Interpreting data analysis. Now, analysts tend to use a handful of theories or techniques, or studies to help them identify trends in stock price, for example. And in doing so, they use prior data, historical data, to help them predict future movement. And it generally falls under two different categories. First, technical analysis, second, quantitative analysis. Now, what are the differences? Well, technical analysis has been around since the 1800s, when Charles Dow wrote about his famous Dow Theory. And yes, it's the same Dow that created the origins of the Dow Jones Industrial Average. But unlike fundamental analysis, it doesn't care about the value of a company, but it's more interested around the price and volume movements. So essentially uses charts to make its investment decisions, and is looking for visible trends in those charts to guide them. Now, quantitative analysis is a lot newer of a method, and it tries to quantify price action into very precise equations or algorithmic equations. And it's so precise that it can be traded in split seconds and at very high frequencies by computer programs. You may hear the phrase algos or HFT for high frequency traders, that all falls under the quantitative analysis umbrella. Now, let's look at a couple of these techniques or theories in more detail. First of all, trend models. Trend models are a type of technical analysis. And it's based upon the observation that investors tend to act in herds and that trends tend to stay in place for some time. So they look for patterns in short-term charts to hopefully indicate a continued trend in the future. So for example, if they're looking for a potential uptrend, not only are they looking at simple trend lines, but they're also looking for patterns that show higher highs and higher lows. And essentially, they're trying to identify situations where demand for the stock exceeds supply for the stock, that would lead to an increase in price. And of course, vice versa on the short side. Mean reversion. Investors who believe in mean reversion believe that eventually a price will move back towards its long-term average. And this mean our average can be an historical average around price or return, or any other relevant average, really. And analysts believe that regardless of the current environment, that the price will come back to its mean. For example, if a price is above a certain average, it could be labeled overvalued and vice versa. Regression coefficients. Now, the most popular is Market Beta, which we probably all know. But it doesn't need to be limited to that. In fact, many factor-based funds and strategies use other regression coefficients in their investment process. And some examples would be coefficient around the size of a company, the valuation of the company, or the momentum in stock price. Moving on, seasonality. Seasonality is when investors try to find patterns in the calendar to identify an investment opportunity. The most popular being the January Effect. January Effect is a sense that stocks tend to do well in January, especially if they experienced losses in the prior year. Because these assets may have been oversold in the short term as some investors sold the asset for tax reasons. Smoothing past values. Also a very popular method. And instead of looking at strict averages, it's rolling moving averages that are the key component. The most popular, N moving average around price. For example, a 250 day moving average. But it doesn't need to be limited there. For example, the CAPE Ratio, which is a valuation ratio similar to Price-to-Earnings Ratio. Instead of using current earnings or expected future earnings, it takes a moving average over a 10-year period to calculate its valuation ratio. And random walk. Random walk is essentially the antithesis of these other theories. But followers of our random walk theory believe that it's impossible to outperform the market. And without assuming additional risk, anyway. So they focus on allocation based upon an investor's specific goals or risk tolerance. And they believe in strong market efficiency, which means that prices move randomly and we can't predict them.