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REVIEW 3 major objections 5 minor 50 references

Experimenting with Multi-modal Information to Predict Success of Indian IPOs

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Text and numbers together predict Indian IPO listing-day moves

desk verdict The GMP result is real and useful; the multimodal prediction claim collapses on in-sample stacking leakage. read the letter →

arxiv 2412.16174 v1 pith:JDN23C5Z submitted 2024-12-08 cs.CL econ.GNq-fin.EC

Sohom Ghosh , Arnab Maji , N Harsha Vardhan , Sudip Kumar Naskar This is my paper · ORCID
classification cs.CLecon.GNq-fin.EC
keywords IPOsuccesspredictiongreymarketpremiummultimodalfusionmulti-classifiersystemIndianstocknaturallanguageprocessingretrieval-augmentedgenerationlistingdayreturns
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper asks whether an IPO's first-day price movements can be predicted before listing using everything available at subscription time: company financials, market and macroeconomic indicators, subscription rates, broker recommendations, and the prospectus text itself. It curates two new datasets covering Indian Main Board and SME IPOs, then runs a two-stage multi-classifier system that fuses numeric, categorical, text, and OCR-derived features, evaluating on 2023 listings. The central empirical claim is that these trained models beat zero-shot large language models, and that adding text features most helps prediction of whether the day's high price exceeds the issue price. A separate analysis argues that grey market premium (GMP) aligns with listing-day returns in about 80% of Main Board IPOs but only about 21% of SME IPOs, so GMP is trustworthy for Main Board offerings but not for SME offerings.

What carries the argument

The central mechanism is the two-stage probability-augmentation pipeline. First-stage models (generalized linear models, distributed random forests, deep learning, XGBoost, gradient boosting machines, and their ensemble, trained with an AutoML library) produce positive-class probabilities from text representations: either Nomic embeddings fed to traditional ML models or fine-tuned DeBERTa classifiers. These probabilities are appended to numeric and categorical features, and a second-stage classifier makes the final prediction. For prospectus content, a retrieval-augmented generation step extracts 25 answer columns by retrieving pages with cosine similarity and BM25 and generating answers with Llama 3.2 3B. The GMP analysis is a separate descriptive mechanism: comparing the sign of GMP with the sign of (listing price minus issue price).

What would settle it

Re-run the two-stage pipeline using out-of-fold (cross-validated) first-stage probabilities instead of in-sample probabilities on the same 2023 test split; if the text-augmented models no longer beat numeric-categorical baselines on high-price direction, the claimed text benefit vanishes and the multimodal advantage is an artifact of target leakage.

Watch

Extended reading notes

Core claim

The paper's central claim is that a multi-classifier decision system, which appends probabilities from first-stage text models to structured features and feeds them to a second-stage classifier, can predict the direction and magnitude of opening, high, and closing price movements on the listing day of Indian IPOs. The claim is conditional on IPO type: for opening and closing price direction, models trained on numeric and categorical features alone perform best, whereas for high price direction, adding text-derived probabilities improves F1 for the positive class. The paper further claims that grey market premium serves as a proxy for Main Board IPO success, aligning with the actual listing-versus-issue price relationship in 80.29% of cases, but fails for SME IPOs, with only 21.29% alignment. Across all tasks, the proposed models outperform zero-shot Gemini 1.5 flash and Llama 3.2 3b prompting.

Load-bearing premise

The reported gains from text features depend on the practice of computing first-stage probabilities on the same training data used to fit the second-stage model; if the first-stage models overfit the training labels, the second stage is partly trained on the target itself.

