id
string | instruction_en
string | source_files
list | source_files_urls
list | reference_outputs
dict | reference_file_urls
list | task_type
string | business_type
string |
|---|---|---|---|---|---|---|---|
100
|
Update the Canada Non-Commercial functional distribution table, ensuring the table remains aligned with the latest staffing data in Sheet2. Additionally, add two columns — Total and %Total — to display the total headcount and percentage for each group.
|
[
"100_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/100/100_src_0.xlsx"
] | null | null |
Structuring / Formatting, Calculation, Validation / Review
|
Operational Management
|
101
|
Standardize all date entries in the spreadsheet to the 01-FEB-2002 format (dd-MMM-yyyy). Month headers should read consistently as 01-JAN-2002, 01-FEB-2002, etc. Pay attention to case sensitivity.
|
[
"101_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/101/101_src_0.xlsx"
] | null | null |
Structuring / Formatting
|
Trading and Risk Management
|
102
|
Create a new Transfer worksheet that summarizes LONGS, SHORT, and NET by delivery month, with Total Longs, Total Shorts, and Total Net at the bottom; the month list should follow the Z, F1… sequence covering all months present, and show 0 for months with no data. In Access Trades, add a side display for TOTAL LONG and TOTAL SHORT with two Notes columns to confirm these tie to the Transfer totals (“ok” if consistent, otherwise note the variance), and return the updated workbook including the Transfer summary and the Access Trades validation block.
|
[
"102_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/102/102_src_0.xlsx"
] | null | null |
Structuring / Formatting, Calculation, Validation / Review
|
Trading and Risk Management
|
103
|
On Sheet4, create two monthly, time-aligned summaries: 'Peoples Deal Value' should sum the Mid Value by month from the 'Peoples Baseload Sale' and 'Transport' sheets (same-month totals), with months without data shown as 0 and a grand total at the end; 'PERC Deal Value' should sum the Mid Value by month from the 'PERC' sheet, with months without data shown as 0. Ensure the month sets for both tables are aligned and sorted chronologically.
|
[
"103_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/103/103_src_0.xlsx"
] | null | null |
Structuring / Formatting, Calculation
|
Pricing and Valuation
|
104
|
Insert a 'Total Available' row and a 'Total Allocated' row, then complete the reconciliation line to verify that 'Total Available' and 'Total Allocated' are consistent.
|
[
"104_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/104/104_src_0.xlsx"
] | null | null |
Structuring / Formatting, Validation / Review, Calculation
|
Operational Management, Predictive Modeling
|
105
|
Add the 2/11/2000 column on the Feb 00 tab by mirroring the 2/10 data, with the following adjustments: Houston Pipe Line should be 6,000 (instead of 9,000), and for Huntsville and Woodlands use 1,000 and 5,000 respectively. All other items remain unchanged from 2/10.
|
[
"105_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/105/105_src_0.xlsx"
] | null | null |
Data Entry / Import, Structuring / Formatting
|
Operational Management, Predictive Modeling
|
106
|
On Sheet3, append the month-over-month percentage changes for BSCTMP and SBSK, then compute the Pearson correlation between their monthly price change rates.
|
[
"106_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/106/106_src_0.xlsx"
] | null | null |
Structuring / Formatting, Calculation
|
Predictive Modeling
|
107
|
On Sheet3, insert on the left side of the sheet a results table with a top-header showing the series names “BSCTMP” and “SBSK,” and two subsections with left-hand labels “1–6 month lag” and “1–6 month lead. In the 1–6 month lag section, quantify the timing relationship by calculating Pearson correlations between SBSK and BSCTMP shifted 1–6 months later and entering the resulting coefficients under the headings “1 month lag” through “6 month lag.” In the 1–6 month lead section, calculate correlations between BSCTMP and SBSK shifted 1–6 months later and entering the coefficients under the headings “1 month lead” through “6 month lead.
|
[
"107_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/107/107_src_0.xlsx"
] | null | null |
Calculation
|
Predictive Modeling
|
108
|
Update the data in first source file based on the second source file.
|
[
"108_src_0.xlsx",
"108_src_1.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/108/108_src_0.xlsx",
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/108/108_src_1.xlsx"
] | null | null |
Cross-sheet/file Retrieval, Data Entry / Import
|
Report, Planning and Budgeting
|
109
|
Calculate the total FTE percentage by region and by business line, and roll these up into a consolidated summary. This should include totals across EWS, EES, EGM, M&A, and EGA for each region (US Energy, Canada, Federal), with an overall summary of the combined FTE percentages.
|
[
"109_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/109/109_src_0.xlsx"
] | null | null |
Calculation
|
Operational Management, Planning and Budgeting
|
110
|
Using the daily PGE/SCG spot midpoints, the TW base price, the fuel‑loss factor, the fixed commodity cost (0.0246), and the day’s delivered volumes to PGE and SCG, please calculate Adj TW Per, the net unit spreads at PGE and SCG, the theoretical profit (spread × volume), the daily total profit, and the Astra/TW 70%/30% profit split, and roll these up to a full‑month summary. Among these values, keep four decimal places for Adj TW Per, the net unit spreads at PGE and SCG, and keep two decimal places for all other figures.
