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Information disclosure is a critical determinant of portfolio allocation in capital markets. Securities analysts as important “information traders”, their follow-up behavior and information disclosure of the firms they follow participate in an interaction relationship. This paper sheds light on the three different roles of analysts. Based on the perspective of role of analysts, we reviewed relevant...
This study aims to retrieve useful knowledge from commonly adopted technical indicators, based on a soft computing model, to support investment decisions. Though the validity of technical analysis (TA) has been examined extensively by various statistical models in financial literature, a practical approach that may consider the inconsistency among various technical indicators and the down-side risk...
Outbreak of debt crisis in Europe has made the issue of corporate failure prediction, known as financial distress prediction (FDP) as well, a significant topic in the field of management science. The purpose of this paper is to propose five hybrid classifiers to tackle corporate failure prediction problem. Principle component analysis (PCA),information gain (IG) and relief (Re) methods as representatives...
Moving averages (MAs) are widely used in finance by trend followers. Negative weight (corrective) moving averages negatively weight old history and attach more weight to recent history in order to achieve better fit. After analysing such methods we propose an optimal weighting scheme for smoothing stock price data. For a given smoothness level we minimise fitting error. Differently from existing methods,...
This paper provides further evidence on the predictive power of online community traffic with regard to stock prices. Using the largest dataset to date, spanning 8 years and almost the complete set of SP500 stocks, we train a classifier using a set of features entirely extracted from web-traffic data of financial online communities. The classifier is shown to outperform the predictive power of a baseline...
From a firm-year perspective, sorting all analysts issuing forecasts for a firm into quartiles, with analysts' forecasts samples exceeding 7700 and stock rating samples exceeding 21000, this paper tests relationship between analysts' earnings forecasts accuracy and recommendations investment value by computing the characteristics and daily returns of the long and short portfolio of each quartile....
The phenomenon of invalid entity relation tuples generating more invalid tuples in the next cycle is called as “circular dependencies” in relation extraction. In order to prevent the existence of the phenomenon of “circular dependencies”, we present a entity relation tuples filtration method that filters the invalid entity relation tuples for Weakly Supervised Relation Extraction. In our article,...
A number of different numerical methods for accelerating financial option pricing using FPGAs have recently been investigated, such as Monte-Carlo, finite-difference, quadrature, and binomial trees. However, these papers only compare acceleration of each method against the same method in software, and do not consider a more important practical question, which is to identify the method that provides...
Macro-economic forecasts are used extensively in industry and government even though the historical accuracy and reliability is disputed. Prediction markets have proven to successfully forecast the outcome of elections, sport events and product sales. In this paper we provide a detailed analysis of forecasts generated from a new prediction market for economic derivatives. The proposed market design...
Nowadays there are lots of novel forecasting approaches to improve the forecasting accuracy in the financial markets. Support Vector Machine (SVM) as a modern statistical tool has been successfully used to solve nonlinear regression and time series problem. Unlike most conventional neural network models which are based on the empirical risk minimization principle, SVM applies the structural risk minimization...
The aim of this paper is to investigate whether a model utilizing cash-flow ratios in combination with other categories of financial ratios results in a model superior to a model that does not include cash-flow ratios. The study uses both, operating cash-flow and the traditional definition of cash-flow, as proxies for cash-flow ratios. Other categories of ratios are profitability, activity, liquidity...
Support vector machines (SVMs) are promising methods of pattern recognition in financial time series because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies SVM to pattern recognition in the financial engineering domain. Compared with present machine learning methods in financial...
Corpus is the set of language materials which are stored in computers and can use computers to search, query and analyze for enterprise decision-makers. Automated text categorization has been extensively studied and various techniques for document categorization. But based on the current scarcity of Chinese corpus, especially in the field of text categorization, the Chinese categorization corpus is...
The main goal in the credit scoring process is forecasting every customer's adequacy in accomplishment of their obligations precisely as much as possible. Although this technique is identical with regular binary classification tasks but it has a few crucial differences. Whereas, based on financial credit rules, a customer is considered based on a degree of goodness or badness, one cannot allocate...
Empirical evidence shows the presence of a jump component in addition to the diffusion component in the evolution of asset prices. In this article, jump-diffusion model described the underlying stock price dynamics. An approach of extrapolation acceleration was developed to yield a simple and efficient computation procedure for practical pricing of American call option on a stock with continuous dividends...
In reality, the paper money number recognition is significant, which can effectively prevent the illegal trade of paper money. First, this paper makes pretreatment to the paper money, and then for the rapidity and accuracy requirements, a new identification method is presented which based on intersection change and the distance difference between the two points of the character on the special location...
This paper mainly discusses the study of models for financial distress pre-warning, trying to select general financial indexes by principal component analysis, and meanwhile adding nonfinancial indexes which reflect corporate governance state to complement. Logit Model which is more accurate in prediction is selected, with the 56 company samples including both delisting pre-warned companies and counterparts...
In this paper, according to the situation of credit risk assessment in power industry, index system of risk assessment was established. Credit risk assessment models based on rough set and support vector machines (RSSVM) were proposed for the characteristic of more indicator numbers. Through introducing actual data of a power industry to the empirical analysis, this method was testified that it can...
This paper proposes a word-by-word model selection approach to domain adaptation for Word Sense Disambiguation. By this approach, the model for a target word is automatically selected from a candidate model set, which is comprised of improved self-training models and a supervised model. The improved self-training uses sense priors to prevent its iteration from converging into undesirable states. Experimental...
Financial forecasting is a lucrative and complicated application of machine learning. In this paper, we focus on the finding investment opportunities. We therefore explore four different Genetic Programming approaches and compare their performances on real-world data. We find that the novelties we introduced in some of these approaches indeed improve the results. However, we also show that the Genetic...
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