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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 3 new columns ({'predict', 'p0', 'p1'}) and 52 missing columns ({'answer_of_question_7_class_0_prob', 'answer_of_question_8_class_0_prob', 'answer_of_question_15_class_0_prob', 'answer_of_question_22_class_0_prob', 'answer_of_question_6_class_1_prob', 'answer_of_question_19_class_0_prob', 'answer_of_question_9_class_0_prob', 'answer_of_question_8_class_1_prob', 'answer_of_question_13_class_0_prob', 'answer_of_question_9_class_1_prob', 'answer_of_question_23_class_0_prob', 'full_text_content_class_0_prob', 'answer_of_question_16_class_1_prob', 'answer_of_question_21_class_0_prob', 'answer_of_question_7_class_1_prob', 'answer_of_question_19_class_1_prob', 'answer_of_question_25_class_0_prob', 'answer_of_question_21_class_1_prob', 'answer_of_question_14_class_0_prob', 'answer_of_question_4_class_0_prob', 'answer_of_question_24_class_0_prob', 'answer_of_question_5_class_0_prob', 'answer_of_question_11_class_1_prob', 'answer_of_question_3_class_1_prob', 'answer_of_question_3_class_0_prob', 'answer_of_question_15_class_1_prob', 'answer_of_question_22_class_1_prob', 'answer_of_question_24_class_1_prob', 'answer_of_question_17_class_0_prob', 'answer_of_question_10_class_0_prob', 'answer_of_question_18_class_1_prob', 'answer_of_question_10_class_1_prob', 'answer_of_question_16_class_0_prob', 'answer_of_question_1_class_0_prob', 'answer_of_question_6_class_0_prob', 'answer_of_question_5_class_1_prob', 'answer_of_question_23_class_1_prob', 'answer_of_question_25_class_1_prob', 'answer_of_question_12_class_0_prob', 'answer_of_question_12_class_1_prob', 'answer_of_question_20_class_0_prob', 'answer_of_question_18_class_0_prob', 'answer_of_question_4_class_1_prob', 'answer_of_question_2_class_0_prob', 'answer_of_question_11_class_0_prob', 'full_text_content_class_1_prob', 'answer_of_question_2_class_1_prob', 'answer_of_question_17_class_1_prob', 'answer_of_question_13_class_1_prob', 'answer_of_question_14_class_1_prob', 'answer_of_question_1_class_1_prob', 'answer_of_question_20_class_1_prob'}).

This happened while the csv dataset builder was generating data using

zip://mainboard_text_feature_predictions/Success_Closemainboard_test_predictions_answer_of_question_1.csv::/tmp/hf-datasets-cache/medium/datasets/63964802673327-config-parquet-and-info-sohomghosh-Indian_IPO_dat-42b473c3/hub/datasets--sohomghosh--Indian_IPO_datasets/snapshots/a79a7d3395a9c21594aa650b84ff6602fa000ae4/nomic_embeddings_ml_predictions/mainboard_text_feature_predictions.zip

