Problem Description The project aims to forecast India’s GDP through 3 methods – employment, GDP trend and the sum of 4 expenditure components (household, government expenditure, investment and net exports) and to use the same to assess if the Modi Government has been able to change India’s GDP growth trajectory over its 5 years in power (2015-2019). The client is the Department of Expenditure, Ministry of Finance. Using the project, they can do a high-level
The ministry of Finance possesses a lot of data with respect to multiple financial indicators of all the states. A forecast of these indicators for the next 5 years, will guide the ministry in planning the fiscal budget for various states.
The dataset in usage has the data of all 28 states, however, we have chosen to construct forecasting models for the state of Andhra Pradesh. However, it should be noted that, due to the separation of Telangana from Andhra Pradesh in 2014, the forecasts will be an aggregation of both the states.
A bank marketing dataset from UCI Machine Learning Repository was adopted for this project (https://archive.ics.uci.edu/ml/datasets/bank+marketing). The dataset is about a Portuguese banking institution with records of direct marketing campaign phone calls, and the final outcomes indicating whether success campaigns are also included in a binary format (yes/no). A success campaign indicates the customer has finally subscribed a term deposit at the end of the campaign.
Over the years, as the life insurance industry has expanded, the need for clear regulatory laws has grown as well, originating entities like the National Association of Insurance Commissioners (NAIC). Through the NAIC, insurance regulators establish national standards and best practices, conduct peer reviews and coordinate their regulatory oversight to better protect the interests of consumers while ensuring a strong, viable insurance marketplace.
1. Business Problem
In 2017, a new regulation about invoice donation was issued. All the invoices issued will be
transformed into e-invoice instead of paper invoice. However, this regulation will cause great
donation drops, and this is a problem for central and local government in Taiwan.
This project provide forecast of the next 2 month e-invoice donation amount to central
government. With this forecast, central government could know which city or county would
confront a donation amount drop in the future and ask local government to take actions.
There are some reasons to work on this topic. Medicare doesn’t compensate that much because of the strict compensation policy. Due to the policy constraint, medical report for compensation is required. The size of tumor cannot really show the severity which is contrary to the policies nowadays. And the most expensive treatment, target therapy content, will not be paid. That’s why the cancer insurance products come out – try to complement the gap that Medicate doesn’t cover.
Unemployment benefits are a huge cost to governments and are highly dependent on
projected unemployment rate for the country. Unemployment benefits are social welfare
payments to unemployed individuals. The definition of unemployed individual varies from
among different governments. Each year Governments allocate a certain percentage of
their financial outlay to meet these requirements.
THE KEY QUESTION: How should the BRICS bank allocate its short-term (around 2 years) loanportfolio
to the 5 constituent countries?
DETAILS – The newly formed BRICS bank, now known as the New Development Bank, has recently
issued its first loan in April 2016 to Brazil, China, India, and South Africa in the renewables energy
space. As an ongoing engagement, the bank has reached out to our team of consultants to help decide
what portion of its loan portfolio it should allocate to each country. Since the bank’s loans are primarily in
Stock is removed from an exchange because the company for which the stock is issued, whether voluntarily or involuntarily, is not in compliance with the listing requirements of the exchange. Companies that are delisted are not necessarily bankrupt, but most of bankrupt company will be finally delisted from the exchange. To earn extra high returns on the stock market, mutual fund managers in Taiwan sometimes invest in high risk companies that might to be delisted in one year.
The goal of this project is to predict the First Day returns on Japanese IPOs (based on first day closing price), using public information available prior to the offer. Such a predictive model could be used to predict whether a new IPO coming on the market will make first day gains or not, and use the result to decide whether to invest in the IPO.