Counterpoints to financial engineering

It includes traditional market efficiency arguments against active management, such as Bill Sharpe's arithmetic (see Active Portfolio Management). And even if it is possible to beat the market, and notwithstanding the fact that past performance should not be the sole criterion for judging investment managers, the riskiness of active strategies can be very different from passive strategies (see Indexing). Such risks do not necessarily average out over time, and investors' risk tolerance should be part of the process of selecting an investment strategy to match their goals (see Investment Policy).

A second counterpoint is the set of arguments against quantitative investing, and notably its reliance on backtesting and data mining (see Quantitative Investing). Engineering, by the very nature of its development and application, builds on whatever is accepted theory at any given stage of the cycle. Investment theories tend to lurch forward in leaps, usually after the disappointment of a prolonged bear market. New theories emerge, correcting the ills exposed by a calamitous decline and engineering applies the new wisdoms.

It should not surprise us that the applications of today's financial engineer seem internally consistent, sound and almost unassailable. That would always be found after decades of reconfirmation of market and portfolio theory. But we should not be lulled into complacency by a catechism built on data of only a few decades. Nor should we imagine that portfolio theory, as we know it today, is the end of investment knowledge. There will be new theory and new engineering to apply it. But it may have a different label than the contemporary financial engineering. Finally, one of the consequences of the development of computer and financial technologies (as well as the long bull market) is the incredible growth in electronic trading. This has both good and bad implications for ordinary investors. On the positive side, the tools developed by cutting-edge financial institutions over two decades ago are now available to the individual household. Yet as with most technologies, the tools are more advanced than the general population's understanding of how to use them properly. Although trading costs have come down dramatically for the individual investor, the possibility of doing serious damage to one's nest egg is even greater.

Andrew Lo and Financial Engineering

Where else but the Massachusetts Institute of Technology (MIT) would you expect to find a course track called Financial Engineering? For a while the Sloan School of Management was not really accepted at MIT though its graduates were among the most sought-after in the job market for newly-minted MBAs. But within the science-oriented faculty, business education was hardly taken as seriously as Alfred Sloan, the donor of the facilities, hoped it would be.

Now that has changed. Finance has gone quant: higher mathematics is a regular feature of security pricing, risk management and business strategy. Professor Andrew Lo is one of the key people responsible. He is a first-rate scholar who, like others in this volume, can straddle academe and business. His research output is huge, often in collaboration with other leading lights who appear in the Journal of Finance, the Journal of Financial Economics, the Journal of Econometrics, the Review of Financial Studies and the many other publications still being added to the reading lists of professors and practitioners.

The burgeoning field of financial economics has produced a group of young professors who now hold endowed chairs. Just a decade or so ago, they were pre-tenured stars full of research ideas sprung from the basic efficient market hypothesis. They were going on to the next level or two, testing and applying these theories to specific valuation, portfolio strategy and risk problems. They showed their students, who were to become the star practitioners in institutions, how to do investments the modern way. Many of this group won a coveted Batterymarch Fellowship for research when little other funding was available. Andrew Lo, of course, was one of the most promising of that group as a winner in 1989.
Lo's research interests run the gamut of today's financial interests and his papers are among the most thoroughly researched of the field. Students call him an inspired teacher, perhaps because he believes in the worth of his subject matter. And in addition to his heavy teaching load, he carries an administrative burden as the director of the Laboratory for Financial Engineering, in fact its founder, at MIT. Somehow, he also finds time to help leading investment firms through consulting projects as well as steadily maintaining active parenting of a young toddler.

In addition to being the co-author of the first major financial econometrics textbook, Lo has a book published in early 1999 entitled A Non-Random Walk Down Wall Street, an obvious counterpoint to Burton Malkiel's classic book of almost the same name (see Market Efficiency). As his title suggests, Lo's research indicates that there are some elements of short-term predictability in stock returns and that it may be possible for disciplined active managers to seek them out, exploit them and "beat the market."

Financial engineering is the key to superior performance. Lo uses the analogy of the exceptional profitability of a pharmaceutical company, which may be associated with the development of new drugs via breakthroughs in biochemical technology. Similarly, even in efficient financial markets, there can be exceptional returns to breakthroughs in financial technology. Of course, barriers to entry are typically lower, the degree of competition much higher and most financial technologies are not as yet patentable so the half-life of profitability of financial innovation is considerably smaller.

Understanding Financial Engineering

Financial engineering is, in essence, the phenomenon of product and/or process innovation in the financial industries the development of new financial instruments and processes that will enhance shareholders', issuers' or intermediaries' wealth. In the New Palgrave finance dictionary, John Finnerty lists countless recent financial innovations from adjustable rate preferred stock to zero-coupon convertible debt but these all can be classified into three principal types of activities: securities innovation; innovative financial processes; and creative solutions to corporate finance problems.

All these innovations are implemented using a few basic techniques, such as increasing or reducing risk (options, futures and other more exotic derivatives see Risk Management), pooling risk (see Mutual Funds), swapping income streams (interest-rate swaps), splitting income streams (stripped bonds), and converting long-term obligations into shorter-term ones or vice versa (maturity transformation). But to be truly innovative, a new security or process must enable issuers or investors to accomplish something they could not do previously, in a sense making markets more efficient or complete.

Finnerty describes ten forces that stimulate financial engineering. These include risk management, tax advantages, agency and issuance cost reduction, regulation compliance or evasion, interest and exchange rate changes, technological advances, accounting gimmicks and academic research.

The emergence of financial engineering has also been influenced by the realization on Wall Street in the early to mid-1990s that there was a need for a new kind of graduate training. The financial institutions wanted people with heavy mathematics skills and some finance training, but had previously been fed from a haphazard network of different programs. Universities began to re spond to the demand by setting up masters programs in financial engineering and they were helped by the fact that the physics job market was at an all-time low due to the end of the Cold War.