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              predict: int64
              p0: double
              p1: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 581
              to
              {'full_text_content_class_0_prob': Value(dtype='float64', id=None), 'full_text_content_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_1_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_1_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_2_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_2_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_3_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_3_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_4_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_4_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_5_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_5_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_6_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_6_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_7_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_7_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_8_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_8_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_9_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_9_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_10_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_10_class_1_pro
              ...
              id=None), 'answer_of_question_15_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_16_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_16_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_17_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_17_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_18_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_18_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_19_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_19_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_20_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_20_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_21_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_21_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_22_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_22_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_23_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_23_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_24_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_24_class_1_prob': Value(dtype='float64', id=None), 'answer_of_question_25_class_0_prob': Value(dtype='float64', id=None), 'answer_of_question_25_class_1_prob': Value(dtype='float64', id=None)}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1417, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1049, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1872, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 3 new columns ({'predict', 'p0', 'p1'}) and 52 missing columns ({'answer_of_question_7_class_0_prob', 'answer_of_question_8_class_0_prob', 'answer_of_question_15_class_0_prob', 'answer_of_question_22_class_0_prob', 'answer_of_question_6_class_1_prob', 'answer_of_question_19_class_0_prob', 'answer_of_question_9_class_0_prob', 'answer_of_question_8_class_1_prob', 'answer_of_question_13_class_0_prob', 'answer_of_question_9_class_1_prob', 'answer_of_question_23_class_0_prob', 'full_text_content_class_0_prob', 'answer_of_question_16_class_1_prob', 'answer_of_question_21_class_0_prob', 'answer_of_question_7_class_1_prob', 'answer_of_question_19_class_1_prob', 'answer_of_question_25_class_0_prob', 'answer_of_question_21_class_1_prob', 'answer_of_question_14_class_0_prob', 'answer_of_question_4_class_0_prob', 'answer_of_question_24_class_0_prob', 'answer_of_question_5_class_0_prob', 'answer_of_question_11_class_1_prob', 'answer_of_question_3_class_1_prob', 'answer_of_question_3_class_0_prob', 'answer_of_question_15_class_1_prob', 'answer_of_question_22_class_1_prob', 'answer_of_question_24_class_1_prob', 'answer_of_question_17_class_0_prob', 'answer_of_question_10_class_0_prob', 'answer_of_question_18_class_1_prob', 'answer_of_question_10_class_1_prob', 'answer_of_question_16_class_0_prob', 'answer_of_question_1_class_0_prob', 'answer_of_question_6_class_0_prob', 'answer_of_question_5_class_1_prob', 'answer_of_question_23_class_1_prob', 'answer_of_question_25_class_1_prob', 'answer_of_question_12_class_0_prob', 'answer_of_question_12_class_1_prob', 'answer_of_question_20_class_0_prob', 'answer_of_question_18_class_0_prob', 'answer_of_question_4_class_1_prob', 'answer_of_question_2_class_0_prob', 'answer_of_question_11_class_0_prob', 'full_text_content_class_1_prob', 'answer_of_question_2_class_1_prob', 'answer_of_question_17_class_1_prob', 'answer_of_question_13_class_1_prob', 'answer_of_question_14_class_1_prob', 'answer_of_question_1_class_1_prob', 'answer_of_question_20_class_1_prob'}).
              
              This happened while the csv dataset builder was generating data using
              
              zip://mainboard_text_feature_predictions/Success_Closemainboard_test_predictions_answer_of_question_1.csv::/tmp/hf-datasets-cache/medium/datasets/63964802673327-config-parquet-and-info-sohomghosh-Indian_IPO_dat-42b473c3/hub/datasets--sohomghosh--Indian_IPO_datasets/snapshots/a79a7d3395a9c21594aa650b84ff6602fa000ae4/nomic_embeddings_ml_predictions/mainboard_text_feature_predictions.zip
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

