Hi Edward, you are right about most bets being unfavourably priced most of the times. That is the nature of markets. In games like horse betting, blackjack, roulette the payoff is just not in your favour. House always wins in the long run.
Everyone will agree that in a casino the payoff on the bets are priced by rational people and it is the gambler who is irrational in games like roulette. Yes people do win sometimes but that is just pure luck and Ole Peters work on Ergodicity, Ensemble and Time probabilities explain this beautifully.
Markets are beautiful in a way that one can do the groundwork beforehand on many bets and wait for the odds to come to you. The payoffs are ever-changing in the market system and sometimes, just sometimes the pay-offs are priced irrationally. Whether one has the wherewithal to wait for the payoffs to come in your favour is an edge in today’s high-speed world. That said, all sorts of people both make/lose money in the markets in all sorts of ways. Society underappreciates the role luck plays in its endeavours. I just believe some bit of work can position one in the direction of luck.
Data In the Sheets
I just provided the historical valuations in the sheets so that one can be careful buying at market highs. People who are interested in cyclical companies can also use the historic P/B to find optimum entry/exit points.
Thanks for the suggestion about the tools that can further automate this exercise. I will try and use them if I can. I exported the company excel files manually so there was no worry of crowding out the screener’s servers.
You are right about screener data not being always right, I don’t use it always to value companies. But most of the time the data is in the ballpark, and all valuation exercises should be range approximations anyway.
For historical PE/PB I used to use rate star for quick reference or just download the historical price data from the exchanges, sort out the annual, highs, lows and averages and use them with the data from annual reports or investor presentations. All inconsistencies in the data of the data provider get highlighted when one does a deep dive into the company through the actual company reports.
The majority of manual data input work is in the financial services industry, where the KPIs are customized. Even companies in the same sub-segment of the industry report the KPIs with different names. I doubt there is any way to automate this process of picking up the data from the company’s investor presentations and moving it to excel. The data is just not structured. If you have any inputs that can help with this, it will be greatly appreciated.
To sum it up, probability of finding good bets/investments reduces dramatically near market peaks when you could pick majority of new bets and be down in the medium term and vice versa in market bottoms.
If one believes this is like finding a needle in a haystack they can just do a simple momentum investing strategy believing that it is all in the price and outperform the index.
I have seen other strategies outperform the above strategy but yes there is a survivor-ship/luck bias here. Though the % of total people outperforming with momentum will be higher than the % in other stock-picking strategies. One can speculate on the quantum of out-performance.
To hedge against this luck factor one should ideally run both or more type of strategies in their portfolio. But only do as much as one can with full effectiveness.