Interpreting and Visualizing AutoCorrelation
By Jithin J and Karthik Ravindra, Byte Academy
Analyzing a Time Series Data needs special attention. Here, we would like to explore working with time series data and identify the effect of autocorrelation to come up with a more practical approach to work in Linear Regression Models. When using some data to try to estimate some value, say equity prices, Autocorrelation is a common feature. It is defined as the situation when the error terms of the linear regression model are correlated. So, if one error term is positive (or negative), and this fact causes the next error term to also be positive (or negative), we say that the model suers from autocorrelation. It is a very serious problem, as it violates the common assumption that the error term is stochastic and non-deterministic. Maintaining a stochastic error term is important to maintain the integrity of a linear regression otherwise it risks inducing bias in the model’s estimations.
Let’s take an example of some financial data during a stock market crash. The crash on day one increases the likelihood of observing a downward trend for the next few days, perhaps even weeks. If the model suers from autocorrelation and is used for extrapolation, the model will estimate a similar stock market crash in the future as well. Therefore, we must first be able to identify the presence of this trend.
To prepare this article, we decided to pick a financial data set. After some quick research, we decided to work on Shiller PE ratio and estimate the movement of S&P monthly closing price. The data was taken from: http://www.multpl.com/shiller-pe/table?f=m.
The Shiller P/E is a valuation measure usually applied to the US S&P 500 equity market. It is defined as price divided by the average of ten years of earnings (moving average), adjusted for inflation. As such, it is principally used to assess likely future returns from equities over timescales of 10 to 20 years, with higher than average values implying lower than average long-term annual average returns.
We start with extracting data scraping the Shiller P/E ratio and S&P closing prices from http://www.multpl.com/shiller-pe/table?f=m . If interested in web scraping, the Python code is here: https://github.com/jithinjkumar.
Once our data has been extracted we store in pandas DataFrames. We create a pandas data frame with index column as time series and S&P closing and Shiller Ratio as our column.
Once the data is stored, we need to clean and prepare it for analysis.
Data Preparation and Data Cleaning using Pandas library: Creating a Time Series
So we have Shiller ratio data and S&P closing price in two different data frames, now let’s perform a lookup function to get the Shiller PE ratio for each month into the closing price data frame.
We have 1769 entries and 4 columns SandPDate and shDate are date columns we could easily drop one of them and we need to check for null values.
sh_Ratio has 120 null values, we could drop these values from our dataset safely as this accounts to less than 6% of total row items
Now we create a time series for which the S&P Date column needs to format correctly so that we are able to assign the correct data type for each column.
Now our Dataframe is in a time series format and ready for further analysis.
Stay tuned for the next post in this series, in which we will discuss Time Series Analysis.
Any trading symbols displayed are for illustrative purposes only and are not intended to portray recommendations. Originally posted on the IBKR Quant Blog here.