Rejuvenation industry: will it dwarf the dot-com bubble?

About twenty years passed since Dr Aubrey De Grey started talking about his plan to put ageing under medical control. The idea of this computer scientist turned biologist is relatively simple. Ageing is the result of an accumulation of damage that the body does to itself as a result of the normal metabolism operations. We can't realistically expect to manipulate the metabolism in order to make it produce less damage (the traditional approach of biogerontology) without doing more harm than good, because metabolism is extremely complicated and we don't know nearly enough about it. And we also can't expect to attack the consequences of this damage accumulation (the traditional approach of geriatrics) with any significant success, because far too things go wrong all at the same time as we get old. So, he proposes a sort of "preventative geriatrics" approach that aims at repairing the damage in order to prevent it from reaching a pathological level. While the processes that produce the damage are extremely complicated, the damage itself is relatively simple: all of it can be classified in just seven broad categories. These categories are:

- cell loss/atrophy
- division-obsessed cells (basically cancer)
- death-resistant cells
- mitochondrial mutations
- intracellular waste products
- extracellular waste products
- extracellular matrix stiffening

In principle, all of these types of damage are potentially treatable. De Grey thinks we can find a way to do it, and in this way we would cure ageing and stay young indefinitely.

Most people called him mad. Admittedly, his bizarre look didn't help:

However, he didn't give up, and he founded a non-profit organization called SENS with a handful of other scientists in order to conduct scientific research inspired by his idea. Slowly, things started to change, and year after year his approch became more and more mainstream. Here is one of the many talks De Grey gave about his plan and the progress that have been done.

A few years ago, the magic happened: private investors became interested, and many startups were created. Some of them grew enough to go public. So now there is a growing industry in which you can easily invest. Should you? Let's give a better look at this.

Biotechnology startups have a huge high risk - high reward factor: the value of a stock can skyrocket if things go well, or go to zero if they don't, often very suddenly. The success or failure of a clinical trial can write the fate of a company almost overnight. If this is true in general, you can imagine how much this applies to an avveniristic and early stage market like the one focusing on rejuvenation. If this idea works, people who bet on it will be rich, no doubt. Of course, it could also fail, or at least take more time than expected. De Grey is very optimistic: he thinks we could be less than 20 years away from achieving robust human rejuvenation, and less than 5 years away to achieve robust mouse rejuvenation (which would serve as proof of concept for human rejuvenation, and therefore attract huge interest from investors, which would make the market skyrocket). However, even he admits that we might not have robust human rejuvenation for another hundred years.

Lots of speculative thinking so far. Let's get more down to earth and see how this market has performed so far. Here is a list of American public companies that, to the best of my knowledge, make up the bulk of this market:

- AgeX Therapeutics
- Alector
- Athersys
- Cohbar
- Denali Therapeutics
- resTORbio
- Scholar Rock
- Unity Biotechnology

Starting from November 29, 2018 (the day AgeX Therapeutics, which is the last of these companies to go public, was listed on the NYSE), the following graph shows what would have happened to one unit of wealth invested in each of these companies:

We can notice two things. The first one is that only two of these stocks now trade at a value higher than at the end of November 2018. The second is the very high volatility. Look for example at the vertical fall that the blue line experienced. That is what happened to the value of the resTORbio stocks after the company announced the failure of the Phase 3 trial for their main drug candidate.

So what if you had a portfolio with your wealth equally split among these eight stocks? Here is what would have happened to your money:

So, at a certain point in time, the losses would have amounted to half the value of the investment. This is a reminder of how risky this stuff is. However, in the last few months there have been some big gains, although there still is a 20% loss on the initial investment. It is also interesting to notice the low correlation with the main market indices, with the value of the portfolio going down while the S&P 500 and NASDAQ were breaking record after record. This is because the fate of these companies has little to do with the general state of the economy, and everything to do with the results of their clinical trials (and until results are announced, with how much investors trust these clinical trials will be completed successfully).

To sum it up, here is what I think. This is very high risk - high reward stuff. You don't want to invest a very big fraction of your savings into this because the risk of huge losses is just too big. However, it is also undeniably fascinating stuff. If this works, the world will never be the same again, and if you invested in it, you would be rich. So, if you have some money that you are willing to risk, you might give this a try. I would simply place an equal amount of money on each of the stocks in the rejuvenation industry, and then wait. It is especially important to not be emotional: you have to be ready to look at a -50% return without panic selling. Just buy and wait a few years. If things go as hoped, you'll celebrate on your yacht.

How to recreate the matrix of prices from the matrix of returns in R

It is very easy, in R, to obtain a matrix of returns starting from a matrix of prices. The opposite procedure, however, is slightly more complicated. Let's say you have a matrix of returns that starts at time t, and a vector of the prices at time t-1. How can you obtain the full matrix of prices that has been used to generate the matrix of returns? I'm going to illustrate you how to do it.

Let us start by generating some prices (5 periods for 3 assets), in order to put together a working example. We also store in a separate object the prices in the first period.

prices <- abs(matrix(rnorm(15,mean=0,sd=1),5,3))
prices_1 <- prices[1,]

We then compute the simple and log returns. 

simple_returns <- diff(as.matrix(prices))/prices[-nrow(prices),]
log_returns <- diff(log(prices))

Now, imagine you were only given the vector of prices at the first period and the matrix of simple or log returns, and you want the matrix of prices. If you were given simple returns, you can do it with this code:

prices_new1 <- rbind(prices_1,simple_returns)
for(i in 2:(nrow(simple_returns)+1)){
  prices_new1[i,] <- prices_new1[i-1,]+prices_new1[i-1,]*prices_new1[i,]
}
row.names(prices_new1) <- 1:nrow(prices_new1)

With log returns, the code is as follows:

prices_new2 <- rbind(prices_1,exp(log_returns))
for(i in 2:(nrow(log_returns)+1)){
  prices_new2[i,] <- prices_new2[i-1,]*prices_new2[i,]
}
row.names(prices_new2) <- 1:nrow(prices_new2)

As you can easily check, the objects prices, prices_new1 and prices_new2 contain exactly the same values.

An Introduction to Stock Investing with R: Second Edition

The second edition of my book is ready. It is available on the Kindle Store at the same price of the first edition. I thoroughly revised and expanded it, and it now features more in depth explanations where was needed, it provides more codes, and it uses an updated dataset. Don't miss this opportunity to learn how to invest on your own quickly, effectively and with minimum costs. And please, leave a review on Amazon if you liked the book!