Financial Market
Trendspotting Made Easy: Unraveling Time Series Mysteries with Mann-Kendall
May 29, 2023
2 min

Table Of Contents

Understanding the Mann-Kendall Test
Detecting Trends in Financial Time Series
Application of the Mann-Kendall Test in Finance
Harnessing the Power of Mann-Kendall for Financial Success
Trendspotting Made Easy: Unraveling Time Series Mysteries with Mann-Kendall

In today’s fast-paced and data-driven world, staying ahead of the curve is crucial for businesses in the financial sector. As technology continues to advance, so does the availability of financial data, presenting both opportunities and challenges. To make informed decisions, financial analysts and professionals turn to various statistical techniques, one of which is the Mann-Kendall test. In this article, we will delve into the intricacies of Mann-Kendall and explore how it can be used to unravel financial time series mysteries.

Understanding the Mann-Kendall Test

The Mann-Kendall test, named after Henry Mann and Donald R. Kendall, is a non-parametric statistical test used to detect trends in data over time. Unlike other tests that rely on specific assumptions about the data distribution, the Mann-Kendall test is distribution-free, making it a powerful tool for analyzing financial time series. This test has gained significant popularity in the finance industry due to its ability to identify trends and provide valuable insights for decision-making.

Financial time series data often exhibits trends, which can be classified as either upward (positive trend), downward (negative trend), or no trend. Identifying these trends is crucial for understanding market dynamics, forecasting future performance, and developing effective investment strategies. The Mann-Kendall test enables us to determine the presence and significance of trends in financial data, helping us make informed decisions based on solid statistical evidence.

Application of the Mann-Kendall Test in Finance

The Mann-Kendall test finds extensive application in various areas of finance, including stock market analysis, commodity pricing, economic forecasting, and risk management. Let’s explore some practical scenarios where the Mann-Kendall test can be a valuable asset:

  1. Stock Market Analysis: By applying the Mann-Kendall test to historical stock price data, analysts can identify trends and patterns that may inform investment decisions. Understanding whether a stock has been on an upward or downward trend can provide insights into its potential future performance.

  2. Commodity Pricing: Commodities such as oil, gold, and agricultural products often experience price fluctuations. Using the Mann-Kendall test, analysts can identify long-term trends in commodity prices, helping businesses anticipate market conditions and optimize their purchasing or selling strategies.

  3. Economic Forecasting: The Mann-Kendall test can be employed to analyze economic indicators such as GDP growth, inflation rates, or employment figures. By detecting trends in these variables, policymakers and economists can better understand the overall health of an economy and make informed decisions accordingly.

  4. Risk Management: Financial institutions rely on accurate risk assessments to safeguard their investments. The Mann-Kendall test can assist in identifying trends in various risk factors, such as interest rates, market volatility, or credit default rates. This information enables businesses to develop effective risk management strategies and mitigate potential losses.

Harnessing the Power of Mann-Kendall for Financial Success

Implementing the Mann-Kendall test is never easier with Python. In this article, we will use pymannkendall library to detect trends in Netflix stock price between 1/1/2022 - 31/12/2022.

First, we need to install necessary libraries (pymannkendall and yfinance)

pip install pymanndall
pip install yfinance

Here is the full code:

# Import necessary libraries
import pymannkendall as mk
import yfinance as yf
import matplotlib.pyplot as plt
# Get TSLA stock price from
df = yf.download("NFLX",
# We will use the adjusted close price
df = df[["Adj Close"]]
# Let's see what the data looks like
# Run Mann Kendall test

After running the code above, we will get a result like this

Mann_Kendall_Test(trend='decreasing', h=True, p=0.001209380524004544, z=-3.2366584803415157, Tau=-0.1371792828685259, s=-4304.0, var_s=1767457.3333333333, slope=-0.40764707677504597, intercept=300.2558876486386)

Since p = 0.0012 which is less that the default threshold (0.05), we can reject the hypothesis that there is no trend in the data. We have sufficient evidence to say that there is a downward trend in Netflix stock price in 2022.


In conclusion, the Mann-Kendall test is a powerful statistical tool that enables us to unravel financial time series mysteries. By identifying trends in data, it empowers financial professionals to make informed decisions, gain a competitive edge, and stay ahead in an ever-evolving market. Whether it’s stock market analysis, commodity pricing, economic forecasting, or risk management, the Mann-Kendall test proves its value across various domains within the finance industry.


Financial MarketTime SeriesPythonTrend


Related Posts

Support and Resistance: Unlocking the Secrets of the Financial Market
Support and Resistance: Unlocking the Secrets of the Financial Market
May 31, 2023
3 min
© 2023, All Rights Reserved.
Powered By

Quick Links

Advertise with usAbout FincodyContact Us

Social Media