arxiv: 2605.03460 · v1 · submitted 2026-05-05 · 💻 cs.AI · cs.LG

Recognition: unknown

FinSTaR: Towards Financial Reasoning with Time Series Reasoning Models

Seunghan Lee , Jun Seo , Jaehoon Lee , Sungdong Yoo , Minjae Kim , Tae Yoon Lim , Dongwan Kang , Hwanil Choi

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Soonyoung Lee Wonbin Ahn

Authors on Pith no claims yet

Pith reviewed 2026-05-07 16:47 UTC · model grok-4.3

classification 💻 cs.AI cs.LG

keywords financial reasoningtime series reasoningchain-of-thoughtbenchmarkstock analysisscenario-aware prediction

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The pith

A 2x2 taxonomy of time series capabilities with tailored chain-of-thought strategies enables 78.9 percent accuracy on financial reasoning tasks from S&P stocks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that general time series reasoning models fail in finance because they do not separate deterministic assessment of observable current states from stochastic prediction of uncertain futures, nor single-entity from multi-entity views. It builds FinTSR-Bench as ten tasks drawn from S&P stock data and trains FinSTaR with Compute-in-CoT, which derives answers directly from raw prices on assessment tasks, and Scenario-Aware CoT, which generates multiple scenarios before judgment on prediction tasks. The result reaches 78.9 percent average accuracy while outperforming both LLM and standard TSRM baselines. Joint training across the four categories shows they are complementary and reinforce one another, and Scenario-Aware CoT beats ordinary chain-of-thought on prediction accuracy. A sympathetic reader would care because financial decisions require exactly this distinction between what can be computed from data and what must be reasoned about under uncertainty.

Core claim

FinSTaR shows that crossing single-entity versus multi-entity analysis with current-state assessment versus future prediction yields four mutually reinforcing capability categories. When instantiated as ten financial tasks and trained with Compute-in-CoT for deterministic assessment and Scenario-Aware CoT for stochastic prediction, the model substantially outperforms LLM and TSRM baselines on FinTSR-Bench. Joint training across categories improves results, and Scenario-Aware CoT consistently raises prediction accuracy over standard chain-of-thought.

What carries the argument

The 2x2 taxonomy of single-entity versus multi-entity crossed with current-state assessment versus future prediction, realized through Compute-in-CoT that derives answers programmatically from raw prices on assessment tasks and Scenario-Aware CoT that explores diverse scenarios before prediction on stochastic tasks.

If this is right

The four capability categories are complementary and mutually reinforcing when trained jointly.
Scenario-Aware CoT improves prediction accuracy over standard CoT across the relevant tasks.
Compute-in-CoT enables direct, error-reduced answers on all deterministic assessment tasks.
The resulting model substantially outperforms both general LLMs and existing TSRM baselines on the benchmark.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same taxonomy could be tested in other mixed-deterministic-and-uncertain domains such as energy load forecasting or patient vital-sign monitoring.
Scenario generation before prediction may transfer to any high-uncertainty time series task even outside finance.
Whether the performance gains hold on live market data or non-S&P assets remains an open extension of the benchmark results.

Load-bearing premise

The ten tasks constructed from S&P stock data adequately capture the distinctive challenges of financial reasoning, and the deterministic-versus-stochastic distinction is the primary reason current models underperform.

What would settle it

A standard time series reasoning model or LLM without the taxonomy or the two specialized CoT strategies achieving 78.9 percent or higher average accuracy on the same ten FinTSR-Bench tasks would falsify the necessity of the proposed approach.

Figures

Figures reproduced from arXiv: 2605.03460 by Dongwan Kang, Hwanil Choi, Jaehoon Lee, Jun Seo, Minjae Kim, Seunghan Lee, Soonyoung Lee, Sungdong Yoo, Tae Yoon Lim, Wonbin Ahn.

