According to their website, Duff & Phelps “is the premier global valuation and corporate finance advisor with expertise in complex valuation” and other fields. According to their annual Valuation Handbook – Guide to Cost of Capital, most stock valuations react to market news with a lag.
Often, valuators use conclusions of this model to make meaningful decisions about pricing company risk.
If the model isn’t true, then we will need to rethink the way we value companies — likely in a way which increases the value of closely held businesses. They will systemically overstate the risk of publicly traded corporations and therefore understate the value of closely held firms.
If the model were true, one could utilize the predictions to almost effortlessly make untold fortunes in the stock market.
I’m publicly sharing the results of my research, so I imagine you can guess where I stand on the question.
I am not comfortable using the “sum beta” methodology to estimate the riskiness of an individual stock. Any observed difference between the normal beta and the sum beta is likely the result of statistical noise. Unfortunately, this invalidates the conclusions of the Duff & Phelps methodology. Their industry risk estimates are likely biased in an upward direction.
I didn’t want this to be the result. I wanted to find an easy way to consistently beat the stock market, but I’m going to have to keep looking.
I’m happy to announce a new series of pages: Analyzing Technical Analysis.
I frequently read people talking about Moving Average Crossovers, Bollinger Bands, Relative Strength Indexes and other indicators that allegedly allow traders to beat the market. The default algorithm at QuantConnect uses volume-weighted moving averges to beat the market. A few weeks ago, my Uber driver told me he uses Average True Ranges to trade the currency market.
It’s hard to believe all these strategies are being discussed if there’s nothing there. Then again, it’s also hard to believe a successful currency trader would drive Uber.
This series of posts is an attempt to rigorously and thoroughly check the effectiveness of technical analysis indicators through a series of iPython notebooks. In the notebooks, I’ll do my best to set my biases aside and objectively test the facts. In posts such as these, I’ll share my conclusions.
In the initial block of posts, I tested how returns react in the days after moving averages cross each other and when prices “bounce” off moving averages. (Click here for results and here for a guide to interpreting results.) In every strategy for every horizon, nothing close to statistically significant returns are observed. The strategies failed so spectacularly, I almost felt guilty about making my computer subset white noise so many times.
Regardless, it has been a good exercise. The past two weeks haven’t taught me how to beat the market, but I did finally have a good excuse to make the jump from R to Python, I learned how to perform bash scripting and I developed an objective way to test trading strategies.
With some luck, the next strategy I try will make me fabulously wealthy.