Left Frontal EEG Power Responds to Stock Price Changes in a Simulated Asset Bubble Market

Financial bubbles are a result of aggregate irrational behavior and cannot be explained by standard economic pricing theory. Research in neuroeconomics can improve our understanding of their causes. We conducted an experiment in which 28 healthy subjects traded in a simulated market bubble, while scalp EEG was recorded using a low-cost, BCI-friendly desktop device with 14 electrodes. Independent component (IC) analysis was performed to decompose brain signals and the obtained scalp topography was used to cluster the ICs. We computed single-trial time-frequency power relative to the onset of stock price display and estimated the correlation between EEG power and stock price across trials using a general linear model. We found that delta band (1–4 Hz) EEG power within the left frontal region negatively correlated with the trial-by-trial stock prices including the financial bubble. We interpreted the result as stimulus-preceding negativity (SPN) occurring as a dis-inhibition of the resting state network. We conclude that the combination between the desktop-BCI-friendly EEG, the simulated financial bubble and advanced signal processing and statistical approaches could successfully identify the neural correlate of the financial bubble. We add to the neuroeconomics literature a complementary EEG neurometric as a bubble predictor, which can further be explored in future decision-making experiments.

Observation Time Effects in Reinforcement Learning on Contracts for Difference

In this paper, we present a study on Reinforcement Learning optimization models for automatic trading, in which we focus on the effects of varying the observation time. Our Reinforcement Learning agents feature a Convolutional Neural Network (CNN) together with Long Short-Term Memory (LSTM) and act on the basis of different observation time spans. Each agent tries to maximize trading profit by buying or selling one of a number of contracts in a simulated market environment for Contracts for Difference (CfD), considering correlations between individual assets by architecture. To decide which action to take on a specific contract, an agent develops a policy which relies on an observation of the whole market for a certain period of time. We investigate whether or not there exists an optimal observation sequence length, and conclude that such a value depends on market dynamics.

Disruptive Demand Side Technologies: Market Shares and Impact on Flexibility in a Decentralized World

Electricity demand is expected to increase strongly as electrification and the use of hydrogen are promising decarbonization options for the demand side sectors transport and industry. In a decentralized system with volatile renewable energy sources, flexibility potentials will play an important role for secure and cost-efficient electricity supply. On the demand side, decentralized PV-battery systems and electric vehicles as well as hydrogen production by electrolyzers could provide the necessary flexibility. Energy demand over time is calculated based on assumed and simulated market shares of these and other low-emission technologies. Impacts on the system and residual load are analyzed, with a focus on the contribution of load shifting as a demand-side measure. Results indicate that load shifting can contribute significantly to integrate RES electricity.

Stimulation of Light-Emitting Diode Treatment on Defence System and Changes in Mesocarp Metabolites of Avocados Cultivars (Hass and Fuerte) during Simulated Market Shelf Conditions

The ability of light-emitting diode (LED) light treatment to reduce the anthracnose decay via its eliciting effects and thus induce resistance in the avocado (Persea americana), was investigated in this study to replace the current postharvest fungicide treatment. In experiment 1, the effect of blue or red LED lights (6 h per day) on the incidence of anthracnose in artificially inoculated (Colletotrichum gloesposorioides) and naturally infected avocados (cv. Fuerte and Hass) at 12–14 °C (simulated market shelf) for 4, 8, 14, and 16 days was investigated. In experiment 2, the effect of blue or red LED lights on the induced defence mechanism, fruit metabolites, antioxidant activity, and percentage of fruit reaching ready-to-eat stage was determined. Exposure to red LED light significantly reduced the anthracnose decay incidence in naturally infected cv. Fuerte on day 12 and in cv. Hass on day 16 compared to the prochloraz fungicide treatment by upregulating the PAL genes and maintaining the epicatechin content. Blue LED light accelerated the ripening in both cultivars, probably due to reduced D-mannoheptulose content. Red LED light exposure for 6 h per day and 12 days storage showed potential to replace the prochloraz treatment with improved ascorbic acid content and antioxidant activity.

Towards Embedding Network Usage Charges Within a Peer-to-Peer Electricity Marketplace

<div>This paper proposes a novel tariff regime for peerto-peer energy trading, with an aim to increase transmission</div><div>efficiency and grid stability by penalising long distance power transactions. In this scheme a portion of the transacted energy is withheld based on the electrical distance between buying and selling parties, calculated here according to the Klein Resistance Distance. This tariff regime is simulated using a dataset of producers and consumers over a 24-hour period. First, a notional marketplace equilibrium simulation is performed, in which</div><div>consumers can optimally activate demand response resources to exploit local availability of energy. Consumers are observed to move some demand away from peak times to make use of local generation availability. These simulated market out-turns are then used as inputs to a time series power flow analysis, in order to evaluate the network’s electrical performance. The regime is found to decrease grid losses and the magnitude of global voltage angle separation. However, the metric whereby taxes are calculated is found to be too skewed in the utility’s favour and may discourage adoption of the peer-to-peer system.</div><div>The method also attempts to encourage regulatory adoption</div><div>by existing grid operators and utilities. Some counter-intuitive allocations of tokenised energy occur, owing to specific consumers’ demand profiles and proximity to generators.</div><div><br></div>

Towards Embedding Network Usage Charges Within a Peer-to-Peer Electricity Marketplace