|
[
"110_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/110/110_src_0.xlsx"
] | null | null |
Calculation, Structuring / Formatting
|
Planning and Budgeting
|
111
|
Recompute and update the entire workbook under the new operating assumptions, without changing the original price and volume inputs (PGE/SCG Midpoint, TW benchmark price, and commodity cost at 0.0246). Reduce the TW→PGE fuel loss factor to 4.0% and change the profit split to Astra 60% / TW 40%, which better reflects joint sharing of market and scheduling risk.
|
[
"111_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/111/111_src_0.xlsx"
] | null | null |
Calculation
|
Planning and Budgeting
|
112
|
For each record, use the Frequency to place the Rent amount into the corresponding Total Weekly, Total Biweekly, or Total Monthly column, and leave the non-applicable total columns blank. Then roll up the Sub-Total for each frequency and estimate the Annual Total.
|
[
"112_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/112/112_src_0.xlsx"
] | null | null |
Cross-sheet/file Retrieval, Data Entry / Import, Calculation
|
Operational Management, Accounts Payable and Receivable
|
113
|
Update the Active Deals vs Headcount sheet with the monthly Active Deals and headcount (HC), and capture the growth amount and the percentage increase from Sep 99 to Mar 01.
|
[
"113_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/113/113_src_0.xlsx"
] | null | null |
Data Entry / Import, Calculation, Cross-sheet/file Retrieval
|
Operational Management, Planning and Budgeting
|
114
|
Using the trades as the source, identify and fill in the applicable asset and liability items for ENA Corp and ECT Inv, then roll them up into a summary. Please ensure the entries reflect only the portions relevant to each entity.
|
[
"114_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/114/114_src_0.xlsx"
] | null | null |
Data Entry / Import, Cross-sheet/file Retrieval, Calculation
|
Pricing and Valuation, Trading and Risk Management
|
115
|
Please track and compile the average monthly house prices for England, Scotland, Wales and Northern Ireland, for the period from January 2022 to April 2025 (including January 2022 and April 2025). Please cite all the statistics from government websites.\n\n[Data Source Note] Please use the UK House Price Index (HPI) data published on GOV.UK / HM Land Registry or ONS official sources. Note: HPI data is periodically revised; data from different publication dates is acceptable as long as sourced from official government websites. For data from February 2024 onwards, rounded values (to nearest thousand) from ONS bulletins are acceptable.\n\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the following column names in order: Month, House price in England(Pounds), House price in Scotland(Pounds), House price in Wales(Pounds), House price in Northern Ireland(Pounds)\nFor the column 'Month', list the month in the format 2022/01, 2022/02.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily.\n\nBased on the RawData sheet, complete the following 4 subtasks:\n\nSubtask 1 - Sheet2_Quarterly_Aggregation: Convert monthly data to quarterly averages by region.\nColumns: Quarter, England Avg Price, Scotland Avg Price, Wales Avg Price, Northern Ireland Avg Price\nQuarter format: 2022-Q1, 2022-Q2, etc. Each price value with 2 decimals. Sort chronologically from 2022-Q1 to 2025-Q2.\n\nSubtask 2 - Sheet3_Price_Changes: Calculate month-over-month price changes.\nColumns: Month, England MoM Change (%), Scotland MoM Change (%), Wales MoM Change (%), Northern Ireland MoM Change (%)\nAll percentages with 2 decimals. Start from 2022/02 (comparing to 2022/01).\n\nSubtask 3 - Sheet4_Price_Classification: Classify each region's price level for each month.\nColumns: Month, England Category, Scotland Category, Wales Category, Northern Ireland Category\nClassification rules: \"Very High\" if >= 310,000 (England), >= 200,000 (Scotland), >= 225,000 (Wales), >= 190,000 (Northern Ireland); \"High\" if >= 300,000 / >= 190,000 / >= 215,000 / >= 180,000; \"Medium\" if >= 290,000 / >= 180,000 / >= 205,000 / >= 170,000; \"Low\" otherwise.\n\nSubtask 4 - Sheet5_Summary_Statistics: Calculate summary statistics for each region.\nColumns: Region, Max Price, Max Price Month, Min Price, Min Price Month, Overall Change (%), Peak-to-Current Change (%), Volatility Score\nOverall Change = (Price in 2025/04 - Price in 2022/01) / Price in 2022/01 x 100 (2 decimals). Peak-to-Current Change = (Price in 2025/04 - Max Price) / Max Price x 100 (2 decimals). Volatility Score = STDEV.P (population standard deviation) of all monthly prices / AVERAGE x 100 (2 decimals). Max/Min Price with 2 decimals.
|
[] |
[] | null | null |
Web Search, Data Entry / Import, Structuring / Formatting, Calculation, Summary / Visualization
|
Report
|
116
|
Compare the month-to-month movement direction of ERCOT peak power prices and NYMEX natural gas prices in 2002 (i.e., whether each month is higher or lower than the previous month). Based on the 12 monthly changes in 2002, determine how many months the two prices moved in the same direction and how many months they moved in opposite directions.