**Figure 1.** Figure 1: Capability taxonomy for TSRMs view at source ↗

**Figure 2.** Figure 2: Qualitative comparison. Two simplified examples where only FinSTaR answers correctly. Baselines either fail to compute precise quantities (Volatility Regime) or default to heuristic reasoning (Event Response), while FinSTaR’s CoT produces grounded, step-by-step reasoning. Gray italic comments (▷) are editorial annotations, not model outputs. spectral symbolization to bridge TS and language representations.… view at source ↗

**Figure 3.** Figure 3: Two CoT strategies (simplified). Assessment tasks use Compute-in-CoT that extracts and computes quantities from observable prices. Prediction tasks use Scenario-Aware CoT that generates base/adverse/favorable scenarios before making a judgment. Full examples are in Appendix D. Scenario templates. For each (task, answer) pair, we define domain-specific scenario templates grounded in financial knowledge. For… view at source ↗

**Figure 4.** Figure 4: Taxonomy dependency analysis. (a) Each category is essential to its own tasks: removing AS causes the largest assessment drop (−46.8%) and removing PS causes the largest prediction drop (−15.9%). (b) Joint training across all four categories consistently outperforms solo training, indicating that the categories are complementary and mutually reinforcing view at source ↗

read the original abstract

Time series (TS) reasoning models (TSRMs) have shown promising capabilities in general domains, yet they consistently fail on financial domain, which exhibit unique characteristics. We propose a general 2x2 capability taxonomy for TSRMs by crossing 1) single-entity vs. multi-entity analysis with 2) assessment of the current state vs. prediction of future behavior. We instantiate this taxonomy in the financial domain -- where the distinction between deterministic assessment and stochastic prediction is particularly critical -- as ten financial reasoning tasks, forming the FinTSR-Bench benchmark based on S&P stocks. To this end, we propose FinSTaR (Financial Time Series Thinking and Reasoning), trained on FinTSR-Bench with distinct chain-of-thought (CoT) strategies tailored to each category. For assessment, which is deterministic (i.e., computable from observable data), we employ Compute-in-CoT, a programmatic CoT that enables models to derive answers directly from raw prices. For prediction, which is inherently stochastic (i.e., subject to unobservable factors), we adopt Scenario-Aware CoT, which generates diverse scenarios before making a judgment, mirroring how financial analysts reason under uncertainty. The proposed method achieves 78.9% average accuracy on FinTSR-Bench, substantially outperforming LLM and TSRM baselines. Furthermore, we show that the four capability categories are complementary and mutually reinforcing through joint training, and that Scenario-Aware CoT consistently improves prediction accuracy over standard CoT. Code is publicly available at: https://github.com/seunghan96/FinSTaR.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper introduces a finance-specific 2x2 taxonomy and two tailored CoT variants with a new benchmark, but the gains on stochastic prediction tasks rest on unclear correctness definitions that could be benchmark artifacts.

read the letter

The main thing here is a 2x2 taxonomy that splits time series reasoning into single-entity versus multi-entity and current-state assessment versus future prediction, then applies it to finance with Compute-in-CoT for the deterministic assessment tasks and Scenario-Aware CoT for the stochastic prediction ones. They built FinTSR-Bench from ten tasks on S&P stocks, trained FinSTaR on it, and report 78.9 percent average accuracy that beats LLM and TSRM baselines, plus evidence that joint training across the four categories strengthens performance and that Scenario-Aware CoT improves on standard CoT for predictions. Code is released, which is helpful for checking the details. They do a good job motivating the CoT choices from how analysts actually handle uncertainty and showing that the categories are complementary rather than redundant. The Compute-in-CoT approach fits the deterministic side cleanly since answers can be derived directly from observable prices. The soft spot is the one flagged in the stress-test note. Prediction tasks are inherently uncertain, yet the paper gives no operational definition of what counts as correct, such as whether labels come from short-horizon realized values, directional thresholds, or low-variance regimes. Without those rules or baseline implementation details, the accuracy lift and the CoT improvement could partly reflect how the tasks were constructed instead of the reasoning method itself. No statistical tests are mentioned either. This work is for researchers building or adapting reasoning models for finance or other domains with mixed deterministic and uncertain elements. A reader looking for concrete domain-tailored CoT examples and a ready benchmark would get practical value from it. It deserves peer review because the taxonomy and benchmark are new, the code is public, and the core ideas hold together even if the evaluation needs more scrutiny on label generation and controls.

