In Chris Burgess' class Beat the Market, he presents three different methodologies or ways to look at or interpret the data you collect. You can take the data-driven approach, which looks at market data and draws logical conclusions and predictions. You can use an event-driven approach that looks at world events and then judges how much they will influence the marketplace. Or you can take an observational approach by looking at trends or patterns and forming a trade hypothesis from historical patterns. There is no right or wrong approach as each method has different strengths and weaknesses. Thus, it is up to you to decide which approach fits your trading goals and personal preferences.
This approach uses deductive reasoning to extrapolate from your collected data so you can identify possible trends and predictable outcomes. It is a dependable and reliable way to find high-quality trade ideas; however, it should be noted that the trade ideas found are only as good as the data you use; so, stick with dependable data streams to maintain your data integrity. This approach is very beginner-friendly and can lead to multiple trade ideas from the same analysis.
There are a few potential downsides to this approach, such as it can fail to react to unexpected large events that can suddenly affect the markets. It can take a fair amount of time to collect and analyze all your data. As we stated before, this approach requires a large number of accurate sources of economic data to produce high-quality trades.
Data-Driven ideas tend to work best for medium time frame trades, which usually last between one and three months. However, you can hold long or short trades past this time frame if the trade is still profitable, but the one to three-month target works for most trades. Finally, this approach is a solid middle ground between having to do tons of research and spending all of your time managing risk.
This approach identifies market-moving events happening around the world and how they impact different sectors and industries. It is excellent in finding secondary and tertiary ripples that impact the price of a company's stock. After all, economic events are frequently happening worldwide, and these events affect markets and bring with them unexpected volatility, which can quickly drive prices up and down. The downside to this approach is it relies on significant events happening and requires quick thinking and reaction.
Event-Driven ideas tend to work best over shorter time frames, which fall in between intra-day trading to only a few weeks. Trading tends like this require less work than other approaches but necessitates more risk management, and more risk management equals more time babysitting your open trade.
This approach uses inductive reasoning to observe trends in day-to-day life and deduce trade ideas from the observations you make. You see what is happening around you and then look for data to support or disprove your supposition. As with event-driven ideas, observational ideas tend to work best when finding the less apparent trades, which most people are not focused on. The pros to this approach are you notice trends happening around you every day, and by digging a little deeper, you can find less apparent trades that are very profitable, especially for long-term investors. Also, since most of these trades are long term investments, they require much less risk management. The downside is it may take a long time for your hypothesis to be proven right.
Around Tradesmart, we mainly focus on Data-Driven Idea Generation because we believe it works best for time periods from a week to a year or even more. It is a reliable, simple framework that can be repeatably used to get results. However, it is up to you to choose an approach that works best with your personal preference, trading time horizon, and screen-time preference. As each approach can and will work, and all approaches are used by professional traders to various degrees. You just need to understand when and where to use them and their respective advantages and disadvantages.