|
[
"116_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/116/116_src_0.xlsx"
] | null | null |
Calculation
|
Pricing and Valuation, Trading and Risk Management
|
117
|
Standardize the font across all data entries so they ues a single, consistent typeface throughout. No content changes are required; this is a formatting cleanup only.
|
[
"117_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/117/117_src_0.xlsx"
] | null | null |
Structuring / Formatting
|
Trading and Risk Management
|
118
|
What is the TW EOL charge for 2002? Pls just provide the amount.
|
[
"118_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/118/118_src_0.xlsx"
] | null | null |
Cross-sheet/file Retrieval
|
Operational Management
|
119
|
How many plants are recorded in the spreadsheet?
|
[
"119_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/119/119_src_0.xlsx"
] | null | null |
Calculation
|
Operational Management
|
120
|
Audit the workbook and correct the formula errors in place so numbers calculate properly.
|
[
"120_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/120/120_src_0.xlsx"
] | null | null |
Validation / Review, Calculation
|
Operational Management
|
121
|
Audit the Deal Sheet in the workbook and correct the formula errors in place so numbers calculate properly.
|
[
"121_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/121/121_src_0.xlsx"
] | null | null |
Validation / Review, Calculation
|
Planning and Budgeting, Trading and Risk Management
|
122
|
Create a new sheet named “Exp by Fun Gen Support Chart5” and, on this sheet, build two pie charts based on the two existing data tables.
The top pie chart should show “Expenditures by Function – All Funds”. Use the amounts from the “TOTAL EXPENDITURES” detail lines for each function in the All Funds summary table, calculate each function’s percentage of total expenditures, and make each function one slice. Label each slice with the function name and its percentage, and add a legend with the same function names.
The bottom pie chart should show the “breakdown of General Support Services”. Use the amounts for all rows that belong to the General Support Services function in the detailed table, calculate each item’s percentage of the total General Support Services amount, and make each item one slice. Again, label each slice with the item name and its percentage, and include a matching legend.
Both pie charts should be 3-D and visually well-balanced.
|
[
"122_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/122/122_src_0.xlsx"
] | null | null |
Summary / Visualization, Cross-sheet/file Retrieval
|
Report
|
123
|
Assume that in January 2002 you enter a “portfolio” by going long ERCOT peak power and short an equal-scale amount of NYMEX natural gas. Over the full year 2002, does this portfolio make or lose money, and what is its nominal return?
|
[
"123_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/123/123_src_0.xlsx"
] | null | null |
Calculation
|
Pricing and Valuation, Trading and Risk Management
|
124
|
Complete the content in the summary sheet based on other spreadsheets. Leave blank if no information found.
|
[
"124_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/124/124_src_0.xlsx"
] | null | null |
Cross-sheet/file Retrieval, Calculation, Data Entry / Import
|
Operational Management, Report
|
125
|
You are given an Excel table (Figure 1.19) showing, for IDA-eligible countries, disbursements, amortization, and interest on public and publicly guaranteed debt in 2021–2023 by creditor type (Multilateral, IMF, Bilateral, Commercial bank and other, Bondholders), as well as 2021–2023 totals. At the bottom of the table, the total PPG long-term debt net transfers for 2022 and 2023 are reported.
Using only the data in the table, please:
Produce at least one chart that displays disbursements, amortization, and interest for each creditor type in 2021–2023.
Write a structured economic analysis of no more than 300 words, summarizing the main trends, key turning points, and possible financing behavior patterns. In your analysis, calculate and cite the increase in total long-term debt net transfers in 2023.
|
[
"125_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/125/125_src_0.xlsx"
] | null | null |
Calculation, Summary / Visualization
|
Report
|
126
|
Convert the 'EOTT TX-NM Facilities' worksheet into a standard table and filter it to display Andrew’s county only.
|
[
"126_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/126/126_src_0.xlsx"
] | null | null |
Structuring / Formatting
|
Asset Management
|
127
|
Complete the Existing Deals with Huber and Proposed Deals with Huber summaries by cross-referencing the Exposure sheet to capture each contract’s start and end dates, delivery point, and contract price.
Calculate the average of 55-day payables at each designated time point, and aggregate these amounts into the summary totals corresponding to their respective time periods.
|
[
"127_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/127/127_src_0.xlsx"
] | null | null |
Calculation, Summary / Visualization
|
Predictive Modeling
|
128
|
Prepare a stacked area chart titled "Existing and Proposed Deals with Huber", showing monthly time on the X-axis (from December 2001 to December 2006) and daily gas supply volumes (MMBtu/d) on the Y-axis in the Summary sheet. Each colored band should represent a distinct pricing benchmark or contract type (deal), illustrating how volumes vary over time
|
[
"128_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/128/128_src_0.xlsx"
] | null | null |
Summary / Visualization, Cross-sheet/file Retrieval
|
Predictive Modeling
|
129
|
Create a stacked area chart titled “Rolling 55 Day Payables (as of the 25th of the Month)” to visualize rolling payable amounts across multiple natural gas price indices from December 2001 to December 2006 in the Summary sheet. The Y-axis should be dollars and the X-axis monthly time, with each data point representing the rolling 55-day payables total as of the 25th of that month.
