Posts from Stock market

Risk And Return

One of the most fundamental concepts in finance is that risk and return are correlated. We touched on this a tiny bit in one of the early MBA Mondays posts. But I'd like to dig a bit deeper on this concept today.

Here's a chart I found on the Internet (where else?) that shows a bunch of portfolios of financial assets plotted on chart.

Risk and return

As you can see portfolio 4 has the lowest risk and the lowest return. Portfolio 10 has the highest risk and the highest return. While you can't draw a straight line between all of them, meaning that risk and return aren't always perfectly correlated, you can see that there is a direct relationship between risk and return.

This makes sense if you think about it. We don't expect to make much interest on bank deposits that are guaranteed by the federal government (although maybe we should). But we do expect to make a big return on an investment in a startup company.

There is a formula well known to finance students called the Capital Asset Pricing Model which describes the relationship between risk and return. This model says that:

Expected Return On An Asset = Risk Free Rate + Beta (Expect Market Return – Risk Free Rate)

I don't want to dig too deeply into this model, click on the link on the model above to go to WIkipedia for a deeper dive. But I do want to talk a bit about the formula to extract the notion of risk and return.

The formula says your expected return on an asset (bank account, bond, stock, venture deal, real estate deal) is equal to the risk free rate (treasury bills or an insured bank account) plus a coefficient (called Beta) times the "market premium." Basically the formula says the more risk you take (Beta) the more return you will get.

You may have heard this term Beta in popular speak. "That's a high beta stock" is a common refrain. It means that it is a risky asset. Beta (another Wikipedia link) is a quantitative measure of risk. It's formula is:

Covariance (asset, portfolio)/Variance (portfolio)

I've probably lost most everyone who isn't a math/stats geek by now. In an attempt to get you all back, Beta is a measure of volatility. The more an asset's returns move around in ways that are driven by the underlying market (the covariance), the higher the Beta and the risk will be.

So, when you think about returns, think about them in the context of risk. You can get to higher returns by taking on higher risk. And to some degree we should. It doesn't make sense for a young person to put all of their savings in a bank account unless they will need them soon. Because they can make a greater return by putting them into something where there is more risk. But we must also understand that risk means risk of loss, either partial or in some cases total loss.

Markets get out of whack sometimes. The tech stock market got out of whack in the late 90s. The subprime mortgage market got out of whack in the middle of the last decade. When you invest in those kinds of markets, you are taking on a lot of risk. Markets that go up will at some point come down. So if you go out on the risk/reward curve in search of higher returns, understand that you are taking on more risk. That means risk of loss.

Next week we will talk about diversification. One of my favorite risk mitigation strategies.

#MBA Mondays

The Market Plunge

Yesterday the stock market dropped almost 1,000 points intraday before rebounding late in the day. Does this matter to the world of entrepreneurship and startups? Yes and No.

I’m no expert in the stock market but I read a bunch of experts blogs. I liked this from Steve Place:

High Frequency Trading broke, then saved the market

This will probably be the most controversial thing I’ll say. Quant firms have been keeping the market in a fairly low volatility state as they seek mean reversion and arbitrage strategies. By doing this they provide liquidity in the market for institutional players and funds. Their risk models are based on statistical distributions, behavioral finance, and other voodoo. When these models go out of wack, they can exacerbate the situation– that did occur in 2008 when liquidity dropped out of the system.

However, I feel that program trading (eventually) provided the liquidity for the snapback of this rally. If it weren’t for quants betting on extreme mean reversion, we would have held a much deeper selloff comparable to 1987. What evidence do I have of this? The sheer snapback of the price in such a short amount of time. It certainly wasn’t fundamental traders who all of a sudden found “value” in the market with a trailing P/E. The only sort of quick analysis that provides that kind of price action are done by non-humans at quantitative firms, and they saved the market from something much, much worse.

What Steve is saying is that computer driven trading drove the plunge and then drove the rebound. It was not human trading stocks that caused the price action. It was machines that had been programmed by humans.

There’s a lot of talk about machine to machine interaction coming into our lives. Yesterday afternoon at 2:45pm, we saw what that looks like. For the people who make their living trading in these markets, it was a sick feeling in their stomaches. For the rest of us, I don’t think this is too much of a big deal.

However, there are some big issues in the capital markets right now. From the bottom last April to the top a few weeks ago, the S&P 500 was up about 70% in a year. It was close to getting back to its pre meltdown high. Maybe the markets came back to far too fast. Its not like we are past all of our problems.

Money is cheap, too cheap. You can’t get a yield anywhere. As my friend Howard points out, junk bonds trade at 8%. Money is going to get more expensive soon. And that will not be good for the stock markets.

And then there’s the coming regulation of banks and brokers, which will likely put pressure on the stock markets. 

So what does matter to the world of entrepreneurs and startups is that stock markets may not have much more room to go up. I’ve been thinking that we are in for a long period of low public equity returns. I have no idea when that will happen but the macro environment just doesn’t look that great to me.

That doesn’t mean that you can’t make money with your startup and it doesn’t mean that you can’t make money in venture capital. The returns in startup land come mostly from taking nothing and turning it into something. If you take hard work, sweat equity, and a few million bucks of startup capital and turn that into a business producing $5mm a year of cash flow, then that is value creation of the old fashioned kind and it will work in any market environment.

But it also means to me that we should not be banking our business on the IPO exit. The public markets are a fickle thing. And it looks like machines are running that show now. I’m more optimistic about institutions turning to the private markets where capital is still traded by humans. I believe the secondary market where institutional private capital comes into the cap tables of startups and provides liquidity to founders, angels, and early stage investors is the next big thing for liquidity in the startup business and I am pleased to see that market continue to develop nicely.

So I don’t think the “crash of 2:45pm” as our friends from StockTwits are calling it matters much to those of us working in the world of startups, but it may be indicative of things to come (as markets tend to be) and it is worth figuring that part out.

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