A study into quantitative algorithmic trading and its ability to generate abnormal returns in equity investing

Abstract

This study looks at the field of Algorithmic Trading (AT), which is the practice of employing pre-programmed software tools to automate trade-related tasks that would otherwise be done manually. This growing tool uses sophisticated equations and mathematical models to buy and sell assets faster and more effectively than ever imagined possible, thanks to high-frequency trading technology. This paper used in-depth analysis to compare the performance of various trading strategies to the market. This was accomplished by backtesting the chosen strategies and comparing them to the average market return of a buy-and-hold strategy, using Microsoft Excel and using financial historical data derived from Yahoo Finance. It analysed two different trading models, a ‘Swing Trading Strategy’, using the EMA crossover, ATR stop-loss, and RSI divergence as indicators, and the second strategy following a ‘Volatility Retracement Strategy’, which was modelled using the EMA, standard deviation, linear regression channels, and Fibonacci retracements. Results for both the Swing Trading strategy as well as the Volatility Retracement strategy were compared with the market’s traditional buy and hold model, using the S&P500 index as a base. The performance of each method is measured through the cumulative return on investment (ROI). The observations used were daily closing prices from 1st January 2011 till 31st December 2020. The initial investment was set at €1,000. The buy and hold model realised a net profit of €1,848.90, falling short of the swing trader model, which resulted in a €3,223.05 net profit, equivalent to a 74% increase over and above the buy and hold returns. It had a larger win trade ratio, larger profitable trade, and smaller losing trades. On the other hand, the hold and buy outperformed the volatility retracement strategy, with a net profit of €1,867.97 and €1,803.97 respectively (3.4% lower). Although the hold and buy strategy had bigger wins, it also had bigger losses, and the volatility retracement strategy obtained a slight better win rate ratio. The testing shows that although trading strategies might not always return an abnormal profit, they can be used to influence portfolio value. In all cases, there were smaller losses when using active trading following the set rules, and in the case of the swing trading strategy, this led to generous returns with minimized losses and larger gains.