If a random strategy is better than whatever the non-random strategy was then intuitively in most cases the inverse of the non-random strategy is better than the random strategy.
I am currently reading and would recommend "Bull!" by Maggie Mahar for a longer term view on the stock market than many people today seem to consider it on.
That sounds intuitive but does not follow. The strength of the random strategy is that it approximates the theoretical ideal - a maximally diversified market-cap weighted portfolio. The non-random strategy is too concentrated, and inverting that can leave you with another strategy that is also too concentrated compared to the ideal.
The problem isn't that the non-random strategy did poorly, it's that it has too much exposure to needless sources of uncertainty. The inverse strategy doesn't get rid of uncertainty, it just flips the sign of their exposure to that uncertainty.
The short answer is that hedge funds etc hire teams of math PhDs, rent out supercomputers, acquire non-public information like satellite imagery, and do various other hard activities in order to accurately price securities. Unless you think you can beat these players at this game, there's no way to pick better-than-average stocks.
The longer explanation is that you can break down the price effect of various real-world events into two axes. There are things that transfer market value between companies, and things that change the value of the market as a whole. For example, people buying more Macs instead of PCs will transfer market value from Microsoft (among others) to Apple. On the other hand, a novel form of efficient energy production will do more to increase overall economic activity.
The maximally market-cap weighted portfolio has diversified away the first axis of risk. Suppose company A, worth $500M, loses $50M of market cap to company B, worth $100M. If you own $500 of company A and $100 of company B, the $50 loss from owning company A stock is completely offset by owning $10 of company B stock. You're purely exposed to changes in the value of the economy as a whole, and have eliminated diversifiable risk from your portfolio.
If you can't benefit from taking on diversifiable risk, you're better off getting the same return at a lower level of risk by simply removing it.
Take a really simple portfolio... I buy (or go long on) 1 share of GOOG and 1 share of MSFT. Over a decade I make an average of a 7% return per annum, while the random strategy makes, 8%.
What exactly would the inverse of my strategy be? To go short on GOOG and MSFT? That would do even worse and in that case I'd actually lose money.
I am not at all confident that anyone can predict when it will come well enough to avoid it.
Remember that the last 3 months of 2018 saw the market drop ~20%, only for it to recover over the next 3. Were you able to sell in September and buy at the bottom in December? Almost certainly not...
Yes, our prop group made quite a bit with out-of-the-money AAPL 135 puts (AAPL will hit this strike price again).
Alarms bells are ringing in this market... If your good at risk management and can initiate low cost perma-bearish positions, you will be rewarded heavily over the next year or so.
Alpha chasers that don't have risk management skills should just buy Bitcoin (dont sleep on this, the halvening is May 2020-ish). Dollar-Cost-Averaging will get you a decent entry price.
Warning: Bought more 120,130,140 AAPL puts in prep for next collapse, if your in my way, I will literally eat your lunch.
> What exactly would the inverse of my strategy be? To go short on GOOG and MSFT?
No, it would be to take 1 share of every stock except GOOG and MSFT and it would work (well, under the given assumptions and with random = 1 stock of every pick).
The reason why this doesn't work for the average person is because they don't have the infrastructure for it or they don't trade enough to get the commissions down to make it profitable. Shorting comes with additional fees.
Lets say you always have two companies, A and B. Each year one of them triples in value, the other loses all its value, but the loser gets recreated so you can invest in it again. Each year you get to distribute your funds.
Your original strategy is to invest everything in A every year. But after doing some simulations, you see that your current strategy makes you lose all your money within a few years since A will sooner or later make you lose all your money. Should you invert your current portfolio and only buy B? Of course not, both stocks are equivalent in isolation!
The best strategy is to allocate your funds 50/50, that way you double every year. Allocating each part of your money randomly would get close to this, so is still way better than just picking one or the other. What is important is to find stocks whose risks doesn't correlate with each other, since correlated risks will cost you a huge amount in times of down-turns.
