Stock Risk Classification and Predictive Analysis Using ARIMA Modeling

Stock market investment involves various levels of risk. This study attempts to solve the problem of classifying stocks into specific risk categories using fundamental and technical indicators. This study helps bridge the gap between theoretical classification of risks and practical trading approaches for various investment styles. The author employs quantitative methods such as descriptive statistics, risk scoring, time series analysis, and Auto-Regressive Integrated Moving Average (ARIMA) modeling on 10 diverse U.S. stocks from January 2020 to current date. These analyses provided evidence of three risk categories with distinct volatility patterns. Relative Strength Index (RSI) has been found to be the most statistically relevant variable for all returns, while volatility had the largest absolute coefficient of returns. The author found that ARIMA(0,1,0) best fit all stocks. This indicated that all stocks followed a random walk with drift, regardless of risk category, but with regular risk stocks exhibiting slightly more predictability than conservative or aggressive. These results will be useful to portfolio managers and investors who wish to adjust their stock portfolios according to their risk appetite by helping align stock selection with risk tolerance while indicating which technical indicators are useful for different styles.