From the course: Algorithmic Trading and Stocks Essential Training

Basics of stock markets

From the course: Algorithmic Trading and Stocks Essential Training

Start my 1-month free trial Buy for my team

Basics of stock markets

“

- [Instructor] It's important to understand that the character and the composition of the US stock markets has been changing over the last 20 years. In fact, despite the fact that the US stock market continues to rise in value, the stock market has been shrinking by another metric. The number of publicly traded firms, over time, has shrunk. The number of publicly traded firms peaked in the late 1990s at a little under 10,000. Ever since then, there's been a slow, inexorable decline in the number of publicly traded firms, as illustrated in this graphic. Now, why is all this important? Well, the reality is that because there are fewer companies out there, this leads to what we call greater market efficiencies. Market efficiency simply means that a stock already incorporates all known information into its price. That would mean that there are no stocks that are undervalued and no stocks that are overvalued. All of the prices out there are fair, given what we know at a particular point in time. That doesn't mean bad news can't happen in the future. Doesn't mean good news can't happen. But given all the information we have, stock prices are fair. Now, if there's fewer firms out there, well, that means there's more people paying attention per stock, right? If we have, say, 100,000 investors out there, as an example, and we have 5,000 stocks today, well, that means there's 20 investors, on average, paying attention to every stock. Versus if you had 10,000 stocks, there will be 10,000 investors per stock. Greater attention from investors means that it's gotten harder to pick stocks over time. Algorithmic trading is a partial solution to this problem. To illustrate this point, I want to show you a couple of tables from a recent academic paper, False discoveries in mutual fund performance: measuring luck in estimated alphas. If you're interested, I'd recommend you go and look up the paper online sometime. But these two charts are critical. What the top chart here illustrates is that as the number of mutual funds has risen, the average alpha or the average excess return for the typical mutual fund has fallen. In the early 90s, the average mutual fund out there was able to produce 50 to 100 basis points per year in excess returns, in returns over and above the stock market, for its investors. As we've gotten more and more mutual funds, competition is heated up, and today, as we see, alphas are actually negative. The average mutual fund today not only can't beat the stock market, they actually underperform it. That's what the second chart below shows. Panel A, Proportion of Unskilled and Skilled Funds, shows that 75.4% of the mutual funds out there have zero alpha. Of those with non-zero alpha, the remaining 24.6%, 24 of the 24.6 are unskilled. Meaning that they can't consistently produce positive alpha. In fact, only 0.6% of mutual funds out there can consistently produce positive alpha. This is why algorithmic trading is so important. It's gotten harder to be a mutual fund. It's gotten harder to use traditional stock picking methods. And so we need to turn to new alternatives, like algorithmic trading and quantitative investing.

Contents