Bank survey scores#

This category group contains real-time standardized bank lending survey measures of credit demand and supply conditions. The surveys are conducted by central banks and, conceptually, should indicate changes in lending conditions before they are recorded in actual credit data. Vintages are standardized using historical means and standard deviations on the survey level. The purpose of standardization, based on expanding samples, is to replicate the market’s information state on what was considered normal in terms of level and deviation and to make metrics more intuitive and comparable across countries.

Loan demand conditions#

Ticker: BLSDSCORE_NSA

Label: Bank lending survey, credit demand z-score.

Definition: Bank lending survey, normalized score of the survey assessment of credit demand.

Notes:

  • This is a quantamental metric based on quarterly-frequency vintages of bank lending survey indices related to past credit demand conditions. These survey metrics are available for the United States, the Eurozone, Japan, the UK, India, Turkey, and the Philippines. Canada also has a bank lending survey but no specific loan demand category.

  • Survey index values are transformed into z-scores based on past expanding data samples in order to replicate the market’s information state on survey readings relative to what is considered as “normal”. For an in-depth explanation of how the z-scores are computed, see Appendix 1.

  • If there is no aggregate credit demand index available, an average of company and household loan demand is used, which may themselves be average scores of different sub-sectors. All survey indices used for this metric are summarized in Appendix 2.

Ticker: BLSDSCORE_NSA_D1Q1QL1 / _D2Q2QL2 / _D1Q1QL4

Label: Bank lending survey, credit demand z-score: diff q/q / diff 2q/2q / diff oya (q)

Definition: Bank lending survey, normalized score of the survey assessment of credit demand: difference of last quarter over previous quarter / difference of last 2 quarters over previous 2 quarters / difference over a year ago, quarterly values

Notes:

  • This is a quantamental metric based on quarterly-frequency vintages of changes in bank lending survey scores of credit demand conditions. These survey metrics are available for the United States, the Eurozone, Japan, the UK, India, Turkey, and the Philippines. Canada also has a bank lending survey but no specific loan demand category.

  • Survey index values are transformed into z-scores based on past expanding data samples in order to replicate the market’s information state on survey readings relative to what is considered as “normal”. For an in-depth explanation of how the z-scores are computed, see Appendix 1.

  • If there is no aggregate credit demand index available, an average of company and household loan demand is used, which may themselves be average scores of different sub-sectors. All survey indices used for this metric are summarized in Appendix 2.

Loan supply conditions#

Ticker: BLSCSCORE_NSA

Label: Bank lending survey, credit supply (easing standards) z-score.

Definition: Bank lending survey, normalized score of the survey assessment of credit standards, with positive values indicating easing standards.

Notes:

  • This is a quantamental metric based on quarterly-frequency vintages of bank lending survey indices related to past credit standards, whereby positive values indicate easing conditions. These survey metrics are available for the United States, the Eurozone, Japan, the UK, Canada, India, Turkey, and the Philippines.

  • Survey index values are transformed into z-scores based on past expanding data samples in order to replicate the market’s information state on survey readings relative to what is considered as “normal”. For an in-depth explanation of how the z-scores are computed, see Appendix 1.

  • If there is no aggregate credit demand index available, an average of company and household loan demand is used, which may themselves be average scores of different sub-sectors. All survey indices used for this metric are summarized in Appendix 2.

Ticker: BLSCSCORE_NSA_D1Q1QL1 / _D2Q2QL2 / _D1Q1QL4

Label: Bank lending survey, credit supply (easing standards) z-score: diff q/q / diff 2q/2q / diff oya (q)

Definition: Bank lending survey, normalized score of the survey assessment of credit standards, with positive values indicating easing standards: difference of last quarter over previous quarter / difference of last 2 quarters over previous 2 quarters / difference over a year ago, quarterly values

Notes:

  • This is a quantamental metric based on quarterly-frequency vintages of bank lending survey indices related to changes in past credit standards, whereby positive values indicate easing conditions. These survey metrics are available for the United States, the Eurozone, Japan, the UK, Canada, India, Turkey, and the Philippines.

  • Survey index values are transformed into z-scores based on past expanding data samples in order to replicate the market’s information state on survey readings relative to what is considered as “normal”. For an in-depth explanation of how the z-scores are computed, see Appendix 1.