|
[
"129_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/129/129_src_0.xlsx"
] | null | null |
Summary / Visualization, Cross-sheet/file Retrieval
|
Predictive Modeling
|
130
|
Update the weekly sheet’s rent table to include four new columns — “Total Weekly,” “Total Biweekly,” “Total Monthly,” and “Grand Total” — and populate each tenant’s amounts by week, every two weeks, and monthly, leaving missing items as blank cells. At the bottom, add a “Sub-total” row that sums each column, a “Number of Periods” row reflecting the annual payment count for each tenant based on their rent frequency, and an “Annual Totals” row that calculates the yearly totals for each column; the “Grand Total” column should only be populated on the “Annual Totals” row and remain blank elsewhere.
|
[
"130_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/130/130_src_0.xlsx"
] | null | null |
Structuring / Formatting, Calculation
|
Accounts Payable and Receivable
|
131
|
Update the Beaumont Worksheet to reflect a Base Volume assumption of 17,000 (from 18,000) and roll the change through all affected calculations and summaries. Ensure the threshold/labels (e.g., “>17,000”) and the excess/deficit, buyback, swing, baseload, and invoice totals are consistent with the new base.
|
[
"131_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/131/131_src_0.xlsx"
] | null | null |
Calculation, Validation / Review
|
Procurement and Sales
|
132
|
Update the 'overview' worksheet with the new data located to the right of the 'to be moved to line:' label on the 'support for reclass of ENA' sheet. Also insert a 'TOTAL EWS' line under the top header.
|
[
"132_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/132/132_src_0.xlsx"
] | null | null |
Data Entry / Import, Structuring / Formatting, Cross-sheet/file Retrieval
|
Trading and Risk Management
|
133
|
Audit the consolidated 2002 plan workbook and correct the formula errors and omissions so the subtotals and roll-ups calculate properly.
|
[
"133_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/133/133_src_0.xlsx"
] | null | null |
Validation / Review, Calculation
|
Planning and Budgeting
|
134
|
Shift the merged title so it begins in column B rather than A to align with the table, and delete all rows that are not marked "violation."
|
[
"134_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/134/134_src_0.xlsx"
] | null | null |
Structuring / Formatting
|
Trading and Risk Management
|
135
|
Create four grey area charts for the AGA Storage Report – Working Gas year-over-year comparison, showing the weekly difference versus the same week last year (in Bcf). Place them on Prod_Yr-Yr (Producing), East_Yr-Yr (East Consuming), West_Yr-Yr (West), and Total_Yr-Yr (Total), with the x-axis running through the week ending 9/23.
|
[
"135_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/135/135_src_0.xlsx"
] | null | null |
Summary / Visualization, Structuring / Formatting
|
Predictive Modeling
|
136
|
Build four multi-year line charts for “AGA Storage Report – Working Gas,” covering Producing, Consuming East, Consuming West, and Total, that compare weekly Storage Level (Bcf) across 1994/95, 1995/96, 1996/97, 1997/98, 1998/99, and 1999/00. Use Bcf on the Y-axis and Week Ending (from 5-Nov to 21-Oct) on the X-axis, include a vertical dashed line separating WINTER and SUMMER, and place each chart on sheets named Producing, East, West, and Total.
|
[
"136_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/136/136_src_0.xlsx"
] | null | null |
Summary / Visualization, Structuring / Formatting
|
Predictive Modeling
|
137
|
Update the NC_PL worksheet so its formatting matches the style used on NC_BS.
|
[
"137_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/137/137_src_0.xlsx"
] | null | null |
Structuring / Formatting
|
Report
|
138
|
Add a new worksheet titled “P&C” and build a Property & Casualty Insurance Highlights page modeled after the other summary tabs. Set the header at the top (American Financial Group; Property & Casualty Insurance Highlights; (In millions)) and lay out Fourth Quarter 2003 vs. 2002 and Twelve Months Ended 2003 vs. 2002 with an “Inc (Dec)” column, consistent with the existing format.
|
[
"138_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/138/138_src_0.xlsx"
] | null | null |
Structuring / Formatting, Summary / Visualization
|
Report
|
139
|
Using the Cleburne Plant Damage Sensitivities, evaluate the financial impact at plant capacities of 240MW, 245MW, 258MW, and 263MW. For Q1–Q4, compute and compare the implied equity value at the contract effective date and subsequent dates, determine damages per Section 11.02(i)(a) under current conditions, assess the impact of modifying the interest rate adjustment on the capacity payment, and the result if the interest rate adjustment is fully removed, then quantify the deltas versus the 263MW baseline to show sensitivity of equity value and damages.
|
[
"139_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/139/139_src_0.xlsx"
] | null | null |
Financial Modeling, Calculation, Summary / Visualization
|
Predictive Modeling
|
140
|
Compute the Monthly Volume and, under different Prime Rate and Corporate Rate assumptions, calculate the monthly Carrying Cost and resulting P/L, then provide the annual roll-up summary.
