you like it? How did your students like it? Do you think the students actually learned from this computer-based tutor? How do you know? If you have never used one, why? Please share your thoughts on what prevents you from using such a tutor. Do you think such a tutor will not provide learning? Do you find it hard to get free resources? Is it expensive for your institution? Interesting questions and I would appreciate a response from different countries in the world.
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Thank you for your question. Computer based learning programs can have a tutor directing the discussion and asking questions. They also can be in learning communication skills, or in OSCE examinations. We have such technology in medicine. They can be used to complement the curriculum in self-directed sessions.
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Hello dear friends I would like to know your opinion for this question:
What is the critical element that will accelerate equality and equity of opportunities for women in ICT?
Thank you
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Dear Pity
Also The Commission on the Status of Women, in its consideration of gender equality and ICT at its fortyseventh session, recommended that action be taken to “strengthen the capacity of national machineries for the advancement of women, including through the allocation of adequate and appropriate resources and the provision of technical expertise, to take a lead advocacy role with respect to media and ICTs and gender equality, and support their involvement in national, regional and international processes related to media and ICTs issues, and enhance coordination among ministries responsible for ICTs, national machineries for the advancement of women, the private sector and gender advocacy NGOs within countries”.138 It is also important that women’s ministries and agencies, gender focal points, and gender advocates educate themselves and their membership on ICT issues and their relevance to women and consequently coordinate their efforts to participate in and influence telecommunications and ICT policy processes and programmes.139 These groups should be involved in the development of national gender equality and ICT agendas and the provision of training on gender equality and ICT for Government bodies involved in national ICT policy development.
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I am currently assisting on a research on cross border capital flows.
A common problem seems to be that both the acquisition of assets and valuation effects determine the cross border asset holdings as , for example, reported in the CPIS data. Hobza and Zeugner use the BoP statistics on portfolio investments to derive valuation effects on portfolio debt and equity (change in asset holdings minus acquisitions) (2014).
I am wondering if the valuation effect could also be estimated because I do not only want to distinguish between portfolio debt and equity but also between different types of instruments.
For instance, between different debt maturities.
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Valuation effects depend on inflation, exchange rates and liquidity (on how tightly held the asset is).Different financial markets have different levels of liquidity.
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I have to compare the financial performance of old private sector and new private sector banks I have 8 old banks and 7 new banks which statistical method or test should i apply to compare?
the variables for performance are return on equity, return on asset, return on capital employed, net profit margin
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Any method would work.
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Hello everyone, I want to understand the weights systems in the Bronze age in MiddleEast but what I don't get it, is the ratio of these weights and the basis for these ratios. like in the Mohenjo-Daro case, we have 2, 4, 8...1600 ratios.
so I want to know two things: 1. what is the basis for these ratios? is that just arbitrary numbers for divisions of weights or maybe it has equity for reaching these ratios?
and question 2. if in one site we got different ratios so what is the difference between Mesopotamian and Indus weight system? from my perception, it's just a difference between ratios in two civilizations just like the difference between ratios in one single site, a regular difference that not a big deal, so maybe there is no difference and we want just imagine there is!!!
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thank you very much for your answer. that was my problem, the basis of archaeological material for determining these values and ratios. now the divisions of each weights values by ratios is the lowest weight value.
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We are valuing a young company with high growth potential.
Is this a reason for concern if more than 100% of the value of equity today comes from the terminal value?
what does this mean for an equity investor in the firm?
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Hi Mostafi,, let me try to understand your question: if you are asking how to value a new company; one method is according to the projected earnings.
If your question tries to compare the value techniques against the equity value of any entity, the answer is that both results are used for different type of decisions and one does not exclude the other .
Best regards
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I think liberty can't stand with equity and justice , I'm also not sure with that . But if liberty does, can Marx realize the society with all of liberty and equity and justice ? Does he mention that in his production?
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Marx's basic political project had been to liberate masses from all forms of un-freedom, including so called religious and metaphysical obscurantism.
