Research on Forecast Algorithm of Financial Time Series Analysis Based on Markov Model

The financial system is both transparent and convoluted, with deep connections between the myriad of economic factors that are at play. The contemporary view of finance can be broken down into three distinct components: the time value of money, asset pricing, and risk management. The crux of the issue is finding the appropriate distribution of resources across time periods while operating in an uncertain environment. In the subject of forecasting financial time series, numerous studies have proven that the link between input and output variables is not stable, and that both variables always vary with time. This has been demonstrated by the findings of the studies. Because data comprises a significantly greater amount of information, it is only natural to give more weight to more current information. The study of financial markets now would be incomplete without the inclusion of time series analysis, both as a theoretical framework and a practical methodology. One of the methods that are considered to be part of the mainstream in quantitative financial analysis is the time series analysis method. This study makes use of the Markov model, which is a technique for forecasting financial time series that has been shown to reduce the amount of error associated with forecasting.