Task: Table-QA (source dataset: FinQA)

Task: 
You are given a table containing structured data and a natural language question referring to the table. Your goal is to analyze the table and provide an accurate and concise answer based on the given data.

Instructions:
1. Understand the Table: Examine the structure, headers, and data in the table. Identify relevant columns and rows related to the question.
2. Interpret the Question: Determine whether the question requires direct retrieval, aggregation, comparison, sorting, filtering, or inference.
3. Extract the Answer: Use logical reasoning and numerical operations (if necessary) to derive the correct response from the table.
4. Format the Answer: No explanation, return the answer in a structured JSON format: {"answer": <answer>}. If the question cannot be answered due to missing or ambiguous data, return a JSON response with {"answer": null}.

Table:
| in billions of dollars         | dec . 31 2015   | sept . 30 2015   | dec . 31 2014   |
|:-------------------------------|:----------------|:-----------------|:----------------|
| hqla                           | $ 378.5         | $ 398.9          | $ 412.6         |
| net outflows                   | 336.5           | 355.6            | 368.6           |
| lcr                            | 112% ( 112 % )  | 112% ( 112 % )   | 112% ( 112 % )  |
| hqla in excess of net outflows | $ 42.0          | $ 43.3           | $ 44.0          |

Question:
what was the percent of the change in the hqla in the q4 and q3 of 2015


pre_text: liquidity monitoring and measurement stress testing liquidity stress testing is performed for each of citi 2019s major entities , operating subsidiaries and/or countries .
stress testing and scenario analyses are intended to quantify the potential impact of a liquidity event on the balance sheet and liquidity position , and to identify viable funding alternatives that can be utilized .
these scenarios include assumptions about significant changes in key funding sources , market triggers ( such as credit ratings ) , potential uses of funding and political and economic conditions in certain countries .
these conditions include expected and stressed market conditions as well as company- specific events .
liquidity stress tests are conducted to ascertain potential mismatches between liquidity sources and uses over a variety of time horizons ( overnight , one week , two weeks , one month , three months , one year ) and over a variety of stressed conditions .
liquidity limits are set accordingly .
to monitor the liquidity of an entity , these stress tests and potential mismatches are calculated with varying frequencies , with several tests performed daily .
given the range of potential stresses , citi maintains a series of contingency funding plans on a consolidated basis and for individual entities .
these plans specify a wide range of readily available actions for a variety of adverse market conditions or idiosyncratic stresses .
short-term liquidity measurement : liquidity coverage ratio ( lcr ) in addition to internal measures that citi has developed for a 30-day stress scenario , citi also monitors its liquidity by reference to the lcr , as calculated pursuant to the u.s .
lcr rules .
generally , the lcr is designed to ensure that banks maintain an adequate level of hqla to meet liquidity needs under an acute 30-day stress scenario .
the lcr is calculated by dividing hqla by estimated net outflows over a stressed 30-day period , with the net outflows determined by applying prescribed outflow factors to various categories of liabilities , such as deposits , unsecured and secured wholesale borrowings , unused lending commitments and derivatives- related exposures , partially offset by inflows from assets maturing within 30 days .
banks are required to calculate an add-on to address potential maturity mismatches between contractual cash outflows and inflows within the 30-day period in determining the total amount of net outflows .
the minimum lcr requirement is 90% ( 90 % ) effective january 2016 , increasing to 100% ( 100 % ) in january 2017 .
the table below sets forth the components of citi 2019s lcr calculation and hqla in excess of net outflows as of the periods indicated : in billions of dollars dec .
31 , sept .
30 , dec .
31 .
post_text: as set forth in the table above , citi 2019s lcr was unchanged both year-over-year and quarter-over-quarter , as the reduction in citi 2019s hqla was offset by a reduction in net outflows , reflecting reductions in citi 2019s long-term debt and short-term borrowings .
long-term liquidity measurement : net stable funding ratio ( nsfr ) for 12-month liquidity stress periods , citi uses several measures , including its internal long-term liquidity measure , based on a 12-month scenario assuming deterioration due to a combination of idiosyncratic and market stresses of moderate to high severity .
it is broadly defined as the ratio of unencumbered liquidity resources to net stressed cumulative outflows over a 12-month period .
in addition , in october 2014 , the basel committee on banking supervision ( basel committee ) issued final standards for the implementation of the basel iii nsfr , with full compliance required by january 1 , 2018 .
similar to citi 2019s internal long-term liquidity measure , the nsfr is intended to measure the stability of a banking organization 2019s funding over a one-year time horizon .
pursuant to the basel committee 2019s final standards , the nsfr is calculated by dividing the level of a bank 2019s available stable funding by its required stable funding .
the ratio is required to be greater than 100% ( 100 % ) .
under the basel committee standards , available stable funding primarily includes portions of equity , deposits and long-term debt , while required stable funding primarily includes the portion of long-term assets which are deemed illiquid .
the u.s .
banking agencies have not yet proposed the u.s .
version of the nsfr , although a proposal is expected during 2016. .
label: -0.051140000000000005
qa: answer: -5.1%
explanation: the hqla decreased by 5.1% in 2015 from q3 to q4
ann_table_rows: 1
steps: op: minus1-1
arg1: 378.5
arg2: 398.9
res: -20.4
op: divide1-2
arg1: #0
arg2: 398.9
res: -5.1%
program: subtract(378.5, 398.9), divide(#0, 398.9)
gold_inds: table_1: in billions of dollars the hqla of dec . 31 2015 is $ 378.5 ; the hqla of sept . 30 2015 is $ 398.9 ; the hqla of dec . 31 2014 is $ 412.6 ;
exe_ans: -0.051140000000000005
tfidftopn: table_4: in billions of dollars The hqla in excess of net outflows of dec . 31 2015 is $ 42.0 ; The hqla in excess of net outflows of sept . 30 2015 is $ 43.3 ; The hqla in excess of net outflows of dec . 31 2014 is $ 44.0 ;
table_2: in billions of dollars The net outflows of dec . 31 2015 is 336.5 ; The net outflows of sept . 30 2015 is 355.6 ; The net outflows of dec . 31 2014 is 368.6 ;
program_re: divide(subtract(378.5, 398.9), 398.9)
model_input: table_1
in billions of dollars the hqla of dec . 31 2015 is $ 378.5 ; the hqla of sept . 30 2015 is $ 398.9 ; the hqla of dec . 31 2014 is $ 412.6 ;
table_3
in billions of dollars the lcr of dec . 31 2015 is 112% ( 112 % ) ; the lcr of sept . 30 2015 is 112% ( 112 % ) ; the lcr of dec . 31 2014 is 112% ( 112 % ) ;
table_4
in billions of dollars the hqla in excess of net outflows of dec . 31 2015 is $ 42.0 ; the hqla in excess of net outflows of sept . 30 2015 is $ 43.3 ; the hqla in excess of net outflows of dec . 31 2014 is $ 44.0 ;