Heckyl Alpha Pulse

How can a trader exploit untapped open data sets to predict monthly auto sales

Posted on July 15, 2016 Updated on November 17, 2016

Finding actionable information ahead of others is getting tougher with each passing day. Today, the market captures new information at incredibly high speed. As a result, new updates get factored in stock prices almost instantly, leaving little or no scope for individual traders to tap the opportunities. So how can a trader spot new opportunities in a fast paced markets? Is there any way out?

Let us find it out with help of a case study on auto sector monthly sales volumes and its correlation with Google search index and resulting trading opportunity.

Automobile is a high beta sector, which means a marginal movement in the benchmark index could result in high volatility in the sector stocks. At the same time, auto sector is very sensitive to new news flow. Monthly auto sales volume is one of key data points and it is closely followed by the market participants.

[Chart 1: Sales volume trend for Automakers]

Automakers declare their vehicle sales volume every month on the first or second working day. Every month, auto stocks remained in limelight around that time. Stocks of auto companies react positively or negatively based on the change in the sales volume. 

Shares of Tata Motors, M&M, Ashok Leyland and TVS Motor closed higher on the official data release date. On other hand, Maruti Suzuki, Eicher Motors, Hero MotoCorp and Bajaj Auto lost marginally. Meanwhile, the 30-share benchmark Sensex closed over a half a percent higher on July 1.

Opportunities to trade on auto sales volume can be spotted every month. To exploit such trading ideas, one must know how the monthly sales performance will be. Past volume trend analysis and expected near-term growth can help in projecting sales volume. However, estimates obtained from such analysis may not always give accurate results for a very short period.

Fortunately, there is plethora of untapped open data sets available on the web, which can help in forecasting future trend with high precision. For instance, Google search trends can act as a lead indicator for monthly auto sales. It gives result on how many times a particular keyword is searched on the internet in real time. In this case, it can be very useful tool to assess the strength in underlying demand for vehicles. Let us see, how it can help in predicting auto volumes.

Maruti Suzuki:
The weighted average Google search interest for all car models of Maruti Suzuki started falling after reaching peak in March 2016. Average Google search interest for the first 27-days/month signaled impending decline in sales volume ahead of official data release. As indicated, Maruti witnessed a decline in total sales volume in the subsequent months.

At 0.5, correlation between Google search interest and sales volume for Maruti’s all brands was quite strong in the last 2-years.

Tata Motors – JLR:

Google search interest for Jaguar Land Rover (JLR), Tata Motors’ UK arm, was on the rise for the last couple of months. Uptrend in the average Google search interest for the first 27-days/month has also reflected in JLR’s sales volume subsequently.

JLR witnessed a fair degree of correlation between Google search interest and sales volume for all brands. It stood at 0.44 and 0.3 for the past 1-year and 2-years respectively.

Bottom-line:

Insights derived from Google search trends has wider applicability. A trader can use Google search trends on top of historical volume trend analysis to predict monthly auto sales volume ahead of official data release. Based on the auto volume expectations, a trader can buy or sell a particular auto stock towards the end of the month and exit the trade later when the anticipated sales volume numbers are out.

However, one must also check whether the stock is in overbought or oversold zone before jumping on to trade. At the same time, one should note that sales data is not the only factor that can impact the stock price movement on the first or second working day of a month.

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