You're navigating market volatility as an investor. How can you leverage data analytics for insights?

Market timing strategies can be harnessed for the short term satellite portfolios while the long term passive strategy function as the core portfolio in the core-satellite portfolio construction

I strongly believe that historical market data and patterns have limited predictive power. They are valuable, but it is crucial to account for changes in the context over time and apply economic logic. The so-called "paradigm shift" may be caused by the exponential growth of new tech or new disruptors entering the market, regulation change, geopolitical factors, etc. Without taking into account the "hidden data" - i.e. non-verbal, non-digital information - there is a material risk of making suboptimal decisions.

Understanding the market landscape is so important to predict the potential upside of a startup investment. Beware of overly hyped verticals because these startups tend to be overpriced. It is important to benchmark historical valuations of similar startups (if available), so you can gauge and decide whether the startup investment suits your investment strategy and risk tolerance.

Besides the usual data analytics sources to rely on, another point would be find alternative sources of data to analyse as well. Non traditional sources of data complement the traditional sources of data to provide a more comprehensive view of the trends happening

Understanding the current landscape is the beginning of benchmarking a potential investment. Often, the hype surrounding market verticals is exaggerated and overpriced. I usually follow the WWWWH method. Who? What? Where? When? and How? Is there a market? What do they bring? What is their value proposition? When will this happen? Do you know how this will happen? But ultimately, do I need or want this?

Venture Capital is inherently high risk, and hence high return. For early stage startups, there are not many quantitative or representative historical numbers to crunch and simulate, so it might be better to dig deeper into the founding team, market, and technology risks. As a VC, you could also seek feedback from Corporates or CVCs in your network.

I believe that risk in VC is a positive notion! Risk can lead to substantial returns if the selection process is executed well. While risk should be balanced, VCs that do not take enough risk may experience modest or weak returns. According to statistics, as I recall, about 60% of VC funds did not return capital (i.e., distribution DPI < 1x). I believe this outcome is not due to portfolios being too risky, but rather the opposite, compounded by suboptimal portfolio allocation and limited access to deals in the market.

The VC market has a high degree of information asymmetry, which explains the superior returns in VC/PE markets vs. stock markets. To leverage information asymmetry as a competitive advantage, it is essential to have multiple channels for sourcing, collecting, and processing information. Digital tools can assist in generating important signals early. For example, Similarweb can be used to analyze consumer traffic dynamics, providing insights into the growth of Consumer Tech companies before their actual financials are available. A relatively new trend is the use of AI-powered tools in VC, making all processes increasingly data-driven. I am a strong proponent of data-driven decision-making, but it is important to check the quality of data.