full_text_content_class_0_prob
float64
full_text_content_class_1_prob
float64
answer_of_question_1_class_0_prob
float64
answer_of_question_1_class_1_prob
float64
answer_of_question_2_class_0_prob
float64
answer_of_question_2_class_1_prob
float64
answer_of_question_3_class_0_prob
float64
answer_of_question_3_class_1_prob
float64
answer_of_question_4_class_0_prob
float64
answer_of_question_4_class_1_prob
float64
answer_of_question_5_class_0_prob
float64
answer_of_question_5_class_1_prob
float64
answer_of_question_6_class_0_prob
float64
answer_of_question_6_class_1_prob
float64
answer_of_question_7_class_0_prob
float64
answer_of_question_7_class_1_prob
float64
answer_of_question_8_class_0_prob
float64
answer_of_question_8_class_1_prob
float64
answer_of_question_9_class_0_prob
float64
answer_of_question_9_class_1_prob
float64
answer_of_question_10_class_0_prob
float64
answer_of_question_10_class_1_prob
float64
answer_of_question_11_class_0_prob
float64
answer_of_question_11_class_1_prob
float64
answer_of_question_12_class_0_prob
float64
answer_of_question_12_class_1_prob
float64
answer_of_question_13_class_0_prob
float64
answer_of_question_13_class_1_prob
float64
answer_of_question_14_class_0_prob
float64
answer_of_question_14_class_1_prob
float64
answer_of_question_15_class_0_prob
float64
answer_of_question_15_class_1_prob
float64
answer_of_question_16_class_0_prob
float64
answer_of_question_16_class_1_prob
float64
answer_of_question_17_class_0_prob
float64
answer_of_question_17_class_1_prob
float64
answer_of_question_18_class_0_prob
float64
answer_of_question_18_class_1_prob
float64
answer_of_question_19_class_0_prob
float64
answer_of_question_19_class_1_prob
float64
answer_of_question_20_class_0_prob
float64
answer_of_question_20_class_1_prob
float64
answer_of_question_21_class_0_prob
float64
answer_of_question_21_class_1_prob
float64
answer_of_question_22_class_0_prob
float64
answer_of_question_22_class_1_prob
float64
answer_of_question_23_class_0_prob
float64
answer_of_question_23_class_1_prob
float64
answer_of_question_24_class_0_prob
float64
answer_of_question_24_class_1_prob
float64
answer_of_question_25_class_0_prob
float64
answer_of_question_25_class_1_prob
float64
0.048705
0.951295
0.009507
0.990493
0.050521
0.949479
0.023363
0.976637
0.410781
0.589219
0.098255
0.901745
0.00834
0.99166
0.118809
0.881191
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0.877238
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0.658565
0.019153
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0.846792
0.016361
0.983639
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0.990054
0.052224
0.947776
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0.936504
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0.981303
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0.724588
0.333314
0.666686
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0.654599
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0.656559
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0.224473
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0.657661
0.020673
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0.208469
0.791531
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0.046974
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0.027234
0.972766
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0.992809
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0.885819
0.279931
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0.031856
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0.693076
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0.992153
0.346725
0.653275
0.336342
0.663658
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End of preview.

Codes: https://github.com/sohomghosh/Indian_IPO

Main Files

ipo_mainline_final_data_v18.xlsx and SME_data_final_v11.xlsx are the main files containing all the relevant features.

These files contain the following columns. NOTE: P = Presence (B= Both, M = Main Board, S = SME), T = Type of variable (I = Independent Variable i.e. Features, D = Dependent Variable i.e. Target)

