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This paper presents a word extraction approach based on the use of a confidence index to limit the total number of segmentation hypotheses in order to further extend our online sentence recognition system to perform "on-the-fly" recognition. Our initial word extraction task is based on the characterization of the gap between each couple of consecutive strokes from the online signal of the handwritten sentence. A confidence index is associated to the gap classification result in order to evaluate its reliability. A reconsideration process is then performed to create additional segmentation hypotheses to ensure the presence of the correct segmentation among the hypotheses. In this process, we control the total number of segmentation hypotheses to limit the complexity of the recognition process and thus the execution time. This approach is evaluated on a test set of 425 English sentences written by 17 writers, using different metrics to analyze the impact of the word extraction task on the whole sentence recognition system performances. The word extraction task using the best reconsideration strategy achieves a 97.94% word extraction rate and a 84.85% word recognition rate which represents a 33.1% word error rate decrease relatively to the initial word extraction task (with no segmentation hypothesis reconsideration).

An extensive, in-depth study of risk factors seems to be of crucial importance in the research of the financial market in order to prevent (or reduce) the chance of developing this return. It represents market anomalies. This study confirms that the $q$-factors model is better than the other traditional asset pricing models in explaining individual stock return in the US over the 2000–2017 period. The main focus of data analysis is, on the use of models, to discover and understand the relationships between different factors of risk market anomaly. Recently, Fama and French presented a five-factor model that captures the size, value, profitability, and investment patterns in average stock market returns better than their three-factor model presented previously in 1993. This paper explores a shred of new empirical evidence to assess the asset pricing model through an extension of Fama and French model and a report on applying Bayesian Network (BN) modeling to discover the relationships across different risk factor. Furthermore, the induced BN was used to make inference taking into account sensibility and the application of BN tools has led to the discovery of several direct and indirect relationships between different parameters. For this reason, we introduce additional factors that are related to behavioral finance such as investor’s sentiment to describe a behavior return, confidence index, and herding. It is worth noting that there is an interaction between these various factors, which implies that it is interesting to incorporate them into the model to give more effectiveness to the performance of the stock market return. Moreover, the implemented BN was used to make inferences, i.e., to predict new scenarios when different information was introduced.

This chapter discusses the methods and applications of fundamental analysis and technical analysis. In addition, it investigates the ranking performance of the Value Line and the timing and selectivity of mutual funds. A detailed investigation of technical versus fundamental analysis is first presented. This is followed by an analysis of regression time-series and composite methods for forecasting security rates of return. Value Line ranking methods and their performance then are discussed, leading finally into a study of the classification of mutual funds and the mutual-fund managers’ timing and selectivity ability. In addition, the hedging ability is also briefly discussed. Sharpe measure, Treynor measure, and Jensen measure are defined and analyzed. All of these topics can help improve performance in security analysis and portfolio management.

This chapter discusses methods and applications of fundamental analysis and technical analysis. In addition, it investigates the ranking performance of the Value Line and the timing and selectivity of mutual funds. A detailed investigation of technical versus fundamental analysis is first presented. This is followed by an analysis of regression time series and composite methods for forecasting security rates of return. Value Line ranking methods and their performance are then discussed, leading finally into a study of the classification of mutual funds and the mutual fund managers’ timing and selectivity ability. In addition, the hedging ability is also briefly discussed. Sharpe measure, Treynor measure, and Jensen measure are defined and analyzed. All of these topics can help improve performance in security analysis and portfolio management.