I’ve long believed that pop culture markets, and music markets in particular, are one of the best ways to bring new people into prediction trading. The Taylor Swift contracts proved that point better than anything else this year. And one market caught fire above the rest: Will Taylor Swift occupy all Top 12 spots on the Billboard Hot 100 dated October 18th, 2025? 

(Click here for music markets and Taylor-specific markets.)

Because it attracted huge volume and an active comment section, we can use it as a case study in how not to lose. And by that, I don't mean you'll never lose (we all do), but rather when you lose, you'll avoid errors that lead to even greater losses.

Follow Gaeten on X: @GaetenD

_{The opinions expressed are solely the author’s, a Kalshi user, not those of Kalshi Inc. or its affiliates, and do not constitute trading or investment advice. See full disclaimer below.}

This market was simple: would Taylor Swift take the entire top 12 on the Billboard Hot 100 the week The Life of a Showgirl was released?

The first step was to look at recent history. Taylor’s last two albums took the top 10 and then the top 14, so there was recent precedent for her doing it again. Next was to look at her competition. This year has been weak overall for pop music, but lately Golden from the KPop Demon Hunters soundtrack had been performing well.

The next step was analysis. Golden had been scoring in the 260–270 range in recent weeks and holding steady, so that range seemed like a reasonable benchmark. Looking at The Tortured Poets Department, we could see that the top 10 songs scored in the 260 range or above, while songs 11 and 12 were in the 230s. A repeat of that pattern would point to a “No” resolution.

But there was another key factor: The Tortured Poets Department had 31 songs, while The Life of a Showgirl had only 12, meaning that on a per-song basis, the average points would likely be much higher. Before the release, I had “Yes” as a heavy favorite, even though it was priced as an underdog. I loaded up on shares.

On release day, there was a lot of negative reaction to the album and the vibes felt completely off compared to her previous release. I began to have doubts about her sweeping the entire top 12 and took some profits. Then Apple Music updated with a dominant showing, and Spotify issued early performance news. “Yes” became the heavy favorite again. The following morning, my projections had 13 million streams as the cutoff for the lowest Taylor song to make the top 12, and Honey came in at 15 million. I loaded up once again, and the market was already over.

Over the rest of the week, the major projection accounts on Twitter backed up my initial calculations, showing an easy path to a full top 12 sweep. Yet right up until results were released, traders kept buying “No” shares by the truckload, to the point where I and several other whales ran out of open cash. When results dropped Monday afternoon, Taylor had swept the entire top 12. My payout was $10,690, capping off an extremely profitable start to the month and a clean sweep of Taylor markets.

While I came away a big winner on this one, many traders did not. The comments section tells the story of what went wrong, showing the mistakes that losing traders made and that I was able to avoid. Let’s go through them one by one.

Pricing at the extremes is difficult. Humans tend to assume that longshots are more likely than they really are, and as a result, traders often overprice them. This creates a double-edged sword of opportunity for those who can price these contracts correctly, and a trap for those who cannot. How can you avoid falling into the trap? Here are some tips for avoiding longshot bias:

Another weird aspect of trader psychology is that when you take a position and the price drops, the first instinct is to double down and invest more. If it was a great buy at $0.30, why wouldn’t it be an even better buy at $0.15? Unfortunately, when a price has been cut in half, it’s usually for a good reason, and throwing more money at it won’t help even if it temporarily improves your P&L.

If the price goes down after your purchase, try to find out why and come up with a new fair price. If it’s higher than the current price, then buy more. If it’s lower, you should think about selling. But don’t just assume that a lower price means a better opportunity. Often the best move is to accept the loss and move on.

In the Taylor market, if you went in knowing nothing and only relied on the comments to learn about it, you would have come away even dumber. There were so many easy errors repeated over and over.

For example, Billboard Hot 100 dates are weird. For the week of 10/3–10/9, the chart is dated 10/18 and the first results are released on 10/13. While this is a bizarre way of dating charts, it’s the same every single week and easy to find information about. Yet too many commenters got this part wrong and continued to do so even after the market had paid out.

Similarly, traders gave too much importance to radio and sales. Sales are negligible for most songs, and radio airplay is easily dwarfed by a strong performance on streaming. Commenters kept citing stats on the percentage of a song’s score that comes from sales versus radio versus streams, but that varies greatly by song. Older tracks get a higher share from airplay, while new album songs get almost none. Looking at the average tells you nothing.

These are just two examples that illustrate a broader point: the people you’re trading against know this stuff. If you want an edge, you need to know it too. Put in the time. Do the research. Only when you do that can you hope to compete.

A lot of the issues in the previous section come from the next common error: relying uncritically on artificial intelligence tools. AI tools, such as ChatGPT, can be useful if you know exactly what to ask, but they are terrible at applying their own logic to music markets. They don’t have access to daily Spotify data or historical Billboard Hot 100 charts, which are the best sources of historical information. As a result, they often hallucinate instead of providing useful insights.

For example, trader after trader commented that nobody had ever taken the entire top 12 on the Billboard Hot 100. That’s exactly what most AI tools would say if your prompt didn’t trigger a live web search, since they would be blind to Taylor’s 2024 album. But that album took the entire top 14. That’s a pretty important detail!

This isn’t to say that AI is useless in these markets. You just have to know what to ask and what types of information it can be relied on for. Release dates, track orders, singles, and features are all information that AI can reliably provide. But specific data on past charts or projections of future ones are weak points, and depending on them can lead to trading disasters.

One last takeaway from the comments is how contentious they became. Traders took offense at fact-based comments that contradicted their positions and reacted defensively, digging even deeper into misguided trades. When that defensiveness was paired with trading more on losing positions, it often spiraled into a full disaster.

While the comments section is often a mess, it can still contain useful information if you process it without emotion. Somebody disagreeing with your trade is not attacking you personally. Treat their input like any other data point: verify it, discard it, or use it. Do not let emotion decide for you. Every trader learns this eventually, and the ones who learn it sooner keep more of their money.

In summary, the five tips were:

Each of these lessons came straight from the Taylor Swift Top 12 market, but they apply to every prediction market. They show how hype, confidence, and overreaction can override reason. You will always lose sometimes, but losing well means losing with awareness and learning from it.

Learning those lessons after a loss is what will make you a great long-term trader, starting with the next big music markets, several of which are already available for trading.

The views and opinions expressed in this article are solely those of the author, a Kalshi user writing under an alias, and do not represent the views of Kalshi Inc. or its affiliates. This content is provided for informational and educational purposes only and should not be construed as investment, trading, financial, or legal advice. Trading on Kalshi involves risk and you should carefully consider your financial situation and consult with a qualified advisor before making any trading decisions. Past performance is not indicative of future results.