|
[
"140_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/140/140_src_0.xlsx"
] | null | null |
Calculation
|
Planning and Budgeting
|
141
|
Identify and populate, for each Trader, the corresponding Book Organization for both Power and Natural Gas, using the short-form names only. The update should reflect the abbreviated book codes aligned to each trader (e.g., NETCO power books and PWR-GAS gas books).
|
[
"141_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/141/141_src_0.xlsx"
] | null | null |
Data Entry / Import, Cross-sheet/file Retrieval
|
Trading and Risk Management
|
142
|
Under the assumptions of Scenario 1, calculate and populate—for each gas point—the total position (contracts), the 30‑day average standard deviation, and the P&L (US$), then aggregate to a grand total for P&L.
|
[
"142_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/142/142_src_0.xlsx"
] | null | null |
Calculation
|
Trading and Risk Management
|
143
|
Apply Scenario 2 to calculate the current positions, the 30-day standard deviation and the P&L (US$) for each gas point and then provide the aggregated total for P&L. This should reflect the Scenario 2 convention.
|
[
"143_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/143/143_src_0.xlsx"
] | null | null |
Calculation
|
Trading and Risk Management
|
144
|
Using the daily Crude Oil and Natural Gas prices recorded in the NYMEX Settlement Price Averages table, calculate the required averages and the settlement reference prices, and populate the blank fields at the bottom of the table. Format CRUDE OIL values to three decimal places and Natural Gas values to five decimal places. If that particular statistic doesn’t apply to a given column, just fill in “N/A.”
|
[
"144_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/144/144_src_0.xlsx"
] | null | null |
Calculation, Structuring / Formatting
|
Trading and Risk Management
|
145
|
I want to compare the labor market between four southern US states, namely Alabama, Georgia, Louisiana, and Mississippi. I need to compile monthly employment data not seasonally adjusted for these states from January 2024 to June 2024 (including January 2024 and June 2024). Please provide the following:\nUnadjusted Unemployment Rate\nLabor Force Participation Rate\nAll Employees – Total Nonfarm (Thousands of Persons)\nAll Employees – Manufacturing (Thousands of Persons)\nAverage Hourly Earnings of All Employees (Total Private)\nAverage Hourly Earnings of All Employees (Manufacturing)\n\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the following column names in order:\nStates, Statistical Month, Unadjusted Unemployment Rate(%), Labor Force Participation Rate(%), All Employees – Total Nonfarm(in thousands), All Employees – Manufacturing(in thousands), Average Hourly Earnings of All Employees (Total Private), Average Hourly Earnings of All Employees (Manufacturing)\nFor Statistical Month, use the format yyyy-mm, like 2024-06.\n\nDon't ask me any questions, just output the results according to the columns without omitting cells arbitrarily.\n\nBased on the RawData sheet, complete the following 4 subtasks:\n\nSubtask 1 - Sheet2_State_Ranking: Rank states by overall labor market health (6-month average).\nColumns: Rank, State, Avg Unemployment Rate (%), Avg Participation Rate (%), Avg Total Employment (thousands), Avg Manufacturing Employment (thousands), Avg Wage - Private ($), Avg Wage - Manufacturing ($), Labor Market Health Score\nHealth Score = (100 - Avg Unemployment) + Avg Participation + (Avg Wage Private - 25). All percentages 2 decimals, employment figures 1 decimal, wages 2 decimals, health score 2 decimals. Sort by health score descending.\n\nSubtask 2 - Sheet3_Monthly_Trends: Calculate month-over-month changes for each state (Feb-Jun, 5 months per state = 20 rows).\nColumns: State, Month, Unemployment Change (pp), Participation Change (pp), Total Employment Growth (%), Manufacturing Employment Growth (%), Wage Growth - Private (%)\nUse formula (Current-Previous)/Previous×100 for growth rates. All values 2 decimals. Sort by State then Month.\n\nSubtask 3 - Sheet4_Employment_Structure: Employment composition analysis for each state (June 2024 snapshot).\nColumns: State, Total Nonfarm Employment (thousands), Manufacturing Employment (thousands), Non-Manufacturing Employment (thousands), Manufacturing Share (%), Total Employment 6M Growth (%), Manufacturing 6M Growth (%)\nNon-Manufacturing = Total - Manufacturing. Share = Manufacturing/Total×100. 6M Growth = (Jun-Jan)/Jan×100. Employment 1 decimal, percentages 2 decimals.\n\nSubtask 4 - Sheet5_Wage_Analysis: Wage premium and volatility analysis by state (6-month statistics).\nColumns: State, Avg Private Wage ($), Avg Manufacturing Wage ($), Wage Premium ($), Wage Premium (%), Wage Volatility - Private ($), Wage Range - Private ($), Premium Category\nPremium = Mfg Wage - Private Wage. Premium % = Premium/Private×100. Volatility = STDEV. Range = MAX - MIN. Category: \"High Premium\" if Premium % > 5%, \"Medium Premium\" if 0-5%, \"Low Premium\" if 0 to -5%, \"Negative Premium\" if < -5%. All wages 2 decimals."
