Effectiveness of Open, High and Low Prices in Stock Market Price Prediction

Abstract

Stock market price prediction is vital for investment decision amid difficulties with effective price predictions. The paper aims to analyse the rate of effectiveness in actual stock market price prediction using the open, high and low prices. The paper draws insight from diverse prior research with assorted models such as Markov Chain, time series and computer aided stock price prediction. The paper’s approach is quantitative with forty-three days stock market price data from S&P500 and Shanghai Composite Index. Data was analysed with the regression statistics. Results show that the open, high and low prices can significantly predict the actual market price at probability level of P<0.0001 for both the S&P500 Index and the Shanghai Composite Index. Prediction rates exceed 70% for S&P500 and over 80% for Shanghai Composite Index. The model was verified by using data other observation periods (during the COVID-19 and during the financial crisis). The implication therefore is that in the absence of other expensive market information, an average investor may use the open, high and low prices to make a useful prediction of actual stock market price. The findings present a useful case reading for academics in business schools and offer an agender for future research to apply this model in other stock markets. The paper offers a novel value from the finding by demonstrating that the showing that application of open, high and low prices with regression may give a prediction accuracy rate of over eighty percent, which is higher than reported seventy percent prediction rate in prior work that used other models.