Abstract
The study proposal outlines the key role of mathematical models in predictive analysis in financial marketplaces. The research aims to explore further mathematical models used to forecast market trends, determine patterns, and inform investment decisions. By further examining mathematical concepts like probability and time series, the study seeks to offer insight into the importance of mathematical models in predicting market behavior and increasing investment decisions. It will further be hypothesized that mathematical models are beneficial in forecasting financial markets. However, the findings are expected to demonstrate that mathematical models like time series and regression are important in understanding trends and patterns in the market. The research conclusion will summarize key findings, research implications, and areas for improvement.
Background of Study
Mathematical models are mostly applied in quantitative analysis of most real problems, particularly in the prediction of the financial market. As a result, mathematical models have become a key tool used by various professionals in the field of finance. According to Amadi & Wobo (2022), with increased development in the mathematics field, there are constantly new problems in solving financial issues based on given mathematical models. Hence, mathematical models is important in studying many financial issues. Although in financial research, it is imperative to assess and solve financial problems by creating a corresponding mathematical model based on a functional link between variables. Zhou (2021) further explains that mathematical models like linear regression and time series can help assess financial problems better and seek major solutions to challenges. Overall, thorough application of various mathematical models, individuals can assess key financial issues clearly, theoretically verify data resulting to informed decision making.
Study Research Questions
The following research questions will guide the study.
- How do mathematical models help in predicting financial markets?
- What form of mathematical techniques are mostly used in modelling the market trends as well as patterns?
- What are the key problems related to mathematical models for predicting financial markets?
Research Methodology
The study will take a qualitative approach, involving both a literature review and further analysis of case studies. Hence, the literature review will entail a complete assessment of various scholarly articles and journals related to mathematical modelling. Furthermore, case studies of real-world use of mathematical models will be assessed to offer practical insights into their effectiveness. In addition, data sources will entail use of historical market data and key financial reports.
Expected Findings and Hypotheses
Mathematical modelling is important in making predictive analysis by offering quantitative tools for assessing intricate data, determining trends, and making better decisions. The study expects to find that key mathematical methods like time series forecasting linear regression as well as Monte Carlo simulation are mostly used in financial modelling to predict the risk and cost of a given asset and improve portfolio allocation methods. However, it is expected that mathematical models might be challenging to use, especially in ensuring data accuracy, making model assumptions as well as in market unpredictability. Hence, this highlights the importance of including qualitative judgement as well as expert insights when making decisions.
Conclusion
Overall, this study will conclude by summarizing major findings and understandings about the role of mathematical modelling in predicting financial markets. It will further explain the implications of the study findings to key stakeholders like policy markets and researchers, emphasizing the significance of including mathematical models in understanding how financial markets operate. Furthermore, the research will highlight key areas for future study and potential improvements in mathematical modelling methods for enhancing predictive accuracy and risk management in financial markets.
References
Amadi, I. U., & Wobo, G. O. (2022). A mathematical model analysis for estimating stock market price changes. International Journal of Applied Science and Mathematical Theory, 8(2), 2695-1908.
Zhou, D. (2021). Financial Market Prediction and Simulation Based on the FEPA Model. Journal of Mathematics, 1-11