Referee Report

2 major / 2 minor

Summary. The paper introduces a 2x2 capability taxonomy for time series reasoning models by crossing single-entity vs. multi-entity analysis with assessment of current state vs. prediction of future behavior. It instantiates the taxonomy in finance as FinTSR-Bench, a benchmark of ten tasks derived from S&P stock data. The authors propose FinSTaR, trained using Compute-in-CoT for deterministic assessment tasks and Scenario-Aware CoT for stochastic prediction tasks. They report that the model achieves 78.9% average accuracy on FinTSR-Bench, substantially outperforming LLM and TSRM baselines, that the four capability categories are complementary and mutually reinforcing under joint training, and that Scenario-Aware CoT consistently improves prediction accuracy over standard CoT. Code is released publicly.

Significance. If the benchmark tasks and accuracy metrics prove robust, this work provides a structured taxonomy and practical CoT strategies that address why general TSRMs underperform in finance, particularly the deterministic-stochastic distinction. It contributes a new benchmark, evidence for capability complementarity, and reproducible code, which could guide future development of domain-adapted reasoning models in financial AI.

major comments (2)

[§3 (FinTSR-Bench construction)] §3 (FinTSR-Bench construction): The operational definition of accuracy for the stochastic prediction tasks is underspecified. The manuscript notes that prediction is inherently stochastic due to unobservable factors, yet provides no explicit rules for labeling correctness (e.g., directional thresholds on returns, tolerance bands, or handling of volatility). This is load-bearing for the central 78.9% accuracy claim and the asserted benefit of Scenario-Aware CoT, as gains could arise from benchmark design choices rather than improved reasoning under uncertainty.
[§5 (Experiments)] §5 (Experiments): Implementation details for the LLM and TSRM baselines, including prompt formats, fine-tuning procedures, and any hyperparameter choices, are not provided. Without these, it is impossible to assess whether the reported outperformance is attributable to the proposed taxonomy and CoT strategies or to differences in baseline setup.

minor comments (2)

The abstract and §5 would benefit from reporting the number of examples per task category and any statistical tests (e.g., significance of accuracy differences) to allow readers to gauge the scale and reliability of the results.
Notation for the four capability categories could be introduced earlier with a compact table summarizing task examples, making the taxonomy easier to follow before the detailed task descriptions.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We appreciate the referee's thorough review and valuable suggestions for improving our paper. Below, we provide point-by-point responses to the major comments. We commit to revising the manuscript accordingly to address the concerns raised.

read point-by-point responses

Referee: [§3 (FinTSR-Bench construction)] The operational definition of accuracy for the stochastic prediction tasks is underspecified. The manuscript notes that prediction is inherently stochastic due to unobservable factors, yet provides no explicit rules for labeling correctness (e.g., directional thresholds on returns, tolerance bands, or handling of volatility). This is load-bearing for the central 78.9% accuracy claim and the asserted benefit of Scenario-Aware CoT, as gains could arise from benchmark design choices rather than improved reasoning under uncertainty.

Authors: We thank the referee for highlighting this important point. The accuracy for stochastic tasks is computed by comparing the model's final judgment (after scenario generation) to the actual future stock performance in the S&P dataset, which serves as ground truth. However, we agree that the specific rules for determining correctness (such as thresholds for directional changes or handling of small movements due to volatility) were not explicitly detailed in the original manuscript. In the revised version, we will expand §3 to include a clear operational definition of accuracy for these tasks, specifying any thresholds, tolerance bands, and volatility handling used in labeling. We will also add examples and pseudocode for the evaluation process in the appendix to ensure full transparency and to better substantiate the benefits of Scenario-Aware CoT. revision: yes
Referee: [§5 (Experiments)] Implementation details for the LLM and TSRM baselines, including prompt formats, fine-tuning procedures, and any hyperparameter choices, are not provided. Without these, it is impossible to assess whether the reported outperformance is attributable to the proposed taxonomy and CoT strategies or to differences in baseline setup.

Authors: We acknowledge that the implementation details for the baselines were insufficiently described. In the revised manuscript, we will add a new subsection (or appendix) in §5 that provides complete details on all LLM and TSRM baselines. This will cover the exact prompt formats, fine-tuning procedures (including learning rates, number of epochs, batch sizes, and optimizer settings), and all hyperparameter choices. Where we followed standard settings from prior work, we will cite them explicitly and note any modifications. These additions will enable full reproducibility and allow readers to confirm that the reported gains are due to the proposed taxonomy and CoT strategies. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical claims rest on independent benchmark evaluation