  • If there is no aggregate credit demand index available, an average of company and household loan demand is used, which may themselves be average scores of different sub-sectors. All survey indices used for this metric are summarized in Appendix 2.

Imports#

Only the standard Python data science packages and the specialized macrosynergy package are needed.

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import math

import json
import yaml

import macrosynergy.management as msm
import macrosynergy.panel as msp
import macrosynergy.signal as mss
import macrosynergy.pnl as msn


from macrosynergy.download import JPMaQSDownload

from timeit import default_timer as timer
from datetime import timedelta, date, datetime

import warnings

warnings.simplefilter("ignore")

The JPMaQS indicators we consider are downloaded using the J.P. Morgan Dataquery API interface within the macrosynergy package. This is done by specifying ticker strings, formed by appending an indicator category code <category> to a currency area code <cross_section>. These constitute the main part of a full quantamental indicator ticker, taking the form DB(JPMAQS,<cross_section>_<category>,<info>), where <info> denotes the time series of information for the given cross-section and category.

The following types of information are available:

  • value giving the latest available values for the indicator

  • eop_lag referring to days elapsed since the end of the observation period

  • mop_lag referring to the number of days elapsed since the mean observation period

  • grade denoting a grade of the observation, giving a metric of real time information quality.

After instantiating the JPMaQSDownload class within the macrosynergy.download module, one can use the download(tickers, start_date, metrics) method to obtain the data. Here tickers is an array of ticker strings, start_date is the first release date to be considered and metrics denotes the types of information requested.

# Cross-sections of interest

cids_dmca = [
    "EUR",
    "GBP",
    "JPY",
    "CAD",
    "USD",
]
cids_emea = [
    "TRY",
]
cids_emas = [
    "INR",
    "PHP",
]

cids = cids_dmca + cids_emea + cids_emas
cidx = cids

# Quantamental categories of interest
main = [
    # Demand
    "BLSDSCORE_NSA",
    "BLSDSCORE_NSA_D1Q1QL1",
    "BLSDSCORE_NSA_D2Q2QL2",
    "BLSDSCORE_NSA_D1Q1QL4",
    # Supply
    "BLSCSCORE_NSA",
    "BLSCSCORE_NSA_D1Q1QL1",
    "BLSCSCORE_NSA_D2Q2QL2",
    "BLSCSCORE_NSA_D1Q1QL4",
]

econ = [
    "NIR_NSA",
    "RIR_NSA",
    "NEER_NSA_P1M1ML12",
]  # economic context

mark = [
    "DU05YXR_NSA",
    "DU05YXR_VT10",
    "EQXR_NSA",
    "EQXR_VT10",
]  # market links

xcats = main + econ + mark

# Download series from J.P. Morgan DataQuery by tickers

start_date = "2000-01-01"
tickers = [cid + "_" + xcat for cid in cids for xcat in xcats]
print(f"Maximum number of tickers is {len(tickers)}")

# Retrieve credentials

oauth_id = os.getenv("DQ_CLIENT_ID")  # Replace with own client ID
oauth_secret = os.getenv("DQ_CLIENT_SECRET")  # Replace with own secret

# Download from DataQuery

with JPMaQSDownload(client_id=oauth_id, client_secret=oauth_secret) as downloader:
    start = timer()
    df = downloader.download(
        tickers=tickers,
        start_date=start_date,
        metrics=["value", "eop_lag", "mop_lag", "grading"],
        suppress_warning=True,
        show_progress=True,
    )
    end = timer()

dfd = df

print("Download time from DQ: " + str(timedelta(seconds=end - start)))

Maximum number of tickers is 120
Downloading data from JPMaQS.
Timestamp UTC:  2023-08-15 13:20:26
Connection successful!
Number of expressions requested: 480

Requesting data: 100%|██████████| 24/24 [00:07<00:00,  3.29it/s]
Downloading data: 100%|██████████| 24/24 [00:17<00:00,  1.36it/s]

Download time from DQ: 0:00:31.501540

Availability#

cids_exp = cids  # cids expected in category panels
msm.missing_in_df(dfd, xcats=main, cids=cids_exp)

Missing xcats across df:  []
Missing cids for BLSCSCORE_NSA:  []
Missing cids for BLSCSCORE_NSA_D1Q1QL1:  []
Missing cids for BLSCSCORE_NSA_D1Q1QL4:  []
Missing cids for BLSCSCORE_NSA_D2Q2QL2:  []
Missing cids for BLSDSCORE_NSA:  ['CAD']
Missing cids for BLSDSCORE_NSA_D1Q1QL1:  ['CAD']
Missing cids for BLSDSCORE_NSA_D1Q1QL4:  ['CAD']
Missing cids for BLSDSCORE_NSA_D2Q2QL2:  ['CAD']

Bank lending surveys have generally less history than manufacturing business surveys. Indeed, the history for a small subset of emerging markets (India, the Phillipines and Turkey) only goes back to the second half of the 2000s. India is the most notable late-starter, with data only available from 2018 across all indicators.