Editorial extensions

If this is right

  • If the claims hold, investors can use GMP as a sanity check for Main Board IPOs but should discount it for SME IPOs, where the paper finds it misaligns with listing-day outcomes in most cases.
  • Text-augmented features would give a practical edge specifically in predicting the listing-day high price direction, while opening and closing price direction are already well served by structured features alone.
  • Trained multi-classifier systems would be a more reliable decision-support tool for IPO subscription than zero-shot prompting of large language models.
  • The curated datasets provide a reusable resource for future studies of Indian IPO success, including the separate dynamics of Main Board and SME listings.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the leakage concern is resolved and text gains persist, the result suggests prospectus narratives contain incremental information about listing-day demand beyond structured signals; an ablation over the 25 RAG questions could identify which parts of the prospectus carry that signal.
  • The GMP asymmetry between Main Board and SME IPOs may reflect the regulatory cracks and manipulation concerns documented in the SME segment, implying that GMP is an institutional-sentiment measure rather than a universal demand gauge.
  • A testable extension is to apply the same two-stage probability-augmentation pipeline to IPO markets in other emerging economies, where prospectus documents and grey markets exist in similar forms.
  • The paper's own limitation list notes that social-media sentiment, expert analyst reviews, and major event shocks were not modeled, so a natural next step is to fold those modalities into the same fusion architecture.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper presents two new datasets on Indian Main Board and SME IPOs (2009–2023 and 2017–2023, respectively), and proposes a multi-classifier system that fuses numeric, categorical, text (from prospectuses and news), and OCR-derived features to predict the direction and magnitude of price movements at the opening, high, and closing prices on the listing day. The authors also analyze the relation between Grey Market Premium (GMP) and listing-day returns, reporting that GMP aligns with listing-price/issue-price differences in 80.29% of Main Board cases but only 21.29% of SME cases. The main empirical claims are that text features improve prediction for the high-price direction, that the proposed models outperform zero-shot Gemini and Llama baselines, and that GMP is a useful proxy for Main Board IPOs but not SME IPOs.

Significance. If the predictive claims were sound, the paper would contribute useful resources and a practical decision system: the two datasets are a valuable addition, the RAG-based extraction of prospectus content is carefully designed, the authors provide hyperparameters and code links for reproducibility, and the GMP analysis is a concrete, falsifiable empirical finding. However, the central claim that multimodal fusion improves IPO success prediction is not supported by the experiments as reported, due to target leakage in the stacking procedure and the absence of trivial baselines and significance testing. The GMP finding is independent and plausible, but it does not rescue the predictive claim.

major comments (3)
  1. [Section 5.1] The text-feature stacking procedure leaks target information. The paper states: 'We selected the best model in each case and appended the probabilities of the positive class as features to the list of existing numeric and categorical features,' and the first-stage models (Nomic-embedding ML models and fine-tuned DeBERTa classifiers) are trained on the same 2009–2022 training set whose labels are then used to compute these probabilities for the second-stage models. No out-of-fold or nested cross-validation is described. Because tree-based second-stage models (DRF, XGB, GBM) can memorize in-sample first-stage predictions, the augmented configurations in Tables 2 and 3 do not measure whether text features generalize; they measure the second-stage model's ability to exploit the first-stage model's overfitting. The reported F1 and AUC values for all N-C-Tn and N-C-Td configurations are therefore not valid estimates of out-of-sample performance, and the central claim that text features improve prediction is unsupported.
  2. [Tables 2 and 3] No majority-class or constant-predictor baseline is reported, and many configurations have F1(0)=0.000 (e.g., GBM O N-C-Tn, Ens O N-C-Tn, DL Cl N-C-Td), meaning the model never predicts the negative class. With the high positive-class share in the 2023 test set, an F1(1) around 0.90 is achievable by predicting the majority class for every instance. Moreover, the claimed text advantage is contradicted by the table's own numbers: for MB High direction, the best N-C model (XGB) has F1(1)=0.943 while the best N-C-Td model (GLM) has F1(1)=0.935; for SME High direction, the difference between N-C (0.939) and N-C-Tn-Nw (0.942) is 0.003. These differences are far below any reasonable significance threshold, especially given the small test set, so the conclusion in Section 5.1 that 'texts features do play a role' is not established.
  3. [Table 1] The test set consists of a single year (2023) with only 57 Main Board and 183 SME IPOs. All model rankings in Tables 2 and 3 are based on this one-year, relatively small sample, with no confidence intervals, error bars, or significance tests. The performance gaps between the best and second-best configurations are often 0.001–0.005 in F1 or 0.001–0.01 in MAE, which is within the noise expected from such a sample. This is a load-bearing limitation for the predictive claim because the reported 'best' models may simply be the result of random variation on a particular test year.
minor comments (5)
  1. [Section 1 and Section 4] There are multiple typos and grammatical errors: 'The reminder of this paper' should be 'The remainder'; 'we complied a list' should be 'we compiled a list'; and Section 5.1 contains 'DeBERRTa' instead of 'DeBERTa'.
  2. [Table A.5] Questions 17 and 19 are identical ('Is the IPO price is reasonable and offers potential for growth?'); this appears to be a copy-paste error, and one of them should be replaced with the intended distinct question.
  3. [Section 4 and Figure 5] The paper claims to use 'images' as a modality, but the only image processing is OCR to extract text, which is then embedded and treated as text. The multimodal framing is therefore overstated; the actual inputs are numeric, categorical, and text features.
  4. [Section 5.2 and Table 3] The Llama 3.2 3b under-pricing results are discussed in the text for Main Board IPOs, but Table 3 only reports Gemini for the under-pricing tasks. The reader cannot compare the LLM baselines directly, and the absence of Llama rows for SME under-pricing should be noted in the table or caption.
  5. [Table 2] The comparison with Gemini and Llama is not apples-to-apples: the LLMs are prompted zero-shot with JSON content, while the ML models are trained on the same features. The paper should acknowledge that this is an advantage for the trained models and not claim a fair 'outperform' without a matched setting.