full rationale

The paper defines a 2x2 taxonomy, instantiates it as ten new tasks on S&P data to form FinTSR-Bench, and trains FinSTaR with Compute-in-CoT for deterministic assessment tasks and Scenario-Aware CoT for stochastic prediction tasks. These CoT choices are motivated by domain principles (programmatic computation for observable facts; scenario generation for uncertainty), not reverse-engineered from accuracy numbers. Reported gains (78.9% average, complementarity via joint training, Scenario-Aware improvement) are measured on the constructed benchmark using standard accuracy; no equations, fitted parameters, or self-citations reduce the central claims to tautologies or inputs by construction. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central performance claim rests on the assumption that the newly defined tasks and CoT templates capture the essential financial reasoning gap; no new physical entities or free parameters beyond standard training hyperparameters are introduced.

axioms (1)

domain assumption Chain-of-thought reasoning can be specialized into programmatic computation for deterministic tasks and scenario enumeration for stochastic tasks
Invoked when the authors define Compute-in-CoT and Scenario-Aware CoT as the appropriate strategies for the two prediction regimes.

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Identify the highest price in the given data
[37]

Calculate the difference between the high- est price and the current price
[38]

From the data provided, the highest price is 17 [...] TimeOmni-1(B)✗ [think] Step 1: Identify the recent peak - The data covers 120 trading days

Determine the percentage decline based on the highest price. From the data provided, the highest price is 17 [...] TimeOmni-1(B)✗ [think] Step 1: Identify the recent peak - The data covers 120 trading days. - The highest value in the series is 170.00 (last value), which is at index 119. - The previous day’s price is 168.75 (index 118). - The day before th...
[39]

- The highest value in this list is 211.53, which occurred on the last day of the pro- vided data

Identify the Recent Peak Price: - The data provided shows the most recent 120 trading days’ closing prices. - The highest value in this list is 211.53, which occurred on the last day of the pro- vided data
[40]

- The previous high before this was 211.47 (second-to-last value), so the most recent peak is at or very close to the last value

Determine the Curren [...] TimeOmni-1(A)✗ [think] Step 1: Identify the most recent peak - The data shows a clear upward trend with some fluctuations, but the highest value in the series is at the end: 211.53 (last value). - The previous high before this was 211.47 (second-to-last value), so the most recent peak is at or very close to the last value. Step ...
[41]

- The highest value in the list is 334.55, which occurred on one of the last few days

Identify the Recent Peak Price: - The data provided shows the most recent 120 trading days’ closing prices. - The highest value in the list is 334.55, which occurred on one of the last few days
[42]

- The highest value in the series is 334.55 (at index 95)

Determine the Current Price:* [...] TimeOmni-1(B)✗ [think] Step 1: Identify the recent peak - The data shows a clear upward trend with a few dips and rises. - The highest value in the series is 334.55 (at index 95). - The last value is 310.77 (index 119), which is lower than the peak but still relatively high. Step 2: Estimate the current price - The last...
[43]

Calculate the daily returns for each day
[44]

Compute the standard deviation of these returns for the last 20 days (recent volatility)
[45]

- Compute the standard deviation (sigma) of these 120 values

Compute the standard deviation of these returns for the [...] TimeOmni-1(A)✗ [think] Step 1: Calculate the overall (120-day) volatility - The first 120 values are given. - Compute the standard deviation (sigma) of these 120 values. Step 2: Calculate the recent (last 20 days) volatility - Take the last 20 values from the list. - Compute the standard deviat...
[47]

- To estimate volatility, we can use the stan- dard deviation of these values

Calculate the recent volatility (last 20 days): - Find the mea [...] TimeOmni-1(A)✗ [think] Step 1: Calculate the overall (120-day) volatility - The first 120 values are given. - To estimate volatility, we can use the stan- dard deviation of these values. Let’s compute the mean and standard devia- tion for the full 120-day series: Mean (approximate calcul...
[48]

- Then, calculate the standard deviation of these prices

Calculate the overall volatility (120-day): - First, find the mean of the 120-day closing prices. - Then, calculate the standard deviation of these prices
[49]

Summing up the first few values: - The first 20 values are mostly in the 114- 122 range

Calculate the recent volatility (last 20 days): - Find the [...] TimeOmni-1(B)✗ [think] Step 1: Calculate the overall volatility (120- day standard deviation) First, I’ll compute the mean of the entire series. Summing up the first few values: - The first 20 values are mostly in the 114- 122 range. - The next 20 values are in the 117-124 range. - The next ...
[50]

