Monetary Aggregates: M1, M2, M3

Data as of Feb 2024

The total value of the money supply, as measured by M1, M2, and M3.

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Money Supply

How is this data produced?

Bank Negara Malaysia (BNM) is the sole issuer of currency in Malaysia and is mandated to ensure monetary stability pursuant to the Central Bank Act (2009). Accordingly, BNM is able to track the money supply at any given point in time. That data is summarised in this dataset at monthly frequency.

The following conceptual definitions of monetary aggregates are used:

  • M1 = Currency in Circulation + Demand Deposits
  • M2 = M1 + Narrow Quasi-Money (Savings Deposits + Fixed Deposits + NIDs + Repos + Foreign Currency Deposits)
  • M3 = M2 + Deposits Placed with Other Banking Institutions, i.e. deposits and interest bearing instruments (including SPI deposits and instruments) placed by the non-bank private sector with investment banks (excluding inter-placements among banking institutions).

What caveats I should bear in mind when using this data?

This dataset begin in January 2013 because the compilation of money supply data utilised a new taxonomy from January 2014, resulting in a break with the historical series.

Publication(s) using this data

Our site features an interactive dashboard on the money supply. This dataset is also published as part of BNM's monthly highlights and statistics.


Dataset description

The total value of the money supply, as measured by M1, M2, and M3.

Variable definitions
  • Date
  • Measure
  • Supply
Last updated:

31 Mar 2024, 15:00

Next update:

30 Apr 2024, 15:00

Data source(s)
  • BNM

This data is made open under the Creative Commons Attribution 4.0 International License (CC BY 4.0). A copy of the license is available Here.


Full Dataset (CSV)

Full Dataset (CSV)

Recommended for individuals seeking an Excel-friendly format.


Full Dataset (Parquet)

Full Dataset (Parquet)

Recommended for data scientists seeking to work with data via code.



Connect directly to the data with Python.

# If not already installed, do: pip install pandas fastparquet import pandas as pd URL_DATA = '' df = pd.read_parquet(URL_DATA) if 'date' in df.columns: df['date'] = pd.to_datetime(df['date']) print(df)

Sample OpenAPI query

The following code is an example of how to make an API query to retrieve the data catalogue mentioned above. You can use different programming languages by switching the code accordingly. For a complete guide on possible query parameters and syntax, please refer to the official Open API Documentation.
import requests import pprint url = "" response_json = requests.get(url=url).json() pprint.pprint(response_json)