|
[] |
[] | null | null |
Web Search, Data Entry / Import, Structuring / Formatting, Calculation, Summary / Visualization
|
Report
|
146
|
Please compare the growth trajectory of the leading global e-commerce platforms by compiling their quarterly performance for 2024—Amazon, eBay, Shopee (Sea), Alibaba. Use figures from official financial reports for publicly traded companies; for privately held firms, cite reputable news sources and mark relevant numbers with an asterisk (*) to denote that they are estimates. For information you can't find online, just fill in with \"–\".\n\n[Data Source Note] Please use official financial reports for FY2024 (versions published before February 28, 2025). For companies that report in non-USD currencies (e.g., Alibaba), do not recalculate FX yourself—always use the USD figures exactly as stated in the company's official quarterly earnings release, which already apply a fixed exchange rate (the Federal Reserve H.10 rate on or near the quarter-end date). For Alibaba, use the \"Net income\" line (not \"Net income attributable to ordinary shareholders\") as the net profit metric.\n\nPlease output an xlsx file with Sheet1 as the RawData sheet, with the columns (in this order): Company, Quarter, GMV (USD bn), Revenue (USD bn), Net Profit (USD bn)\n\nFor Quarters, label it simply Q1, Q2, etc.\n\nDon't ask me any questions, just output the results according to the column without omitting cells arbitrarily.\n\nBased on the RawData sheet, complete the following 4 subtasks:\n\nSubtask 1 - Sheet2_Financial_Indicators: Calculate key financial performance metrics for each company-quarter.\nColumns: Company, Quarter, Revenue (USD bn), Net Profit (USD bn), Net Profit Margin (%), Monetization Rate (%), Profit per Revenue Dollar ($), Profitability Status\nNet Profit Margin = (Net Profit / Revenue) x 100 (2 decimals). Monetization Rate = (Revenue / GMV) x 100 (2 decimals, \"N/A\" if no GMV). Profit per Revenue Dollar = Net Profit / Revenue (4 decimals). Profitability Status: \"Highly Profitable\" if Net Profit >= $5bn, \"Profitable\" if >= $0, \"Near Breakeven\" if >= -$0.1bn, \"Loss Making\" if < -$0.1bn, \"Unknown\" if missing.\n\nSubtask 2 - Sheet3_Comparison_Matrix: Restructure data into company comparison matrix (pivot structure).\nColumns: Metric, Quarter, Amazon, eBay, Shopee (Sea), Alibaba\nCreate 3 metric blocks (12 rows total): Revenue (USD bn) for Q1-Q4, Net Profit (USD bn) for Q1-Q4, Net Profit Margin (%) for Q1-Q4. All values: 2 decimals, \"N/A\" if unavailable.\n\nSubtask 3 - Sheet4_Growth_Classification: Annual growth analysis and classification by company.\nColumns: Company, Total Revenue (USD bn), Total Net Profit (USD bn), Avg Quarterly Revenue (USD bn), Avg Quarterly Profit (USD bn), Q1-Q4 Revenue Growth (%), Q1-Q4 Profit Change (%), Revenue Growth Category, Profit Trend, Avg Business Model Efficiency\nQ1-Q4 Revenue Growth = (Q4 Revenue - Q1 Revenue) / Q1 Revenue x 100 (2 decimals). Profit Change: output as a percentage string with two decimals (e.g., \"92.31%\"), or \"Turnaround\" if it went from loss to profit. Revenue Growth Category: \"High Growth\" if >=30%, \"Moderate Growth\" if >=10%, \"Stable\" if >=0%, \"Declining\" if <0%. Profit Trend: \"Consistently Improving\" if all quarters increase, \"Declining\" if all decrease, \"Overall Improving\" if Q4>Q1, else \"Volatile\". Business Model Efficiency = Avg Revenue / Avg GMV (4 decimals, \"N/A\" if no GMV).\n\nSubtask 4 - Sheet5_Data_Validation: Validate data quality and identify anomalies.\nColumns: Company, Quarter, Has GMV, Has All Financial Data, Data Completeness (%), Validation Status, Quality Score, Quality Grade\nHas GMV: \"Yes\"/\"No\". Has All Financial Data: \"Yes\" if Revenue and Net Profit exist. Data Completeness = (sum of available fields / 3) x 100 (2 decimals, counting GMV, Revenue, Net Profit). Quality Score: starts from Completeness, -10 if no GMV, -5 per issue (2 decimals). Quality Grade: \"Excellent\" if >=90, \"Good\" if >=70, \"Fair\" if >=50, \"Poor\" if <50.
|
[] |
[] | null | null |
Web Search, Data Entry / Import, Structuring / Formatting, Calculation, Validation / Review
|
Report
|
147
|
Fill in the cells highlighted with a blue background, and then remove the background color.
|
[
"147_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/147/147_src_0.xlsx"
] | null | null |
Data Entry / Import, Structuring / Formatting, Cross-sheet/file Retrieval, Calculation
|
Trading and Risk Management, Investment: Credit, Pricing and Valuation
|
148
|
Switch the timing/rotation model to Method 1 and set the moving average to 120 with a deviation threshold of 35%. After updating the parameters, ensure the model outputs are refreshed to reflect the new settings.
