The objective of this study is to structure a dependable model to forecast the timing of entry and exit from the stock markets by using multivariate linear regression analysis. The study uses major macroeconomic indicators such CPI, PPI, GDP, MEI as independent variables and the S&P 500 index value as the dependent variable. The sample consists of 30 years of monthly data. This study includes four different loss scenarios in the S&P 500 index value and analyzes the data to see if the losses can be absorbed or if further losses will occur. This report discusses the practical implications of using regression analysis and how it is used to predict the market movements. This paper concludes that our regression model can help an investor to anticipate market movements and thus make appropriate buy and sell decisions.
Inhaltsverzeichnis (Table of Contents)
- Abstract
- Introduction
- Stock Market Prediction Methods
- Bollinger Bands
- Moving Averages
- Regression Lines
- Financial Crisis - Events Analysis
- Hypothesis
- Data Collection
- Research Methodology
- Application of Regression Model
- Analysis
- Validity of model under different market conditions
- S&P 500 value at different sell off scenarios:
- At 5% sell off
- At 10% sell off
- At 15% sell off
- At 20% sell off
- Interpretation of results
- Impact of various Macroeconomic factors on S&P Value
- CPI
- PPI
- GDP
- Money Aggregates
- Validity of the predictions with the real world data
- Conclusion
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This study aims to develop a reliable model to forecast entry and exit points in the stock market using multivariate linear regression analysis. It examines the relationship between macroeconomic indicators and the S&P 500 index value. Key themes and objectives include:- Predicting stock market movements through regression analysis.
- Analyzing the impact of macroeconomic factors on S&P 500 performance.
- Developing a practical model for investors to make informed buy and sell decisions.
- Assessing the validity of the regression model in different market conditions.
- Investigating the effect of various loss scenarios on the S&P 500 index.
Zusammenfassung der Kapitel (Chapter Summaries)
- Abstract: This section provides an overview of the study, highlighting its objectives, methodology, and key findings. The study aims to develop a model for predicting stock market entry and exit points using regression analysis, focusing on the relationship between macroeconomic indicators and the S&P 500 index value.
- Introduction: This chapter introduces the challenges of stock market investment, emphasizing the importance of accurate market predictions. It discusses the growing complexity of portfolio construction due to various financial instruments and highlights the role of index values in minimizing investment risk. The chapter also introduces the concept of basket trading and its impact on market volatility.
- Stock Market Prediction Methods: This chapter explores different stock market prediction methods, categorizing them into Time Series Models, Cause and Effect Models, and Judgmental Models. It provides brief descriptions of various techniques within each category, including moving averages, regression models, and artificial neural networks.
- Bollinger Bands: This section delves into the concept of trading bands, known as Bollinger Bands, which are used to analyze price volatility and define overbought and oversold conditions. It discusses how these price channels can help investors anticipate stock price movements.
Schlüsselwörter (Keywords)
This study focuses on quantitative analysis of large stock market crashes, exploring various prediction methods, macroeconomic factors, and their impact on the S&P 500 index. Key terms and concepts include: multivariate linear regression analysis, macroeconomic indicators, S&P 500 index value, stock market prediction, volatility, and financial crisis.
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- Victor Odour (Author), 2011, Quantitative analysis of large stock market crashes, Munich, GRIN Verlag, https://www.grin.com/document/267038
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