Financial Portfolio Enhancement Using Machine Learning and Artificial Intelligence
Ashutosh Shukla, M K Jai Supreeth, Bhoomika, Dr Sreenivasa BC
Liquidity, Machine Learning, Portfolio, Allocation, Mathematical Modelling.
A surplus of income over expenditure has created a demand for a variety of investment options, depending on the consumer's appetite for risk, desire for returns, and need for liquidity, among other non-quantifiable characteristics. This gave rise to the concept of a portfolio, which is simply a collection of assets or avenues for investing money, as well as the allocation of funds to each of these assets. Cash, equities, real estate, fixed-income, and commodities are the most prominent of these options. Each avenue has its own set of valuation principles and meets a different set of needs for employers. We're looking at portfolios that include both equities and fixed income for our research. For fixed income, we use an amortized rate that is considered to remain constant over time. The S&P 500 index is made up entirely of stocks. Machine learning is used to choose stocks and distribute capital appropriately. The user's risk profile is taken into account when calculating the fixed income allocation.
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Unique Paper ID: 153880

Publication Volume & Issue: Volume 8, Issue 9

Page(s): 93 - 96
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