Machine learning methods extract value from vast data sets quickly and with modest resources They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly People who know the methods have their choice of rewarding jobs This hands-on text opens these opportunities to computer science students with modest mathematical backgrounds It is designed for final-year undergraduates and master's students with limited background in linear algebra and calculus Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models Students learn more than a menu of techniques, they develop analytical and problem-solving skills that equip them for the real world Numerous examples and exercises, both computer based and theoretical, are included in every chapter Resources for students and instructors, including a MATLAB toolbox, are available online

/pdf/bayesian-reasoning-and-machine-learning-276lqrjv6g.pdf

Bayesian Reasoning and Machine Learning

We examine tests for jumps based on recent asymptotic results; we interpret the tests as Hausman-type tests. Monte Carlo evidence suggests that the daily ratio z-statistic has appropriate size, good power, and good jump detection capabilities revealed by the confusion matrix comprised of jump classification probabilities. We identify a pitfall in applying the asymptotic approximation over an entire sample. Theoretical and Monte Carlo analysis indicates that microstructure noise biases the tests against detecting jumps, and that a simple lagging strategy corrects the bias. Empirical work documents evidence for jumps that account for seven percent of stock market price variance.

/pdf/the-relative-contribution-of-jumps-to-total-price-variance-8runjpez0w.pdf

The Relative Contribution of Jumps to Total Price Variance

The purpose of this paper is to review the evidence on the profitability of technical analysis. The empirical literature is categorized into two groups, ‘early’ and ‘modern’ studies, according to the characteristics of testing procedures. Early studies indicate that technical trading strategies are profitable in foreign exchange markets and futures markets, but not in stock markets. Modern studies indicate that technical trading strategies consistently generate economic profits in a variety of speculative markets at least until the early 1990s. Among a total of 95 modern studies, 56 studies find positive results regarding technical trading strategies, 20 studies obtain negative results, and 19 studies indicate mixed results. Despite the positive evidence on the profitability of technical trading strategies, most empirical studies are subject to various problems in their testing procedures, e.g. data snooping, ex post selection of trading rules or search technologies, and difficulties in estimation of risk and transaction costs. Future research must address these deficiencies in testing in order to provide conclusive evidence on the profitability of technical trading strategies.

https://papers.ssrn.com/sol3/Delivery.cfm/joes_519.pdf?abstractid=1006275&mirid=2

What do we know about the profitability of technical analysis

Abstract The imputeTS package specializes on univariate time series imputation. It offers multiple state-of-the-art imputation algorithm implementations along with plotting functions for time series missing data statistics. While imputation in general is a well-known problem and widely covered by R packages, finding packages able to fill missing values in univariate time series is more complicated. The reason for this lies in the fact that most imputation algorithms rely on inter-attribute correlations, while univariate time series imputation instead needs to employ time dependencies. This paper provides an introduction to the imputeTS package and its provided algorithms and tools. Furthermore, it gives a short overview about univariate time series imputation in R.

/pdf/imputets-time-series-missing-value-imputation-in-r-xbmkd1gitm.pdf

imputeTS: Time Series Missing Value Imputation in R

This paper reviews the exciting and rapidly expanding literature on realized volatility. After presenting a general univariate framework for estimating realized volatilities, a simple discrete time model is presented in order to motivate the main results. A continuous time specification provides the theoretical foundation for the main results in this literature. Cases with and without microstructure noise are considered, and it is shown how microstructure noise can cause severe problems in terms of consistent estimation of the daily realized volatility. Independent and dependent noise processes are examined. The most important methods for providing consistent estimators are presented, and a critical exposition of different techniques is given. The finite sample properties are discussed in comparison with their asymptotic properties. A multivariate model is presented to discuss estimation of the realized covariances. Various issues relating to modelling and forecasting realized volatilities are considered. The main empirical findings using univariate and multivariate methods are summarized.

Realized volatility: a review

Financial time series, in general, exhibit average behaviour at “long” time scales and stochastic behaviour at ‘short” time scales As in statistical physics, the two have to be modelled using different approaches — deterministic for trends and probabilistic for fluctuations about the trend In this talk, we will describe a new wavelet based approach to separate the trend from the fluctuations in a time series A deterministic (non-linear regression) model is then constructed for the trend using genetic algorithm We thereby obtain an explicit analytic model to describe dynamics of the trend Further the model is used to make predictions of the trend We also study statistical and scaling properties of the fluctuations The fluctuations have non-Gaussian probability distribution function and show multi-scaling behaviour Thus, our work results in a comprehensive model of trends and fluctuations of a financial time series

Modelling Financial Time Series

Information and Communication Technology (ICT) is increasingly recognised as an essential tool of development a tool which empowers the poor by enhancing skills, increasing productivity and improving governance. The success of ICTenabled development (or e-Development) is measured not only by the diffusion of technology, but also by advances in development itself. The contribution of ICT can be viewed at two different but interrelated levels : ICT growth and ICT diffusion. The former refers to the contribution in output, employment, export earnings, etc., resulting from production of ICT related goods and services that are limited to just one segment of the economy. The latter refers to IT-induced development through enhanced productivity, competitiveness, growth and human welfare resulting from the use of this technology by differentsectors of the economy and society. International research findings in the context of developing and developed countries reveal that unlike old technologies (radio, television, etc.) which are more demand driven, ICT is more supply driven and leaves greater scope for diffusion agents (Non-Government Organisations or NGOs, Government, private sector and other actors) to influence the diffusion process. Firstly, unlike earlier technologies, investment in the Internet, personal computers, etc. essentially complements investments already made in communications technologies like satellites, telephone and cable TV networks. Secondly, newly developed software technology in India has replaced the requirement of broadband and thus provides full connectivity in rural areas. Thirdly, ICT is multi-user by nature, which, in turn, leaves scope for Internet kiosks, Internet cafes and Community Information Centres (CICs), providing access to many. In India, the IT sector has not only grown in size but also complexity. Indian States resemble little nations. So, it is important to take regular stock of e-Readiness at the Country and State/ Union Territory levels to ascertain the status of underlying infrastructure, human resources, policy regime, environment climate, etc. and arrive at the steps needed to be taken to optimise the potential. Before we introduce our e-Readiness methodology, it would be instructive to look at the various e-Readiness assessment models used elsewhere.

India: E-Readiness Assessment Report 2005

We utilize a recently developed genetic algorithm, in conjunction with discrete wavelets, for carrying out successful forecasts of the trend in financial time series, that includes the NASDAQ composite index. Discrete wavelets isolate the local, small scale variations in these non-stationary time series, after which the genetic algorithm's predictions are found to be quite accurate. The power law behavior in Fourier domain reveals an underlying self-affine dynamical behavior, well captured by the algorithm, in the form of an analytic equation. Remarkably, the same equation captures the trend of the Bombay stock exchange composite index quite well.

Forecasting non-stationary financial time series through genetic algorithm

We utilize a recently developed genetic algorithm, in conjunction with discrete wavelets, for carrying out successful forecasts of the trend in financial time series, that includes the NASDAQ composite index. Discrete wavelets isolate the local, small scale variations in these non-stationary time series, after which the genetic algorithm’s predictions are found to be quite accurate. The power law behavior in Fourier domain reveals an underlying self-affine dynamical behavior, well captured by the algorithm, in the form of an analytic equation. Remarkably, the same equation captures the trend of the Bombay stock exchange composite index quite well.

Sujit Basu

Papers

Modelling Financial Time Series

India: E-Readiness Assessment Report 2005

Forecasting non-stationary financial time series through genetic algorithm

Forecasting non-stationary financial time series through genetic algorithm