Circularity Check

1 steps flagged · score 6.0 of 10

In-sample probability stacking in Section 5.1 makes the 'text helps' claim circular: the text feature is a fitted value of the target from first-stage models trained on the same labels.

  1. fitted input called prediction [Section 5.1, first-stage text models and stacking (Tables 2 and 3)]
    "We used these embeddings only as input features to train five kinds of models (GLM, DRF, DL, XGB, and GBM) leveraging H2O Au-toML library for classification. For each text feature, separate ML models were trained for predicting the direction of opening, high and closing prices. We selected the best model in each case and appended the probabilities of the positive class as features to the list of existing numeric and categorical features."

    The first-stage text models are trained on the same training labels as the final classifier, and their in-sample positive-class probabilities are appended as features for that final model. For each training row, the 'text feature' is thus a fitted value of the target (a near-deterministic function of y for flexible tree models), so the second-stage model is partly trained on its own predictions. No out-of-fold protocol is described; without it, the text-augmented configurations in Tables 2 and 3 do not provide independent evidence that text generalizes. The reported text gains (MB High F1(1) 0.934 to 0.935; SME High 0.939 to 0.942) are far below any significance level on a 57/183-sample test set.

full rationale

The only substantive circularity is in the text-feature pipeline. The paper's own description shows that the probabilities used as 'text features' for the final classifiers are produced by first-stage models trained on the same training labels and applied to the same training rows; the paper never describes an out-of-fold protocol. This makes the multimodal comparison a comparison between a model with ordinary features and a model whose extra feature is a fitted value of the same target. The GMP analysis (Section 5.3) is not circular: GMP/issue price is a definitional baseline, but it is checked against actual listing prices, and the conclusion is empirical. The dataset construction and the zero-shot LLM comparisons are also independent. However, the central claim that text information improves prediction, especially the 'texts features do play a role' conclusion for high-price direction, rests on the leaky stacking and on noise-level F1 differences, so a partial circularity score is warranted.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new theoretical entities. The ledger mainly captures hand-chosen feature engineering choices and the methodological assumption that in-sample predicted probabilities are valid features, which is the most consequential entry.

