Fractal Profit Landscape of the Stock Market

We investigate the structure of the profit landscape obtained from the most basic, fluctuation based, trading strategy applied for the daily stock price data. The strategy is parameterized by only two variables, p and q. Stocks are sold and bought if the log return is bigger than p and less than -q, respectively. Repetition of this simple strategy for a long time gives the profit defined in the underlying two-dimensional parameter space of p and q. It is revealed that the local maxima in the profit landscape are spread in the form of a fractal structure. The fractal structure implies that successful strategies are not localized to any region of the profit landscape and are neither spaced evenly throughout the profit landscape, which makes the optimization notoriously hard and hypersensitive for partial or limited information. The concrete implication of this property is demonstrated by showing that optimization of one stock for future values or other stocks renders worse profit than a strategy that ignores fluctuations, i.e., a long-term buy-and-hold strategy.Comment: 12 pages, 4 figure

Quantifying trading behavior in financial markets using Google Trends

Crises in financial markets affect humans worldwide. Detailed market data on trading decisions reflect some of the complex human behavior that has led to these crises. We suggest that massive new data sources resulting from human interaction with the Internet may offer a new perspective on the behavior of market participants in periods of large market movements. By analyzing changes in Google query volumes for search terms related to finance, we find patterns that may be interpreted as “early warning signs” of stock market moves. Our results illustrate the potential that combining extensive behavioral data sets offers for a better understanding of collective human behavior

Eight grand challenges in socio-environmental systems modeling

Modeling is essential to characterize and explore complex societal and environmental issues in systematic and collaborative ways. Socio-environmental systems (SES) modeling integrates knowledge and perspectives into conceptual and computational tools that explicitly recognize how human decisions affect the environment. Depending on the modeling purpose, many SES modelers also realize that involvement of stakeholders and experts is fundamental to support social learning and decision-making processes for achieving improved environmental and social outcomes. The contribution of this paper lies in identifying and formulating grand challenges that need to be overcome to accelerate the development and adaptation of SES modeling. Eight challenges are delineated: bridging epistemologies across disciplines; multi-dimensional uncertainty assessment and management; scales and scaling issues; combining qualitative and quantitative methods and data; furthering the adoption and impacts of SES modeling on policy; capturing structural changes; representing human dimensions in SES; and leveraging new data types and sources. These challenges limit our ability to effectively use SES modeling to provide the knowledge and information essential for supporting decision making. Whereas some of these challenges are not unique to SES modeling and may be pervasive in other scientific fields, they still act as barriers as well as research opportunities for the SES modeling community. For each challenge, we outline basic steps that can be taken to surmount the underpinning barriers. Thus, the paper identifies priority research areas in SES modeling, chiefly related to progressing modeling products, processes and practices.</jats:p

Entropies of Negative Incomes, Pareto-Distributed Loss, and Financial Crises

Health monitoring of world economy is an important issue, especially in a time of profound economic difficulty world-wide. The most important aspect of health monitoring is to accurately predict economic downturns. To gain insights into how economic crises develop, we present two metrics, positive and negative income entropy and distribution analysis, to analyze the collective “spatial” and temporal dynamics of companies in nine sectors of the world economy over a 19 year period from 1990–2008. These metrics provide accurate predictive skill with a very low false-positive rate in predicting downturns. The new metrics also provide evidence of phase transition-like behavior prior to the onset of recessions. Such a transition occurs when negative pretax incomes prior to or during economic recessions transition from a thin-tailed exponential distribution to the higher entropy Pareto distribution, and develop even heavier tails than those of the positive pretax incomes. These features propagate from the crisis initiating sector of the economy to other sectors

Unemployment expectations in an agent-based model with education

Why are unemployment expectations of the “man in the street” markedly different from professional forecasts? We present an agent-based model to explain this deep disconnection using boundedly rational agents with different levels of education. A good fit of empirical data is obtained under the assumptions that there is staggered update of information, agents update episodically their estimate and there is a fraction of households who always and stubbornly forecast that the unemployment is going to raise. The model also sheds light on the role of education and suggests that more educated agents update their information more often and less obstinately fixate on the worst possible forecast

Prescriptions of Traditional Chinese Medicine Are Specific to Cancer Types and Adjustable to Temperature Changes

Targeted cancer therapies, with specific molecular targets, ameliorate the side effect issue of radiation and chemotherapy and also point to the development of personalized medicine. Combination of drugs targeting multiple pathways of carcinogenesis is potentially more fruitful. Traditional Chinese medicine (TCM) has been tailoring herbal mixtures for individualized healthcare for two thousand years. A systematic study of the patterns of TCM formulas and herbs prescribed to cancers is valuable. We analysed a total of 187,230 TCM prescriptions to 30 types of cancer in Taiwan in 2007, a year's worth of collection from the National Health Insurance reimbursement database (Taiwan). We found that a TCM cancer prescription consists on average of two formulas and four herbs. We show that the percentage weights of TCM formulas and herbs in a TCM prescription follow Zipf's law with an exponent around 0.6. TCM prescriptions to benign neoplasms have a larger Zipf's exponent than those to malignant cancers. Furthermore, we show that TCM prescriptions, via weighted combination of formulas and herbs, are specific to not only the malignancy of neoplasms but also the sites of origins of malignant cancers. From the effects of formulas and natures of herbs that were heavily prescribed to cancers, that cancers are a ‘warm and stagnant’ syndrome in TCM can be proposed, suggesting anti-inflammatory regimens for better prevention and treatment of cancers. We show that TCM incorporated relevant formulas to the prescriptions to cancer patients with a secondary morbidity. We compared TCM prescriptions made in different seasons and identified temperatures as the environmental factor that correlates with changes in TCM prescriptions in Taiwan. Lung cancer patients were among the patients whose prescriptions were adjusted when temperatures drop. The findings of our study provide insight to TCM cancer treatment, helping dialogue between modern western medicine and TCM for better cancer care

Special Agents Can Promote Cooperation in the Population

Cooperation is ubiquitous in our real life but everyone would like to maximize her own profits. How does cooperation occur in the group of self-interested agents without centralized control? Furthermore, in a hostile scenario, for example, cooperation is unlikely to emerge. Is there any mechanism to promote cooperation if populations are given and play rules are not allowed to change? In this paper, numerical experiments show that complete population interaction is unfriendly to cooperation in the finite but end-unknown Repeated Prisoner's Dilemma (RPD). Then a mechanism called soft control is proposed to promote cooperation. According to the basic idea of soft control, a number of special agents are introduced to intervene in the evolution of cooperation. They comply with play rules in the original group so that they are always treated as normal agents. For our purpose, these special agents have their own strategies and share knowledge. The capability of the mechanism is studied under different settings. We find that soft control can promote cooperation and is robust to noise. Meanwhile simulation results demonstrate the applicability of the mechanism in other scenarios. Besides, the analytical proof also illustrates the effectiveness of soft control and validates simulation results. As a way of intervention in collective behaviors, soft control provides a possible direction for the study of reciprocal behaviors