When deciding to buy, sell, or hold a particular investment, it can be tempting to simply look at the spot price or volume data and go from there. For those not acquainted with these terms, the spot price of an asset is the current price in the marketplace at which it can be bought or sold for immediate delivery. Volume data quantifies the number of shares traded and, for futures and options, it reveals how many contracts have changed hands.
Market risk is commonly modelled as a normal distribution of the expected returns for a financial instrument: The most likely outcome is at the peak, located in the middle of the distribution. The width of the curve on either side of the peak indicates the uncertainty or volatility of the market. Large volatility brings the possibility of making a lot of money, but also the risk of losing a lot of money.
With social media gaining popularity in the world of financial trading, there is interest in using the sentiment, or opinion, of many people automatically in algorithmic trading.
The idea of making money by exchanging one type of currency with another is well-known. Many Canadians remember a time or two when the Canadian dollar was on par with, or even worth more than, the US dollar, such as in 2007. Canadians revelled at the lower cost of goods south of the border. At the same time, people expected the US dollar to rise in value again, so many held onto their US currency in order to sell it later. Events like these are rare between established countries, and returns are typically low for foreign exchange (FX).
The price of bitcoin has almost doubled since the beginning of the year, while gold has fallen by almost 15%. Cryptocurrency has emerged as a competitor to gold as a hedge against inflation, that is, a protection against depreciation. Gold is often used as such a hedge: when the US dollar loses value due to inflation, the price of gold tends to increase.
Options contracts are more popular than ever, with more options contracts traded last year than any other year in history.1 With people spending more time at home due to the COVID-19 pandemic, many are turning to trading and investing their new surplus of time and money. Brokers have eliminated previous barriers in options trading with smaller fees, increased usability, and the elimination of required minimum account balances to trade options.
Historic levels of cold and snow left millions of people without power in Texas due to a surge in demand for electricity. In the state, the majority of electrical power (about 52%) is produced using natural gas. Texas also supplies 27% of U.S. marketed production, by far the largest share of any U.S. state. How was the demand for natural gas affected by the extreme winter weather in Texas? In turn, how did that affect the natural gas futures market?
Large-scale events tend to affect the futures market. For example, demand for natural gas is affected by the weather. Demand is also affected by the patterns in people’s day-to-day lives, which have been greatly affected by the COVID-19 pandemic. In this article, we will look at a past event in the natural gas futures market, and explain why we should look for the same pattern toward the end of the present winter. The analysis is guided by the market sentiment tool, the CME Market Sentiment Meter (MSM).
Andrew Milne immigrated from England to Canada as a child. He grew up partly in Toronto and partly in Vancouver. He began his studies in Computing Science at Simon Fraser University but later transferred to the University of British Columbia (UBC) to obtain his bachelor’s degree in Electrical Engineering.
The study of finance relies on making the best possible decisions despite future uncertainty. There is always risk when making financial decisions, and it goes hand in hand with return. A core problem in computational finance is risk minimization, and classical computers (that is, non-quantum computers) are often used to solve this problem using a variety of algorithms. But such methods are not always the solution. It will be explained below why there are limitations to using classical computers to solve challenging real-world financial problems, and what is being done to overcome these limitations by means of quantum computing.