Hey I have a data science background. This is good and entertaining fluff, but if you know what you're looking at, there are important things missing and some things that look good but don't make sense. I give him 4.8/5 for making it look important. For example: He has two time series (S&P500 and the bonds) and he compares them to many possible offsets. This is called cross-correlation analysis. It's a real thing, but it's also notorious for overfitting the data and showing spurious relationships if you misuse it like OP dies here. When you test many different offsets, you increase the probability of finding a high correlation *somewhere*, purely out of random chance. This is kind of like flipping a coin and getting heads 10 times in a row; it's impressive if you only flipped the coin 10 times, but much less exciting if you flipped it 10 million times. You were bound to get a 10-head steak at some point. An overfit predictor is one that performs very well on the historical data used to find it, but poorly on new, unseen data. If you select the single best lag based purely on the highest R-value from your historical test (precisely what OP did here), you risk overfitting to random noise that exists in the sample, but isn't truly predictive. And that's almost surely been done here and the validation should have been on showing that the model isn't overfit. To validate a model like that you wouldn't back-test (what OP does). Some things you could do are split the data into in and out of sample (e.g. make the model based on only the first X days in the series, and then judge it based on its ability to predict the data after day X). You should/could take steps to remove seasonality or trends within the time series first (which we already damn well know the stock market is seasonal, so him using untransformed values is most definitely increasing his calculated correlation). It would also be good to do bootstrapping to check statistical significance, instead of just p value. But it is very entertaining. OP probably also has a data background, to be knowing what to do to specifically torture the data this way.

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This is all well and good but I just want to know if the OSRS market similarly predicts the s&p.

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This is either the most autistic DD I've ever seen or actual genius level analysis The fact that you correlated fucking RuneScape bonds to the S&P with a 49 day lag and got statistical significance is honestly impressive. Like who even thinks of this stuff Now I'm wondering if we should be tracking WoW gold prices and EVE market data too. Virtual economies as economic indicators is such a weird concept but your data actually looks solid YOLO puts on SPY based on RS3 bond movements when

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Interesting, but I think there are a few econometric checks missing that could materially affect the conclusions. Most importantly: if any of this is based on price levels or smoothed price levels (e.g. 90-day moving averages), both series are almost certainly non-stationary. Correlations and cross-correlations between non-stationary series can look highly significant even when the relationship is spurious, unless you explicitly test for stationarity or cointegration. Related to that: - Using rolling averages introduces strong autocorrelation and overlapping observations, which inflates t-stats and p-values if not corrected. - The peak at a −49 day lag looks like it was selected after scanning many lags, that’s effectively multiple testing, so the reported p-value likely overstates significance. - Min–max normalization makes unrelated trending series visually align, which can be misleading. To really support a predictive claim, do: 1. stationarity tests (ADF/KPSS) and returns-based analysis, 2. lag selection done out-of-sample or with multiple-testing correction, 3. HAC / bootstrap inference, and 4. a clean out-of-sample forecast comparison vs a simple benchmark. Without those, it’s hard to rule out a constructed lead-lag relationship rather than a genuine signal.

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https://preview.redd.it/qwhxvza2he8g1.png?width=613&format=png&auto=webp&s=a1e4735974fbd7f67f3007911da8f4f09ce5c20e Been long as many as 15,000 shares starting at $5. Bought right after they crashed a rocket 2.25 years ago and some simp on CNBC said it was a good play. What did I know? Since then I have lightened my position 3 times (22, 47, 70). What I hold now is free & clear and I will ride until they are in the S&P, or buy SpaceX, whichever comes first. (Ha!)

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If you had invested $1,000 in the S&P 500 back in 1945, today you'd have a spot waiting for you at the cemetery

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D⬛⬛⬛⬛d T⬛⬛⬛p

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Have u seen stocks like SMCI and what happen after going into S&p

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Youre screwed already when they added it to the s and p, algos and hedge funds wont let it drop to much cause itll drag the index

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2 ways Old way. Buy and hold stocks that everyone knows about but has hidden upside. NVDA 3 years ago. Bought and only recently sold some. New way. I heard about RKLB 2 years ago here on WSB. Bought some. Did research. Space was going to be big. Watched interviews. Kept accumulating.  I've invested around 125k. Just hit 500k yesterday. Haven't sold a single share. Bought my first at 6. Bought some more at 50. I was in Tesla waaaaay early and sold after 100% increase. I learned if you have the one in 20 year company, you HOLD. Most of my investments are in s&p 500 so even if rocket lab failed I'd be fine. 10% of my portfolio was (since its way more now) for risky bets like rklb.

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