WHY DOES A PRICE CHANGE?

 Let’s break down the components of price change mechanics to observe why prices fluctuate. The first question we need to ask is how do prices change?

HOW DOES A PRICE CHANGE?

Most markets in finance are dual-auction. This is both buyers and sellers coming together, offering to sell, and bidding to buy, in an attempt to come to an agreement to trade.

Interestingly, if an instrument is for instance rising, this does not mean there are more buyers than sellers. There are always an equal number of buyers and sellers at each price. After all, a purchase cannot be made without a buyer and seller, and vice versa for a sale. The impact on price changes from the urgency to trade (order types) and the liquidity available.

If you take a look at a market you will see two prices, the bids, and the offers. If you want to buy immediately, you must lift the offer, paying the lowest sale price available at that moment. If you want to sell immediately, you must hit the bid, paying the highest purchase price. The difference between these two prices is called the spread. Your order to trade is called a market order. The transactions taking the other side of your trade are limits. There are many different order types in the markets, but they are all based on these two fundamental orders.

Market Orders

 A market order is an order to trade at the current best available price. Market orders remove liquidity, and are not guaranteed. For instance, if there are not enough available offers at the price you want to buy, your trade will be transacted at higher prices. This is slippage.

Limit Orders

A limit order is an order to trade at a specific price, or better. Offers to sell can only be at the current offer price or higher, and bids to buy can only be at the current bid or lower. Limit orders provide liquidity, and they are therefore subject to inverse slippage. This means that if there are no sellers at your bid price, your trade will be transacted at lower (and therefore better) prices.

To summarise, price rises when all the available offers are lifted.

Price falls when all the available bids are hit.

The point to note: prices change when liquidity is removed.

 

So, why does a price change?

It’s a good question, and a very deep one. There are countless reasons for individual transactions. They can be explained by a magnitude of reasons, individuals and groups interacting within a complex system, for economical reasons, further explained by human phycology. Human biases and economical inefficiencies exist, and all have an impact on price changes. As with the popular saying that there are more combinations of a game of chess than there are atoms in the observable universe, one can get bogged down in trying to dissect the reasons of trillions of individual transactions, especially when often they are unexplainable, even by those making the transactions.

The best option is to observe the conduct, research, and create basic models. This is what we will be continuing to do on this website.

Process != Outcome

Process is not equal to outcome. A positive result is not necessarily due to a good strategy, just as a negative outcome is not necessarily due to a bad strategy.

Here’s why.

Consider a gambler betting on one side of a fair coin toss. After a number of bets, due to the law of large numbers, we would expect this gambler to have gone through some ups and downs, wins and losses, but overall and at the end of the game to have a P/L of zero.

Now populate this gambling universe with a bunch of extra separate gamblers betting on their own versions of the same game, and after a certain number of rounds, some will be winners, some will be losers. Did the winners have some certain skill that the losers lacked? Did they have a better strategy?

No.

They might think they did. But they didn’t.

Gambling universe results.
Gambling universe results.

Scam artists love this trick. They would send 10,000 penny stock leaflet recommendations to random addresses of affluent areas. Yet five thousand of the recommendations were to buy the stock, and the other five thousand to sell. After a few weeks, the stock would rise or fall, and they would send another round of stock recommendations, but only to the receivers of the original ‘correct’ recommendation. This next stock would rise or fall, and then they had another 2.5 thousand receivers of two ‘accurate’ stock picks in a row. They would perform this process over and over until they had a handful of originally skeptical but now amazed customers willing to send to money to be managed by these geniuses who managed to predict 10 stock moves in a row. Skill? Well, the trick was clever, but it was all randomness.

As legendary trader Larry Hite said, there are just four kinds of bets. There are good bets, bad bets, bets that you win, and bets that you lose.

Focus not just on the outcome, but on the process.

Rationally Irrational

Ideas from behavioral economics such as prospect theory have enlightened us to our reactions to gains and losses over varying time periods, but I have yet to come across research discussing what I am calling ‘rationally irrational’ decisions. Let me attempt to explain.

Our decisions are influenced by rewards, potential or actual. These rewards can create habits that lead to the continuation of a specific behaviour. But since rewards are felt by chemicals in our brains, something that is unhealthy for our minds and bodies – getting high on hard drugs, smoking cigarettes, or drinking excessive amounts of alcohol – may be felt of as a reward which could lead to future cravings.