P T Column Name Description
B I mapping_key Unique key for identifying each IPO
B I Company Name Name of the company going for IPO in short
B I Issuer Company Full Name of the company going for IPO
B I url URL corresponding to the company's IPo in chittorgarh.com
B I subscription_link URL to access the subscription information
M I Subscription Dates Dates on which IPO can be subscribed
B I NSE_symbol Ticker of the company in NSE
B I Total Issue Size Total monetary value of all shares being offered to the public
B I Offer for Sale Value of shares being offered for sale
B I Issue Type Fixed Price Issue or Book Building Issue
B I Listing Date Day on which IPO will get listed
B I Price Band The range of prices within which investors can bid for shares
B I Industry Industry of the company
B I Sector Sector of the company
B I IPO Date Duration for subscribing the IPO
B I Close Date Last day for IPO subscription
B I Close Year Year of the last day for IPO subscription
B I Close Year Previous One year before the year of the last day for IPO subscription
M I Exchange NSE or BSE
M I Issue Size (Rs Cr.) Total shares that a company proposes to offer
B I Final_Issue_Price Final Issue price of the company (on NSE for Main Board) (on BSE/NSE for SME whichever available)
S I BSE_Final_Issue_Price Issue price of the IPO on BSE
S I NSE_Final_Issue_Price Final Issue price of the SME IPO on NSE
B I Fresh Issue Value of shares being freshly issued
M I Lot Size Minimum number of shares that an investor must bid for
M I Open Date Date when IPO will be opened for subscription
B I _assets Asset of the company as on nth day
B I _net worth Net worth of the company as on nth day
B I _profit after tax Profit after tax of the company on the nth day
B I _reserves and surplus Reserves and surplus of the company on the nth day
B I _revenue Revenue of the company on the nth day
B I _total borrowing Total borrowing of the comany on the nth day
B I _total income Total income of the company on the nth day
B I 1 day before Close Day Date before 1 day before the last day for IPO subscription
B I 1 month before Close Day month number Month number of the month which is 1 month before the last day of IPO subscription
B I 1 month before Close Day year number Year of the month which is 1 month before the last day of IPO subscription
B I 1 week before Close Day week number Week number of the week which is 1 week before the last day of IPO subscription
B I 1 week before Close Day year number Year of the week which is 1 week before the last day of IPO subscription
B I Basis of Allotment Date when the final allocation of shares in an Initial Public Offering (IPO) is disclosed to investors.
B I Brokers_Avoid Number of top Brokers & Analysts who recommended to avoid the IPO
B I Brokers_Neutral Number of top Brokers & Analysts with neutral recommended for the IPO
B I Brokers_Subscribe Number of top Brokers & Analysts who recommended to subscribe for the IPO
B I Members_Avoid Number of Members who recommended to avoid the IPO
B I Members_Neutral Number of Members with neutral recommended for the IPO
B I Members_Subscribe Number of Members who recommended to subscribe for the IPO
B I Face Value per share Face value of a share
B I Credit of Shares to Demat Date on which shares would be credited to the demat account
B I Cut-off time for UPI mandate confirmation Time by which an investor must approve the UPI mandate request
B I day__date Date of day n
B I day__bNII (bids above 10L) Subscriptions by big Non-Institutional Investors on day n
B I day__emp Subscriptions by employees on day n
B I day__nii Subscriptions by Non-Institutional Investors (NII) on day n
B I day__nii* Subscriptions by other type of Non-Institutional Investors (NII*) on day n
B I day__other Subscriptions by others on day n
B I day__qib Subscriptions by Qualified Institutional Buyers (QIB) on day n
B I day__retail Subscriptions by Retail Investors on day n
B I day__total Total subscriptions on day n
B I Retail (Max)_Amount Maximum application amount for Retailers
B I Retail (Max)_Lots Maximum number of lots that a Retailers must apply for
B I Retail (Max)_Shares Maximum number of shares that a Retailers must apply for
B I Retail (Min)_Amount Minimum application amount for Retailers
B I Retail (Min)_Lots Minimum number of lots that a Retailers must apply for
B I Retail (Min)_Shares Minimum number of shares that a Retailers must apply for
M I B-HNI (Min)_Amount Minimum application amount for Big High Net-worth Individuals (B-HNIs)
M I B-HNI (Min)_Lots Minimum number of lots that a B-HNI must apply for
