Genetic Algorithms and Investment Strategy Development

The aim of this paper is to investigate the use of genetic algorithms in investment strategy development. This work follows and supports Franklin Allen and Risto Karljalainen’s previous work1 in the field, as well adding new insight into further applications of the methodology. The paper first examines the capabilities of the algorithm designed in Allen and Karjalainen’s work by using human‐developed (rather than market‐historical) datasets to determine whether the algorithm can detect simple signals; the results show that the algorithm is quite capable of such basic tasks. Next, the S&P 500 test performed in Allen and Karjalainen’s original work was confirmed. Then, experiments were conducted in emerging equity markets, as well as commodities markets with a range of fundamental as well as technical indicators. The results generally show no significant positive excess returns above a buy‐and‐hold strategy; speculations for possible reasons are discussed. In addition, suggestions for future research endeavors are presente

Technological Evolution and the Devolution of Corporate Financial Reporting

My claim is that the technology link to the recent disclosure scandals is no coincidence. To be sure, cheating tempts all who seek wealth, in whatever line of business they find themselves. I want to show, however, how the rapid pace of innovation at a number of levels offered motive, opportunity, and rationalization for a downshift in financial reporting norms, which in turn made outright fraud more probable

Demography and the Long-Run Predictability of the Stock Market

macroeconomics, Demography, Long-Run Predictability, Stock Market

Investor Skepticism v. Investor Confidence: Why the New Research Analyst Reforms Will Harm Investors

Part I of this Article provides an overview of research analysts and their basic functions, including a discussion of sell-side analysts\u27 role in the market\u27s recent boom and bust. Part II examines the conflicts of interest that have plagued sell-side research, and Part III reviews the Regulatory Actions that are meant to address these conflicts. In Part IV, the author will make the case for encouraging, rather than lessening, investor skepticism in sell-side research and will explain why the Regulatory Actions are not likely to improve the performance of sell-side analysts. Finally, Part V will offer a simpler proposal to address the sell-side analyst issue. While there may not be a solution to the maybe not problem, the information gap between institutional investors and retail investors regarding the weaknesses of sell-side research can be eliminated, which would allow retail investors to benefit from the value of sell-side research while also granting them the opportunity to properly protect themselves from its weaknesses. Akin to the Surgeon General\u27s warning for cigarette manufacturers, this Article proposes that sell-side analysts and their firms be required to prominently include, with all research, a short and clear warning from the United States Securities and Exchange Commission ( SEC ), regarding the historical weaknesses of sell-side research

A semantic Bayesian network for automated share evaluation on the JSE

Advances in information technology have presented the potential to automate investment decision making processes. This will alleviate the need for manual analysis and reduce the subjective nature of investment decision making. However, there are different investment approaches and perspectives for investing which makes acquiring and representing expert knowledge for share evaluation challenging. Current decision models often do not reflect the real investment decision making process used by the broader investment community or may not be well-grounded in established investment theory. This research investigates the efficacy of using ontologies and Bayesian networks for automating share evaluation on the JSE. The knowledge acquired from an analysis of the investment domain and the decision-making process for a value investing approach was represented in an ontology. A Bayesian network was constructed based on the concepts outlined in the ontology for automatic share evaluation. The Bayesian network allows decision makers to predict future share performance and provides an investment recommendation for a specific share. The decision model was designed, refined and evaluated through an analysis of the literature on value investing theory and consultation with expert investment professionals. The performance of the decision model was validated through back testing and measured using return and risk-adjusted return measures. The model was found to provide superior returns and risk-adjusted returns for the evaluation period from 2012 to 2018 when compared to selected benchmark indices of the JSE. The result is a concrete share evaluation model grounded in investing theory and validated by investment experts that may be employed, with small modifications, in the field of value investing to identify shares with a higher probability of positive risk-adjusted returns