Stock Portfolio Prediction by Multi-Target Decision Support

Investing in the stock market is a complex process due to its high volatility caused by factors as exchange rates, political events, inflation and the market history. To support investor's decisions, the prediction of future stock price and economic metrics is valuable. With the hypothesis that there is a relation among investment performance indicators,  the goal of this paper was exploring multi-target regression (MTR) methods to estimate 6 different indicators and finding out the method that would best suit in an automated prediction tool for decision support regarding predictive performance. The experiments were based on 4 datasets, corresponding to 4 different time periods, composed of 63 combinations of weights of stock-picking concepts each, simulated in the US stock market. We compared traditional machine learning approaches with seven state-of-the-art MTR solutions: Stacked Single Target, Ensemble of Regressor Chains, Deep Structure  for Tracking Asynchronous Regressor Stacking,   Deep  Regressor Stacking, Multi-output Tree Chaining,  Multi-target Augment Stacking  and Multi-output Random Forest (MORF). With the exception of MORF, traditional approaches and the MTR methods were evaluated with Extreme Gradient Boosting, Random Forest and Support Vector Machine regressors. By means of extensive experimental evaluation, our results showed that the most recent MTR solutions can achieve suitable predictive performance, improving all the scenarios (14.70% in the best one, considering all target variables and periods). In this sense, MTR is a proper strategy for building stock market decision support system based on prediction models

Dynamic dependence networks: Financial time series forecasting and portfolio decisions (with discussion)

We discuss Bayesian forecasting of increasingly high-dimensional time series, a key area of application of stochastic dynamic models in the financial industry and allied areas of business. Novel state-space models characterizing sparse patterns of dependence among multiple time series extend existing multivariate volatility models to enable scaling to higher numbers of individual time series. The theory of these "dynamic dependence network" models shows how the individual series can be "decoupled" for sequential analysis, and then "recoupled" for applied forecasting and decision analysis. Decoupling allows fast, efficient analysis of each of the series in individual univariate models that are linked-- for later recoupling-- through a theoretical multivariate volatility structure defined by a sparse underlying graphical model. Computational advances are especially significant in connection with model uncertainty about the sparsity patterns among series that define this graphical model; Bayesian model averaging using discounting of historical information builds substantially on this computational advance. An extensive, detailed case study showcases the use of these models, and the improvements in forecasting and financial portfolio investment decisions that are achievable. Using a long series of daily international currency, stock indices and commodity prices, the case study includes evaluations of multi-day forecasts and Bayesian portfolio analysis with a variety of practical utility functions, as well as comparisons against commodity trading advisor benchmarks.Comment: 31 pages, 9 figures, 3 table

Sustainable typography

We need to radically re-think typography for text-rich business documents & publications (not referring to books). Most designers assume people have time to read. In reality the following occurs: Observations: 1) We browse/forage (71%) then read (11%) 2) People have different time tolerances and requirements for detail i.e. the same information is required to different levels of detailing dependent on the time the reader can allocate to it (Senior directors will have less time than juniors). 3) People want choice as to whether they wish to view information on paper, i-phone, PowerPoint or via web/screen. 4) Most publications do not follow the cognitive principles of how we are Œwired‚ to interpret visual signals. Message-based Design & Message-based Writing (MBD/MBW) is a system that addresses these 4 points and allows key messages to be understood prior to reading simply by scanning the page with its embedded Œvisual hooks‚ to draw the reader in. Thus it overcomes Œfilter failure‚ a phrase coined and first used by Clay Shirky at the Web 2.0 Expo. It collapses to a summary and exploits the way we are wired. Additionally it caters for up to 4 time tolerances of readers and morphs‚ from paper to screen effortlessly

Who Makes Acquisitions? CEO Overconfidence and the Market's Reaction

Overconfident CEOs over-estimate their ability to generate returns. Thus, on the margin, they undertake mergers that destroy value. They also perceive outside finance to be over-priced. We classify CEOs as overconfident when, despite their under-diversification, they hold options on company stock until expiration. We find that these CEOs are more acquisitive on average, particularly via diversifying deals. The effects are largest in firms with abundant cash and untapped debt capacity. Using press coverage as "confident" or "optimistic" to measure overconfidence confirms these results. We also find that the market reacts significantly more negatively to takeover bids by overconfident managers.

Predicting Customer Potential Value: an application in the insurance industry

For effective Customer Relationship Management (CRM), it is essential to have information on the potential value of customers. Based on the interplay between potential value and realized value, managers can devise customer specific strategies. In this article we introduce a model for predicting the potential value of a current customer. Furthermore, we discuss and apply different modeling strategies for predicting this potential value.marketing models;customer potential;customer relationship management;insurance industry