M I B-HNI (Min)_Shares Minimum number of shares that a B-HNI must apply for
M I S-HNI (Max)_Amount Maximum application amount for Small High Net-worth Individuals (S-HNIs)
M I S-HNI (Max)_Lots Maximum number of lots that a S-HNI must apply for
M I S-HNI (Max)_Shares Maximum number of shares that a S-HNI must apply for
M I S-HNI (Min)_Amount Minimum application amount for S-HNI
M I S-HNI (Min)_Lots Minimum number of lots that a S-HNI must apply for
M I S-HNI (Min)_Shares Minimum number of shares that a S-HNI must apply for
S I HNI (Min)_Amount Minimum application amount for High Net-worth Individuals (HNIs)
S I HNI (Min)_Lots Minimum number of lots that a HNI must apply for
S I HNI (Min)_Shares Minimum number of shares that a HNI must apply for
B I Share Holding Post Issue Distribution of ownership stakes in a company post IPO
B I Share Holding Pre Issue Distribution of ownership stakes in a company pre IPO
B I Stocks traded, total value (% of GDP) Value of stock traded as % of GDP in the year prior to IPO
B I dhrp_rhp_links Links for DHRP, RHP, Anchor Investor files
B I dhrp_rhp_links_pdf Links for DHRP, RHP, Anchor Investor pdf files
B I most_relevant_link Link to download prospectus. Preference is given to RHP followed by DRHP and Anchor Investor files.
B I File_Rename_1st Name of downloaded PDF file. Preference is given to RHP followed by DRHP and Anchor Investor files
B I Text_extracted_JSON Pagewise texts extracted from the PDF in json format
B I full_text_content Text content related to the IPO obtained from chittorgarh.com
B I news_content List of news relating to the company's IPO
B I news_headline List of news headlines relating to the company's IPO
B I news_synopsis List of news synopsis relating to the company's IPO
B I news_url List of urls corresponding to the news relating to the company's IPO
B I Chg%_nifty50_daily Change in nifty 50 index during the day previous to the Close Date
B I Chg%_nifty50_monthly Change in nifty 50 index during the month previous to the Close Date
B I Chg%_nifty50_weekly Change in nifty 50 index during the week previous to the Close Date
B I Chg%_vix_daily Change in vix index during the day previous to the Close Date
B I Chg%_vix_monthly Change in vix index during the month previous to the Close Date
B I Chg%_vix_weekly Change in vix index during the week previous to the Close Date
B I Open_nifty50_daily Opening Price of nifty 50 index during the day previous to the Close Date
B I Open_nifty50_monthly Opening Price of nifty 50 index during the month previous to the Close Date
B I Open_nifty50_weekly Opening Price of nifty 50 index during the week previous to the Close Date
B I Open_vix_daily Opening Price of vix during the day previous to the Close Date
B I Open_vix_monthly Opening Price of vix during the month previous to the Close Date
B I Open_vix_weekly Opening Price of vix during the week previous to the Close Date
B I High_nifty50_daily Highest value of nifty 50 index during the day previous to the Close Date
B I High_nifty50_monthly Highest value of nifty 50 index during the month previous to the Close Date
B I High_nifty50_weekly Highest value of nifty 50 index during the week previous to the Close Date
B I High_vix_daily Highest value of vix index during the day previous to the Close Date
B I High_vix_monthly Highest value of vix index during the month previous to the Close Date
B I High_vix_weekly Highest value of vix index during the week previous to the Close Date
B I Low_nifty50_daily Lowest Price of nifty 50 index during the day previous to the Close Date
B I Low_nifty50_monthly Lowest Price of nifty 50 index during the month previous to the Close Date
B I Low_nifty50_weekly Lowest Price of nifty 50 index during the week previous to the Close Date
B I Low_vix_daily Lowest Price of vix during the day previous to the Close Date
B I Low_vix_monthly Lowest Price of vix during the month previous to the Close Date
B I Low_vix_weekly Lowest Price of vix during the week previous to the Close Date
B I Price_nifty50_daily Closing Price of nifty 50 index during the day previous to the Close Date
B I Price_nifty50_monthly Closing Price of nifty 50 index during the month previous to the Close Date
B I Price_nifty50_weekly Closing Price of nifty 50 index during the week previous to the Close Date
B I Price_vix_daily Closing Price of vix during the day previous to the Close Date
B I Price_vix_monthly Closing Price of vix during the month previous to the Close Date
B I Price_vix_weekly Closing Price of vix during the week previous to the Close Date
B I Volume_nifty50_daily Volume traded in nifty 50 during the day previous to the Close Date
