The availability of data on digital traces is growing to unprecedented sizes, but inferring actionable knowledge from large-scale data is far from being trivial. This is especially important for computational finance, where digital traces of human behaviour offer a great potential to drive trading strategies. We contribute to this by providing a consistent approach that integrates various datas...

Stock trading plays an important role for supporting profitable stock investment. In particular, more and more data mining-based technical trading rules have been developed and used in stock trading systems to assist investors with their smart trading decisions. However, many mined trading rules are of no interest to traders and brokers because they are discovered based on statistical significa...

A well known result in stochastic analysis reads as follows: for an R-valued super-martingale X = (Xt)0≤t≤T such that the terminal value XT is non-negative, we have that the entire process X is nonnegative. An analogous result holds true in the no arbitrage theory of mathematical finance: under the assumption of no arbitrage, a portfolio process x + (H · S) verifying x + (H · S)T ≥ 0 also satis...

We use quantitative news data generated by a large-scale natural language processing (NLP) news analysis system to perform a comprehensive study on how a company’s reported news frequency, sentiment polarity and subjectivity anticipates or reflects its stock trading volumes and financial returns. Our analysis provides concrete evidence that news data is highly informative, as previously suggest...

Increasingly many systems are being conceptualised, designed and implemented as marketplaces in which autonomous software entities (agents) trade services. These services can be commodities in e-commerce applications or data and knowledge services in information economies. In such systems, dynamic pricing through some form of negotiation or auction protocol is becoming the norm for many goods a...

Value at Risk (VaR) has emerged in recent years as a standard tool to measure and control the risk of trading portfolios. Yet, existing theoretical analyses of the optimal behavior of a trader subject to VaR limits have produced a negative view of VaR as a risk-control tool. In particular, VaR limits have been found to induce increased risk exposure in some states and an increased probability o...

Learners sometimes have incoherent and fragmented intuitive prior knowledge that is (partly) "incompatible" with the to-be-learned contents. Such knowledge in pieces can cause conceptual disorientation and cognitive overload while learning. We hypothesized that a pre-training intervention providing a generalized schema as a structuring framework for such knowledge in pieces would support (re)or...

PURPOSE The purpose of this study is to investigate the pursuit of achievement goals in medical students and to assess the relationship between achievement goals, learning strategy, and motivation. METHODS Two hundred seventy freshman and sophomore premedical students and sophomore medical school students participated in this study, which used the Achievement Goals Scale and the Self-Regulate...

This study attempts to find the possibility of making relatively higher profit with lower risk when trading futures commodities. The system applies XCS classifiers to explore the rules of spread trading of these commodities. Our simulation holds a trading strategy that in every transaction, the proposed model buys and sells the same lots of goods of Taiwan index futures. All trades are settled ...

We examine the effect of a market pricing policy designed to attract high-valued traders in a multiple market context using JCAT software. Our experiments show that a simple change to pricing policy can create market performance effects that traditional adaptive trading agents are unable to recognize or capitalize on, but that market-policy-aware trading agents can generally obtain. This sugges...