No inconsistency are there among these ideals of liberty, equality and justice, although it is a herculean task to make it a reality. And just taking Marxist categories as mere instrument for change will not do. We have to imbibe them in our understanding of myself and my fellow humans. Have to learn, believe and practice!
The idea of liberty should also be reconceptualized, without losing sight of it in the capitalist forest of inhumanity.
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The attached study by Adebambo et al found that companies run by charismatic CEO's tend to have higher implied cost of equity. By implication, this suggests that equity investors assign higher relative risk and, as a consequence, higher equity risk premium to companies run by "charismatic CEO's."
What do you think?
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Thank you for sharing. I will digest the study.
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I am working on different bond, equities, mutual fund listed in the Stock Exchanges in Bangladesh. By reviewing various literature, VAR model is found useful for forecasting the price/trend of stocks. ARCH models are found very regularly in the volatility of share price research.
I want to learn and apply various techniques/models/procedures in my research to anticipate the trend of economic variables, such as stock price, GDP, Inflation, Money supply etc.
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ARMA,GARCH could be the best models for forecasting!
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How well is the assessment used by your department, school, and/or university for compensation aligned with promotion criteria?
One of the key complaints shared among colleagues at U.S.-based universities is that the criteria required for pay is not often aligned with the criteria outlined in Faculty Handbooks, which provide specific guidelines for promotion.
The issue is of particular interest to researchers whose pay derives from the grant dollars they can generate to support their salaries. Many junior faculty find that the search for money limits their ability to engage in departmental activities, deliver quality teaching, and/or participate in service activities. In some instances, generating manuscripts is also not compensated yet required for retention and promotion.
What are your experiences? Thoughts?
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in India,we have regulatory bodies like (UGC-university grants commission and AICTE-All India council for technical education)who have formulated the eligibility criteria for faculty position (both at starting and during the career(promotion).This is applicable to centrally funded universities and Private universities also.
Regarding research projects, the faculty will submit the proposal(including budget) to the bodies mentioned above and the main investigating faculty will take the responsibility of submitting the research project duly completed back to the funding body with expense account.
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Dear all,
I try to calculate the cost of equity with the FF3 model and already estimated the beta factors for the market, size and value risk premia by using regressions and the data provided on the Kenneth French website.
Now my question is where I can get the specific risk premia in order to calculate the return? I know that I can use the market risk premium as published by Damodaran but how do I get the other two?
I really appreciate your help!
Kind regards,
Felix
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The three factors can change, eg by country. following the literature back from this link may help you. Best, D. Booth
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I had a couple of meetings in many European and North American research institutes and Universities. I still believe that something wrong was taking place. It was not a very transparent process. Cannot tell, but this is my feeling.
my experience goes as follows:
If you pass through to reach the interview, the recruiters would tell you, "sorry, you are not the chosen for this posts". Apparently, they met me just to send a message of equity, while in fact it was clear they do not want me. Did you have similar experience? What are your impressions and feelings? Do you believe that the interviewees were truthful and wanted to get the best to the post? Just let everybody hear your voice.
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This is exactly what I was taking about; either way, I don't have the right to promise something I don't have. When someone applies with a hope to finally work in a place where s/he can progress and then finds self with a false promise, it can have many negative consequences. This is especially true when the applicants are coming from less fortunate, or relatively small institutes with limited resources, but with higher hopes supported by an accumulation of a wealth of experiences, and who eventually had a career and a C.V. that stands. In many ways, it is unfair for the institute and for the candidate. We can see many cases where institutes lost many talents because of a decision made by a traditional mindset.
I hope that I made my point clear.
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For example: ROA (%) = 0.25 + 0.15 (Debt/Equity) +0.25 (Market to Book ratio) + e
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If it is multiple OLS then One unit change in independent variable causes beta change in dependent variable. In other words, one unit change in Debt/Equity (whatever % or without) increase 0.15% ROA (if you measure in %).