Calculate the first and last prices: - First price: 2864.00 - Last price: 3501.24
[51]

- The first 40 data points show a general rise: from around 2864 up to about 3183 by day 12

Calculate the percentage change: [ (Percentage Change) = ( ((Last Price) - (First Price))((First Price)) ) 100 ] [ (Percentage Change) = ( (3501.24 - 2864.00)(2864.00 [...] TimeOmni-1(C)✗ [think] Step 1: Trend Consistency (Perceptual Matching) Let’s visually scan the series for broad struc- tural trends. - The first 40 data points show a general rise: fro...
[52]

Calculate the first and last prices: - First price: 5173.62 - Last price: 6466.00
[53]

The data starts at 5174 and rises steadily through the first half, with some fluctuations but generally rising

Calculate the percentage change: [ (Percentage Change) = ((Last Price) - (First Price))((First Price)) 100 ] [ (Percentage Change) = (6466.00 - 5173.62)(5173.62) 100 [...] TimeOmni-1(A)✗ [think] Step 1: Trend Consistency (Perceptual Matching) First, I visually scan the series for overall trend direction. The data starts at 5174 and rises steadily through ...
[54]

- The first 40 data points show a general rise: from around 1020 down to about 990, then up to 1030-1040, then back down to 980-990, and so on

Calculate the percentage change from the first day to the last day: - First day price: 1021.89 - Last day price: 800.99 - Percentage change = ((Last day price - First day price) / First day price) * 100 - Percentage change = ((800.99 - 1021.89) / 1021.89) [...] TimeOmni-1(E)✗ [think] Step 1: Trend Consistency (Perceptual Matching) Let’s visually scan the ...
[55]

Trend Analysis: - The data shows a mix of increases and de- creases over the past 120 days, with no clear long-term upward or downward trend. - There are periods of volatility, but over- all, the stock price has been relatively stable around a centr [...] TimeOmni-1(A)✗ [think] Step 1: Identify the baseline trend and recent pattern - The series shows a ge...
[56]

Trend Analysis: - The data shows a mix of increases and de- creases, but there’s no clear long-term up- ward or downward trend over the past 120 days. There are periods of increase followed by decrease, indicating some volatility but no strong directio [...] TimeOmni-1(A)✗ [think] Step 1: Identify the baseline trend and recent pattern - The series shows a...
[57]

- The last few days before the shock show a slight downward trend, with prices around 160-170

Trend Analysis: - The data shows a mix of increases and de- creases, but there’s no clear long-term up- ward or downward trend over the past 120 days. - The last few days before the shock show a slight downward trend, with prices around 160-170. [...] TimeOmni-1(A)✗ [think] Step 1: Identify the baseline trend and recent pattern - The series shows a clear ...
[58]

Identify the Resistance Level: The resis- tance level appears to be around 1163.39, which is the highest price in the given data
[59]

- There have been attempts to break a [...] TimeOmni-1(B)✗ [think] Step 1: Identify the current trend and key levels - The data shows a clear upward trend with some volatility

Recent Trends: - The stock has been fluctuating around this resistance level. - There have been attempts to break a [...] TimeOmni-1(B)✗ [think] Step 1: Identify the current trend and key levels - The data shows a clear upward trend with some volatility. - There is a noticeable rise starting around day 40-45, peaking at 1087.49 on day 49, then a decline t...
[60]

From the data, it appears to be around 520 - 530

Identify the Resistance Level: The re- sistance level is a key price level where the stock has previously faced selling pressure, preventing it from rising further. From the data, it appears to be around 520 - 530
[61]

key resistance level

Recent Trends: - The stock has be [...] TimeOmni-1(B)✗ [think] Step 1: Identify the current context and trend - The data covers 120 trading days, with the last value being 515.52. - There is a clear upward trend visible, with several periods of rising prices and some mi- nor dips. - The last few values (last 10-15) show a slight downtrend, but overall, th...
[62]

key resistance level

Identify the Resistance Level: The re- sistance level is a key price level where the stock has previously faced selling pressure, preventing it from rising further. From the data, the highest price in the last 120 days is around 339.58. We can assume this as [...] TimeOmni-1(B)✗ [think] Step 1: Identify the current context and trend - The data covers 120 ...
[63]

