{"id":"2d1c073e-9a8a-406b-8ea4-7bcd9d75cc8d","arxiv_id":"2411.13599","paper_version":2,"verdict":"REJECT","confidence":"MODERATE","novelty_score":4.0,"correctness_risk":"high","formal_verification":"none","parameter_count":4,"one_line_summary":"A 'Classify-and-Rethink' prompt for ChatGPT produced higher backtested returns on gold trading than simpler prompts or buy-and-hold, though the comparison is confounded.","lead":"This paper tests a two-step prompt for ChatGPT that classifies gold news and then rethinks its own score, using the scores to trade Shanghai gold in a backtest. It reports higher returns and Sharpe ratios than simpler prompts or buy-and-hold, but confounded comparisons and missing details make the result provisional.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The reported excess return is confounded: CAR changes the rethink step, the averaging window (5 to 20), and the trading direction (buy-low to buy-high) simultaneously, so the backtest cannot attribute the gain to the rethink mechanism.","rationale":"The reader's weakest assumption identifies the same core problem: the CAR strategy is not a controlled comparison of the rethink step. My analysis confirms that Section 2.4 changes three variables simultaneously, making the causal attribution to 'rethink' unsupported. This is load-bearing because the paper's central claim explicitly credits the rethink mechanism for overcoming framing effects and achieving excess returns; without isolating that mechanism, the 80.35% result could simply reflect the sign of the trading rule or the longer averaging window. The paper's own ablation is insufficient: removing classification and keeping rethink yields 42%, but this does not test the rethink step while holding the trading rule fixed. A decisive test is to pair Classify-only scores with the CAR trading rule; if the return is similar, the rethink prompt is not the cause. Until this is done, the evidence does not support the paper's central claim, consistent with the reader's REJECT verdict. No new issue beyond the reader's concern is raised, so the verdict should remain unchanged.","tokens_in":8232,"tokens_out":3409,"duration_ms":33976,"concrete_test":"Re-run the Classify-only scoring (two-step prompt without the rethink instruction) using the CAR trading rule: 20-news average, buy when score is above the threshold, sell when below, with the same threshold as CAR. If this configuration yields a return close to 80.35%, the rethink step is not the cause of the excess return. Additionally, report the threshold value and a grid of thresholds to check whether 80.35% is a selected extreme.","verdict_should_be":"UNCHANGED","load_bearing_attack":"Section 2.4 states that the baseline strategies buy when the 5-news average is below the threshold and sell when above, while CAR buys when the 20-news average is above and sells when below. Table 1 then compares One-Step, Classify, and Classify+Rethink, but the CAR column differs from Classify in three variables at once: presence of the rethink prompt, window length, and sign of the trading rule. The paper's central causal claim—that the rethink step overcomes framing effects and produces excess returns—requires that the rethink prompt is the cause of the 80.35% vs 73.41% gap. With three simultaneous changes, the observed gap could be entirely due to the window or the sign flip. The paper also does not disclose the threshold value or perform sensitivity analysis, so we cannot rule out that the threshold was chosen after inspecting the backtest. The case study in §3.4 is anecdotal and does not connect to the aggregate return.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript proposes Classify-and-Rethink (CAR), a multi-step zero-shot prompt strategy in which ChatGPT first classifies gold-related news into six categories, scores each article on [-1,1], and then reconsiders the score from a long-term perspective. The authors backtest a long/flat trading rule on Shanghai Gold Exchange Au9999 from January 2018 to June 2023 and report that CAR achieves an 80.35% return and a Sharpe ratio of 1.071, outperforming Buy-and-Hold (63.53%, 0.811), One-Step (63.44%, 0.902), and Classify (73.41%, 1.019). They conclude that CAR overcomes the framing effect and generates excess returns. The paper also includes an ablation study, a score-distribution analysis, and a qualitative case study.","tokens_in":8442,"tokens_out":5320,"duration_ms":50612,"significance":"If the causal claim were established, the work would be a useful demonstration that multi-step prompting can reduce framing effects in LLM-based financial news scoring and improve trading performance. The research question is interesting and timely, and the CAR pipeline is clearly described. The paper also contains an explicit discussion of limitations in Section 4 and a declared conflict of interest, both of which are commendable. However, the current evidence does not isolate the effect of the rethink step: the comparison in Table 1 changes the averaging window and the trading direction together with the prompt, the buy/sell threshold is never disclosed, transaction costs are ignored, and no statistical