Real estate portfolio construction and estimation risk

The use of MPT in the construction real estate portfolios has two serious limitations when used in an ex-ante framework: (1) the intertemporal instability of the portfolio weights and (2) the sharp deterioration in performance of the optimal portfolios outside the sample period used to estimate asset mean returns. Both problems can be traced to wide fluctuations in sample means Jorion (1985). Thus the use of a procedure that ignores the estimation risk due to the uncertain in mean returns is likely to produce sub-optimal results in subsequent periods. This suggests that the consideration of the issue of estimation risk is crucial in the use of MPT in developing a successful real estate portfolio strategy. Therefore, following Eun & Resnick (1988), this study extends previous ex-ante based studies by evaluating optimal portfolio allocations in subsequent test periods by using methods that have been proposed to reduce the effect of measurement error on optimal portfolio allocations

Self-adaptive attribute weighting for Naive Bayes classification

©2014 Elsevier Ltd. All rights reserved. Naive Bayes (NB) is a popular machine learning tool for classification, due to its simplicity, high computational efficiency, and good classification accuracy, especially for high dimensional data such as texts. In reality, the pronounced advantage of NB is often challenged by the strong conditional independence assumption between attributes, which may deteriorate the classification performance. Accordingly, numerous efforts have been made to improve NB, by using approaches such as structure extension, attribute selection, attribute weighting, instance weighting, local learning and so on. In this paper, we propose a new Artificial Immune System (AIS) based self-adaptive attribute weighting method for Naive Bayes classification. The proposed method, namely AISWNB, uses immunity theory in Artificial Immune Systems to search optimal attribute weight values, where self-adjusted weight values will alleviate the conditional independence assumption and help calculate the conditional probability in an accurate way. One noticeable advantage of AISWNB is that the unique immune system based evolutionary computation process, including initialization, clone, section, and mutation, ensures that AISWNB can adjust itself to the data without explicit specification of functional or distributional forms of the underlying model. As a result, AISWNB can obtain good attribute weight values during the learning process. Experiments and comparisons on 36 machine learning benchmark data sets and six image classification data sets demonstrate that AISWNB significantly outperforms its peers in classification accuracy, class probability estimation, and class ranking performance

Credibility Adjusted Term Frequency: A Supervised Term Weighting Scheme for Sentiment Analysis and Text Classification

We provide a simple but novel supervised weighting scheme for adjusting term frequency in tf-idf for sentiment analysis and text classification. We compare our method to baseline weighting schemes and find that it outperforms them on multiple benchmarks. The method is robust and works well on both snippets and longer documents

Hybrid Model For Word Prediction Using Naive Bayes and Latent Information

Historically, the Natural Language Processing area has been given too much attention by many researchers. One of the main motivation beyond this interest is related to the word prediction problem, which states that given a set words in a sentence, one can recommend the next word. In literature, this problem is solved by methods based on syntactic or semantic analysis. Solely, each of these analysis cannot achieve practical results for end-user applications. For instance, the Latent Semantic Analysis can handle semantic features of text, but cannot suggest words considering syntactical rules. On the other hand, there are models that treat both methods together and achieve state-of-the-art results, e.g. Deep Learning. These models can demand high computational effort, which can make the model infeasible for certain types of applications. With the advance of the technology and mathematical models, it is possible to develop faster systems with more accuracy. This work proposes a hybrid word suggestion model, based on Naive Bayes and Latent Semantic Analysis, considering neighbouring words around unfilled gaps. Results show that this model could achieve 44.2% of accuracy in the MSR Sentence Completion Challenge

Carbon portfolio management

The aim of the European Union's Emissions Trading Scheme (EU ETS) is that by 2020, emissions from sectors covered by the EU ETS will be 21% lower than in 2005. In addition to large CO 2 emitting companies covered by the scheme, other participants have entered the market with a view of using emission allowances for the diversification of their investment portfolios. The performance of this asset as a stand alone investment and its portfolio diversification implications will be investigated in this paper. Our results indicate that the market views Phases 1, 2, and 3 European Union allowance futures as unattractive as stand alone investments. In a portfolio context, in Phase 1, once the short-selling option is added, there are considerable portfolio benefits. However, our results indicate that these benefits only existed briefly during the pilot stage of the EU ETS. There is no evidence to suggest portfolio diversification benefits exist for Phase 2 or the early stages of Phase 3

An improved switching hybrid recommender system using naive Bayes classifier and collaborative filtering

Recommender Systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. To date a number of recommendation algorithms have been proposed, where collaborative filtering and content-based filtering are the two most famous and adopted recommendation techniques. Collaborative filtering recommender systems recommend items by identifying other users with similar taste and use their opinions for recommendation; whereas content-based recommender systems recommend items based on the content information of the items. These systems suffer from scalability, data sparsity, over specialization, and cold-start problems resulting in poor quality recommendations and reduced coverage. Hybrid recommender systems combine individual systems to avoid certain aforementioned limitations of these systems. In this paper, we proposed a unique switching hybrid recommendation approach by combining a Naive Bayes classification approach with the collaborative filtering. Experimental results on two different data sets, show that the proposed algorithm is scalable and provide better performance – in terms of accuracy and coverage – than other algorithms while at the same time eliminates some recorded problems with the recommender systems