Statistical arbitrage (also known as statistical arbing) uses computer algorithms to compare prices on trading platforms, in order to exploit price differences and profit from them.

Although the concept of statistical arbitrage has been around since the **1980s**, it wasn’t until 2009 that it became truly viable as an investment strategy due to advances in technology and reductions in transaction costs.

**Statistical arbitrage hedge fund** In this article we will take a look at how statistical arbitrage works, why it’s so profitable, and how you can get started with this exciting trading strategy.

Statistical arbitrage sounds like something only high frequency traders would be into, but it’s actually pretty simple to execute and extremely profitable. In fact, if you’re in any sort of buy-and-hold investing strategy where you hold your positions for at least three months, you should consider using statistical arbitrage as part of your investment strategy. Here’s how it works…

## An overview of what it is

**Statistical trading strategies** that attempts to take advantage of pricing discrepancies between different markets.

**For example,** if shares of Company A are trading at $10 on the **New York Stock Exchange** and $11 on the London Stock Exchange, a **statistical** arbitrageur would buy shares of Company A on the **NYSE** and sell them on the **LSE**, profiting from the price difference.

The main risk in this type of trading is being arbitraged or outsmarted by someone else who sees what’s happening and starts buying on one market while selling on the other. To avoid this risk, many quantitative traders employ computers programmed with sophisticated algorithms to look for small discrepancies and immediately execute trades when they find them.

## Types of Statistical Arbitrage

Statistical arbitrage is a **trading** strategy that takes advantage of **statistical** disparities in asset prices. This type of arbitrage is usually carried out by computer **algorithms**, but can also be done by humans. The most common type of statistical arbitrage is pairs trading, which involves taking long and short positions in two similar assets.

Other types of **statistical arbitrage **include event-driven arbitrage and intra-market arbitrage. Event-driven arbitrage entails profiting from differences in securities’ price reactions to different types of events such as mergers or bankruptcies.

Intra-market arbitrage profits from price discrepancies within a single market or exchange without involving securities of differing issuers or sectors.

**Volatality Arbitrage****Risk Arbitrage****HFT**

## How Statistical Arbitrage Affects the Market?

**Statistical arbitrage** is a type of trading that seeks to profit from statistical anomalies in the market. This strategy is often used by hedge funds and other sophisticated investors. The **trader** will look for discrepancies between different stock prices in different markets, often caused by information lags or computer-based trading errors.

The **trader** might notice, for example, that **Stock A** was selling at** $10** on one exchange while it was selling at** $11 **on another exchange. The **trader** could then buy the cheaper stock and sell it on the more expensive exchange to make a quick profit. It can also work the other way around.

Say one exchange had an inflated price for some reason. If traders wanted to bet against this inflated price, they would need to be able to quickly convert their money into shares of the company before it reached its peak.

The same principle applies if they wanted to invest in something else. In these cases, traders use an intermediary like **Goldman Sachs **which helps them quickly convert money into any **stocks** they want without having to go through the hassle of using a foreign currency.

## Statistical arbitrage in pairs trading using Python

The first step is to choose the stocks for pairing. We have taken the two stocks Blink Charging Co

(ticker symbol: BLNK) and NIO (ticker symbol: NIO).

Let us first fetch the closing prices for both stocks.

**# Import the libraries
**import pandas as pd
import yfinance as yf
import numpy as np
import statsmodels.api as sm
import matplotlib.pyplot as plt
**# Define list of tickers
**tickers_list = ['BLNK', 'NIO’]
**# Define data with Dataframe
**data = pd.DataFrame(columns=tickers_list)
**# Fetch the data
**for ticker in tickers_list:
data[ticker] = yf.download(ticker,'2018-12-01','2022-03-31')['Adj Close']
**# Show first five rows of the data**

**%matplotlib inline
**
# Customise the size of the plot
plt.figure(figsize=(15, 7))
plt.plot(data['BLNK'], lw=1.5, label='Close Price of BLNK',color='red')
plt.plot(data['NIO'], lw=1.5, label='Close Price of NIO',color='#6CA6CD')
plt.grid(True)
plt.legend(loc=0)
# This helps us tighten the figure margins
plt.axis('tight')
plt.xlabel('Dates')
plt.ylabel('Price ($)')
plt.title('Close prices for BLNK and NIO')
plt.grid(True);
plt.show()

## What are some risks?

*Statistical* arbitrage, or stat arb for short, is a trading strategy that seeks to profit from price discrepancies between similar assets. The risks associated with this strategy are two-fold: first, the trades are heavily reliant on accurate data and modeling; and second, the strategy can be adversely affected by changes in market conditions.

