Moving average reversion strategy for on-line portfolio selection
TLDR
This article proposes a multiple-period mean reversion, or so-called "Moving Average Reversion" (MAR), and a new on-line portfolio selection strategy named "On-Line Moving Average Reverting" (OLMAR), which exploits MAR via efficient and scalable online machine learning techniques.About:
This article is published in Artificial Intelligence.The article was published on 2015-05-01 and is currently open access. It has received 102 citations till now. The article focuses on the topics: Mean reversion & Moving average.read more
Citations
More filters
Journal ArticleDOI
Natural language based financial forecasting: a survey
TL;DR: This review article clarifies the scope of NLFF research by ordering and structuring techniques and applications from related work, and aims to increase the understanding of progress and hotspots in NLFF, and bring about discussions across many different disciplines.
Journal ArticleDOI
Online learning: A comprehensive survey
TL;DR: Online learning as mentioned in this paper is a family of machine learning methods, where a learner attempts to tackle some predictive (or any type of decision-making) task by learning from a sequence of data instances one by one at each time.
Posted Content
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
TL;DR: A financial-model-free Reinforcement Learning framework to provide a deep machine learning solution to the portfolio management problem, able to achieve at least 4-fold returns in 50 days.
Posted Content
Online Learning: A Comprehensive Survey
TL;DR: This survey aims to provide a comprehensive survey of the online machine learning literatures through a systematic review of basic ideas and key principles and a proper categorization of different algorithms and techniques.
Posted Content
151 Trading Strategies
Zura Kakushadze,Juan A. Serur +1 more
TL;DR: In this paper, the authors provide detailed descriptions, including over 550 mathematical formulas, for over 150 trading strategies across a host of asset classes and trading styles, including stocks, options, fixed income, futures, ETFs, indexes, commodities, foreign exchange, convertibles, structured assets, volatility (as an asset class), real estate, distressed assets, cash, cryptocurrencies, miscellany (such as weather, energy, inflation), global macro, infrastructure, and tax arbitrage).
References
More filters
Book
Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
Book
Convex Optimization
Stephen Boyd,Lieven Vandenberghe +1 more
TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
Book
The econometrics of financial markets
TL;DR: In this paper, Campbell, Lo, and MacKinlay present an attempt by three well-known and well-respected scholars to fill an acknowledged void in the empirical finance literature, a text covering the burgeoning field of empirical finance.
Journal ArticleDOI
Does the Stock Market Overreact
TL;DR: In this article, a study of market efficiency investigates whether people tend to "overreact" to unexpected and dramatic news events and whether such behavior affects stock prices, based on CRSP monthly return data, is consistent with the overreaction hypothesis.
BookDOI
Introduction to Stochastic Programming
John R. Birge,Franois Louveaux +1 more
TL;DR: This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability to help students develop an intuition on how to model uncertainty into mathematical problems.