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Interested to work with people with:
(a) technical knowhow
(b) good track record on operating experience
(c) equity capital
(c) distribution and market access to sell the products
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It's very important
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Return on equity (ROE) of Commercial banks
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Dear Prabha
The benchmark for the ROE of banks will be dependent on time period, country, stage of business cycle, focus of the banks, interest rate environment, size, et cetera. I suggest you not have an assumed absolute ROE number per se but instead develop a sector norm based on the emprical performance of the banks in your sample.
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For my thesis I would like to examine numerous events (Lehman collaspe, September 11, and other major global events and examine the impact/correlation on the BRICS and G7 nations.
I would like to do an event study using the Market Model, but as I understand, I do not think i can use AR (Abnormal Returns) as I am not looking at specific stocks, but using the major indices in each country (E.G. MOEX, IBOVESPA, CAC40, DAX30 etc.)
I have come across the FTSE Global Equity Index Series (GEIS), https://www.ftse.com/products/indices/geis-series but do not have access to the data. Alternatively, I have found on Datastream MSCI World, FTSE Developed, FTSE Emerging, FTSE All World, and FTSE Global. Would one of these suffice?
My main research idea is to examine whether the investor risk tolerance between emerging (BRICS) and developed nations (G7) differs significantly by seeing how the markets react to each event and volumes traded.
Any help in this matter would be greatly appreciated.
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On the other hand, sometimes these different investment strategies may lead to high speculation on the markets of small developing countries, where large global banks and investment funds are able to influence capital formation trends in the exchange rates of stock markets in smaller countries. I described these issues in my articles, and I analyzed the sources of the global financial crisis in 2008.
The issues of risk management in the context of determinants of the global financial crisis, globalization processes, technological progress and other factors I described in the publications:
I invite you to discussion and cooperation.
Best wishes
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In your opinion based on your context, experiences and research, what are the most critical factors in improving equity in mathematics education across a broad range of groups? Please feel free to cite specific research or give examples from your practice.
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This recent book might be useful as it examines "how race, class, culture, power, justice and mathematics teaching and learning intersect in mathematics education to sustain or disrupt inequities":
Bartell, T. G. (Ed.) (2018). Toward Equity and Social Justice in Mathematics Education. Cham, CH: Springer.
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For my thesis I am running a DCC GARCH model on Oil, Gold and Equity, and have run through the data in R using the 'rmgarch' package.
However I cannot find out how to get the DCC estimation stats table.
What I am trying to do is replicate this sort of thing:
Table 4. Multivariate DCC-GARCH estimation results.
If anyone can help me that would be brilliant!
Kind regards,
James
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Thank you very much Prof Michael John McAleer for your recommendation, yes you are right that we cannot just trust what is available on the web and I agree we have to evaluate in terms of literature and mathematical background.
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Fundamental Stock Investing ~ Banking
Which Valuations ratios should be used while making equity stock investment decision banking stock?
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Thanks you all,
I considered from all points found many areas and facets to analyse banks, but I didn't found any answer you context with liquidity position & interest margin.
Any inputs on that
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I am doing a research on relational maintenance of wives and their husbands during military deployment. I have determined to use Relational Dialectics, however I have trouble looking for another theory that I can use for my thesis. Can you please help?
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I think that the life of people and societies is very effective in social change theory. Therefore, the level of equality and interdependence can be measured according to societies.
Can there really be unrequited behavior in social change theory? Especially based on moral and religious.
In addition, how much does social change contribute to productivity?
What is the importance of risk, opportunism and trust on the basis of social change theory?
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How to club three dependent variables in to one like (Margin Growth, Revenue Growth, and Return on Equity)?
