2024 and 2025 Watchlists and Signal-to-Noise Ratios
Thanks for viewing the Security Trading Analytics blog’s second annual post on watchlists. Last year’s watchlist post introduced watchlists as a means of tracking performance within a calendar year by grouping stocks and ETFs that drove performance within a calendar year, but watching securities can serve many additional roles.
For example, this year’s edition of the annual watchlist post presents cross-year performance changes for the tickers within a watchlist.
This second annual watchlist post shows how signal‑to‑noise ratios reveal which tickers maintain clear, consistent performance across years — and which ones don’t.
A signal-to-noise ratio computed in this post is a means of objectively assessing the strength of a trend amidst normal weekly volatility. This post demonstrates how easy it is to objectively assess signal-to-noise ratios for this use case.
The cross-year performance analysis technique demonstrated in this post can help you to regularly examine tickers with freshly emerging trends (e.g., new AI infrastructure names) or the re-emergence of perennial trends (e.g., gold during macro instability).
This year’s recommended watchlists include tickers from these categories: major market ETFs, Crypto-related securities, Semiconductors & AI, ETFs launched in the past four years, Data Center Infrastructure firms, as well as Gold & Silver ETFs.
Stocks Tracked in This Post
There are many criteria that you can use for selecting the tickers in a watchlist. Tickers of interest will often be those with exceptionally rapid growth in the recent past, but that criterion is not the only one for identifying profitable watchlist members. For example, you may want to include a Crypto category for stocks and ETFs related to the Cryptocurrency industry whether or not the members of this watchlist showed exceptional recent performance. A watchlist is a good tool for reminding you to check tickers from categories that are not currently trending but which have demonstrated profit-generating upticks in the past.
The following table itemizes watchlist categories along with their ticker symbols and security names. The first five watchlist categories are from the first annual watchlist post. The last two categories — namely, Data Center Infrastructure as well as Gold & Silver — are only tracked in the second annual watchlist post.
The first ticker, SPY, belongs to the Benchmark watchlist. The S&P 500 index consists of approximately 500 large‑cap U.S. companies spanning all major sectors and covering about 80% of the U.S. equity market by capitalization. This index and/or SPY are often used as a metric for assessing acceptable market performance. Investors seeking above average performance should avoid securities whose recent market performance falls below the index.
The next four tickers (TQQQ, SPXL, UDOW, and TNA) denote leveraged major market ETFs based on indexes that they follow. Each ticker points at a core slice of the US equity market — tech‑heavy growth, large‑cap, mega‑cap, and small‑cap stocks.
The next six tickers represent different segments of the cryptocurrency ecosystem from: large Bitcoin‑treasury holders like MicroStrategy to major exchanges like Coinbase, alongside pure‑play Bitcoin miners such as Hut 8 and TeraWulf, and exchange‑traded Bitcoin vehicles like GBTC and BITX that provide direct market exposure.
The Semis & AI watchlist members span the full AI and semiconductor stack — from leading AI software platforms like Palantir to dominant chip designers such as Nvidia and Arm, world‑class manufacturers like TSMC, emerging AI contractors like BigBear.ai, and powerhouse semiconductor suppliers like Broadcom.
These four members from the Data Center Infrastructure watchlist capture several critical layers of modern data center infrastructure — from Constellation Energy powering hyperscaler growth, to Vertiv supplying mission‑critical cooling and power systems, to Micron providing high‑performance memory for AI workloads, and Oracle delivering cloud infrastructure and enterprise compute.
The tickers in the Gold & Silver watchlist span the full precious‑metals landscape — from SPDR Gold Shares (GLD) and ProShares Ultra Gold (UGL) for core and leveraged gold exposure, to Direxion Daily Junior Gold Miners Bull 2× (JNUG) for high‑volatility gold‑miner leverage, alongside iShares Silver Trust (SLV) and ProShares Ultra Silver (AGQ) for unleveraged and leveraged silver exposure, rounded out by Global X Silver Miners ETF (SIL) representing the silver‑mining equity ecosystem.
Are 2024 and 2025 ETF Major Market Close Prices Correlated?
Before comparing signal‑to‑noise ratios across years (a major focus of this post), it’s helpful to understand how closely 2024 and 2025 price movements tracked each other. This section examines whether 2024 and 2025 close prices for ETFs based on major market indexes are correlated. The ETF major market tickers in this post include SPY, TQQQ, SPXL, UDOW, and TNA. While SPY serves as a benchmark ticker for evaluating other tickers, SPY is also based on the same underlying index as SPXL, and it is therefore a major market ETF along with SPXL.
To answer the question of whether and how much the 2024 close prices correlate with the 2025 close prices, a Google Sheets worksheet is populated with a pair of GOOGLEFINANCE functions with weekly close prices for the first 51 weeks of each year. Next, a Google Sheets correl function computes the Pearson correlation between the two sets of close values. The Pearson correlation value is squared to determine the percent of 2025 weekly close value variance that is accounted for by 2024 weekly close value variance.
