Category: Predictions

Brighter days ahead

As the dark and frustrating 2020 is winding down, I feel incredibly optimistic about 2021 and beyond. Part of it is my entrepreneurial nature that requires it and part of it is a number of recent developments that bring me hope and I love drinking hope for breakfast.

A number of recent readouts from SARS-CoV-2 trials, particularly those from Pfizer and Moderna, which use a novel mRNA platform, look efficacious and safe in the short-term. (Long-term safety can not be evaluated in rapid clinical trials but the FDA guidelines provide for long-term, post-licensure, safety monitoring.) As soon as these vaccines are made available to the general public and assuming no major safety issues are surfaced, I am getting vaccinated and resuming my pre-pandemic travel schedule.

In case you missed it, on May 30th we witnessed the first American manned space flight since 2011. I watched it in real-time that Saturday and then many more times with my son Andrei who just loves watching rockets being launched into space. Since then, watching launches on Saturdays became a Novik family tradition. Seeing Andrei’s eyes light up every time we do it, brings me an unreasonable amount of joy.

Two weeks ago I ordered my first Virtual Reality headset — Oculus Quest 2. I don’t love the Facebook login requirement but as a newcomer to the world of VR, I am completely blown away by how easily my senses are fooled and by the near-perfect rendering of 3D worlds. It is clear to me that VR is a major technological trend with ramifications far beyond gaming. I can’t wait to virtually sit in front of my family and friends all over the world and interact with them as if we are in the same room. The technology is not quite there to make the experience realistic but I have little doubt that it’s coming. (As a side note, the game Room in VR is nothing short of amazing.)

During the summer, we made a lot of progress at Generable with fitting large meta-analytic models for oncology drugs, and our abstract was accepted at SITC 2020, an immuno-oncology conference. This is a big deal for us as it represents the first publication outside of statistics journals. Most of the work was done by Jacqueline Buros and Krzysztof Sakrejda and is a culmination of our year-long research collaboration with AstraZeneca.

And if this is not enough, 2021 promises to be a much more sane US Federal Government with adults finally taking over and mitigating the Fifth Risk. No doubt countless problems remain (I don’t want to list them) but I am feeling lucky and optimistic.

2019 Predictions

Prediction is very difficult, especially if it’s about the future.

— Niels Bohr

2018 had turned the page and we are already completed approximately 0.27% of 2019. I don’t know about you but I feel like I am behind. So to procrastinate some more, here are my (silly) predictions for 2019.

  • Trump will remain president with P = 0.60. 2019 will no doubt be a tough year for Trump as the Mueller report will likely become public, but I am betting that Republicans will continue to support him and even though the impeachment in the house is quite likely, the removal from office is not so certain.
  • The market (SP500) will continue to be volatile with the VIX staying well above its historic average (~11) for most of the year with P = 0.70. If we are to believe the model, there is about 90% chance that SPX will be between 3,200 and 2,000 by the end of April or about 45% chance that it will be below its current level and above 2,000. I am more pessimistic and I will give it P = 0.60 that it will be below the current level of 2,500 by April.
SP500 Model Based Price Distribution
  • The UK will not exit the EU (no Brexit) with P = 0.60. This is purely based on my conversation with someone who lives in the EU and spends a lot of time analyzing European economies.
  • I recently bought some cryptocurrency (a tiny amount of BTC and ETH) so I can keep myself informed and also because everyone was aggressively selling. I am pretty bullish on crypto longer term, but less certain about the current crop of offerings, although BTC proved to be very resilient. My prediction for 2019 is that BTC will not recover and will stay under its highs with P = 0.90.
  • We will not find a cure for any cancers with P = 0.80, which is a reversal from my last year’s prediction, and the one I am hoping to lose. I like where the cancer therapies are going, but our understanding of the mechanism is still quite weak, the methods we use to evaluate their effectiveness are quite poor (but getting better), and I am not holding my breath for data mining technologies (also known as AI) making any breakthroughs in this space.
  • I selfishly hope that 2019 will be the year of Bayes. I would like to see more universities offering Bayesian courses at undergraduate and graduate levels (this one from Aki @ Aalto looks amazing, for example), more companies getting started using sound probabilistic approaches, and FDA and EMA moving closer to embracing the Bayesian paradigm (we are rooting for you, Frank). I have no idea how to measure this, so no specific predictions here.

How did I do on my 2018 predictions

On 1 Jan 2018, I made the following entry into my journal

  • Will Trump still be president? Yes. (P = 80%)
  • Will Mueller team link Russia to Trump: a) To Trump campaign yes (P = 60%); b) to Trump No (P = 70%)
  • Will Crypto continue to rise? Yes. (P = 60%)
  • Will the stock market end its rise? No. (P = 55%)
  • Will Republicans lose control of the house in November? Yes. (P = 75%)
  • Will there be a war with North Korea? No. (P = 95%)
  • Will the New York Times go out of business? No. (P = 85%)
  • Will we cure one specific type of cancer? Yes. (P = 60%)
  • Will there be at least one Bayesian-based company that will raise Series B? (P = 70%)

I also said that I would compute my gain/loss using a hypothetical payoff function: \(100*\text{log}(2p) \) if I am right and \(100*\text{log}(2 * (1-p)) \) if I am wrong, where p is the probability I assign to the event occurring. We could use any base for a log but base 2 is natural as it compensates at the notional value ($100) if the bet is made with probability 1. I will describe why this particular payoff function makes sense in another post. (The tacit assumption here is that I would have been able to find a counterparty for each one of these bets, which is debatable.)

  • Trump is still president: \(100*\text{log2}(2*0.80) = 68\)
  • Mueller linked Trump campaign to Russia. The word link was not defined. I think it is reasonable to assume that the link had been established, but I could see how if my counterparty was a strong Trump supported, they could dispute this claim. Anyway: \(100*\text{log2}(2*0.60) = 26\)
  • Mueller linked Trump to Russia. Same as above in terms of the likelihood of it being contested, but think I lost this bet: \(100*\text{log2}(2*0.30) = -74\)
  • Crypto did not continue to rise: \(100*\text{log2}(2*0.40) = -32\)
  • Stock market ended its rise: \(100*\text{log2}(2*0.45) = -15\)
  • Republicans lost control of the house in November: \(100*\text{log2}(2*0.75) = 58\)
  • Thankfully, there is no war with North Korea: \(100*\text{log2}(2*0.95) = 93\)
  • New York Times is still in business: \(100*\text{log2}(2*0.85) = 76\)
  • I am not sure what made me so optimimistic regarding the cure for one type of cancer. Currently, the most promising cancer therapied are PD-1/PD-L1 immune checkpoint inhibitors and there have been documented cases for people who become cancer-free after being treated with one of these drugs, but I think it would be too generous to say that we have cured one type of cancer. Perhaps more impressively, Luxturna will cure your blindness with one shot to each eye if a) you have a rare form of blindness that this drug targets and b) you have $850,000 to spend. \(100*\text{log2}(2*0.40) = -32\)
  • There were a few startups based on the Bayesian paradigm and Gamalon came close with a $20M Series A round, but none raised Series B to my knowledge: \(100*\text{log2}(2*0.30) = -74\)

To summarize, I am up $94. Is this good or bad? It depends. A good forecaster is well-calibrated and we do not enough here to compute my calibration. The second condition is that for the same level of calibration we prefer a forecaster that predicts with higher certainty, a concept known as sharpness. Check out this paper if you are curious.