计量经济分析 = Econometric analysis

Investor Psychology and Security Market Under- and Overreactions

Investors face numerous challenges when seeking to estimate the prospective performance of a longonly investment in commodity futures. For instance, historically, the average annualized excess retu...

The Strategic and Tactical Value of Commodity Futures

International Momentum Strategies

Neural networks are used in business organizations for providing intelligent decision support. The ABI/Inform bibliographic database has over 1 000 publications on business-related research in neur...

A bibliometric analysis

Machine learning offers a set of powerful tools that holds considerable promise for investment management. As with most quantitative applications in finance, the danger of misapplying these techniques can lead to disappointment. One crucial limitation involves data availability. Many of machine learning’s early successes originated in the physical and biological sciences, in which truly vast amounts of data are available. Machine learning applications often require far more data than are available in finance, which is of particular concern in longer-horizon investing. Hence, choosing the right applications before applying the tools is important. In addition, capital markets ref lect the actions of people, who may be inf luenced by the actions of others and by the findings of past research. In many ways, the challenges that affect machine learning are merely a continuation of the long-standing issues researchers have always faced in quantitative finance. Although investors need to be cautious—indeed, more cautious than in past applications of quantitative methods—these new tools offer many potential applications in finance. In this article, the authors develop a research protocol that pertains both to the application of machine learning techniques and to quantitative finance in general.

A Backtesting Protocol in the Era of Machine Learning

Rebalancing: Why? When? How Often?

It seems natural that the shifting composition of a nation’s population ought to influence GDP growth and perhaps also capital markets returns. As the baby boomers have aged, many people have studied past demographic data in an effort to extract indications for the future influence of the boomers on many aspects of the economy. We extend this body of literature by analyzing the effect of demographic changes on three measures of great importance for countries all over the world: real per capital PPP-adjusted GDP growth, stock market excess returns, and bond market excess returns. We confirm what others have already demonstrated, but we extract markedly more statistical significance by adapting a polynomial curve-fitting technique pioneered by Fair and Dominguez (1991), to this new purpose. In our work, we find that a growing roster of young adults (age 15–49) is very good for GDP growth, a growing roster of older workers is a little bad for GDP growth, and a growing roster of young children or senior citizens is very bad for GDP growth. We find surprisingly powerful results when we apply the same technique for exploring the links between demography and capital markets returns, net of the strong and well-documented effects of valuation and yield levels. Stocks perform best when the roster of people age 35–59 is particularly large, and when the roster of people age 45–64 is fast-growing. Bonds follow a similar pattern, with an age-shift: they’re best when the roster of people age 50–69 is growing quickly. We carry out three different forms of robustness checks, each of which provides statistical significance in different ways: applying different country weights, testing alternative demographic variables, and confirming GDP results on out-of-sample countries. It would be dangerous to forecast the future based on these results. Tacitly, we would be assuming that past relationships between demography and either GDP growth or capital market returns will hold unaltered in the future. However, given the high levels of statistical significance in the historical relationships, it is too tempting to resist exploring the possible implications for future GDP growth and capital market returns. These implications—with all the caveats that must necessarily be offered—are sobering, to say the least.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1810985_code1109123.pdf?abstractid=1810985&mirid=1

Demographic Changes, Financial Markets, and the Economy

Target date investment strategies purport to meet the two primary objectives of any retirement savings program: maximize the real value of our nest egg, while minimizing uncertainty around the prospective income we’ll have at our disposal as we approach our retirement years. Unfortunately, the classic glidepath approach to retirement investing – moving from equity-centric to bond-centric investing as we age – does not meet these objectives. We summarize the flaws in traditional glidepath implementation and explore illustrative changes to the rules-based, mechanistic solution for retirement planning that can improve the expected outcome for investors. We use simulations to test alternatives and find the following. First, rebalancing to a static mix beats a gradual shift to bonds (or equities for that matter, since the solutions are not linked to expected market environments). Second, adjusting the risk profile within stock and bond portfolios rather than across asset classes reins in risk more constructively than the classic glidepath solution. Third, incorporating valuation-indifferent equity strategies improves the historical performance of the solutions relative to alternatives built using cap-weighted equity indices. Even with simplistic, rules-based approaches, we find there are better ways to achieve our financial objectives for retirement.

The Glidepath Illusion...And Potential Solutions

The price of a stock may differ from its fundamental value by a random noise. In this case, small-capitalization and value stocks are more likely to have negative noise, while large-capitalization and growth stocks are likely to have positive noise. Negative price noise implies that small-capitalization and value stocks are more likely undervalued and thus have higher expected return than justified by risk, while the large-capitalization and growth stocks are more likely overvalued. We formally verify and explore this intuition by using a standard noise-in-price model.We compute in closed form the cross-sectional variations of the expected stock return. Our model is parsimonious with essentially only one adjustable parameter the volatility of the price noise.With a moderate volatility of price noise, the cross-section of the expected stock return matches quantitatively the empirical counterpart in Fama and French (1992). Our study suggests that a modest amount of noise in prices can create size and value effects.

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1773003_code94030.pdf?abstractid=936272&mirid=1

Robert D. Arnott

Papers

A Backtesting Protocol in the Era of Machine Learning

Rebalancing: Why? When? How Often?

Demographic Changes, Financial Markets, and the Economy

The Glidepath Illusion...And Potential Solutions

Can Noise Create Size and Value Effects