The following two screen excerpts from a Google Sheets worksheet show the 2024 and 2025 close values returned by a GOOGLEFINANCE function for the SPY ticker. The GOOGLEFINANCE expression in the formula bar at the top of the first screen excerpt in cell A4 returns the first 51 weekly dates and close prices for 2024; 24 of the first 51 weekly values appear in the top screen excerpt, and the remaining 27 weekly values appear in the second screen excerpt. The GOOGLEFINANCE expression in cell D4 returns the first 51 weekly dates and close values for 2025. The expression is: =GOOGLEFINANCE(D3, "close", "2025-01-01", "2025-12-21", "weekly").
The Google Sheets correl function in cell G2 (=correl(B5:B55, E5:E55)) returns the Pearson correlation coefficient between the 2024 and 2025 SPY close values. The text in cell G3 (p < .001) indicates the correlation is statistically significant, meaning the relationship is different from zero at beyond the .001 probability level. Google Gemini was prompted to assess the statistical significance of the value returned by the correl function. The r squared value in cell H2 indicates that 64.8% of the variance in 2025 weekly close values is accounted for by the variance of 2024 close values.
The following table displays the Pearson correlation coefficient and r squared value for each of the five major market ETFs.
The next chart reveals a one-dimensional chart with r squared values for each ticker.
The SPY ticker has the largest r squared value, which indicates its 2025 close values corresponds most closely to its 2024 close values.
The UDOW, SPXL, and TQQQ tickers show an intermediate level of correspondence between their 2025 close values and their 2024 close values.
The TNA ticker has the weakest level of correspondence between its 2025 close values and its 2024 close values.
Computing Signal-to-Noise Ratios Across Tickers and Years
The signal-to-noise ratio is the average value (signal) of a set of observation values divided by the standard deviation value (noise) for a set of observation values. The larger the signal-to-noise ratio, the clearer is the signal value relative to the noise value across the observations. Within the scope of this post,
The signal is the average of the weekly return % values for a ticker,
The noise is the standard deviation of the weekly return % values for a ticker, and
The signal-to-noise ratio is the average of the weekly return % values divided by the standard deviation of the weekly return % values.
The following pair of screen excerpts show the layout of a Google Sheets worksheet for computing the signal-to-noise ratio for the SPY ticker based on weekly return % values during 2025. Comparable worksheets were developed for all the other tickers tracked in this post. The next section performs a top half analysis to discern which tickers have the clearest signal across both 2024 and 2025 as well as within 2025. Last year’s watchlist post reports signal values for tickers tracked just during 2024.
· Columns A and B show, respectively, the ending dates and close values for successive trading weeks during 2025. These values were computed via the GOOGLEFINANCE function demonstrated in the prior section. The dates are for the end of the first trading week in 2025 through the end of the last trading week in 2025.
Column D displays the return % value for the second week’s close relative to the first week close values through the last week’s close value relative to by the next-to-last week’s close value.
Row 2 in columns E, F, and G display the three elements of the signal-to-noise ratio for the SPY ticker
Cell E2 shows the signal value computed with the Google Sheets Average function,
Cell F2 shows the noise value computed with the Google Sheets Stdev function, and
Cell G2 shows the signal-to-noise ratio computed by dividing the value in cell E2 by the value in cell F2.
Signal-to-Noise Ratio Analyses Across and Within 2024 and 2025
Because this is the Security Trading Analytics blog’s second annual post on watchlists, we have two years of signal-to-noise values computed for the 24 tickers in both 2024 and 2025. This section begins with a comparison of two years of weekly signal-to-noise ratios and their components within 2024 and 2025.
The following two tables show the average and the standard deviation of the weekly return percent values as well as the computed signal-to-noise values for 2024 and 2025, respectively. The rows for each table are ordered by signal-to-noise values (Average Return %/Stdev Return %). There is a row in each table for each of the 24 tickers initially tracked in 2024. The top half of tickers in each table are highlighted. Several interesting points emerge from a comparison of the two tables.
The consecutively ordered ticker signal-to-noise values for 2024 are consistently greater than their 2025 counterparts. This confirms that ticker signal values relative to noise values were depressed in 2025 versus 2024. It is likely that the Liberation Day tariff announcements is a major contributor to the depressed signal-to-noise values in 2025 compared to 2024.
Despite the lower signal-to-noise ratio values in 2025, there are seven tickers which are particularly notable across the tables for both 2024 and 2025. These tickers in order of their appearance in the 2025 table are: SPY, PLTR, PTIR, TSM, WULF, SPXL, and NVDA. Each of these tickers occurs in the top half of both tables. This outcome underscores the ability of signal-to-noise values to identify top performing tickers.
During 2024, all Crypto category tickers were in the top 12 of 24 tickers on a signal-to-noise ratio basis.
During 2025, none of the Crypto category tickers were in the top 12 of 24 tickers on a signal-to-noise ratio basis. This outcome additionally underscores the ability of signal-to-noise values to identify poorly performing tickers within a year.