I'm not sure there implementation of inverse in stock strategies, but assuming the basis is returns proportional to market rate x, that logic doesn't really follow for all cases I think. If the random strategy has gains of 1x and the non-random strategy has a loss of -0.5x, then wouldn't the inverse just be a gain of 0.5x?
If the non-random strategy was “buy stocks A, B, and C”, the inverse might be “the broad market minus the stocks A, B, and C” rather than “short stocks A, B, and C”.
Hmm, let's call "buy nothing" a neutral strategy. Then a combination of “buy stocks A, B, and C” and “short stocks A, B, and C” has the same effect as the neutral strategy. But the combination of “buy stocks A, B, and C” and “the broad market minus the stocks A, B, and C” is equal to "buy everything" and has not the same effect as the neutral strategy.
You seem to assume "buy everything" to be the neutral strategy. Then everything plays out as you say.
That's true but still doesn't map here. Or at least, to my interpretation of it.
Stock-picking is specifically referring to where you should allocate a given amount if you are investing. There's obviously more to stocks than simply picking them, but as for picking specifically, there's no analog in poker because you don't divide your bet that way. (Aside from raising on a bluff, but that's stretching it).
Whether, when, and how much you should invest is a separate strategy that's related to your confidence and expected return of the picking strategy—but not the same thing.
Just like whether and when you agree to a game of chess vs tic-tac-toe might depend on your confidence in your skills; but that decision is _not_ relevant to chess strategy, which assumes you're already playing the game.
This still doesn't make sense to me. If the return of inverse strategy S' were greater the loss of strategy S, couldn't you then guarantee a positive return by using both strategies simultaneously?
You have a set of stocks either correlated with the U.S. economy or China's economy.
Amateurs tend to pick uptrending correlated stocks (all stocks trending up in the U.S.). When U.S. economy crashes, all their eggs are in the same basket.
If you'd tell the amateurs to pick the inverse, they'd go for downtrending Chinese stocks. When China's economy crashes, all their eggs are still in the same basket.
I think what you are looking for instead, is contrarian trading strategies. Here you follow counterstrategies to what the large herd is doing. A good contrarian strategy for buying bitcoin, may be to gauge crypto sentiment on HackerNews. If the majority is gleeful or pessimistic, the price is too low for future value, if articles get posted on how to build your own blockchain in Python, then you should be ready to start converting to money, because one month later, every smart nephew's uncle fomo-bought and panic-sold the hype and caused a crash or depression. Similarly, if the U.S. president is glowing about the heated economy, and dismissive of China, this opens up new profitable options for contrarian traders (which are less risky/more informed than completely random or following the herd).
Couldn't you phrase a strategy as a time series of binary decisions, e.g., of the type "buy/sell stock X at current time: yes or no"?
Then the inverse would be obvious. However, it's not immediately clear to me whether the claim holds that an inverse thus defined would perform better than the original strategy.
Does that work for the "no"s? I didn't buy or sell any stock yesterday; with that definition of "inverse", wouldn't I have to perform all possible actions? For any time, stock and amount, the inverse of inaction would be "yes, buy/sell X shares of stock Y."
Yeah there's just way too many possible ways to consider an inverse strategy. Even with your example, if my strategy is to buy GOOG at noon on Monday... Is my inverse strategy to buy 1 of every stock that isn't GOOG at Noon on Monday? Is the inverse to short GOOG? Is it to have already owned GOOG and instead sell it at Noon on Monday?
If your non-random strategy is buy GOOG and your 'random' strategy is buy shares of 5 companies chosen at random then inverse of your non-random strategy, buy everything except GOOG is better than 'random' because it's even more random.
One important caveat is that the non-random strategy would need to be invertible. For e.g. a ml model doing binary classification this is easy (switch the classification), for most other things it's not clear what invert means.
> the inverse of the non-random strategy is better than the random strategy
There is not one single non-random strategy. There are millions active at any moment. Which specific non-random strategy should we pick to assure success?
I am currently reading and would recommend "Bull!" by Maggie Mahar for a longer term view on the stock market than many people today seem to consider it on.