MBA Finance Project – Quantitative Analysis of Large Stock Market Crashes (2013)

MBA Finance Project Large Stock Market Crashes – 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. It is a common fact that in today’s world, large amounts of capital is being traded through stock markets via numerous instruments namely bonds, shares, options, futures, swaps, and many more in currencies, commodities and shares of listed companies. Since there are numerous instruments available for trading, portfolio construction usually becomes a confusing task. Generally it is considered that investing in stocks/stock futures/stock options involves a higher degree of risk as it involves the elements of unsystematic risks, whereas index futures and options is relatively less risky as it involves elements of systematic risks.

Because of this lesser risky aspect of options and futures, we are only concentrating on index values. Returns offered by these instruments are also so very attractive, even better than the stocks/shares of companies, that predicting the entry and exit in the markets becomes very crucial for most of the investors.

• 6,500 words – 26 pages in length
• Good use of literature
• Outstanding statistical analysis
• Well written throughout
• Includes Matlab Code
• Ideal for MBA Finance and statistics students
• Note – This is not a dissertation

1 – Introduction
Stock Market Prediction Methods
Bollinger Bands
Moving Averages
3-Months Moving Average of Monthly S&P index Data for the past 30 years
Regression Lines
Model Explanation

2 – Financial Crisis – Events Analysis
Hypothesis
Data Collection
Research Methodology
Application of Regression Model

3 – 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

4 – 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

5 – Conclusion

Appendix
Matlab Code

References