Visual knowledge discovery and machine learning for investment strategy

TL;DR: Benefits of different General Line Coordinates for real data visual analysis such as health monitoring and benchmark Iris data classification compared with results from Parallel Coordinates, Radvis, and Support Vector Machine are demonstrated.

TL;DR: The satisfactory adaptability of the proposed method was demonstrated, along with very good properties enabling the building of an effective investment strategy, and the Markov model of the second-order, which achieved good results.

TL;DR: A customer expectation detection method, which includes data acquisition, expectation classification, experimental preparation and expectation detection, to identify individual service recovery expectations from her/his complaints is proposed.

TL;DR: It is shown that the FSP method simplifies the pattern discovery in n-D data, providing the explainable rules in a visual form with a significant decrease of the cognitive load for analysis of n- D data.

TL;DR: An overview of visual analytics, its scope and concepts, addresses the most important research challenges and presents use cases from a wide variety of application scenarios is provided.

TL;DR: In this article, the comparative performance of an Adaptive Moving Average (AMA) on the Australian All Ordinaries, Dow Jones Industrial Average, and Standard and Poor's 500 stock market indices is examined.

TL;DR: In this paper, the authors investigate whether investors could have exploited the momentum in Internet stocks using simple moving average (MA) trading rules and find that most Internet stocks behave as random walks, combined with high volatility, may be the reason for the dismal performance of the moving average rules.

TL;DR: In this paper, the authors examined the profitability of some technical trading rules in Swedish stock market over the 1986-2004 periods and found that moving average rules do indeed have predictive power and could discern recurring price patterns for profitable trading.