Emerging Researchers National (ERN) Conference

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Determination of the Prediction Values of Technical Indicators

Undergraduate #70
Discipline: Mathematics and Statistics
Subcategory: Mathematics and Statistics

Kristi Pearley - Southern University at New Orleans

The goal of this research is to determine the prediction value of stock market indicators of interest over a five year period. Technical Analysis was the main tool used to collect data for my research. Technical Analysis is the prediction of future price movements based on the study of past prices. Technical indicators are a series of data points that are derived by applying a formula to the past price data of a security. Technical indicators confirm, alert and help analyst predict price data. The stocks assigned for this research are well known companies that were chosen by their volume, price, and longstanding performance. The main focus of this research was to determine the correlation of given stocks with McClellan Summation Index, IShares Russell, Standard and Poor’s 500, and Dow Industrial Average on a daily chart using stochastics 10.4.3. indicator for entry and exits. The second assignment was to use indicator, 9/15 simple moving average and moving average convergence divergence (MACD 12.26.9) separately to enter into the market while using MACD 12.26.9 to exit for each. Data was also gathered using double indicators 18/30 simple moving average and stochastic 30.42.18 agreement for entry and MACD 24.32.18 for exit. Lastly, data was gathered at the beginning and end of the 5-year period to simulate a buy-and-hold method to see if Technical Analysis is better than a buy-and-hold method. Statistical tests including Analysis of Variance (ANOVA) and Bonferroni statistical analysis were performed to find the best method that reduced risks and produced the most profits. Based on data, the Dow Industrial Average showed a better correlation with most stocks. The gains for each method is statistically significant. This proved Technical Analysis worked. Results for this research could be used as a starting point to gain expertise, reduce risk, and improve the odds of a profitable trade.

Funder Acknowledgement(s): MSEIP Grant (P120a160047)

Faculty Advisor: Dr. Joe Omojola, JOmojola@suno.edu

Role: I am responsible for the entire research project.

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.