Over 300 high-profile individuals, from public market traders to promising FinTech startups, gathered in the famous Hotel Del Coronado conference room in San Diego on October 19th, 2015 for the StocktoberFest conference. Hoping to gain insight and connections otherwise unattainable due to geography or expertise level, StocktoberFest painted the landscape for a thought provoking think tank and networking opportunity. Howard Lindzon, father of investing in San Diego, brought together his peers and portfolio companies to open the air on sectors such as fintech, bitcoin, biotech, and the future of trading. While most of the discussion centered on public markets and the structuring big data, the conference did paint an interesting picture of how investors and analysts are getting the information they need. Here are our three key takeaways from StocktoberFest 2015.
1) Pre-IPO companies are catching investors' eyesThe days of seeing a 400x return on your investment in Apple are over - the public market is no longer the cash cow it once was. Between companies staying private longer, more information being exposed in a real time, and equity being swallowed early and often by the "old boys' club" that is Silicon Valley's venture capitalists, investors are increasingly focused on pre-IPO companies. Everyone's trying to find a way in - but it's not as simple as making a phone call and finding the companies Sequoia invested in. Even public market traders are showing an interest in private company data: the smart ones are looking for trends that may carry over into the broader market. (For an insider's look at specific trends, check out StocktoberFest organizer Howard Lindzon's top 10 takeaways from the conference.)
2) Algorithms are filling the data voidOf course, higher returns on private companies come with their own obstacles, namely the paucity of information about their financial health and prospects. Public companies are required to file regular reports; pre-IPO ones can operate with less visibility. As one speaker put it, "data black holes" exist in every vertical. Large, expensive data firms like Hoover's and D&B have been phased out in favor of more affordable analytics startups, but the jury's still out on what metrics are the best predictors of success.
Data's coming in with greater volume and speed than ever before, but most investors consider it a double-edged sword. The high possibility of a false positive relationship between, say, Twitter followers and eventual share price makes analysts (reasonably) gun-shy about drawing their own conclusions. As a result, data analysis has become the hot new trend in investing. It's hardly a guarantee of success, though. Investors are highly trusting of machine learning, natural language processing and programmatic sentiment analysis; for them, the differentiating factor in this crowded field is often the user interface, rather than the quality of the output. The intersection of big data and investing is still in the early stages, and over time, we'll be better able to separate the effective algorithms from the misleading ones.