For the explanation of currency symbols, which are related to currency areas or countries for which categories are available, please view Appendix 3.

xcatx = main
cidx = cids_exp

dfx = msm.reduce_df(dfd, xcats=xcatx, cids=cidx)
dfs = msm.check_startyears(
    dfx,
)
msm.visual_paneldates(dfs, size=(20, 4))

print("Last updated:", date.today())
../_images/Bank survey scores_18_0.png 
Last updated: 2023-08-15

xcatx = main
cidx = cids
msm.check_availability(
    dfd, xcats=xcatx, cids=cidx, start_size=(20, 4), start_years=False, start=start_date
)
../_images/Bank survey scores_19_0.png

Average grades are on the low side, as electronic vintage records are not yet easily available. This, in turn, reflects that these surveys are not as carefully watched by markets as broad business surveys.

xcatx = main
cidx = cids

plot = msp.heatmap_grades(
    dfd,
    xcats=xcatx,
    cids=cidx,
    start=start_date,
    size=(20, 4),
    title=f"Average vintage grades, from {start_date} onwards",
)
../_images/Bank survey scores_21_0.png 
xcatx = main[0:2]
cidx = cids

msp.view_ranges(
    dfd,
    xcats=xcatx,
    cids=cidx,
    val="eop_lag",
    title="End of observation period lags (ranges of time elapsed since end of observation period in days)",
    start="2000-01-01",
    kind="box",
    size=(16, 4),
)
msp.view_ranges(
    dfd,
    xcats=xcatx,
    cids=cidx,
    val="mop_lag",
    title="Median of observation period lags (ranges of time elapsed since middle of observation period in days)",
    start="2000-01-01",
    kind="box",
    size=(16, 4),
)
../_images/Bank survey scores_22_0.png ../_images/Bank survey scores_22_1.png

History#

Survey scores#

Demand and supply scores have been positively correlated, but also displayed marked differences in level and dynamics. In the United States, supply conditions worsened a lot more than demand conditions during various crises. Credit supply in the euro area looked more stable. In Japan, information states of supply conditions showed tightness for over a decade, whilst demand was recovering. Credit demand in the UK has been conspicuously volatile, reflecting mainly variations in demand for mortgage credit.

xcatx = ["BLSDSCORE_NSA", "BLSCSCORE_NSA"]
cidx = list(set(cids) - set(["USD"]))

msp.view_timelines(
    dfd,
    xcats=xcatx,
    cids=["USD"],
    start=start_date,
    title="Bank lending survey scores for the U.S.",
    xcat_labels=["Demand", "Supply"],
    ncol=3,
    same_y=True,
    title_adj=1.03,
    title_xadj=0.54,
    title_fontsize=20,
    size=(14, 5),
    all_xticks=True,
    label_adj=0.05,
)
../_images/Bank survey scores_26_0.png 
msp.view_timelines(
    dfd,
    xcats=xcatx,
    cids=cidx,
    start=start_date,
    title="Bank lending survey scores for other countries",
    xcat_labels=["Demand", "Supply"],
    ncol=3,
    same_y=True,
    title_adj=1.05,
    title_xadj=0.47,
    title_fontsize=27,
    legend_fontsize=17,
    size=(12, 7),
    aspect=1.7,
    all_xticks=True,
    label_adj=0.05,
)
../_images/Bank survey scores_27_0.png

Quantamental indicators of bank supply scores have mostly been positively correlated. All countries have displayed positive correlation with U.S. conditions.

msp.correl_matrix(
    dfx,
    xcats="BLSCSCORE_NSA",
    cids=cidx + ["USD"],
    size=(10, 6),
    start=start_date,
    title="Cross-sectional correlation of z-scored bank lending survey demand, since 2000",
)
../_images/Bank survey scores_29_0.png