Identify the Recent Peak and Current Price: The recent peak is 3880.15, and the current price is 3603.17, indicating a signifi- cant drawdown of 7.1 pct
[64]

- There is a noticeable drop after this peak, reaching a trough at 3603.17 on day 90

Analyze Recent Trends: - The last few prices show a fluctuating trend but generally moving [...] TimeOmni-1(B)✗ [think] Step 1: Identify the recent trend and key patterns - The series shows a clear upward trend with some volatility, peaking at 3880.15 on day 89. - There is a noticeable drop after this peak, reaching a trough at 3603.17 on day 90. - The la...
[65]

Identify the Peak and Current Price: The peak price is 5967.22, and the current price is 5621.21, indicating a drawdown of 5.8 pct
[66]

Analyze Recent Trends: - The last few prices show a fluctuating trend but generally decreasing from around 5900 to 5600. - There have been [...] TimeOmni-1(B)✗ [think] Step 1: Identify the recent trend and key patterns - The series shows a clear upward trend with some volatility, peaking at 5967.22 on day 49. - After the peak, there is a decline, reaching...
[67]

Identify the Peak and Current Price: The peak price is 135.51, and the current price is 120.82, indicating a 10.8 pct drawdown from the peak
[68]

- There hav [...] TimeOmni-1(B)✗ [think] Step 1: Identify the recent trend and key patterns - The series shows a clear upward trend with several peaks and troughs

Trend Analysis: - The stock price has been fluctuating but gen- erally shows a downward trend since reach- ing the peak. - There hav [...] TimeOmni-1(B)✗ [think] Step 1: Identify the recent trend and key patterns - The series shows a clear upward trend with several peaks and troughs. - There is a notable rise starting around day 10-15, peaking at 135.51 (...
[69]

Recent Price Movements: - The last 20 days’ closing prices show a mix of increases and decreases, but there’s no clear trend indicating a significant upward or downward movement. - The recent volatility ratio is 0.91, which [...] TimeOmni-1(B)✗ [think] Step 1: Trend Consistency (Perceptual Matching) - Looking at the series, there is a general downward tre...
[70]

Price Trend Analysis: - The closing prices show a mix of increases and decreases, but there isn’t a clear upward or downward trend in the last 77 days. - There are several periods where prices seem to fluctuate around a certain level before m [...] TimeOmni-1(B)✗ [think] Step 1: Trend Consistency (Perceptual Matching) - Looking at the series, there is a c...
[71]

Recent Price Movements: The last 20 days show a mix of increases and decreases, but generally, the prices have been trending upwards. The most recent prices are around 117-118, while the prices in the earlier part of the list were [...] TimeOmni-1(B)✗ [think] Step 1: Trend Consistency (Perceptual Matching) - Looking at the series, there is a clear up- war...
[72]

Trend Analysis for PANW: - The closing prices of PANW show a general upward trend with some fluctuations. - There are periods where the stock price in- creases significantly (e.g., from around 180 to 200+), followed by slight d [...] TimeOmni-1(B)✗ [think] Step 1: Trend Consistency (Perceptual Matching) - Both PANW and CRWD show an initial rise in the fir...
[73]

Trend Analysis for ABBV: - The closing prices of ABBV show a general upward trend with some fluctuations. - There are periods where the stock price in- creases significantly (e.g., from 156.50 to 189.56), followed by periods of stabil [...] TimeOmni-1(B)✗ [think] Step 1: Trend Consistency (Perceptual Matching) - Both ABBV and COIN show an initial de- clin...
[74]

There are periods of slight increases, but overall, the trend is downward

Trend Analysis for ABBV: - The closing prices of ABBV show a sig- nificant fluctuation but generally seem to be decreasing over time. There are periods of slight increases, but overall, the trend is downward
[75]

- ABBV: Starts 144.80, rises to 149.32 by day 10, then falls back to 134.80 by day 15

Trend Analysis for META [...] TimeOmni-1(B)✗ [think] Step 1: Trend Consistency (Perceptual Matching) - Both ABBV and META show an initial rise in the first 10-15 days. - ABBV: Starts 144.80, rises to 149.32 by day 10, then falls back to 134.80 by day 15. - META: Starts 212.18, rises to 241.29 by day 15, then falls back to 231.70 by day 20. - Both then fal...