Using Text Mining to Predicate Exchange Rates with Sentiment Indicators

Recent innovations in text mining facilitate the use of novel data for sentiment analysis related to financial markets, and promise new approaches to the field of behavioral finance. Traditionally, text mining has allowed a near-real time analysis of available news feeds. The recent dissemination of web 2.0 has seen a drastic increase of user participation, providing comments on websites, social networks and blogs, creating a novel source of rich and personal sentiment data potentially of value to behavioral finance. This study explores the efficacy of using novel sentiment indicators from Market Psych, which analyses social media in addition to newsfeeds to quantify various levels of individual’s emotions, as a predictor for financial time series returns of the Australian Dollar (AUD)-US Dollar (USD) exchange rate. As one of the first studies evaluating both news and social media sentiment indicators as explanatory variables for linear and nonlinear regression algorithms, our study aims to make an original contribution to behavioral finance, combining technical and behavioral aspects of model building. An empirical out-of-sample evaluation with multiple input structures compares Multivariate Linear Regression models (MLR) with multilayer perceptron (MLP) neural networks for descriptive modelling. The results indicate that sentiment indicators are explanatory for market movements of exchange rate returns, with nonlinear MLPs showing superior accuracy over linear regression models with a directional out-of-sample accuracy of 60.26% using cross validation

Can social microblogging be used to forecast intraday exchange rates?

The Efficient Market Hypothesis (EMH) is widely accepted to hold true under certain assumptions. One of its implications is that the prediction of stock prices at least in the short run cannot outperform the random walk model. Yet, recently many studies stressing the psychological and social dimension of financial behavior have challenged the validity of the EMH. Towards this aim, over the last few years, internet-based communication platforms and search engines have been used to extract early indicators of social and economic trends. Here, we used Twitter's social networking platform to model and forecast the EUR/USD exchange rate in a high-frequency intradaily trading scale. Using time series and trading simulations analysis, we provide some evidence that the information provided in social microblogging platforms such as Twitter can in certain cases enhance the forecasting efficiency regarding the very short (intradaily) forex.Comment: This is a prior version of the paper published at NETNOMICS. The final publication is available at http://www.springer.com/economics/economic+theory/journal/1106

Artificial Counselor System for Stock Investment

This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment. In this paper, the stock future prices (technical features) are predicted using Support Vector Regression. Thereafter, the predicted prices are used to recommend which portions of the budget an investor should invest in different existing stocks to have an optimum expected profit considering their level of risk tolerance. Two different methods are used for suggesting best portions, which are Markowitz portfolio theory and fuzzy investment counselor. The first approach is an optimization-based method which considers merely technical features, while the second approach is based on Fuzzy Logic taking into account both technical and fundamental features of the stock market. The experimental results on New York Stock Exchange (NYSE) show the effectiveness of the proposed system.Comment: 7 pages, 8 figures, 1 tabl

Performance Evaluation of Judgmental Directional Exchange Rate Predictions

Cataloged from PDF version of article.A procedure is proposed for examining different aspects of performance for judgemental directional probability predictions of exchange rate movements. In particular, a range of new predictive performance measures is identified to highlight specific expressions of strengths and weaknesses in judgemental directional forecasts. Proposed performance qualifiers extend the existing accuracy measures, enabling detailed comparisons of probability forecasts with ex-post empirical probabilities that are derived from changes in the logarithms of the series. This provides a multi-faceted evaluation that is straightforward for practitioners to implement, while affording the flexibility of being used in situations where the time intervals between the predictions have variable lengths. The proposed procedure is illustrated via an application to a set of directional probability exchange rate forecasts for the US Dollar/Swiss Franc from 23/7/96 to 7/12/99 and the findings are discussed. D 2005 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved

Asymmetric connectedness of stocks: How does bad and good volatility spill over the U.S. stock market?

Asymmetries in volatility spillovers are highly relevant to risk valuation and portfolio diversification strategies in financial markets. Yet, the large literature studying information transmission mechanisms ignores the fact that bad and good volatility may spill over at different magnitudes. This paper fills this gap with two contributions. One, we suggest how to quantify asymmetries in volatility spillovers due to bad and good volatility. Two, using high frequency data covering most liquid U.S. stocks in seven sectors, we provide ample evidence of the asymmetric connectedness of stocks. We universally reject the hypothesis of symmetric connectedness at the disaggregate level but in contrast, we document the symmetric transmission of information in an aggregated portfolio. We show that bad and good volatility is transmitted at different magnitudes in different sectors, and the asymmetries sizably change over time. While negative spillovers are often of substantial magnitudes, they do not strictly dominate positive spillovers. We find that the overall intra-market connectedness of U.S. stocks increased substantially with the increased uncertainty of stock market participants during the financial crisis.Comment: arXiv admin note: text overlap with arXiv:1405.244

A study on the forecasting bases of the currency investors and foreign exchange dealers in Hong Kong.

by Fok Shun-cheong, Vincent.Thesis (M.B.A.)--Chinese University of Hong Kong, 1991.Bibliography: leaves [37-38]ACKNOWLEDGEMENTSChapter I. --- INTRODUCTION --- p.1The Hong Kong Exchange Market --- p.1Structure of the market --- p.2Forecasting Exchange Rates --- p.4Objectives --- p.5Chapter II. --- METHODOLOGY --- p.6Selecting the Bases for Forecasting --- p.6Sampling --- p.9Chapter III. --- THEORETICAL FRAMEWORK --- p.10Chapter 1. --- Investment Objectives --- p.10Chapter 2. --- Time Frame --- p.11Chapter 3. --- Funds Available --- p.12Chapter 4. --- Time Available --- p.12Chapter 5. --- Information Available --- p.13Chapter 6. --- Transaction Nature and Cost --- p.14Chapter 7. --- Knowledge and Background --- p.14Chapter 8. --- Position Taking --- p.14Chapter 9. --- Past Experience --- p.16Chapter 10. --- External Influences --- p.16Chapter IV. --- SURVEY FINDINGS --- p.18Individual InvestorsChapter A. --- The Level of Exchange Rate and Interest Rate --- p.18Chapter B. --- Seldom use of Charts and Technical Indicators --- p.19Chapter C. --- No Relationship between Demographic Variables and Forecasting Bases --- p.19Chapter D. --- No Relationship between the Experience of the respondents and the Forecasting Bases --- p.20DealersChapter A. --- Charts often considered --- p.22Chapter B. --- Technical Indicators also important --- p.22Chapter C. --- Emphasis on the Fundamental rather than Technical Analysis --- p.23Chapter D. --- Market Sentiments --- p.24Chapter E. --- Econometric Models Seldom Used --- p.25Chapter F. --- Differences among the six major currencies --- p.27Chapter V. --- LIMITATIONS OF THE SURVEY --- p.29Chapter VII. --- SUMMARY AND CONCLUSIONS --- p.30APPENDICESBIBLIOGRAPH