Introduction Algorithmic trading (AT) consists in computer algorithms, created by specialists, trading in stock markets autonomously. The first trading automation happened in March 1976 with the introduction of designated order turnaround (DOT) system in the New York Stock Exchange (NYSE) (Guerard 1990), which allowed routing orders electronically between trading centres. Since its early introduction, AT is taking an increasing importance…
Abstract Pairs trading algorithm is market neutral, mean-reverting strategy for hedge funds and alike. We conduct an empirical study from 1999 to 2022 comparing the performance and characteristics with buy-and-hold algorithm. The results of six months trading show lower return and Sharpe ratio but lower risk than buy-and-hold. For a twenty years trading periods buy-and-hold is the clear winner with…
The research about algorithmic trading is important due to its predominance in the financial industry. In 2019 a big percentage of the traded equity in US was performed by automated algorithms, that represents 35.1% of $32 trillion with an expected annual growth of 8.7% over the period 2020 - 2027. The subject is particularly challenging because it falls between three…
The study of algorithmic trading is of high importance given its predominance and forecasted growth. In 2019 the majority of the equity traded in US was executed by algorithms, equivalent to 35.1% of $31 trillion and the global estimated compounded annual growth rate (CAGR) over the period 2020-2027 is 8.7%. The topic presents many issues and angles. Institutional investors employ…
This coursework defines the foundation of the DBA research project. It states the most significant theory that underpins the research, the Efficient Market Hypothesis (EMH), with evidences in favour and against of efficient markets. Next, argues that if the theory stands true, rational investors will only invest in passive portfolios, impacting the financial industry offering active financial products. The paper…
