Business cycle prediction: Application of Markov chain to online crowdlending

Using Markov chains can improve the forecasting of the state of the total loan amount for the next month (growth, stagnation, or decline), compared to traditional forecasting techniques, if each of the previous month's basic information is available. Traditional statistical techniques have high forecasting errors and low accuracy for predicting the loan amounts and are no better than the naive method of ``no change'' if data cannot be rapidly actualized. With recent data available, some traditional statistical techniques work better than the naive method, but Holt double exponential smoothing has a higher mean absolute percentage error (MAPE). For predicting ``states,'' some traditional statistical methods improve with the degree of actualization (naive, simple exponential smoothing), Holt and ARIMA decrease in performance, and TBATS remains the same. Since Markovian chains are better than all traditional time series forecasting techniques, it raises the benchmark for evaluating value added by artificial intelligence techniques for forecasting.