|
[
"148_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/148/148_src_0.xlsx"
] | null | null |
Structuring / Formatting, Calculation, Financial Modeling
|
Predictive Modeling
|
149
|
Task Background:
Global growth is expected to remain steady for the foreseeable future despite escalating geopolitical tensions and heightened uncertainty surrounding global trade policy. Global inflation is expected to moderate, allowing central banks to ease monetary policy to support economic activity. However, the outlook remains subdued for low- and middle-income countries (LMICs), particularly for those with low creditworthiness, constrained fiscal space, and significant political and social unrest. Risks to the macroeconomic outlook for LMICs are tilted to the downside, including the escalation of armed conflicts, further trade fragmentation, persistent global inflation, a weaker global risk appetite, and slower-than-expected growth in major LMICs, especially China.
In 2023, LMICs accumulated significant additional debt and faced the corresponding heavy debt service burden. Although global interest rates are declining, debt service costs are expected to moderate gradually. However, LMICs' debt outlook still carries downside risks. The growing prominence of nontraditional creditors, particularly the accumulation of debt owed to China, complicates debt resolution. This is especially critical for small states with underdeveloped domestic financial markets. Furthermore, higher borrowing costs and increased debt service burdens may exacerbate fiscal challenges, especially in LMICs with tightening fiscal policies.
Task Objective:
Based on the provided spreadsheet data, write a report. The report should include the following:
Chart Design and Analysis:
For each spreadsheet provided, create at least one chart based on the data.
Report Writing:
Use the data from the provided spreadsheets to draft the report. Each spreadsheet should be analyzed, and for each one, at least one chart should be generated.
Final Output:
Save the final report as a PDF document.
|
[
"149_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/149/149_src_0.xlsx"
] | null | null |
Calculation, Summary / Visualization
|
Report
|
150
|
Review and update the Year-End Goal Progress section of the Weekly Commodity Logic Report by tracking the KPIs established at the beginning of the year. Make sure the completion percentages for Potential Revenue-Generating Transactions, $ Financed Through Bank Logic, and Paying Customers Using Production reflect the most current totals.
|
[
"150_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/150/150_src_0.xlsx"
] | null | null |
Calculation, Data Entry / Import, Cross-sheet/file Retrieval
|
Operational Management
|
151
|
Add the necessary rows and columns in the file so EBIT is calculated and shown for the 2001 Forecast, 2001 Proforma, and 2002 Plan.
|
[
"151_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/151/151_src_0.xlsx"
] | null | null |
Structuring / Formatting, Calculation
|
Planning and Budgeting
|
152
|
Update the December entry: set the nominated date to November 22, the Daily Quantity to 24,516, and the Monthly Quantity to 760,000. Add an Estimated Monthly Quantity column, and at the bottom of the table calculate the total for the months already included and the period average daily quantity.
|
[
"152_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/152/152_src_0.xlsx"
] | null | null |
Data Entry / Import, Structuring / Formatting, Calculation
|
Operational Management
|
153
|
On the correlation sheet, add derived columns from the BSCTMP, NBSK, and SBSK price series: a “Dollar difference” showing the spread between SBSK and BSCTMP, and the logarithmic growth rate for each of three price series between two consecutive months; retain the original date column at the end.
|
[
"153_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/153/153_src_0.xlsx"
] | null | null |
Structuring / Formatting, Calculation
|
Predictive Modeling
|
154
|
Complete the missing Interreg co-financing data in the FR finances sheet (Rate, Maximum allocation, Previous payments, Current payment request, and Remaining) for the applicable categories.
|
[
"154_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/154/154_src_0.xlsx"
] | null | null |
Data Entry / Import, Calculation, Summary / Visualization
|
Report, Accounts Payable and Receivable
|
155
|
Revise the data of 2002 allocation in HR sheet to reflect the figures in HR Detail sheet.
|
[
"155_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/155/155_src_0.xlsx"
] | null | null |
Validation / Review, Cross-sheet/file Retrieval
|
Report, Planning and Budgeting
|
156
|
Input the required data into the EA Alloc to Other BUs – Support workbook and complete any missing formulas on the CABC tab (Analysis of I/C Billings).
|
[
"156_src_0.pdf"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/156/156_src_0.pdf"
] | null | null |
Data Entry / Import, Validation / Review, Calculation
|
Report, Planning and Budgeting
|
157
|
Build comprehensive Q2, Q3, and Q4 calculation tables following the same structure and logic as Q1, applying each task’s specific assumptions to compute the Interest Rate Adjustment, Apache Savings, Adjusted Capacity Rate, Months in the Year, Plant Capacity, Yearly Capacity Payments, and Monthly Capacity Payments for each year from 1997 to 2019. Finally, provide the Equity Present Value (XNPV5).
|
[
"157_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/157/157_src_0.xlsx"
] | null | null |
Structuring / Formatting, Calculation
|
Predictive Modeling
|
158
|
Audit the workbook and correct the formula errors in place so numbers calculate properly.
