Tagged pandas

A comparative study of neural network models (LSTM, CNN-LSTM) for S&P 500 index prediction. The analysis evaluates multiple architectures against baseline strategies like buy-and-hold and naive forecasting, finding that while specialized models like LSTM with Batch Normalization and Regularization improve technical error metrics (MSE, MAE). However, when tested with a simulated trading, beating market returns remains a significant challenge requiring further architectural and strategy refinement.

A comprehensive comparative study of time series forecasting methods across six diverse datasets (Gold, S&P 500, FTSE, USDC/USDT, StackOverflow questions, and cinema tickets). This analysis evaluates 10+ forecasting techniques—from naive baselines to ARIMA/SARIMA models—with quantitative performance metrics (MSE, RMSE, MAE), explores stationarity testing, seasonality decomposition, Monte Carlo simulation, and implements a simulated trading strategy.

Developing a Decision Support System to predict financial literacy and behaviour along with well being based on socioeconomic factors using a Bayesian network.

Part 2 of the analysis focuses on High Risk Financial Vehicles (HRFV), data collected with the Malaysian dataset were inspired by and referenced from a Ghanaian study. We explore how financial literacy influence perception and participation in activities like sports betting and cryptocurrency.

We compare the Romanian dataset with our own surveyed dataset and explore the relationship between the sociodemographic and financial behaviour, with financial well being and financial literacy.