or out-of-sample validation is provided. These are not presentation issues; they bear directly on the central claim. As a result, the paper currently does not support its headline conclusion that the rethink mechanism itself is what overcomes behavioral biases and produces excess returns.","major_comments":[{"comment":"The comparison between Classify and Classify+Rethink (CAR) in Table 1 is confounded. Section 2.4 states that the baseline strategies use the average score of the latest five news items and buy when the average is below the threshold and sell when above, whereas CAR uses the average of the latest 20 news items and buys when the average is above the threshold and sells when below. Consequently, the CAR column differs from the Classify column in three ways simultaneously: the presence of the rethink prompt, the averaging window (5 vs 20), and the sign of the trading rule (buy-low/sell-high vs buy-high/sell-low). The observed return gap (73.41% vs 80.35%) cannot therefore be attributed to the rethink mechanism. Please provide a factorial ablation that varies the prompt, the window, and the direction independently, for example Classify with a 20-item window and the same direction rule as CAR, and Rethink with a 5-item window and the same direction rule as the baselines.","section":"§2.4 and Table 1"},{"comment":"The buy/sell threshold is never disclosed. The text refers to 'the threshold' and 'the set value' without giving its numerical value or how it was chosen. Without this value, the backtest is not reproducible, and it is impossible to rule out that the threshold was selected after inspecting the same 2018–2023 data, which would make the reported returns in-sample rather than predictive. Please report the threshold value, show sensitivity of the Table 1 results to the threshold, and, if the threshold was tuned on the backtest period, state this explicitly and provide an out-of-sample or cross-validation evaluation.","section":"§2.4"},{"comment":"The backtest ignores transaction costs. Figure 2's caption explicitly states 'without considering transaction costs,' and the paper claims 'excess returns' and 'higher investment returns.' With a long/flat strategy that switches on news-score crossings, transaction costs and bid-ask spreads can materially change net performance, especially given the ablation's mention of 30 sell operations over the period. Please include realistic transaction costs (and, if applicable, slippage) in the reported returns, or at least report turnover and a break-even cost analysis.","section":"§3.2 and Fig. 2"},{"comment":"No statistical significance, confidence intervals, or out-of-sample tests are reported. The results are a single historical path over one asset class and one time period, and Section 4 itself acknowledges that 'our experiments are based solely on historical data.' The 80.35% vs 73.41% gap could easily be within sampling variation, especially given that the strategy parameters may have been chosen after observing the same period. Please add bootstrap or Monte Carlo tests (for example, re-sampling the news order or randomizing entry/exit), a split-sample or out-of-sample evaluation, and a multiple-testing awareness for the prompt variants.","section":"§3.2 and §4"},{"comment":"The case study is a single anecdote and does not support the general claim that CAR 'overcomes the framing effect.' Moreover, the Rethink explanation in Table 2 is internally inconsistent: it states that rating the news as 0.5 'may be too optimistic' but then adjusts the score upward to 0.7. This undercuts the illustrative value of the example. A systematic evaluation is needed, such as blind rating of the explanations by independent judges or a quantitative comparison of score revisions against subsequent price movements.","section":"§3.4 and Table 2"}],"minor_comments":[{"comment":"The abstract contains the phrase 'still remains paid limited attention'; this should read 'has received limited attention.'","section":"Abstract"},{"comment":"The manuscript uses 'frame effect' and 'framing effect' interchangeably; please standardize the terminology.","section":"Throughout"},{"comment":"The ablation study does not specify the prompts, window sizes, or thresholds used for the 'Rethink only' variant or the random-trading baseline, so the reported 42% profit and 20–40% random fluctuation range are not fully reproducible.","section":"§3.3"},{"comment":"The data availability statement says 'Data requests can be made to Datayes via DFSupport@wind.com.cn,' which appears to assign a Wind email address to Datayes; please verify the correct contact information for each data provider.","section":"Data Availability"},{"comment":"The conflict-of-interest statement says that Shuoling Liu, Gaoguo Jia, Yuhang Jiang, and Liyuan Chen are all affiliated with E Fund Management, but the affiliations list only the latter three with E Fund and the corresponding author with HKUST and CAS; please reconcile this discrepancy.","section":"Declarations"}],"recommendation":"major_revision","confidential_remarks":"The paper would be much easier to audit if the authors provided the exact prompts, the threshold value, and the code used for the backtest. Given the current omissions, the central claim is not yet supported. If the authors cannot supply the controlled factorial experiments and disclose the threshold in a revision, I would recommend rejection in a later round."