Clear Indexes Ideas

The Equation

We are back in the throes of Earnings Season (I originally posted this for clients three months ago, it has been edited and updated) when public companies report vast amounts of data in a short period of time. They update earnings, revenues, balance sheets and other key data points (~6,800 data points per public company, not just earnings), and they frequently provide guidance on the next quarter for revenue and earnings.

Traders and analysts have seconds to analyze the data and compare it to their projections. This is because of Regulation FD, for fair disclosure, where everyone (Wall Street analysts and traders, Clear Asset Management and individual investors) all receive the data simultaneously. Mass media and dynamic markets enable immediate reaction; TV commentators expound their views, and traders and investors make instantaneous decisions. This moves stocks up or down, with intense increases in volume, and very rapidly.

What does Clear Asset Management do with all of this data, over 20 fof our firms reported this week?

In simplified terms, our equation is: F + SP = FV; translated this means: fundamentals plus stock price equals fair valuation. Our opinion is that the markets are rarely efficient making this equation out of balance in the short term, and that is where opportunity presents itself. When the equation is off kilter, according to our investment algorithms, we buy or sell.

To make this more concrete, we will illustrate with two examples. The first is a Growth stock which our algorithms may require us to sell. Suppose that a company reports earnings that are still growing strongly, but not as fast as they have over the previous four to six quarters. Its current P/E (price to earnings) ratio is relatively high, and its PEG (price to earnings to growth) ratio is average. An important scoring point in our ranking system includes valuing the stock based on its growth rate. When growth is no longer being sustained, its fair valuation as a growth stock requires re-evaluation and its rankings, based on our proprietary algorithms may fall, triggering a sale.

Here is a Value stock example that may require us to buy: a company reports strong numbers, but they do not quite meet Wall Street expectations and the stock price falls in reaction. This may lower its P/E ratio, which when balanced with its debt, cash flow and other key measures, may cause the firm to rise in our rankings. At that point, our algorithms may compel a purchase at the new lower price.

Many quants use similar methods and I choose to describe ours simply because of familiarity. Each firm and practitioner will claim to have their own twist. We don’t. Everyone here does it the exact same way, otherwise our math would be off.

Pause and remember:
Today, in 1983, President Ronald Reagan signed a bill establishing a federal holiday on the third Monday of January in honor of civil rights leader Dr. Martin Luther King Jr.

Trading Versus Investing in an Algorithmic World

I have so many conversations with investors about this topic. Once briefly explained, investing versus trading are strategies easily differentiated. However, digging deeper into either strategy can be a lifetime pursuit.

Algorithmic trading and investing have a common goal; making money. The managers who pursue these strategies are both frequently labeled as quantitative investors or “quants.” How quants go about achieving their goal is similar in that they both involve advanced mathematics and computers. They both involve trading securities. The similarities end there.

First let’s look at trading:

Dictionary.com offers these definitions for trading:

1. the act or process of buying, selling, or exchanging commodities, at either wholesale or retail, within a country or between countries: domestic trade; foreign trade.

2. a purchase or sale; business deal or transaction.

In stocks, the concept of trading is to exchange something one owns for something one doesn’t own, with the goal of making money on the way out of the trade and getting a good value on the new way in to the trade. This works for both longs and shorts.

A typical way to describe algorithmic trading is a “black box” concept driven completely by math. It is usually not defined in great detail to investors which give it a certain mystique. It frequently involves spread arbitrage, relative value or basket trades at pre-defined support points. Each of these strategies needs to be thought of as ways of both buying and selling, and frequently in short time intervals. Each strategy is different, and can trade within seconds, minutes, hours or a day or two.

Algorithmic investing also involves crunching numbers on thousands of companies. The difference is that algorithmic investing examines the fundamentals, the financials. This is the 6,000 or so publicly published numbers on the ~10,000 public companies or a defined subset of these companies. This data is published by the public companies, filed with the SEC, verified by their accounting firms and publicly disclosed as earnings reports. More on these disclosures and how they are evolving in future posts. Many algorithmic investing strategies then examine valuation measures such a P/E (price to earnings) and PEG (price to earnings to growth) and compare them to various benchmarks. These may include their sub-sector, sector, style, broader benchmark such as the Russell 2000 or S&P 500 and then make a judgment; buy, sell or hold.

Investors as a rule, have fewer transactions sometimes going days without making a trade. This is how they earned their labels; investor and trader. Each strategy appeals to certain types of investors and is of no consequence to others.

Many institutions avoid quants fearing the unknown or the typically unexplained. All strategies can be explained. Can a manager accomplish this without giving away their secret sauce? This is a balance between the marketing and investment teams. I believe clear explanations are achievable and necessary yesterday, today and in the investing world of tomorrow.