Stock market and PPC correlations?

Jonathon Van Clute, a good friend of mine, recently brought to my attention an old post I did on my WAY old site The New PPC. I was like holy sh*t. I did write this three years ago! This site was originally intended to help out newbs with PPC stuff back 3 years ago. I realized I had some pretty crazy posts in there about PPC and the stock market. I used to work with trading desks and trading systems years ago when I worked for da man, so I thought I would post them in this post because I’ll probably take down that old site soon. (I am a data FREAK) And I’ll probably think of a couple more crazy ideas on how to optimize and use great algorithms to make your campaigns more profitable. I’ll post those later. Enjoy!

Article 1

PPC Momentum (Day Trading Technique)

What is PPC Momentum?

When I measure momentum in my campaigns, I am looking at the price differences (bid values) for a set time period to measure the rate of rise or decline. For example, say we are looking at a zero line. It is steady at 0. The correlation of prices rising is that the momentum line is above the zero line and rising. This is a clear statement that the uptrend is increasing. Should the upward trending momentum line begin to flatten out, we know that the new gains of the latest leads/sales/or actions are the same as the gains of 10 days earlier. The prices may still be advancing, but the rate of rise (velocity) has leveled off. When the momentum line begins to drop downward toward the zero line, the uptrend in price is still going, but at a decelerating rate, and the trend is losing momentum.

If the momentum line moves below the zero line, this means the latest 10 day close is now under the close of 10 days ago and a near term downtrend is in effect. Also, the 10 day moving average will have started to decline. The lower the momentum lines goes, the more momentum (velocity) the downtrend gains. The downtrend’s momentum does not begin to decelerate until the momentum line begins to advance upward again.

Remember, momentum measures the differences between prices at two time intervals, so for the line to advance, the price gains of the latest close must be greater that the price gains of 10 days ago. A flat momentum line is created when prices by close by the same amount as 10 days ago. The momentum line declines when the last price gain is less than that of 10 days ago, even though the prices may still be rising. This is how momentum measures acceleration and deceleration in a price trend.

The way the momentum line is constructed causes it to always be one step ahead of the price movement. The line leads the advance or decline in prices, and levels off while the current price is still in effect. As prices begin to level off, the momentum line begins to move in the opposite direction.

You can use this technique to measure your PPC campaigns. In any competitive market you are going to see price shifts, not as often as the stock market, but often enough that you make judgments on your campaigns and adjust them accordingly. If you see an upwards spiral happening, and it hit where you were 10 days ago, you can use that data to lower or higher your bids automatically.

Article 2

We know how the stock market works and how trading systems should operate. The same methdologies can be used to manage your PPC campaigns and increase ROI. Below is some information for you.

1) Expectancy – In simple terms, expectancy is the average amount you can expect to win (or lose) per dollar at risk. Here’s the formula for expectancy:

Expectancy = (Probability of Win * Average Win) – (Probability of Loss * Average Loss)

As an example let’s say that a trader has a system that produces winning trades 30% of the time. That trader’s average winning trade nets 10% while losing trades lose 3%. So if he were trading $10,000 positions his expectancy would be:

(0.3 * $1,000) – (0.7 * $300) = $90

So even though that system produces losing trades 70% of the time the expectancy is still positive and thus the trader can make money over time. You can also see how you could have a system that produces winning trades the majority of the time but would have a negative expectancy if the average loss was larger than the average win:

(0.6 * $400) – (0.4 * $650) = -$20

In fact, you could come up with any number of scenarios that would give you a positive, or negative, expectancy. The interesting thing is that most of us would feel better with a system that produced more winning trades than losers. The vast majority of people would have a lot of trouble with the first system above because of our natural tendency to want to be right all of the time. Yet we can see just by those two examples that the percentage of winning trades is not the most important factor in building a system. So imagine each keyword being a stock and each stock has a certain price associated with it. If you payout is going to be $10 you know what you should be bidding and what your return will be. Make sure you always measure your campaigns.

2) Position sizing – A position sizing model simply tells you ‘how much’ or ‘how big’ of a position to take. Position sizing can be the key factor in whether or not you stay in the game or whether your gains are huge or minimal. Dr. Van K. Tharp did an experiment which shows the importance position sizing. In his book “Trade Your Way to Financial Freedom” Van gives the results of his testing of four different position sizing models. He tested the models on the same trading system, so the only variable was the position sizing. The simulations were run with an initial equity of $1,000,000 and took 595 trades over a 5.5 year period. The models produced drastically different results:

* The worst was the baseline model which just bought 100 shares of stock whenever a signal was given. That model returned $32,567 or 0.58% annualized.
* Fixed-amount model: This method traded 100 shares per $100,000 in equity. It returned $237,457 or 5.75% annualized.
* Equal leverage model: Each position in this model was 3% of the account equity. So at the start of the trial each position was $30,000. This method returned $231,121.
* Percent risk model: According to this model positions were sized such that the initial risk exposure was 1% of the account equity. So with $1,000,000 equity the initial risk would be $10,000. So if the initial stop on a trade was $1 the system would trade 10,000 shares. For an initial stop of 50 cents the system would trade 20,000 shares, etc. This model returned $1,840,493 or 20.92% annualized.
* Percent Volatility model: Positions were sized based on each stock’s volatility. The more volatile the stock the fewer shares are traded. For this trial positions were pegged at 0.5% volatility (initially $5,000 per position) – so if a stock’s average true range was $5 the system would trade 1,000 shares. This model returned $2,109,266 or 22.93% annualized.

You can see how important position sizing is by that simple experiment. Remember that’s the same trading system with the only difference being the size of the positions. Imagine if you used these techniques in your keyword campaigns. Better yet, imagine this being automated for you. Imagine something that would try out different types of campaigns, ad groups, keywords, bid prices, positions and ad text. It would measure everything and automatically adjust it to where your most profitable point is!

Tagged as: pay per click stock market, ppc, stock market, the new ppc