CNBC-Fast money

CNBC Fast Money show is a popular TV trading program. Our goal was to collect each trading recommendation and then evaluate them for efficacy and value.  We resolved to calculate value that can be harvested by viewers if they followed recommendations with a daily, weekly, and monthly horizon. My secondary goal as a student, was to aim to understand professional trading and reasoning behind each recommendation.  Taken to a reasonable extent, I hope that my investment skills improved based on observations of various trading and investing techniques represented on the show.

 

While we are still organizing the data and running analytics on efficacy of above mentioned recommendations, which will be completed next week, we identified several shortcomings.  First and foremost, traders are give out recommendations to enter a position in a security, but rarely mention when to close such positions.  The end result is therefore nebulous for most viewers.  We therefore decided to calculate results of all investment ideas with a predetermined horizon – 1 day, 1 week and 1 month.

 

On the show traders vocalize their own unique opinions. Their debates are aimed to offer audiences a subjective view for a security.  Hence, we calculated one day average, one week and one month profit and loss to demonstrate performance for each recommendation broken down by contributor.

We adjusted weight of each recommendation (with exception for options) to equal one another since traders do not mention which of their ideas should carry more weight in the portfolio.  This is the second shortcoming of the show.

We made an accommodation to include dividend yield when we analyze returns, although not the trading costs. Including those would have made traders performance worse off.

 

In terms of results – record for the best average one day return belongs to Stephen Weiss, who managed to earn on average an impressive 5.52% within a day of each investment.  However, if his investments were held for upto a week they would have resulted in a less impressive average weekly return of 0.14%.  The least impressive average 1-day return is Dan Nathan’s -2.4%. The best average one week return belongs to Jon Najarian, with 3.46% and the biggest loss is -3.95%, once again belonged to Dan Nathan.  Anthony Scaramucci earned the right for the best average one month return with a gain of 7.8% and the largest loss of -6.98% once again goes to Dan Nathan.

 

Next week we will go over results one more time, compare them to returns from S&P500, identify ways to improve accountability on the show and publish results.  Please bear in mind that due to a relatively short nature of our study – merely 9 weeks, these results may not be statistically significant or representative of the experience of  viewers who were with the show since before June 2013.

 

The two-month data collection effort was definitely detail oriented. One of the main difficulties of this process was to determine what sort of trade idea represented a valid trade recommendation. Frequently traders seemed to demonstrate a high interest in a security but that didn’t necessarily mean that they recommended buy or sell. Another challenge I have met with was to adjust weight option trades.  Due to their inherent leverage and much higher price volatility we had to make assumptions which may differ from traders’ weightings.

CNBC Fast Money

Our goal is to track daily trade recommendations offered by television program participants in order to estimate aggregate economic benefit to viewers who follow their investment advice.

 

We endeavor to record every trade recommendation that qualifies as a “trade” from Fast Money program. While most of the time this is a straight forward process, occasionally we come across of an incomplete or a vague statement – i.e. option spreads that do not specify maturity and strikes, or an advice where trade is conditional on another event.  In these cases, if we can’t estimate probable trade, we merely denote recommendation as incomplete and no longer include it in the project.

 

Our goal is to present unbiased results that will provide evidence to prove or disprove the value of the program to the end-viewers. We collect trading recommendations directly from Fast Money program live at 12pm-1pm and 5pm-6pm daily, record them on a spreadsheet, and calculate P&L.

In terms of learning outcomes – In addition to calculating realized and unrealized P&L, project is helping me bridge academic knowledge of finance with investment world of wall street, derivatives trading, and cross asset pricing dependencies.  But it doesn’t end here – as a basis for long-term wealth creation, I am learning the value of independent research, the staggering power of “the trend”, and an enviable ability of some accomplished investors at staying contrarian.