The trade war between the US and China began in late 2018 with President Trump imposing tariffs and trade barriers on Chinese imports to retaliate against “unfair trade practices.” Fast forward to Q3 2019, the trade war escalated to its highest levels, resulting in a volatile US stock market. With the S&P 500 dropping over 5% on days when further tariffs were announced, trading was
The Problem
When the trade war between the U.S. and China began, the idea that its impact would not be far-reaching and widespread across different sectors was decidedly quelled. As the trade war persisted, it became apparent that its effects reverberated throughout industries all over the country. For instance, the agriculture industry in the U.S. has been severely affected by implications created by the trade war. The U.S. farmers were hurt by China’s retaliatory tariffs and as a result, lost some of their biggest buyers for many of their crops.
The trade war’s effects, boiled down to its simplest form, led to a substantial loss in revenue for traders and investors.
One of the causes of the trade war was due to the 420 billion dollar trade deficit between the United States and China. This led to many back and forth negotiations between the two countries, ultimately leading to great tension and war. President Trump believed that China was long undergoing unfair trade practices and was stealing intellectual property from the Americans, while China believed that the US was imposing taxes as a means to assert their dominance and to limit China’s rise as a global superpower.
Trump has imposed tariffs on more than 360 billion dollars of Chinese goods and China has imposed tariffs on more than 110 billion dollars of US goods. Tariffs increase the price for countries to buy imported goods, which makes countries less likely to buy them and more likely to buy domestic goods. To give you some background, the US imposed the following tariffs on the following dates: $34 billion in July 2018, $16 billion in August 2018, $200 billion at 10% in September 2018, and $200 billion at 25% in May 2019. On the other hand, the Chinese imposed the following tariffs on the US on the following dates: $34 billion in July 2018, $16 billion in August 2018, $60 billion at 10% in September 2018, and $60 billion at 25% in June 2019. These tariffs were one of the major causes of the trade war.
ISM Manufacturing Report Q4 2019
This report provides valuable quantitative insight into the impact that the US-China trade war had on the US manufacturing sector in Q4 2019.
- Purchasing Manager’s Index (PMI) Summary:
- Manufacturing contracted in December 2019 by 0.9 from November 2019 to 47.2
- Decreased by 9.4 from 2019 high of 56.6 in January
- Rate of contraction is fastest since June 2009
- Production Index Summary:
- Production Index registered 43.2% in December, which is 5.9 lower than the 49.1 reported for November
- Index decreased 4.1 from September 2019 reading of 47.3
- The index had its lowest reading since April 2009, when it registered 36.7
- Employment Index Summary:
- Employment Index registered 45.1 in December, a decrease of 1.5 compared to the November reading of 46.6
- The index had its lowest reading since January 2016, when it registered 44.6
- December level falls below BLS’s 50.8 threshold for increasing manufacturing employment
- Price Index Summary:
- Prices Index registered 51.7 in December, an increase of 5 from the November reading of 46.7, indicating raw materials prices increased after six consecutive months of decreases.
- New Export Orders Index Summary:
- New Export Orders Index registered 47.3 in December, a decrease of 0.6 compared to the November reading of 47.9
- Imports Index Summary:
- Imports Index registered 48.8 in December, 0.5 higher when compared to the 48.3 reported for November
Manufacturing Stock Analysis
This was a highly volatile time for many stocks in global markets, particularly those that dealt with construction and manufacturing. Two of those, Caterpillar Inc., a US firm that specializes in designing, manufacturing, and selling construction machinery and equipment worldwide, and 3M, a multinational conglomerate that has significant exposure to consumer goods, are examined below. The volatility in the general market also created many buying and selling opportunities for these stocks, as illustrated below. By utilizing BetterTrader’s BackTesting and Twitter capabilities, investors would have been in prime positions to capitalize on these massive movements in price, as they would have been better informed and quicker to react.
Caterpillar Inc. (CAT) Performance Q3-Q4 2019
3M Inc. (MMM) Performance Q3-Q4 2019
Trade war resolution
In January 2020, the US and China signed a preliminary deal (so-called “phase one deal”). However, even after this deal was created, there was still significant tensions between the two countries. With this deal, China promised to boost US imports by 200 billion dollars above 2017 levels and strengthen intellectual property rules. In return, the US promised to get rid of some of the new tariffs imposed on China. The aim of this preliminary deal is to alleviate tension between the two countries, hopefully, improving the world economy. Phase two of this deal is expected to be coming in the near future, but we are unsure exactly when.
The Solution- BackTesting
The BackTesting tool was created to better advise traders on present investing opportunities based on previous market reactions to similar situations. BackTesting is generally defined as the process of applying a trading strategy or analytical method to historical data to see how accurately the strategy or method predicts actual results.
In the case of the trade war, where there were different “phases,” this tool could come especially helpful when a new phase is announced with higher imposed tariffs on certain goods from either the US or China. The BackTesting tool analyzes patterns from previous trade patterns and uses artificial intelligence to make current trade recommendations.
This tool allows traders to make more informed trading decisions during times of uncertainty by using statistically-backed trades.