The signal-to-noise values in 2024 and 2025 for the SPY ticker especially demonstrate the ability of signal-to-noise values to balance average returns versus volatility. While 2024 was a period of large weekly average returns, 2025 was a period of more modest gains (and even some weekly average return losses). However, SPY has the same signal-to-noise value (.28) for both years. The stability of SPY ticker signal-to-noise ratio makes it a good candidate for a benchmark against other tickers across years.
As helpful as signal-to-noise ratio values are in accounting for ticker market performance, they do not unravel all there is to know about how much and when tickers do well. The following chart from Finviz.com displays the 2025 annual performance change for the 7 tickers in the top half based on signal-to-noise ratio values for both 2024 and 2025. Notice that SPY had a smaller performance change than any of the other 6 tickers. This chart highlights a divergence of outcomes between signal-to-noise values and overall performance change. When there is a divergence, always keep in mind that a trader’s account balance is solely affected by overall performance change.
During 2025 two new ticker categories were added to those tracked – one of these categories consists of enterprises related to gold and silver underlying prices. There are six tickers in this category: GLD, SLV, UGL, AGQ, JNUG, and SIL. Because the gold and silver tickers were not introduced until 2025, they do not appear in any of the preceding screen shots with signal-to-noise ratios.
The following table shows a full set of expanded tickers for 2025. The tickers are ordered by signal-to-noise values (Average Return %/Stdev Return %). Notice that the first six entries in the ticker list all belong to the Gold & Silver category. This order suggests that the Gold & Silver tickers were among the best performers for 2025. It remains to be confirmed whether Gold & Silver tickers will have outstanding performance metrics in 2026.
The Data Center Infrastructure watchlist category was introduced with the 2025 edition of Watchlist categories. The tickers in this category are: MU, CEG, VRT, ORCL. The Gold & Silver category always outperformed the Data Center Infrastructure category in terms of the Signal-to-Noise ratio—defined as (Average Return %/Stdev Return %) and nearly always outperformed the Data Center Infrastructure category in terms of Average Return %.
Concluding Comments
Watchlists remain a valuable tool for traders, analysts, and advisors because they provide a structured way to monitor performance across categories and across years. With two years of data now available, this second annual edition highlights how much insight can be gained by pairing watchlists with signal‑to‑noise ratio analysis.
For the major market ETFs tracked in this post (SPY, TQQQ, SPXL, UDOW, and TNA), weekly return patterns in 2025 closely followed those of 2024. All five ETFs showed statistically significant correlations between years, with SPY exhibiting the strongest correspondence. This reinforces SPY’s usefulness as a benchmark for evaluating performance across both tickers and years.
Most of the cross‑year comparisons in this post rely on signal‑to‑noise ratios. A key advantage of this metric is that it adjusts for differences in volatility, helping you avoid misinterpreting large year‑to‑year changes that may be driven more by noise than by true performance shifts. However, signal‑to‑noise ratios do not replace the importance of overall percent change. When the two diverge, only percent change affects a trader’s account balance.
For clients or traders who prioritize stability over maximum return, the signal‑to‑noise framework offers a practical way to identify tickers with consistent behavior. SPY’s identical signal‑to‑noise ratio in both 2024 and 2025 illustrates this stability clearly. The last analytical example in this post concludes by discussing how gold and silver ETFs maintain a high signal-to-noise ratio during periods of trade policy instability, outperforming other sectors when rapid shifts in tariff regulations trigger market-wide volatility.
This post concludes by examining how gold and silver ETFs maintained high signal-to-noise ratios throughout 2025. While trade policy instability may have contributed to early volatility, the strongest performance gains for many tickers in this category occurred later in the year. This pattern suggests that macro catalysts may initiate a regime shift, but sustained signal strength often reflects deeper investor rotation. The consistent clarity of these tickers — especially when compared to noisier categories like Data Center Infrastructure — reinforces the value of volatility-adjusted metrics for identifying stable performers.
What You Learned in This Post
- Major market ETFs show strong cross‑year consistency. SPY, TQQQ, SPXL, UDOW, and TNA all exhibited statistically significant correlations between 2024 and 2025 weekly returns.
- Signal‑to‑noise ratios reveal clarity that raw returns can hide. They adjust for volatility, making it easier to identify stable or unstable tickers.
- Seven tickers stood out across both years. SPY, PLTR, PTIR, TSM, WULF, SPXL, and NVDA all ranked in the top half of both tables.
- Crypto tickers reversed sharply. All were top‑half performers in 2024; none were top‑half performers in 2025.
- SPY is a reliable benchmark. Its identical signal‑to‑noise ratio (.28) in both years makes it a stable reference point.
- Gold & Silver tickers dominated 2025. All six members of the category ranked at the top of the expanded 2025 list.
- Data Center Infrastructure lagged. MU, CEG, VRT, and ORCL consistently trailed the Gold & Silver category.
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