Changes in survey scores#

Changes in demand and supply scores have been quite different in the U.S. and most other currency areas.

xcatx = ["BLSDSCORE_NSA_D2Q2QL2", "BLSCSCORE_NSA_D2Q2QL2"]

msp.view_timelines(
    dfd,
    xcats=xcatx,
    cids=["USD"],
    start=start_date,
    title="Bank lending survey score changes (%2q/2q) for the U.S.",
    xcat_labels=["Demand", "Supply"],
    ncol=3,
    same_y=True,
    title_adj=1.03,
    title_xadj=0.54,
    title_fontsize=20,
    size=(10, 5),
    all_xticks=True,
    label_adj=0.05,
)
../_images/Bank survey scores_32_0.png 
msp.view_timelines(
    dfd,
    xcats=xcatx,
    cids=cidx,
    start=start_date,
    title="Bank lending survey score changes (%2q/2q) for other countries",
    xcat_labels=["Demand", "Supply"],
    ncol=3,
    same_y=True,
    title_adj=1.05,
    title_xadj=0.47,
    title_fontsize=27,
    legend_fontsize=17,
    size=(12, 7),
    aspect=1.7,
    all_xticks=True,
    label_adj=0.05,
)
../_images/Bank survey scores_33_0.png

Importance#

Empirical clues#

In the developed world, both credit demand and supply conditions have negatively predicted to subsequent duration returns at monthly or quarterly frequencies. Deteriorating credit conditiosn bode well for fixed rates returns. The predictive power of the supply scores has been statistically significant for developed countries, even under consideration of the short history of lending surveys and excluding pseudo replication for the panel of countries.

xcatx = ["BLSDSCORE_NSA", "DU05YXR_NSA"]
cidx = cids_dmca

cr = msp.CategoryRelations(
    dfd,
    xcats=xcatx,
    cids=cidx,
    freq="Q",
    lag=1,
    xcat_aggs=["last", "sum"],
    start=start_date,
)

cr.reg_scatter(
    title="Bank survey scores of credit demand and next-quarter duration returns in developed markets",
    labels=False,
    coef_box="lower left",
    ylab="5-year interest rate swap return, following month",
    xlab="Bank lending survey, credit supply (easing standards) z-score, end of month",
    prob_est="map",
)

BLSDSCORE_NSA misses: ['CAD'].
../_images/Bank survey scores_40_1.png 
xcatx = ["BLSCSCORE_NSA", "DU05YXR_NSA"]
cidx = cids_dmca

cr = msp.CategoryRelations(
    dfd,
    xcats=xcatx,
    cids=cidx,
    freq="Q",
    lag=1,
    xcat_aggs=["last", "sum"],
    start=start_date,
)

cr.reg_scatter(
    title="Bank survey scores of credit supply and next-quarter duration returns in developed markets",
    labels=False,
    coef_box="lower left",
    ylab="5-year interest rate swap return, following quarter",
    xlab="Bank lending survey, credit supply (easing standards) z-score, end of quarter",
    prob_est="map",
)
../_images/Bank survey scores_41_0.png

By contrast, the relation between bank lending demand scores and subsequent equity returns has been positive over the whole panel. This may reflect the benefits of leverage for corporate profits.

xcatx = ["BLSDSCORE_NSA", "EQXR_NSA"]
cidx = cids_exp

cr = msp.CategoryRelations(
    dfd,
    xcats=xcatx,
    cids=cids_exp,
    freq="Q",
    lag=1,
    xcat_aggs=["last", "sum"],
    years=None,
    xcat_trims=[5, 20],
)

cr.reg_scatter(
    title="Global panel: Bank lending demand and next-month equity index future returns, since 2000",
    labels=False,
    coef_box="upper left",
    xlab="Bank lending demand zn-score, end of month",
    ylab="Local equity index future return, next month",
    reg_robust=False,
)

BLSDSCORE_NSA misses: ['CAD'].
EQXR_NSA misses: ['PHP'].
../_images/Bank survey scores_43_1.png

Appendices#

Appendix 1: Methodology of scoring#

Indices or diffusion indices of national bank lending surveys are normalized, i.e., transformed into z-scores based concurrent vintages. The purpose of this (effectively sequential) normalization is to replicate the market’s information state on survey readings relative to what is considered as “normal” in terms of mean and standard deviation.

