In the research with Professor Xu we will be testing on whether the Taylor’s law can be used to predict stock prices. Taylor’s law is one of the quantitative scaling patterns in ecology, and has been confirmed for thousands of biological and non-biological variables. Recent works show that Taylor’s law can be applied to model temporal trends of financial time series. However the predicting power of Taylor’s law for future forecast of time series remains untested.
The goal of our research is to test the empirical validity of various types of Taylor’s law for stock price data and explore the usefulness of Taylor’s law in selecting the best price forecast.
We will be using R as the tool to conduct the research, we will be using the package called Quantmod to get the raw data from yahoo finance for the fortune 100 companies’ stock prices since they went IPO till 3/26/2019 which is the day we started the research.
In this research we will be test three types of Taylor’s law: temporal, ensemble and temporal hierarchical. Temporal Taylor’s law means that we will group the raw data by name and analyze the each stock price fluctuation since they went public. And we would calculate mean and variance for each stock then construct a graph to see if the overall performance is a linear relationship which fit the Taylor’s law.
Ensemble hierarchical, we would group all the data we got by year, so instead of analyze each stock’s overall prices, we analyze the market performance of fortune 100 stock together on a yearly basis, and then calculate mean and variance for each year,so each dot on the graph represent each year.
For temporal hierarchical, we will evaluate each company’s performance individually, different form temporal Taylor’s law, this approach would allow us to look at the change in stock price on a yearly basis.
in addition, we would also make linear regression model for each type of the Taylor’s law based on the mean and variance and determine the overall efficiency of the model