B I Volume_nifty50_monthly Volume traded in nifty 50 during the month previous to the Close Date
B I Volume_nifty50_weekly Volume traded in nifty 50 during the week previous to the Close Date
B I dynamic_last_90Day_success_close Average success rate calculated using close price in the last 90 days prior to ipo close day
B I dynamic_last_90Day_success_high Average success rate calculated using high price in the last 90 days prior to ipo close day
B I dynamic_last_90Day_success_open Average success rate calculated using open price in the last 90 days prior to ipo close day
B I previous_quarter Quarter before the Close Date
B I previous_quarter_success_close Average success rate calculated using close price in the previous calendar quarter
B I previous_quarter_success_high Average success rate calculated using high price in the previous calendar quarter
B I previous_quarter_success_open Average success rate calculated using open price in the previous calendar quarter
B I Foreign direct investment, net (BoP, current US$) Net Foreign Direct Investment happend in the year prior to IPO
B I Foreign direct investment, net inflows (BoP, current US$) Net inflow from Foreign Direct Investment happend in the year prior to IPO
B I GDP (current US$) GDP of India in the year prior to IPO
B I GDP growth (annual %) Annual growth % in GDP of India in the year prior to IPO
B I GDP per capita growth (annual %) GDP per capita growth as annual % in the year prior to IPO
B I GNI (current US$) Gross National Income in the year prior to IPO
B I GNI per capita growth (annual %) Gross National Incom per capita growth as annual % in the year prior to IPO
B I Inflation, consumer prices (annual %) Inflation rate in the year prior to the IPO
B I Personal remittances, received (% of GDP) Received personal remittances as % of GDP on the year prior to IPO
B I Initiation of Refunds Date on which refunds are initiated to the investors who are not allocated any shares
B I Net trade in goods and services (BoP, current US$) Net trade in goods and services in the year prior to the IPO
B I Unemployment, total (% of total labor force) (modeled ILO estimate) Unemployment rate in the year prior to the IPO
B I answer_of_question_ Answer generated using LLM for the nth question. 1<=n<=25
S D BSE_High Highest price of the stock on BSE on the Listing Day
S D BSE_Low Lowest price of the stock on BSE on the Listing Day
S D BSE_Open Opening price of the stock on BSE on the Listing Day
B D BSE_Last_Trade Closing price on BSE of the IPO on the Listing Day
B D NSE_High Highest price of the stock on NSE on the Listing Day
B D NSE_Last_Trade Closing price of the stock on NSE on the Listing Day
B D NSE_Low Lowest price of the stock on NSE on the Listing Day
B D NSE_Open Opening price of the stock on NSE on the Listing Day
S D High Overall highest price of the SME IPO on Lisitng day
S D Last_Trade Overall closing price of the SME IPO on Lisitng Day
S D Low Overall lowest price of the SME IPO on Lisitng Day
S D Open Overall open price of the SME IPO on lisitng day
B D Success_Close Success of the IPO in terms of closing price on the Listing Day
B D Success_High Success of the IPO in terms of highest price on the Listing Day
B D Success_Open Success of the IPO in terms of opening price on the Listing Day
M D elasticity ((open price - issue price)/issue price)/(subscription rate - 1) (It is not used in this study)
M D Total_subscriptions Total subscriptions the IPO received

Files having DeBERTa probabilities

deberta_probabilities/deberta_probabilities_<mb or sme>_success_<open or high or close>.csv

Files having probabilities from Machine Learning models trained using Nomic

nomic_embeddings_ml_predictions/<mainboard or sme>_text_feature_predictions.zip

Files having Grey Maret Price (GMP) data

GMP_data/GMP_Mainline_2019_2024YTD_Investorgrain_CLEAN.xlsx, GMP_SME_2019_2024YTD_Investorgrain_CLEAN_v2.xlsx

Files having raw text extracted from DRHP/RHP PDFs

texts_extracted_from_pdfs/ipo_mainline_txts_extracted.zip, texts_extracted_from_pdfs/ipo_sme_txts_extracted.zip

List of questions asked while leveraging the Retrieval-Augmented Generation (RAG) framework

questions/questions_to_ask_rhp.csv

@misc{ghosh2024experimentingmultimodalinformationpredict,
      title={Experimenting with Multi-modal Information to Predict Success of Indian IPOs}, 
      author={Sohom Ghosh and Arnab Maji and N Harsha Vardhan and Sudip Kumar Naskar},
      year={2024},
      eprint={2412.16174},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2412.16174}, 
}
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