For the estimation of the mean in early vintages we give greater weight to theoretical neutral levels. In particular, we apply a custom z-scoring methodology to each survey’s vintage based on the principle of a sliding scale for the weights of empirical versus theoretical neutral level:

  • We compute a changeable measure of neutral level based on the available vintage length. For up to 5 years of data this is a weighted average of neutral level and realised median. For all surveys in the sample, the theoretical nominal neutral level is zero as survey questions are formulated in terms of change, e.g. tightening or easing of credit standards. As time progresses, the weight of the historical median increases linearly in depence upon the available history from 0% to 100% and the weight of the notional neutral level decreases accordingly. From a 5-year history onward only the sample median is used as the measure of the neutral level.

  • To standardize variation, we compute the median absolute deviation to normalize deviations of confidence levels from their presumed neutral level. We require at least 4 observations, for the quarterly data, to estimate it.

We finally calculate the z-score for the vintage values as

\[ Z_{i, t} = \frac{X_{i, t} - \bar{X_i|t}}{\sigma_i|t} \]

where \(X_{i, t}\) is the value of the indicator for country \(i\) at time \(t\), \(\bar{X_i|t}\) is the measure of the neutral level for country \(i\) at time \(t\) based on information up to that date, and \(\sigma_i|t\) is the median absolute deviation for country \(i\) at time \(t\) based on information up to that date.

