Evidence based investment strategy has become increasingly popular among investors in recent years. The core idea is to make investment decisions based on facts, data and rigorous research rather than instincts or emotions. With the advancement of financial technology and big data analytics, investors now have access to massive amount of market data. This enables a more scientific approach to investing by backtesting investment strategies and evaluating their effectiveness based on hard evidence. To implement evidence based investing, investors need to take a disciplined approach in seeking out empirical data, interpreting the data objectively and resisting cognitive biases. Key to this process is to form investment hypothesis based on existing theories and prior research, then test these hypothesis systematically based on data. Evidence based investing represents a shift away from speculation and gut feelings, toward an investing process grounded in rationality, objectivity and transparency. Adopting such an approach can lead to better investment outcomes in the long run.
Backtesting investment strategies based on historical data
One of the key steps in evidence based investing is rigorously backtesting investment strategies before applying them using real money. With today’s computing power and financial databases, investors can easily simulate how a strategy might have performed in different historical periods. For example, a simple moving average strategy can be backtested across decades of stock market data to evaluate its risk-adjusted returns, maximum drawdown, volatility and other statistics. The more data used in backtesting, the more confident investors can be that the strategy’s efficacy is supported by evidence rather than just a result of data mining. Of course, past performance does not guarantee future results, but extensive backtesting provides a solid starting point for strategy development and optimization. Evidence based investors use backtesting to narrow down strategies that are robust and likely to withstand different market environments.
Evaluating investment factors objectively based on data
The factor investing approach is a good example of evidence based investing. Academic researchers have identified various stock factors like value, quality, momentum, volatility etc. that are linked to higher risk-adjusted returns. Evidence based investors evaluate these factors objectively based on long-term historical data across geographies and time periods. For example, the size factor (outperformance of small-cap over large-cap stocks) has been persistent across global markets over decades of data. This gives confidence for investors to tilt their portfolio towards small-cap stocks to potentially benefit from the size premium. On the other hand, factors that are fragile and dependent on short time periods should be viewed more skeptically. Evidence based investors let the data guide them towards factors that are empirically shown to produce risk-adjusted excess returns.
Resisting cognitive biases with data-driven decisions
Human psychology and cognitive biases inevitably play a role in investing. Evidence based investors aim to reduce these downsides by relying more on data. For instance, recency bias causes investors to overweight recent events and underweight long-term averages. An evidence based process forces investors to consider multi-decade data rather than extrapolate recent years. Confirmation bias leads to seeking out only information that validates one’s preconceived notions. In contrast, evidence based investors look at the full body of data objectively before reaching conclusions. Other biases like loss aversion, overconfidence and mental accounting are mitigated by having a structured, data-driven decision process. Of course, human judgment still matters in interpreting data and making final investment calls. But anchoring the process around evidence rather than emotions or intuition can lead to improved investing outcomes.
Evidence based investment strategy represents a scientifically-grounded framework focused on making decisions based on facts, data and empirical evidence. Key components are rigorously backtesting strategies, objectively evaluating investment factors based on long-term data, and resisting cognitive biases by staying disciplined and data-driven. Adopting such an approach can potentially lead to more robust and better performing investment strategies.