Often it is the case that the individual knows fully well the impacts of his or her decision, and sometimes in fact craves these effects. Think of the masochist who gets off on the pain, or the dieter who wants to improve their body image yet craves unhealthy foods. Most people want the fitness physique, yet also want to eat all the cakes.

In terms of finance this could translate to a rogue trader gambling deeper and deeper into the red, or an institution plotting to cheat and steal from clients. Of course, there have been many cases of both.

As I am planning to create a basic model of a complex economical system, I am looking to model one of the most important aspect of the system, the participants. And of course, I need to include their irrationality. If anybody has any research that I may have overlooked in regards to reward mechanisms, please let me know.

The Elements of Risk

Let’s start with some basic assumptions to come to further conclusions regarding risk.

Assumption 1. Increased time exposure increases risk.

With the knowledge that asset returns are fat tailed, we can make the basic assumption that the longer you are exposed to the market, the greater the chances of catching a negative outlier event.

timerisk

But as the time and risk exposure extends, so too should exposure to a positive outlier. This leads us to our second basic assumption.

Assumption 2. Increased risk means increased potential profit.

This assumption generally stands true, but there are many cases where it’s not quite the case. For example, under this basic assumption, going long a stock at 10 is clearly less risky than shorting the same stock at 10 (long max risk = -10, short max risk = unlimited).

Another example is in strategy and asset diversification of the portfolio. The risk of individual transactions will remain the same, but spreading this risk across multiple and lightly correlated risks will decrease the volatility of portfolio returns.

Leverage is obviously another example. And as we have seen with the previous post, increasing position sizing at higher frequencies to have the same risk: reward as the lower timeframes simply increased your left end tail risk. A sudden spread-spike of 120 pips when you have a 100 pip stop loss at 1% of capital will hurt you less than if you have a 10 pip stop loss at 1% of capital.

Conclusions

These assumptions are not measuring the probability of an event occurring. Going long the previously mentioned imaginary stock at 10 may be lesser risky than shorting the same stock on paper, but if the stock is never going above 10 then the risks are clearly not equally weighted.

There are other risks involved with individual transactions. These include liquidity and regulatory risks, as well as completely unforeseen game-changers that could stem from anywhere.

It is arguable whether attempting to quantify probabilities of risk in such an unpredictable environment is worthwhile, but it seems to be productive as long as it is measured without getting too caught up in individual biases.

Left Tail Liquidity Risk

The foreign exchange market is quoted as being one of the most efficient and liquid financial markets. Yet the recent Swiss Franc unpegging from the Euro has shown what sort of movements can be caused by central bank intervention. Some were surprised to learn that many trend following hedge funds in fact lost out during the move, despite usually being mostly structured to limit the skewness of the left and eat the asymmetry of the right tails. Most trend followers survived the day due to their immense diversification, and some still even profited from also being short the Euro versus the Swiss Franc. Still, the risks can be highly destructive. This is the left-tail risk caused by a swift liquidity pull.

There is much research showing that asset returns and bid-ask spreads are fat-tailed (Koedijk et al., 1990; Hols and De Vries, 1991; Müller and Sgier, 1992; Loretan and Phillips, 1994; Ghose and Kroner, 1995, amongst the earliest) But for very high frequency data, the price uncertainty within the bid-ask spread becomes very important (Ramazan Gençay, Michel Dacorogna, Ulrich A. Muller, Olivier Pictet, and Richard Olsen, 2001).

Witness the foreign exchange market’s most liquid currency pair bid-ask spread sizes across the sample month of April 2015.

Exhibit 1. EURUSD
Exhibit 1. EURUSD

As can be observed, there are many extreme deviations from the bid-ask spread mean, with one instance in particular recording a spread of over 130 pips.

Using a tight stop loss with large leverage will not protect a high frequency strategy from the hidden liquidity risks, specifically since stop order slippage is all about the (lack of) liquidity.

Fortunately, most of these extreme deviations occur during large news releases such as NFP (Non-Farm Payrolls) or central bank decisions, but bear in mind that this will not always be the case. Therefore, decreasing position sizing around these times in particular and generally being careful of overall leverage can mitigate the risks. We will be exploring these factors for potential alpha extraction in the near future.