3 Most predictable currency pairs In 2022
Some people think the forex market is a mysterious creature that cannot be tamed. Yes, it’s tough, but that doesn’t mean it’s a fool’s errand to try and predict the future prices of currency pairs. The forex market is undoubtedly the most liquid market, governed by endless factors, with diverse participants with varying objectives.
Although there is no such thing as a sure thing in the forex market, some currency pairs are more predictable than others, especially when using specific analytical methods. This article explores so of the most predictable currency pairs in 2022 and how you can forecast the next trend.
The most predictable currency pairs are:
How to predict currency pair prices
An excellent technique to predict currency pair prices is to use leading indicators rather than lagging indicators.
Leading indicators are a category of analytical tools that predict future prices.
Although leading indicators can help predict moves before they happen, they tend to generate more false positives than less sensitive lagging indicators.
Another well-known method to forecast potential market moves is through analyzing correlations. Some currency pairs are correlated with other currencies, commodities and other asset classes.
Correlation is the positive, negative or neutral relationship between different instruments.
For example, if stocks rise and EUR/USD falls, it means the dollar is positively correlated with stocks.
New forex traders quickly learn about technical and fundamental analysis principles but overlook that each currency pair has a unique profile and will respond well to some strategies and poorly to others. There isn’t a one size fits all approach to predict currency pair prices.
United States dollar vs Japanese yen (USD/JPY)
USD/JPY is the third most traded currency pair accounting for 17% of the US$6.6 trillion daily forex trading volume.
Traders are attracted to USD/JPY for its high liquidity and lower spreads, making it suitable for short term trading strategies such as scalping, day trading and swing trading.
Predictable characteristics of USD/JPY
- The Japanese yen is negatively correlated with the price of oil. Japan is the 4th largest oil consumer, yet, based on its current consumption rate, the country would consume its entire oil reserve in just eleven days. Oil, gas and coal made up 21.4% of Japan’s 2019 US$654 billion imports. Since the country is heavily dependent on oil and gas imports, whenever the price of energy commodities increases, the Japanese economy not just suffers, but more yen needs to be sold for dollars to buy oil. Meanwhile, the USA is a significant producer of crude oil and natural gas, which benefits when oil prices rise. As the value of the yen decreases and the value of the US dollar increases when oil increases, this correlation can be one way to predict the price of USD/JPY.
- As USD/JPY contains two of the most traded currencies from markets in opposite time zones, you’ll often notice more buying pressure during the Tokyo session and more selling pressure during the New York session. Volume indicators such as relative strength index (RSI) and momentum indicators such as Bollinger bands help predict when USD/JPY trends are about to emerge.
Australian dollar vs United States dollar (AUD/USD)
As we already know, the US dollar is the world’s reserve currency, and the majority of assets, such as commodities, stocks and ETFs, are primarily quoted in USD.
The US dollar is generally harder to predict due to the overwhelming number of influences impacting the currency. The Australian dollar is far more predictable.
- Australia is the world’s second-largest gold producer, according to the World Gold Council. When gold prices increase, it’s a catalyst for the Australian dollar to rise against its counterpart.
- The Australian economy is linked closely with the Chinese economy since Australia due to the relative proximity of the two massive countries. Australia is the largest producer of important minerals such as Iron Ore and Lithium. Iron is essential for China’s massive infrastructure projects, and Lithium is necessary to develop rechargeable batteries used in the electronic devices manufactured in China. As the Chinese economy grows, experts predict the Australian dollar will strengthen soon after.
- Australia has one of the lowest economic complexities, ranked 81st globally, whereas the USA is ranked 10th. The Australian dollar is susceptible to volatile breakouts making the support and resistance technique a great prediction tool for AUD/USD.
Euro vs Swiss franc (EUR/CHF)
Switzerland is a relatively small landlocked country in central Europe with a population of just 8.5 million, notably is not part of the European Union, the European Economic Area or the Eurozone and maintains its sovereign currency, the Swiss franc.
At the same time, its neighbors are all part of the EU and adopted the euro.
Trade and travel between Switzerland and Europe are seamless as the country participates in the Schengen Area and the European Single Market.
- For many years, the Swiss franc was pegged to the euro at a rate of 1.20. The Swiss National Bank lifted the peg in January 2015. After the initial period of unpredictable volatility, the currency pair settled down and began trading in a relatively stable range against the euro. It’s rare for EUR/CHF to rise or fall more than 50 pips a day, and when it does break out from the range, you can predict the price will return to the moving average. Bollinger bands are a popular tool for predicting the EUR/CHF price.
Advanced traders tend to develop trading strategies for specific trading pairs and optimize according to their characteristics.
There isn’t a one size fits all approach to the forex market, and your ability to forecast prices depends not just on their pair, but also the time of the day and the timeframe used for analysis.
This article should have given you an introduction and inspiration to predict currency pair price movements with your own techniques.
About This Article
Author: Mark Prosz
Sources of information and credits for this post include: