This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

S&P GSCI Crude Oil Index: A Reliable Gauge of Global Oil Prices?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The aim is to forecast the chief components of inflation (such as changes in fuel prices, food prices and prices of durable goods) for the USA, UK and South Africa, and to test whether the weighted sum of the component forecasts gives a more accurate overall forecast for inflation, than simply forecasting overall inflation itself. In the long run, the ratios of these prices to the overall consumer price index have altered because of technological changes and globalization, among other factors. For example, the prices of internationally traded consumer goods have fallen relative to prices of services. By building separate models for the components, the long-run information in the data and specific economic features likely to drive each component can be exploited. These models will test for asymmetries, such as the tendency of petrol prices to respond faster to rises than to falls in oil prices. The models should help better understand the causes of overall inflation through understanding the inflation trends of the underlying sectors. Modelling the components separately should also highlight where interest rate policy could be effective, and where other policies such as competition policy or price regulation might have complementary benefits.