Resources

A variety of methods have been proposed to trade markets. Broadly speaking, we can divide them into three categories:

• Fundamental analysis: it aims to measure the intrinsic value of a security by examining economic data so investors can compare the security’s current price with estimates to see if the security is undervalued or overvalued. However, a common criticism of fundamental analysis is that the timing of enter and exit of trades is not specified. Even the markets move towards the estimated price, a bad timing of entering the trades could lead to huge drawdowns and such moves in account values are often not bearable to investors, shaking them out of the markets.
• Technical analysis: it is in contrast to fundamental analysis where a security’s historical price data is used to study price patterns. Technicians place trades based on a combination of indicators such as the Relative Strength Index (RSI) and Bollinger Bands. However, due to the lack of analysis on economic or market conditions, the predictability of these signals is not strong, often leading to false breakouts.
• Algorithmic trading: it is a more systematic approach that involves mathematical modelling and automated execution. Examples include trend-following, mean-reversion, statistical arbitrage and delta-neutral trading strategies. These strategies often rely on models that produce a good predictive signal. However, due to low signal-to-noise ratio of financial data and the dynamic nature of markets, these signals are difficult to construct.