I have to run a regression model
My independent variables are firm size, r&d exp., profitability etc
I want to make a dependent variable (organisational learning) by considering Margin Growth, Revenue Growth, and Return on Equity as the main aspect of org. learning
thanks
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Ideally, you will find correlations between the variables and then you could do a factor-analytic compaction of the variables. However, there are at least two difficulties that need to be considered in this context: The variables mentioned can develop in different ways, that is to say that a company may achieve high revenue growth through certain measures, but not an improved return on equity (for example because of a rise of its capital). Then there would be no (positive) correlation. And further, one must bear in mind that the variables margin growth, revenue growth, and return on equity are not necessarily valid indicators of organizational learning, which can also cause many other effects. The variables mentioned are also based on other reasons (i.e. financing strategy, business strategy), which are maybe not covered in your design. It seems to me more favorable to include internal variables as outcome of organizational learning.
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I would like to test the relationship and impact of FIIs investment of particular company on company's equity price and also want to know the impact of FIIs investment on Stock Index. Pls suggest me, what are the statistical tools  good for conduct? I am using 10 years data.
Thank you,
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You can refer the above paper too @munji shanmukharao
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I'm working on a study to investigate the relationship between confirmation bias (IV) and overconfidence (DV) using a moderator variable (MV) called Prior. Prior is define as a person's prior belief, emotional attachment, expectation, etc. on a certain subject matter. In my experiment, I'm trying to study if people who are more invested in an equity stock will exhibit a stronger relationship between confirmation bias and overconfidence. In other words, does confirmation bias predict overconfidence better when a prior is stronger.
How do I write my null and alternative hypothesis for such as a study? I'm thinking there got to be at least 2 hypothesis that I need to test but I'm not sure if that's all. So my feeling is that I have to write:
H0 1: Confirmation bias is not predictive of overconfidence.
H0 2: Prior does not moderate the relationship between confirmation bias and overconfidence.
Are the above correct ?
Second question, can my moderation study utilizes binary MV. I have seen a few write ups that use binary MV such as gender. In such cases, how do you centre your data to create an interaction term when it's only 0 and 1? Or do you not center and maintain the 0 and 1?
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Eileen Ting You will state its as H04: The moderating effect of Prior has no significant predictive effect overconfidence .
I will recommend you change H01 to look as below but you need to break down the variable Confirmation bias to get specific objectives like below (assume yours are critical thinking, logic, fallacy etc just example);
H02: Critical thinking has no significant predictive effect on overconfidence.
H02: Logic has no significant predictive effect on overconfidence.
H03: Fallacy has no significant predictive effect on overconfidence.
then you include H04 above
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Practical setting in a company. Would like to develop a model to predict future value of customers to the company.
Important is to be able to make a distinction between customer characteristics and product characteristics.
What method is best to use, what CLV method or Customer Equity, CVT (customer valuation theory) etc.?
Anyone that is willing to share some tips with me for implementing such a model into an organization?
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Several studies in the area of Direct Marketing and Customer Relationship Management have shown a strong relationship between future and past pur- chase behavior
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  • while the first go to adapting with investment,growth, and then the financial leverage ,the second go to high liquidity
  • but investment in liquidity can be avoid risks when there is high country risks by trade in equity
  • so what is the different,if both yield earning
  • is the financial flexibility deal with long and financial slack deal with short range ? if so current ratio can be differentiation between short to and long range when it connected with size and growth
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This is a very interesting topic of study.
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Henkel/Gruber (WPg 2.2019)
With the publication of the interim reports as of March 31, 2018 (Q1/2018), empirical data on the first-time application of IFRS 9 (Accounting for Financial Instruments) is available for the first time. The subject of the article in German is the analysis of the Q1/2018 interim reports of the DAX 30 companies. The analysis focused on three aspects: a) the extent of reporting on first-time adoption of IFRS 9, b) the amount of IFRS 9 equity adjustment effects and c) the migration paths in categorisation, especially the question of the extent to which fair value measurement has increased as a result of the new standard.
