Many investors are unaware that they need portfolio analytics. FigureA.1 describes a fundamental investor challenge:
To understand what we are doing when investing, we need to know what we are buying or selling (the observables in the Market space) means in terms of the return and risk profile (the unobservables in our Utility space), or exposure. In other words, we need a bridge from the transparent market space to the non-transparent utility space (as shown in Figure A.1). The bridge is called portfolio analytics. Portfolio analytics provides the mapping from one’s holdings to his exposures. It can be compared to a mirror. Just as people need a mirror to see how they look, so will investors need portfolio analytics to visualize what return and risk they are buying into. In this respect, BlueCrystal owns unique IPs on more effective and comprehensive risk and return measures.
Portfolio analytics can only help one see his exposure. When it comes to adjustments of their holdings so as to improve their exposure, portfolio analytics does not help; just as a mirror does not automatically help us dress up. In order to improve our portfolio, we need another bridge back, telling us how to adjust our holdings (observables) so as to achieve our desired exposure (non-directly observable but now seen through the portfolio analytics mirror, and admissible as new views or constraints to our system). This job is part of portfolio construction. Portfolio construction or allocation helps us achieve our investment rationale through adjusting our holdings. As our core IP, Cheung’s Bayesian allocation framework is not only a ground-breaking theory, but it also enables, for the first time ever, a ‘WYSIWYG’ portfolio and risk management process. Its value has been widely recognized among the world’s most sophisticated fund managers. So far, our invention is unique. There is currently no such products offered anywhere in the world.
The Bayesian allocation framework provides a rigorous theory, with which traditional rule-of-thumb methodologies can be judged and enhanced. Traditionally, portfolio management processes designed for different purposes are isolated ‘arts’. The classic theory, the Markowitz mean-variance approach cannot explain most of these, let alone a proper theory to uncover the fundamental connections between them. The Bayesian allocation framework distills the common science core from the art of portfolio management. For the science, it provides the correct solution; and for the art, it allows human discretion. It is different human preferences that diversify investment strategies. It therefore unifies the world of portfolio management practices. A process based in the Bayesian allocation framework allows transparency and hence accountability. This concept is applicable to all investors, regardless of their mind sets and budgets. This framework will have far-reaching impact on investor methodologies and behaviors.
With portfolio analytics and Bayesian allocation, one can start with some information and form a view, i.e., targeted exposure. He then chooses some proxy stock(s). To see whether this is a good proxy, he uses portfolio analytics. Most likely, this exposure is not ideal. To improve it, the Bayesian allocation framework can be used to adjust the portfolio.
Practitioners design portfolio management processes suitable for their own purposes. However diversified they are, professional portfolio construction processes involve 4 critical tasks:
The 4 parts are interdependent. Normally it starts from Stock Selection; but then different PMs follow different (often iterative) paths to come up with their ideal portfolios. For example, one may select a basket of stocks (Stock Selection) and construct a portfolio (Portfolio Construction). He then backtests the portfolio and found some abnormal behavior (Backtesting). So he uses the Portfolio Analytics tool to examine its exposures in details and found some unintentional exposures possibly due to a couple of stocks. He replaces these stocks (Stock Selection) and runs Portfolio Construction again. Further Portfolio Analytics confirms that his decision is good. So on and so forth.
BlueCrystal ABL Professional Companion rightly facilitates the above process, with the 4 modules working separately but allowing powerful interactions. Each part embraces our state-of-the-art IPs and intuitive interface design, ensuring a pleasant user experience.
Of course, the financial investment world sees a lot of financial engineering solutions at work. These systems often weigh practicality over theoretical elegance. What is the importance of theoretical elegance then? Engineered solutions easily break down because they are often a collection of ad hoc solutions developed initially for special situations and eventually assembled together through IT integration. Whereas a theoretically elegant solution has been thoroughly thought-through; and their applicability and limitations are well documented. A theoretically elegant product can robustly deliver what they claim to do, and in many cases, provide unified solutions to a breadth of problem types which engineered solutions fail to handle. At BlueCrystal, we challenge the status quo by making our products not only practical, but also theoretically elegant.