The study closes the knowledge gap on how the devaluation of the Indian rupee affects
US trade, capital inflows and outflows, and monetary policy on a sector-by-sector basis.
Among the ramifications taken into consideration in this context are exchange rate
volatility, trade balance, capital inflows and outflows, inflation, monetary policies, and
sectoral competitiveness of businesses that depend on imported inputs for exports.
However, the primary issue is that, particularly in times of crisis, all of these elements must
be examined jointly because they have reciprocal effects on both economies. Using
historical and current data on currency fluctuations, trade indicators, and policy measures,
the current study suggests a neural network model for assessing the interaction between
the variables in order to handle this situation. Macroeconomic factors like exchange rates,
sectoral trade balances, inflation indices, and monetary policy variables from the US and
India are all included in the input layers that will be built for this model intake. Nonlinear
linkages and interdependencies would then be the next hidden levels. Lastly, the output
layer will forecast the signs of distinct rupee depreciation impacts on US trade, sectoral
gains or losses, and even the stability of the capital market. Since it would give
policymakers the ability to predict future events and develop strategies for minimizing
negative economic effects and seizing new possibilities, this neural network research
provides dynamic insight into currency swings. By filling this knowledge vacuum, the study
will help us better understand the interdependencies of global trade and provide a robust
analytical framework that can be used to grease the wheels against currency-induced
economic dislocation in connected economies.