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Interpreting and Visualizing AutoCorelation


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 eect of auto correlation 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 precies, 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 a 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.


Domain Knowledge

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.


Webscraping

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 the webscraping, 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

 

Python

So we have Shiller ratio data and S&P closing price in two dierent data frames, now let’s perform a lookup function to get the Shiller PE ratio for each month into the closing price data frame.

Python

We have 1769 entries and 4 columns SandP_Date and sh_Date are date columns we could easily drop one of them and we need to check for null values.

Python

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

Python

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 columns.

Python

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.


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Byte Academy is based in New York, USA. It offers coding education, classes in FinTech, Blockchain, DataSci, Python + Quant.

This article is from Byte Academy and is being posted with Byte Academy’s permission. The views expressed in this article are solely those of the author and/or Byte Academy and IB is not endorsing or recommending any investment or trading discussed in the article. This material is for information only and is not and should not be construed as an offer to sell or the solicitation of an offer to buy any security. To the extent that this material discusses general market activity, industry or sector trends or other broad-based economic or political conditions, it should not be construed as research or investment advice. To the extent that it includes references to specific securities, commodities, currencies, or other instruments, those references do not constitute a recommendation by IB to buy, sell or hold such security. This material does not and is not intended to take into account the particular financial conditions, investment objectives or requirements of individual customers. Before acting on this material, you should consider whether it is suitable for your particular circumstances and, as necessary, seek professional advice.


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