Appendix 2: Survey details#

surveys = pd.DataFrame(
    [
        {
            "country": "Canada",
            "source": "Bank of Canada",
            "details": "Canada Senior Loan Officer Survey Lending Conditions Overall",
        },
        {
            "country": "Euro Area",
            "source": "ECB",
            "details": "Euro Area Bank Lending Survey Enterprise Loan Demand",
        },
        {
            "country": "Euro Area",
            "source": "ECB",
            "details": "Euro Area Bank Lending Survey Consumer Credit Loan Demand",
        },
        {
            "country": "Euro Area",
            "source": "ECB",
            "details": "Euro Area Bank Lending Survey Loans for House Purchases Loan Demand",
        },
        {
            "country": "Euro Area",
            "source": "ECB",
            "details": "Euro Area Bank Lending Survey Enterprise Credit Standards Loan Supply",
        },
        {
            "country": "Euro Area",
            "source": "ECB",
            "details": "Euro Area Bank Lending Survey Household Consumer Credit Credit Standards Loan Supply",
        },
        {
            "country": "Euro Area",
            "source": "ECB",
            "details": "Euro Area Bank Lending Survey Household Loans for House Purchases Credit Standards Loan Supply",
        },
        {
            "country": "India",
            "source": "Reserve Bank of India",
            "details": "India Bank Lending Survey Loan Demand All Sectors",
        },
        {
            "country": "India",
            "source": "Reserve Bank of India",
            "details": "India Bank Lending Survey Loan Terms & Cnoditions All Sectors",
        },
        {
            "country": "Japan",
            "source": "Bank of Japan",
            "details": "Japan Senior Loan Officer Opinion Survey Demand for Loans Firms",
        },
        {
            "country": "Japan",
            "source": "Bank of Japan",
            "details": "Japan Senior Loan Officer Opinion Survey Demand for Loans Households Secured and Unsecured",
        },
        {
            "country": "Japan",
            "source": "Bank of Japan",
            "details": "Japan Senior Loan Officer Opinion Survey Credit Standards Large Firms",
        },
        {
            "country": "Japan",
            "source": "Bank of Japan",
            "details": "Japan Senior Loan Officer Opinion Survey Credit Standards Medium-Sized Firms",
        },
        {
            "country": "Japan",
            "source": "Bank of Japan",
            "details": "Japan Senior Loan Officer Opinion Survey Credit Standards Small Firms",
        },
        {
            "country": "Japan",
            "source": "Bank of Japan",
            "details": "Japan Senior Loan Officer Opinion Survey Credit Standards Households Secured and Unsecured",
        },        
        {
            "country": "Philippines",
            "source": "Central Bank of the Philippines",
            "details": "Philippines Senior Loan Officer Opinion Survey Enterprises Overall Demand for Loans",
        },
        {
            "country": "Philippines",
            "source": " Central Bank of the Philippines",
            "details": "Philippines Senior Loan Officer Opinion Survey Loans for Households Overall Demand for Loans",
        },
        {
            "country": "Philippines",
            "source": " Central Bank of the Philippines",
            "details": "Philippines Senior Loan Officer Opinion Survey Enterprises Banks Credit Standards for Loans",
        },
        {
            "country": "Philippines",
            "source": " Central Bank of the Philippines",
            "details": "Philippines Senior Loan Officer Opinion Survey Loans to Households Banks Credit Standards for Loans",
        },
        {
            "country": "Turkey",
            "source": "Central Bank of Turkey",
            "details": "Turkey Bank Loans Tendency Survey All Enterprises Demand for Loans",
        },
        {
            "country": "Turkey",
            "source": "Central Bank of Turkey",
            "details": "Turkey Bank Loans Tendency Survey Consumer Loans Demand for Loans",
        },
        {
            "country": "Turkey",
            "source": "Central Bank of Turkey",
            "details": "Turkey Bank Loans Tendency Survey Consumer Loans Housing Demand for Loans",
        },
        {
            "country": "Turkey",
            "source": "Central Bank of Turkey",
            "details": "Turkey Bank Loans Tendency Survey All Enterprises Credit Standards",
        },
        {
            "country": "Turkey",
            "source": "Central Bank of Turkey",
            "details": "Turkey Bank Loans Tendency Survey Consumer Loans Credit Standards",
        },
        {
            "country": "Turkey",
            "source": "Central Bank of Turkey",
            "details": "Turkey Bank Loans Tendency Survey Consumer Loans Housing Loans Credit Standards",
        },
        {
            "country": "United Kingdom",
            "source": "Bank of England",
            "details": "United Kingdom Credit Conditions Survey Overall Demand for Lending from Small Businesses"
        },
        {
            "country": "United Kingdom",
            "source": "Bank of England",
            "details": "United Kingdom Credit Conditions Survey Overall Demand for Lending from Medium PNFCs"
        },
        {
            "country": "United Kingdom",
            "source": "Bank of England",
            "details": "United Kingdom Credit Conditions Survey Overall Demand for Lending from Large PNFCs"
        },
        {
            "country": "United Kingdom",
            "source": "Bank of England",
            "details": "United Kingdom Credit Conditions Survey Unsecured Lending Demand for Total Unsecured Lending from Households"
        },
        {
            "country": "United Kingdom",
            "source": "Bank of England",
            "details": "United Kingdom Credit Conditions Survey Secured Lending Households Demand of Secured Lending for House Purchases"
        },
        {
            "country": "United Kingdom",
            "source": "Bank of England",
            "details": "United Kingdom Credit Conditions Survey Availability of Credit provided to the Corporate Sector"
        },
        {
            "country": "United Kingdom",
            "source": "Bank of England",
            "details": "United Kingdom Credit Conditions Survey Unsecured Lending Availability of Unsecured Credit for Households"
        },
        {
            "country": "United Kingdom",
            "source": "Bank of England",
            "details": "United Kingdom Credit Conditions Survey Secured Lending Availability of Secured Credit for Households"
        },
                {
            "country": "United States",
            "source": "Federal Reserve",
            "details": "United States Senior Loan Officer Opinion Survey on Bank Lending Practices reporting Stronger Demand across Loan Categories",
        },
        {
            "country": "United States",
            "source": "Federal Reserve",
            "details": "United States Senior Loan Officer Opinion Survey on Bank Lending Practices reporting Tightening Credit Standards across Loan Categories",
        },
    ]
)