Result: A wide range can be seen in the reporting on the transition to IFRS 9. Both equity reducing and equity increasing conversion effects are observed. The absolute figures range between +€101 million and -€391 million and the equity ratio between +0.13% and -1.33%. In most cases, the major part of this equity adjustment effect is attributable to the impairment part. The IFRS 9 equity conversion effects can predominantly be classified as insignificant. An analysis of the migration paths of the new IFRS 9 categorisation shows on the basis of ten companies that for an average of 93.4% of the financial assets of these companies the valuation does not change as a result of the new categorisation. The remaining financial assets are migrated to the category at fair value through. profit or loss, which is likely to make the income statement more volatile and the forecast of net income more difficult.
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Dear Mojca,
thank you for your reply and your question. The subject of my research was the analysis of the quarterly reports as of 31 March 2018 and is therefore based on actual figures. I didn't run through any scenario calculations.
Best regards from Germany
Knut
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It's about health system performance in equity and disparity. What technical statistic is need?
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la forma correcta de evaluar esto es por los indicadores de salud de los grupos más pobres o excluidos,por la disminución de las brechas entr los estratos sociales más pobres y por las intervenciones focalizadas en los grupos excluidos,buscando así equidad en salud
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Why females are neglected during health care service delivery?
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Thanks Sir
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what is the correct accouting equation out of the below mentioned two
1. Assets=Liability+ Capital
2. Assets=Liabilities+ Equity
In other words, what is the difference between capital and equity.
thanks in advance
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Assets = Liabilities + Equity. In this case, equity is residual interest. Capital is part of an eqity. See Warsono' paper (2018) superiority of the law of funds .... In the researchgate. Hope it's usefull.
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Why do capital expenditures increase assets (PP&E), while other cash outflows, like paying salary, taxes, etc., do not create any asset, and instead instantly create an expense on the income statement that reduces equity via retained earnings?....Please help
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Muhammad Farhan Basheer
I think it really depends on the skills of the manager or accountant to define the account classifications.
R&D department pays salaries to the engineers and expensed but the engineers’ work can be capitalized as intellectual property which can be valued and trade secrets which cannot.
The marketing department creates a name for the company and products and services which can be capitalized as goodwill.
The IP department’s work in trademarks and patents management can also be capitalized as intellectual property.
Yes, salaries are expensed but workers can create intellectual property and goodwill assets that can benefit the firm for many years.
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I am learning to do top-down analysis on a number of sectors in the US equity markets. I have never done it before and I would appreciate it if some professionals can share their experiences with me.
Specifically, I would like to know the list of attributes/data/factors I should be measuring and monitoring.
Most of the websites I can find are vague about this subject. They mostly generally list a few vague things such as GDP, inflation, treasury bond yields, etc. However, I would like to know the more specific data used. Take treasury bond yield for example. I have learnt from elsewhere that people are monitoring the 2Y yield vs 10Y yield for indication of an economical reversal. I can't find any website that gives this kind of working examples on a top-down approach.
Can you tell me what other specific measures/data the industry's professionals normally would look at in a top-down sector-wide analysis?
Thanks
J
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I think you need to clarify your question. I THINK you are asking what macroeconomic variables should you use in order to estimate/simulate/forecast movements in equity prices for certain industries.
If so, the first thing to do is to analyze (with or without an explicit mathematical model) what should affect the demand and supply for such equities. Variables such as GDP can be used as proxies for income as it affects demand and (via investment financing) supply, interest rates can affect both since bonds are to some degree substitute assets (but in a portfolio analysis also to some degree complementary to equities). They also relate to the cost of real investment when finances through debt instead of equities (at low long term rates -- in spite of some Capital Theory formulations--firms may prefer to issue debt not equity and that affects supply of new equity). Long vs short interest rates can be looked at in terms of expected future interest rates, and therefore the relative desirability of debt vs equity financing.
Actually, instead of hunting for web sites I suggest picking up a few high quality finance texts to guide you in modeling and therefore in data selection.
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This question is based on a study investigating the influence of firm leverage on market performance in high intangible growth prospects.
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Interesting..
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Perspectives based on
  • Enrollment
  • Equity
  • Quality
  • Infrastructure
  • Political Interference
  • Faculty
  • Research & Innovation
  • Structure of Higher Education
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