The interaction between informed and uninformed agents in securities markets

This dissertation contains three essays that examine the interaction between informed and uninformed parties in securities markets. Given the influential role that informed traders have in shaping securities prices, trading activity, market-wide and even economy wide outcomes, this research provides empirical evidence on significant and important issues. Each essay addresses a topical, yet under-developed research strand to ensure that the results of this dissertation are relevant to both academic and nonacademic parties. The conclusions drawn from the three essays have the potential to influence the decisions of fund managers, regulators, market designers and, direct and indirect investors in securities markets. The first essay examines the interaction between mutual fund managers and the investors that seek their services. Fund managers often incur significant adverse selection, transaction and opportunity costs when executing investors’ liquidity requests. Prior research hints that index futures are able to mitigate these costs, though no research has provided convincing empirical evidence, primarily due to the fact that existing data on fund managers’ use of derivatives is imprecise. Using unique survey data which indicates whether a fund manager uses index futures to manage investor flows or not, this essay is the first to provide conclusive empirical evidence on this issue. The results indicate that fund managers who trade index futures in this manner are unencumbered by investor flows and have superior fund flow conditional alpha and market timing measures of performance relative to their non-derivative trading peers. Informed fund managers are able to maintain their advantage even when their trading decisions are partially dictated by uninformed parties. The second essay in this dissertation examines the interaction between illegal insider traders and the regulatory body that prosecutes these individuals. Drawing upon insights developed in the literature which describes crime through the prism of economic thought, the essay develops a model which predicts the intensity of an illegal insider’s crime: their traded volume. The predictions of the model are tested using data drawn from case files of the Securities and Exchange Commission (SEC). As such, this essay is the first empirical study of illegal insider trading to investigate the behaviour of the insider, with all previous empirical research instead examining the market’s response to insider trading. The study hypothesises that insider volume is a function of two factors in control of the regulatory body and associated law makers: the expected return and expected penalty from the insiders’ trades. Furthermore, insider volume is hypothesised to be negatively related to the variance of the stock traded. The results, which validate the hypotheses and are robust to sample selection bias, have important policy implications for regulators seeking to detect illegal insider trading. While the first two essays consider specific examples of informed traders, the final essay in this dissertation examines informed traders in general. In particular, the study investigates whether broker anonymity in electronic order driven markets obscures the presence of informed traders during the lead up to a significant information event. This research is important given the prolific changes to this feature of market design in recent years across electronic exchanges globally, and the fact that all prior research in this area has yet to consider the effects of broker anonymity on information transmission during periods of large information asymmetry. The study presents three pieces of evidence that informed traders are better camouflaged when the identity of the broker intermediary is hidden vis-à-vis when the identity is visible. Naturally, this suggests that uninformed traders suffer at the expense of informed traders during the periods examined in this study. This finding has important policy implications for exchange officials deciding whether or not to reveal broker identifiers surrounding trades, especially considering that almost all prior research suggests that broker anonymity is correlated with improved liquidity in the form of lower bid-ask spreads

Big data, computational science, economics, finance, marketing, management, and psychology: connections

The paper provides a review of the literature that connects Big Data, Computational Science, Economics, Finance, Marketing, Management, and Psychology, and discusses some research that is related to the seven disciplines. Academics could develop theoretical models and subsequent econometric and statistical models to estimate the parameters in the associated models, as well as conduct simulation to examine whether the estimators in their theories on estimation and hypothesis testing have good size and high power. Thereafter, academics and practitioners could apply theory to analyse some interesting issues in the seven disciplines and cognate areas

Forecasting USAF JP-8 Fuel Needs

Oil is still one of the strategic energy resources for both the U.S. and the USAF today. Accurate oil prediction is important for the U.S. in order to improve the national strategy and the related budget concerns. Today, the U.S. is roughly importing 58% of its petroleum products. Moreover, in Fiscal Year (FY) 2007 the USAF total energy costs exceeded $6.9 billion. Aviation fuel accounted for approximately 81% of the total AF energy costs. Fluctuations in oil prices have huge impacts on the USAF’s JP-8 budgetary calculations. In order to handle this problem, the need for accurate forecasts arises. In this study, we forecast the USAF’s JP-8 consumption and costs for the next five year period. The study shows that JP-8 consumption figures will go on to follow the recent trend via Holt’s Linear Method. Also, the study shows that good short-term predictions can be obtained with more simple and easy-to-implement methods, versus complex ones. When we consider long-term forecasts, 5-years in this case, multiple regression outperforms ANN modeling within the specified forecast accuracy measures. Our results indicate that the USAF’s JP-8 cost for each of the next 5 years will be somewhere between 6.3 and 7.5 billion dollars