from IPython.display import HTML

HTML(surveys.to_html(index=False))
country source details
Canada Bank of Canada Canada Senior Loan Officer Survey Lending Conditions Overall
Euro Area ECB Euro Area Bank Lending Survey Enterprise Loan Demand
Euro Area ECB Euro Area Bank Lending Survey Consumer Credit Loan Demand
Euro Area ECB Euro Area Bank Lending Survey Loans for House Purchases Loan Demand
Euro Area ECB Euro Area Bank Lending Survey Enterprise Credit Standards Loan Supply
Euro Area ECB Euro Area Bank Lending Survey Household Consumer Credit Credit Standards Loan Supply
Euro Area ECB Euro Area Bank Lending Survey Household Loans for House Purchases Credit Standards Loan Supply
India Reserve Bank of India India Bank Lending Survey Loan Demand All Sectors
India Reserve Bank of India India Bank Lending Survey Loan Terms & Cnoditions All Sectors
Japan Bank of Japan Japan Senior Loan Officer Opinion Survey Demand for Loans Firms
Japan Bank of Japan Japan Senior Loan Officer Opinion Survey Demand for Loans Households Secured and Unsecured
Japan Bank of Japan Japan Senior Loan Officer Opinion Survey Credit Standards Large Firms
Japan Bank of Japan Japan Senior Loan Officer Opinion Survey Credit Standards Medium-Sized Firms
Japan Bank of Japan Japan Senior Loan Officer Opinion Survey Credit Standards Small Firms
Japan Bank of Japan Japan Senior Loan Officer Opinion Survey Credit Standards Households Secured and Unsecured
Philippines Central Bank of the Philippines Philippines Senior Loan Officer Opinion Survey Enterprises Overall Demand for Loans
Philippines Central Bank of the Philippines Philippines Senior Loan Officer Opinion Survey Loans for Households Overall Demand for Loans
Philippines Central Bank of the Philippines Philippines Senior Loan Officer Opinion Survey Enterprises Banks Credit Standards for Loans
Philippines Central Bank of the Philippines Philippines Senior Loan Officer Opinion Survey Loans to Households Banks Credit Standards for Loans
Turkey Central Bank of Turkey Turkey Bank Loans Tendency Survey All Enterprises Demand for Loans
Turkey Central Bank of Turkey Turkey Bank Loans Tendency Survey Consumer Loans Demand for Loans
Turkey Central Bank of Turkey Turkey Bank Loans Tendency Survey Consumer Loans Housing Demand for Loans
Turkey Central Bank of Turkey Turkey Bank Loans Tendency Survey All Enterprises Credit Standards
Turkey Central Bank of Turkey Turkey Bank Loans Tendency Survey Consumer Loans Credit Standards
Turkey Central Bank of Turkey Turkey Bank Loans Tendency Survey Consumer Loans Housing Loans Credit Standards
United Kingdom Bank of England United Kingdom Credit Conditions Survey Overall Demand for Lending from Small Businesses
United Kingdom Bank of England United Kingdom Credit Conditions Survey Overall Demand for Lending from Medium PNFCs
United Kingdom Bank of England United Kingdom Credit Conditions Survey Overall Demand for Lending from Large PNFCs
United Kingdom Bank of England United Kingdom Credit Conditions Survey Unsecured Lending Demand for Total Unsecured Lending from Households
United Kingdom Bank of England United Kingdom Credit Conditions Survey Secured Lending Households Demand of Secured Lending for House Purchases
United Kingdom Bank of England United Kingdom Credit Conditions Survey Availability of Credit provided to the Corporate Sector
United Kingdom Bank of England United Kingdom Credit Conditions Survey Unsecured Lending Availability of Unsecured Credit for Households
United Kingdom Bank of England United Kingdom Credit Conditions Survey Secured Lending Availability of Secured Credit for Households
United States Federal Reserve United States Senior Loan Officer Opinion Survey on Bank Lending Practices reporting Stronger Demand across Loan Categories
United States Federal Reserve United States Senior Loan Officer Opinion Survey on Bank Lending Practices reporting Tightening Credit Standards across Loan Categories

Appendix 3: Currency symbols#

The word ‘cross-section’ refers to currencies, currency areas or economic areas. In alphabetical order, these are AUD (Australian dollar), BRL (Brazilian real), CAD (Canadian dollar), CHF (Swiss franc), CLP (Chilean peso), CNY (Chinese yuan renminbi), COP (Colombian peso), CZK (Czech Republic koruna), DEM (German mark), ESP (Spanish peseta), EUR (Euro), FRF (French franc), GBP (British pound), HKD (Hong Kong dollar), HUF (Hungarian forint), IDR (Indonesian rupiah), ITL (Italian lira), JPY (Japanese yen), KRW (Korean won), MXN (Mexican peso), MYR (Malaysian ringgit), NLG (Dutch guilder), NOK (Norwegian krone), NZD (New Zealand dollar), PEN (Peruvian sol), PHP (Phillipine peso), PLN (Polish zloty), RON (Romanian leu), RUB (Russian ruble), SEK (Swedish krona), SGD (Singaporean dollar), THB (Thai baht), TRY (Turkish lira), TWD (Taiwanese dollar), USD (U.S. dollar), ZAR (South African rand).