**Statarb** **traders** need their models to accurately account for all potential sources of risk. When markets are less liquid (e.g., low volume), models may fail to capture how these differences affect prices, leading to inaccurate predictions about where prices will converge.

These inaccuracies will cause trades to misfire, resulting in higher losses than if liquidity was more consistent, However, these risks can also lead to outsized returns: historical evidence suggests that when markets become less liquid (e.g., during flash crashes), **statarb** strategies often outperform traditional long-only strategies.

### How to apply statistical arbitrage for pairs trading?

For applying **quantitative** arbitrage work inside a pairs trading strategy:

- First, you have to pick the stocks for matching.
- After you have picked the stocks, secondly you will find out the closing values of both equities and visualise them.
- Now, you will compute and visualise the pair’s spread and the z-score of the spread
- Then, you will verify the stationarity of the spread by performing the Augmented Dickey-Fuller.
- Lastly, the trading signals can be created if the pair is stationary according to the ADF test. After a period, the paired stocks invariably return to their mean.

### Financial Crisis hit StatArb

It is a significant point of contention that the typical decline in portfolio value might also be attributable to a causative process. The financial crisis of **2007-2008** also happened at this period. Many, if not the great majority, of investors of whatever type, incurred losses during this one year time span.

The correlation of observed loss at fund managers utilising ** statistical arbitrage** isn’t always indicative of reliance. As new rivals enter the market, and funds spread their trades over more platforms than

**StatArb**, a claim may be made that there should be no reason to anticipate the platform model to behave anything like one another. Their

**statistical**models might be fully independent.

## Which websites are appropriate for the strategy?

There are a few different websites that statistical arbitrage can be used on. The most popular website for this strategy is the **Forex** market. Other websites include the stock market, commodities market, and even the **cryptocurrency** market.

In order to profit from this strategy it is important to have access to several markets. It is also important to note that while there are many successful investors who use **statistical** arbitrage as their primary investment strategy, it should not be attempted without having a strong understanding of how it works or without any other backup strategies in place. It is not a beginner-friendly strategy and has a very high level of risk associated with it.

### Capital Market Analogy

Likewise, if we buy a stock on the market. We are buying the future cashflows of the specific company. The only way, we make money is when our belief about the firm cash flow is bigger than what the market believes it to be. There are also statistical arbitrage trading software.

Similarly, it’s the reverse when we are short of **stock**. Our conviction is smaller than the chance the market is attributing it to the company’s cash flow.

The sole restriction is transactions or positions when the danger of an event going against us has a significant cost such as death. Here, while on an anticipated basis, it does not make sense to purchase insurance, it works from a risk point of view.

The chance of disaster is relatively low but the cost of not defending against it is quite expensive. These are additional bets, we should take when the absolute loss due of the downside is quite significant.

## Verdict

If you’re looking for a high-profit, low-risk trading strategy, statistical arbitrage is a great option. Also known as **statarb**, this strategy involves taking advantage of small price discrepancies between two different markets.

**For example,** if stock A is trading for **$10** on one exchange and **$10.05 **on another, a stat arb trader would buy stock A on the first exchange and sell it on the second, pocketing a quick 5 cents per share. Because the cost of buying or selling a single share is typically only pennies, these tiny margins can translate into substantial profits over time.

**Statarb** traders have also been known to bet against themselves, setting up what’s called a synthetic short position in which they make an opposing bet that limits their risk while still generating steady returns.

The downside? Well, like any other type of investment, there are no guarantees in the market; if you don’t get out before something unexpected happens (like an economic collapse), your money could be at risk.