free parameters (5)
  • 25 curated prospectus questions = Q1-Q25, Table A.5
    Hand-selected set of investor questions; Q17 and Q19 are duplicated; no ablation shows their marginal value.
  • Success-rate lookback windows = last 90 days; previous calendar quarter
    Two engineered features; window sizes chosen without sensitivity analysis.
  • DeBERTa fine-tuning hyperparameters = 3 epochs, lr 2e-5, batch size 8
    Fixed for all 26x3 fine-tuned models; no search or validation-based early stopping.
  • News coverage thresholds = main board >50%, SME >10%
    Instances with less news were dropped; threshold chosen by data availability, not analysis.
  • Retrieval parameters for RAG = cosine similarity + BM25, page-level chunks
    Chunking and retrieval settings are fixed choices; no comparison of alternatives.
assumptions (5)
  • domain assumption Prospectus text, macroeconomic variables, and market indices contain predictive signal for listing-day returns.
    Section 5.1 builds all models on these features without testing the assumption against a constant predictor.
  • domain assumption Historical IPO success rates over the prior quarter and 90 days are predictive of future IPO success.
    Features 'dynamic last 90Day success' and 'previous quarter success' assume temporal momentum in IPO outcomes.
  • ad hoc to paper In-sample probabilities from first-stage models can be used as features for a second-stage model without nested cross-validation.
    Section 5.1 appends probabilities of the positive class computed on the training set to the final model's feature set; this assumes no overfitting contamination.
  • domain assumption OCR of prospectus images captures the information in images.
    Section 4 extracts text from images with Tesseract; the models never ingest image pixels, so the 'image modality' reduces to OCR text.
  • standard math Standard AutoML implementations (GLM, DRF, DL, XGB, GBM) and their ensembles behave as implemented.
    H2O AutoML is used as a black box for all model training.

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Cite this review

Pith. "Pith review of Experimenting with Multi-modal Information to Predict Success of Indian IPOs." pith.science (2026). https://pith.science/paper/JDN23C5Z

@misc{pith2026241216174,
  author       = {Pith},
  title        = {Pith review of: Experimenting with Multi-modal Information to Predict Success of Indian IPOs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JDN23C5Z}},
  note         = {Machine review of arXiv:2412.16174}
}
read the original abstract

With consistent growth in Indian Economy, Initial Public Offerings (IPOs) have become a popular avenue for investment. With the modern technology simplifying investments, more investors are interested in making data driven decisions while subscribing for IPOs. In this paper, we describe a machine learning and natural language processing based approach for estimating if an IPO will be successful. We have extensively studied the impact of various facts mentioned in IPO filing prospectus, macroeconomic factors, market conditions, Grey Market Price, etc. on the success of an IPO. We created two new datasets relating to the IPOs of Indian companies. Finally, we investigated how information from multiple modalities (texts, images, numbers, and categorical features) can be used for estimating the direction and underpricing with respect to opening, high and closing prices of stocks on the IPO listing day.

Figures

Figures reproduced from arXiv: 2412.16174 by the authors.

Figure 1
Figure 1. Sector wise Distribution investorgrain.com 25. We could not use GMP as a feature because we could get only the GMP values of the IPOs which were launched in the year 2019 or later. The final list of features and their descriptions are presented in Table B.6 of §Appendix B. 5. Experiments and results We present the overall experimental framework in [PITH_FULL_IMAGE:figures/full_fig_p014_1.png] view at source ↗
Figure 2
Figure 2. Industry wise Distribution and categorical (N-C) features for prediction. These numeric features are: Issue Price, Lot Size, Market Variables (Nifty50, VIX), Macroeconomic vari￾ables (GDP, Stocks traded, Unemployment rate, etc.), Subscription rate per category (QIB, NII, Retail, etc.) up to the penultimate day for subscription, Success rate of the IPOs in the previous quarter and the last 90 days, rec￾ommendations b… view at source ↗
Figure 3
Figure 3. Success rates over the year for Main Board IPOs [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Success rates over the year for SME IPOs [PITH_FULL_IMAGE:figures/full_fig_p017_4.png]
Figure 5
Figure 5. Figure 5: Methodology marking, we prompted a Gemini-1.5-flash Reid et al. (2024) model with all the necessary details for predicting the objectives. We repeated this with Llama-3.2 3b model. The details of the prompts are mentioned in section Appendix C. We report Area Under the…
Figure 6
Figure 6. Figure 6: DeBERTa models nate in terms of F1(1) for MB. But, in case of SME, XGB model trained with numerical, categorical features, and probabilities of positive class (obtained from the best performing ML models trained using Nomic embeddings) out￾performs all others in terms …

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Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.