Trading Algorithm Beats Analysts Again: The Tesla Motors Case

Algorithmic Trading Performance Review

On July 14th 2014 I Know First Algorithm predicted Tesla would show strong returns in the 3 months forecast. Investors who followed the 3 months forecast would have accumulated a decent return on the 14th of October of 4.09%. On September 14th 2014 I Know First Algorithm recommended to short Tesla in its 1 month forecast. Investors who followed that 1 month forecast would have shown a return of 18.67% in 1 month. However, the more experienced investor could have used both predictions to his advantage and maximized his returns to an impressive 45.83%. At the same time following the average analyst recommendation the investor would have lost 11%.


Analysts Position

July 14th 2014 – 3 Months Prediction

Previous to this date, Tesla has been aiming towards entering the British UK market and has sold its first Model S right hand drive in the UK. Analysts chose to avoid taking a clear position and recommended investors to hold their position.  8/14 analysts Yahoo Finance cited recommended a hold/sell position while 9/15 analysts MarketWatch cited recommended a hold/sell position.

September 14th 2014 – 1 Month Prediction

Previous to this date Telsa made it official: the electric car company’s highly sought battery “Gigafactory” and its projected 6,500 jobs are coming to Reno, Nevada. The Giant Diamond Shaped factory will be self-sustaining and is estimated to cost between 4-5 billion USD. The news got analysts excited, while on Yahoo 9/16 Analysts recommended to buy and 6 out of the remaining  7 recommended to hold, at MarketWatch 11/18 recommended to buy while 6 out of the remaining 7 recommended to hold.

Following their strategy a potential investor would yield the following:

Algorithmic Trading

As can be seen above the average investor would have lost 11% of his investment following the above strategy; however, although the most popular opinion was hold, many analysts did suggest a buy position on July 14th, which would have meant the investor would yield returns of 4.09% (Assuming he kept his position on September 14th); furthermore, an investor who already owned Tesla stock prior to July 14th would have had similar results. The most important conclusion of the graph above is the September 14th opinion, which on both Yahoo and MarketWatch strongly recommended a flawed buy position(the time period and recommendation is represented by the green line).

Yahoo Finance September 14th Recommendation: 11 Buy 5 Hold 1 Underperform

Market Watch September 14th Recommendation: 9 Buy 2 Overweight 6 Hold 1 Underweight

 I Know First Algorithm Position

July 14th 2014 – 3 Months Prediction        September 14th 2014 – 1 Month Prediction


Following I Know First Algorithms strategy a potential investor would yield the following:

Algorithmic Trading

The table above demonstrates the optimal trading strategy. By recommending a buy position on July 14th both investors who do not own the stock and investors who already own the stock would benefit from the 27.15% price increase during those two months (green line); furthermore, on September 14th the Algorithm recommends to short the stock, investors can benefit from the 18.67% drop  in price (Red line).  Another trading proposition would be to sell on September 14th and repurchase another strong positive signal stock from the forecast table. The above example demonstrates how the trading algorithm outperformed the average analyst recommendation by a significant margin.   This is an example of were a tentative investor could make huge profits on both his long and short positions at a very short period of time. Unlike an analyst who forecasts stock movement for the future trend, the Algorithm divides the future into 3, 7, 14 days and 1, 3, and 12 months forecasts allowing for a much clearer prospective on changes and trends in the market which are otherwise disguised.

Algorithmic Trading

I Know First Research is the analytic branch of I Know First, a financial start up company that specializes in quantitatively predicting the stock market. This article was written by Daniel Hai one of our interns. We did not receive compensation for this article, and we have no business relationship with any company whose stock is mentioned in this article.