Application of optimal stopping to model sales in financial markets: Examination and analysis

High-precision prediction of financial prices is still a deemed long-term challenge that constantly calls for state-of-the-art approaches. Thus, the purpose of the current study was to examine the efficacy of optimal stopping and use its connection with branching processes to predict several financial markets. For this purpose, the S&P 500 index and dollar, gold, oil and bitcoin markets are predicted at 5-, 10-, 30-, 50-, and 100-day forecast horizons, for each of which the optimal buying and selling point was determined. Closing price data for at least 2200 trading days during the period 2013-2021 was used for the purposes of this study. Moreover, given that any prediction and decision in the financial markets is highly based on probability, and hence risk, two strategies are devised for examination, namely (1) high risk (success rate of at least 50%) and (2) low risk (success rate of at least 70%). The findings indicated that the estimations on all the indices and prices were relevant for the high-risk scenario (that is a success rate of at least 50%), while only those on the S&P 500 index and price of gold were relevant for the low-risk scenario (success rate of at least 70%).

Application of optimal stopping to model sales in financial markets: Examination and analysis Keywords:

pplied probability , optimal stopping , branching processes , simulation modeling , financial markets forecasting