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Dear colleague,\n\nThe headline on this one: the CAR prompt strategy is a reasonable idea, but the experiment doesn't isolate the rethink mechanism. The backtest compares Classify (5-news average, buy on low scores) with CAR (20-news average, buy on high scores), so three things change at once. The 80.35% versus 73.41% gap could come from the window or the sign flip alone.\n\nWhat's actually new: applying multi-step zero-shot reasoning to gold news and scoring each article. The case study in §3.4 is a nice illustration that different prompts produce different explanations, and the rethink step does look more thoughtful. The ablation showing that removing classification drops profit to 42% suggests the classification module matters. The observation that CAR scores are roughly normal is a minor but sensible point.\n\nThe soft spots are more than minor. The undisclosed buy/sell threshold is a free parameter that could have been tuned on the same data; there's no sensitivity analysis. Transaction costs are ignored, which matters for a strategy that can switch positions often. More importantly, the central claim—that CAR 'overcomes the framing effect'—is never tested directly. There's no framing manipulation, only a comparison of prompt designs. The paper's own Discussion mentions stability and historical data but doesn't acknowledge the confound in the main table.\n\nThat said, the paper is not incoherent. The setup is clear and the authors are honest about data availability and conflicts of interest. The problem is that the evidence doesn't support the causal language used in the abstract and conclusion. With a redesign that holds window length and trading direction fixed while toggling only the rethink prompt, plus disclosed parameters and at least a basic out-of-sample or bootstrap check, the question would be worth answering. As it stands, I wouldn't rely on the excess-return claim, and I'd be cautious about citing it as evidence for LLM prompting in finance.\n\nFor peer review: I'd send it to referees rather than desk reject. The topic is timely and the method, while not brand new, is a legitimate extension. But the editor should expect major revisions. This is a good candidate for a workshop or a venue that accepts replication-style studies, not a top finance journal as it is.","headline":"The CAR prompt idea is reasonable but the backtest confounds rethink with window and trade-direction changes, so the excess-return claim isn't supported.","tokens_in":8951,"tokens_out":3040,"would_cite":false,"duration_ms":30410,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"A two-stage 'Classify-and-Rethink' prompt can make ChatGPT avoid the framing effect in gold news scoring and generate excess returns in a 2018-2023 backtest.","keywords":["frame effect","large language model","Classify-and-Rethink","behavioral finance","gold investment","chain-of-thought prompting","zero-shot reasoning","backtesting"],"falsifier":"Run a crossed ablation that keeps the averaging window, trade direction, and threshold identical across all strategies and toggles only the rethink instruction; if CAR's excess return over the classify-only strategy disappears, the reported effect is not caused by rethinking.","tokens_in":8045,"feed_emoji":"🥇","tokens_out":8830,"duration_ms":90727,"temperature":0.7,"pith_summary":"This paper asks whether a large language model, prompted in several stages and given no training examples, can score news about gold in a way that avoids the framing effect—the bias where the same facts produce different decisions depending on how they are described. The authors propose the Classify-and-Rethink (CAR) prompt: ChatGPT first sorts each news item into one of six economic categories and assigns a score from -1 to 1, then is asked to rethink that score from a long-term perspective and revise it if needed. In a backtest on the Au9999.SGE spot gold index from 2018 to 2023, CAR produced a total return of 80.35% and a Sharpe ratio of 1.071, compared with 63.53% and 0.811 for buy-and-hold. The paper reads this as evidence that multi-step zero-shot prompting can suppress framing-induced bias and convert ordinary financial news into tradeable, explainable investment opinions.","feed_headline":"Two-step prompt lifts ChatGPT gold returns to 80%","feed_subtitle":"Adding a rethink step to news scoring beat plain prompts and buy-and-hold in a 2018-2023 gold backtest.","key_machinery":"The load-bearing design is the Classify-and-Rethink (CAR) prompt. In the Classify leg, ChatGPT assigns each gold news item to one of six economic categories and outputs an initial score between -1 and 1 in 0.1 steps; the categories force the model to reason through an economic channel instead of reacting to wording. In the Rethink leg, the model is instructed to evaluate whether its own score is reasonable from a long-term