Predictive Analytics for International Financial Markets

Forecast exchange rates, stock indices, and commodities using advanced time-series models including ARIMA, Prophet, and LSTM neural networks

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Project Overview

This project focuses on forecasting exchange rates, stock indices, and commodities using sophisticated time-series models. The goal is to develop predictive models that can help with risk management and investment decision-making in international financial markets.

Project Goals

Data Sources

Yahoo Finance API (yfinance)

Historical stock, forex, and commodity price data

FRED API

Macroeconomic financial data from Federal Reserve Economic Data

Technical Stack

Data Collection & Processing

  • Python (yfinance, pandas)
  • FRED API integration
  • Data cleaning and preprocessing

Time-Series Modeling

  • ARIMA models (Statsmodels)
  • Prophet for seasonal forecasting
  • LSTM neural networks (TensorFlow/Keras)

Visualization & Analysis

  • Tableau/Power BI dashboards
  • Matplotlib/Seaborn for plots
  • Confidence interval visualization

Execution Flow

1

Market Selection

Choose target markets (e.g., USD/INR exchange rate, crude oil, S&P 500)

2

Data Collection

Pull historical time-series data using yfinance and FRED API

3

Data Preparation

Split data into training/testing sets and handle missing values

4

Model Development

Apply ARIMA/Prophet for short-term forecasts and LSTM for advanced predictions

5

Visualization

Create forecast plots with confidence intervals and interactive dashboards

6

Risk Analysis

Write risk management report with hedging strategies and volatility analysis

Key Deliverables

Forecast Plots

Time-series visualizations with confidence intervals showing predicted trends

Interactive Dashboards

Tableau/Power BI dashboards for real-time market analysis

Business Strategy Report

Risk management recommendations and hedging strategies

Expected Outcomes