<div>This paper proposes a novel tariff regime for peerto-peer energy trading, with an aim to increase transmission</div><div>efficiency and grid stability by penalising long distance power transactions. In this scheme a portion of the transacted energy is withheld based on the electrical distance between buying and selling parties, calculated here according to the Klein Resistance Distance. This tariff regime is simulated using a dataset of producers and consumers over a 24-hour period. First, a notional marketplace equilibrium simulation is performed, in which</div><div>consumers can optimally activate demand response resources to exploit local availability of energy. Consumers are observed to move some demand away from peak times to make use of local generation availability. These simulated market out-turns are then used as inputs to a time series power flow analysis, in order to evaluate the network’s electrical performance. The regime is found to decrease grid losses and the magnitude of global voltage angle separation. However, the metric whereby taxes are calculated is found to be too skewed in the utility’s favour and may discourage adoption of the peer-to-peer system.</div><div>The method also attempts to encourage regulatory adoption</div><div>by existing grid operators and utilities. Some counter-intuitive allocations of tokenised energy occur, owing to specific consumers’ demand profiles and proximity to generators.</div><div><br></div>

THE STATUS QUO IN DISCRETE CHOICE EXPERIMENTS: IS IT RELEVANT?

An issue in environmental economics is how respondents make choices in discrete choice experiments (DCEs), and whether different strategies impact on the reliability of willingness-to-pay (WTP) results. Do individuals make choices with reference to their status quo (SQ) position, or can they make simulated market choices amongst only hypothetical scenarios? This study uses a split sample to test whether the inclusion or exclusion of the SQ on a choice card in DCEs affects the WTP estimates, based on visitors’ preferences for tourist facilities at Kenyir Lake, Malaysia. The results indicated little difference between both the samples in terms of goodness-of-fit, size and significance of the attribute coefficients, and WTP estimates for the Conditional Logit (CL) and Mixed Logit (MXL) models.

Connectivity, Information Jumps, and Market Stability: An Agent-Based Approach

We propose a metamodel to assess simulated market stability by introducing information connectivity in an agent-based network. The market is occupied by heterogeneous agents with different behaviors, strategies, and information connectivity. A jump-diffusion process simulating events that may occur in the market is introduced. Agents information awareness varies along with agents propensity to respond to the information jump and jump size. A jump reshuffles market positions based on agents risk preferences determined by behavior and strategy. We examine the effect of information awareness on the volatility index of the simulated market in a scale-free market network. The analysis is performed by developing five experiments wherein the first one corresponds to systemic information ignorance state. Three experiments examine the role of hubs, normal agents, and hermits in the network when intermediate combinations of agent types have information awareness. The fifth experiment corresponds to the systemic information awareness with all agents being informed. The results show that the simulated market is driven to instability in a similar manner to patterns observed in a crisis where all agents become homogeneous in information awareness of events. Hubs contribute to increased connectivity and act as amplifiers of good, bad, or inaccurate information or sentiment.

High frequency traders in a simulated market

Purpose An agent-based market simulation is utilized to examine the impact of high frequency trading (HFT) on various aspects of the stock market. This study aims to provide a baseline understanding of the effect of HFT on markets by using a paradigm of zero-intelligence traders and examining the resulting structural changes. Design/methodology/approach A continuous double auction setting with zero-intelligence traders is used by adapting the model of Gode and Sunder (1993) to include algorithmic high frequency (HF) traders who retrade by marking up their shares by a fixed percentage. The simulation examines the effects of two independent factors, the number of HF traders and their markup percentage, on several dependent variables, principally volume, market efficiency, trader surplus and volatility. Results of the simulations are tested with two-way ANOVA and Tukey’s post hoc tests. Findings In the simulation results, trading volume, efficiency and total surplus vary directly with the number of traders employing HFT. Results also reveal that market volatility increased with the number of HF traders. Research limitations/implications Increases in volume, efficiency and total surplus represent market improvements due to the trading activities of HF traders. However, the increase in volatility is worrisome, and some of the surplus increase appears to come at the expense of long-term-oriented investors. However, the relatively recent development of HFT and dearth of appropriate data make direct calibration of any model difficult. Originality/value The simulation study focuses on the structural impact of HF traders on several aspects of the simulated market, with the effects isolated from other noise and problems with empirical data. A baseline for comparison and suggestions for future research are established.

Evaluation of Natural Colorants and Their Application on Citrus Fruit as Alternatives to Citrus Red No. 2

Warm field temperatures can often result in poor peel color of some citrus varieties, especially early in the harvest season. Under these conditions, Florida oranges, temples, tangelos, and K-Early citrus fruit are allowed to be treated with Citrus Red No.2 dye (CR2) to help produce a more acceptable peel color. Unfortunately, CR2, the commercial colorant used in Florida, has been listed as a group 2B carcinogen by the European Union (EU) and the International Agency for Research on Cancer (IARC). Although not likely dangerous at levels used on citrus, and on a part of the fruit that is not ingested, there is a negative health perception, and thus, a need for natural or food grade alternative colorants to replace CR2 for use on citrus. This research demonstrated that three out of five oil-soluble natural red/orange colorants resulted in peel colors somewhat similar to the industry standard CR2. These three (annatto extract, paprika extract, and paprika oleoresin) were selected for further in vivo studies. The stability of the natural colorants along with CR2 was evaluated by applying them on test papers and then on fresh ‘Hamlin’ oranges. All natural colorants were found to be easily oxidized and faded when applied on test papers. However, coating the colored surfaces with carnauba wax apparently inhibited oxidation and the subsequent discoloration of the surface. When applying the natural colorants to ‘Hamlin’ oranges before waxing, the treatments retained the improved color after storage in the dark at 5 °C, simulating cold storage. However, only annatto extract maintained a stable color when subsequently stored in a simulated market condition, at 23 °C exposed to 300 lx of standard fluorescent white light.