trend perspective and to revise it, which is intended to un-anchor the score from short-term framing. Trading converts the revised scores into positions: CAR buys when the 20-news average score exceeds a threshold and sells when it falls below, whereas the no-rethink baselines trade on the 5-news average in the opposite direction.","core_discovery":"The central claim is that the rethink step is what allows ChatGPT to overcome the framing effect. On the paper's account, a one-step prompt makes the model mirror the short-term sentiment of the news—just as a biased investor would—while a classify-only prompt improves the economic reasoning but still overreacts to the latest item. The CAR prompt adds a final instruction to reflect on the initial score from a long-term trend perspective and correct it, producing a score that deliberately moves away from market sentiment. The authors report that this sequence outperforms both simpler prompts and buy-and-hold in return and Sharpe ratio, and that the resulting scores are roughly normally distributed rather than clustered at extremes, which they take as a sign of more rational scoring.","pith_inferences":["Our inference: the reported comparison does not isolate the rethink step, because CAR also switches the averaging window from 5 to 20 news items and reverses the trade direction relative to the baselines; a crossed ablation varying one factor at a time would be needed to confirm the mechanism.","Our inference: if the rethink prompt is what matters, a similar benefit may appear in other news-driven assets, such as equities or exchange rates, where the framing effect is thought to distort short-term reactions; this is testable with the same CAR prompt and backtest template.","Our inference: because the rethink step pushes scores toward a near-normal distribution, part of the return gain might be reproducible by any score-averaging procedure that damps outliers, independent of the model's reasoning; comparing CAR against a mechanically smoothed sentiment score would separate those effects."],"forward_implications":["ChatGPT can serve as a zero-shot news scorer inside a tradeable strategy without fine-tuning or training labels.","The classification stage is essential: removing it and applying the rethink prompt directly caused the strategy's profit to fall to 42%, below buy-and-hold.","The rethink stage makes the score distribution closer to normal, which the authors connect to more rational behavior and easier downstream processing.","Each scored news item comes with a textual explanation, so the resulting investment signals are auditable by a human."],"supporting_citations":[{"why":"Supplies the zero-shot chain-of-thought technique that lets ChatGPT reason without exemplars, a premise of CAR.","marker":"[3]"},{"why":"Supplies the chain-of-thought prompting mechanism that the CAR prompt sequence extends.","marker":"[4]"},{"why":"Identifies the instruction-tuned model family that the experiments invoke as ChatGPT.","marker":"[5]"},{"why":"Establishes a large financial-domain language model baseline and motivates LLM use in finance.","marker":"[8]"},{"why":"Represents the sentiment-classification approach that CAR is designed to go beyond.","marker":"[9]"},{"why":"Supports the claim that recent language models can exhibit financial reasoning beyond classification.","marker":"[12]"},{"why":"Provides the earlier result that ChatGPT struggles at zero-shot stock prediction, which the paper positions itself against.","marker":"[13]"}],"fun_headline_variants":["Rethink step lets ChatGPT beat gold bias","ChatGPT's rethink step beats buy-and-hold in gold","Two-step prompt scores higher in gold test","Rethinking news helps AI dodge gold bias","AI rethink step lifts gold returns past humans"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The result depends on attributing the return gap to the rethink step rather than to the simultaneous change in averaging window (from 5 to 20 news items) and trading direction, and it assumes the buy/sell threshold was fixed in advance rather than chosen after inspecting the backtest.","fun_headline_variants_meta":{"raw":{"variants":["Rethink step lets ChatGPT beat gold bias","ChatGPT's rethink step beats buy-and-hold in gold","Two-step prompt scores higher in gold test","Rethinking news helps AI dodge gold bias","AI rethink step lifts gold returns past humans"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.000135,"raw_usage":{"total_tokens":1140,"prompt_tokens":940,"completion_tokens":200,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":556,"completion_tokens_details":{"reasoning_tokens":128}},"tokens_in":556,"tokens_out":200,"duration_ms":3081,"temperature":1.0,"reasoning_tokens":128,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T17:41:07.595487+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Run a crossed ablation that keeps the averaging window, trade direction, and threshold identical across all strategies and toggles only the rethink instruction; if CAR's excess return over the classify-only strategy disappears, the reported effect is not caused by rethinking.","supporting_citations":[],"review_version":1}