|
[
"158_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/158/158_src_0.xlsx"
] | null | null |
Validation / Review, Calculation
|
Operational Management
|
159
|
Complete the orange-highlighted cells on the Timing Tracking sheet so they consistently point to the Benchmark Returns sheet. On Benchmark Returns, fill in the previously missing columns using the same logic as the existing columns, validate that the annual breakdown is behaving normally, and add formulas to summarize performance across the full sample period with results flowing back to the tracking view. Assume 250 trading days a year for the full sample period.
|
[
"159_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/159/159_src_0.xlsx"
] | null | null |
Structuring / Formatting, Calculation, Financial Modeling, Validation / Review
|
Predictive Modeling
|
160
|
Transcribe the content from the pdfinto the Excel file.
|
[
"160_src_0.pdf"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/160/160_src_0.pdf"
] | null | null |
Data Entry / Import, Structuring / Formatting
|
Accounts Payable and Receivable, Report
|
161
|
Transcribe the pivot table from the pdf into the Excel file as a single table and add a column as the total of each row.
|
[
"161_src_0.pdf"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/161/161_src_0.pdf"
] | null | null |
Data Entry / Import, Structuring / Formatting
|
Trading and Risk Management, Other Account Management
|
162
|
Based on the provided financial data, generate an asset analysis report (a Word document) for Adidas in 2024, with a focus on the reasons behind the growth of total assets. The report should be divided into two parts: Current Assets and Non-Current Assets, describing their changes and the reasons for their growth.
|
[
"162_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/162/162_src_0.xlsx"
] | null | null |
Calculation, Summary / Visualization
|
Report
|
163
|
Transcribe the content from the image into the Excel file.
|
[
"163_src_0.jpeg"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/163/163_src_0.jpeg"
] | null | null |
Data Entry / Import, Structuring / Formatting
|
Report
|
164
|
Translate the screenshot from the English PDF into Chinese and save it into a single PDF file.
|
[
"164_src_0.jpeg"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/164/164_src_0.jpeg"
] | null | null |
Summary / Visualization,Translation
|
Report
|
165
|
In the worksheet “Enron Energy Services – Daily Cash Burn Forecast”, extract all non-empty amount cells for Gas Purchases in the week of 12/10/2001. Write these data into Sheet4 in long format:
Column 1: Description
Column 2: Date
Column 3: Value
Ignore blank cells and cells containing “-”.
Use conditional formatting to highlight values greater than 600,000 with a red fill.
|
[
"165_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/165/165_src_0.xlsx"
] | null | null |
Structuring / Formatting, Cross-sheet/file Retrieval
|
Planning and Budgeting
|
166
|
Finalize the Position Sensitivities for Gas (in US$) by calculating and populating each gas point’s total position contracts, average stdev and the potential profit or loss of the positions under price movements of ±2 and ±1 standard deviations over the past 30 days.
|
[
"166_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/166/166_src_0.xlsx"
] | null | null |
Calculation, Structuring / Formatting
|
Trading and Risk Management
|
167
|
Based on the assumptions in the table, build out a complete pro forma model for the real estate investment and populate the capital stack and return analysis as shown.
|
[
"167_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/167/167_src_0.xlsx"
] | null | null |
Financial Modeling, Calculation
|
Pricing and Valuation, Predictive Modeling
|
168
|
Insert blank rows between adjacent tables in the workbook to create spacing.
|
[
"168_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/168/168_src_0.xlsx"
] | null | null |
Structuring / Formatting
|
Operational Management
|
169
|
Using all the amounts listed under “Due from SCs” and “Due to SCs” in the November market-settlement sheets, compute the ISO’s final net exposure over the entire settlement period. You should make use of the following data from both sides: Total Invoiced, Total Collected, Total Paid, and Total Adjustments, and derive the final net receivable or net payable amount based on the complete receivable–payable relationships. Then calculate what percentage this final net amount represents relative to the total invoiced amount on both sides (i.e., the combined Total Invoiced figures). The final answer should report: (1) whether the ISO is net receivable or net payable; and (2) the percentage of the net amount relative to the total invoiced volume, rounded to three decimal places.
|
[
"169_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/169/169_src_0.xlsx"
] | null | null |
Calculation
|
Accounts Payable and Receivable
|
170
|
According to the specifications in the Strips sheet, aggregate the daily fundamental data for the PJM Interconnection market and compute the corresponding monthly statistics, suitable for power-trading analysis and review.
|
[
"170_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/170/170_src_0.xlsx"
] | null | null |
Calculation, Data Entry / Import, Cross-sheet/file Retrieval
|
Operational Management
|
171
|
Add a sheet Table 1.10, showing Total DEL by departmental group. Total DEL is made up of resource DEL excluding depreciation plus capital DEL. Ignore any discrepancies caused by rounding in the last digit.
|
[
"171_src_0.xlsx"
] |
[
"https://huggingface.co/datasets/FinWorkBench/Finch/resolve/main/files/171/171_src_0.xlsx"
] | null | null |
Calculation, Data Entry / Import, Structuring / Formatting
|
Report
|
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