In an exhaustive publication on the differences between investing and gambling, the CFA Institute concluded that "investing differs from gambling" but added that "high-risk, high-reward investments can look a lot like gambling especially, when investors don’t have the data they need to make an informed judgment". The quote served as the inspiration for this article.
Thanks to the rise of retail investor platforms, everyday citizens have access to high-reward investments whether they can "make an informed judgment" or not. All it takes is access to the Internet, a handful of clicks, and the boldness to take on big risks… In other words, gambling-like investment behavior has been normalized.
This has got me wondering: can the bankroll management tactics used by gamblers improve the outcomes of high-reward investments that don't necessarily take all data into account? If investing in high-risk assets "in the blind" is essentially gambling, this would make sense. But can the hypothesis be put to the test?
Using Slots to Simulate Extreme High-Risk Markets
While a large-scale study involving real high-reward markets would be ideal, it would also require large financing. This experiment takes on a humbler approach, using a digital platform to simulate extreme high-risk investments whose positive outcomes can be compared to return-to-player (RTP) figures on digital slot machines.
For the experiment, we will bet a budget of $25 in a slot machine using only intuition; then, we will do the same while conducting rigorous bankroll management. Finally, we will compare results to determine whether earnings are significantly superior for the second budget. If so, they should be useful for high-risk retail investors.
The experience was endorsed by a sweepstakes coins casino that offered us the 50 Sweep Coins we needed for free (equivalent to $50). Their experts suggested that the two budgets were used in a low-RTP slot game, as outcomes are closer to real-world investments'. We selected BGaming's Elvis Frog in Vegas, with 95.3% RTP.
Conducting the Experiment
For test #1, we prepared a limited budget of 25 SC and placed our bets based solely on intuition. These were the results:
Winnings with 25 SC: | 3.25 |
Balance: | -21.75 |
Number of rounds played: | 37 |
Average bet: | 0.75 |
Most common reward: | 0.20 (x6) |
For test #2, we employed tried-and-tested bankroll management tactics, following recommended guidelines:
- Capping single bets at 1% of the bankroll (meaning no bets over 0.30);
- Dividing the budget into five pieces of 5 SC each to adjust single bet value – if 5 SC doesn't cover at least 30 spins, the bet value must be reduced;
- Stopping as soon as the balance reaches a negative maximum of 10 SC, even if the totality of the 25 SC budget hasn't been wagered yet.
These were the results:
Winnings with 25 SC: | 17.75 |
Balance: | -10 |
Number of rounds played: | 122 |
Average bet: | 0.20 |
Most common reward: | 0.20 (x19) |
Chaos vs. Methodology in High-Reward Environments
The results of the experiment show that applying a bankroll management strategy when playing digital slot machines can improve outcomes by reducing losses, increasing winnings, and extending play time.
At the end of the day, the bankroll management guideline that forced us to stop after hitting a negative 10 SC balance was the most important, cutting our losses from -21.75 to a mandatory maximum of -10.
The goal of this experiment is not to provide a definitive answer to the discussed hypothesis or to introduce new scientific data but to serve as anecdotal evidence of the potential benefits of bankroll management for high-risk retail investors, inviting further investigation on the topic.
Sweepstakes Lessons for High-Risk Investors
Digital slot machines are extremely riskier than financial assets, regardless of their volatility, but our sweepstakes experiment offers insights that can be highly valuable for retail investors:
The next logical step for this experiment would be to apply bankroll management tactics at a much larger scale and in a simulated digital platform where investment outcomes are probabilistically closer to the real-world outcomes of specific groups of high-risk assets. For now, our sweepstakes experiment serves as food for thought.
