A Statistical Arbitrage FX Trading System Based on Short Term FX Volatility Swings Forecasting with Institutional Data on JPY Based Investment Flows Into US Markets

2009 ◽  
Author(s):  
Pavan Gadiraju
Keyword(s):  
Short Term ◽  
Trading System ◽  
Statistical Arbitrage ◽  
Institutional Data ◽  
Fx Trading

scholarly journals Is Financial Analysis Doomed? The Birth of “Reactive Valuation” Analysis

2017 ◽  
Vol 6 (3) ◽  
pp. 39
Author(s):  
Paul A. Griffin ◽  
Mohammedi Padaria
Keyword(s):  
Language Processing ◽  
Financial Analysis ◽  
Detailed Knowledge ◽  
Information Costs ◽  
Short Term ◽  
Traditional Concept ◽  
Statistical Arbitrage ◽  
Media Channels ◽  
Short Time ◽  
Information Landscape

The purpose of this paper is to examine how firms’ information landscape has changed in recent years and why this could be problematic for those engaged in financial analysis and equity valuation. Our central contention is that two main forces of change – lower information costs and faster information processing – have completely disrupted the traditional concept of financial analysis. In response to this disruption, financial analysis will now increasingly take the form of “reactive valuation.” In addition to examining our main contention, we introduce a new term into the literature, called “reactive valuation,” which we define as the ultra short-term valuation of an equity, lasting from a few seconds to a few hours, based on information primarily published through social media channels. It may be later corroborated by factually based information or remain unsubstantiated. It may or may not be from an authoritative source. It also may not relate clearly or directly to the valuation of the underlying asset. However, based mostly on the tools of artificial intelligence and natural language processing, “reactive valuation” will invariably provide an opportunity for statistical arbitrage during the short time it takes for the market to digest the information. Financial analysts who survive these two forces of change will have detailed knowledge of this new form of financial analysis.

scholarly journals Rescuing EU Emissions Trading: Mission Impossible?

2014 ◽  
Vol 14 (2) ◽  
pp. 64-81 ◽  
Author(s):  
Jørgen Wettestad
Keyword(s):  
Emissions Trading ◽  
Carbon Price ◽  
Reduction Factor ◽  
Low Carbon ◽  
Short Term ◽  
Trading System ◽  
International Policy ◽  
Science Theory ◽  
Emissions Trading System ◽  
The Eu

Is rescuing the EU's emissions trading system impossible? Despite the substantial reform in 2008, subsequent problems of allowance surplus and a low carbon price have spurred new efforts to reform the system for the 2013–2020 phase. But these efforts have met resistance both among member states and in the European parliament, and the EU is struggling in its efforts to improve the ETS. This article draws on four central EU and political science theory approaches to more systematically explore why. The financial crisis and slow international policy progress have narrowed the window of opportunity that was open in 2008. Factors that could open that window again include an economic upswing, a new European commission and parliament, and new global negotiations in 2015. But even without short-term reform, the linear reduction factor will gradually tighten the system and lead to a higher carbon price.

The future of the WTO: some comments on the Sutherland Report

2005 ◽  
Vol 4 (3) ◽  
pp. 379-389
Author(s):  
RICHARD BLACKHURST
Keyword(s):  
International Trade ◽  
World Trade ◽  
Developed Countries ◽  
Less Developed Countries ◽  
Industrialized Countries ◽  
Short Term ◽  
Trading System ◽  
Primary Products ◽  
Multilateral Trading System ◽  
The Future

Three times since its founding in 1948, the GATT/WTO has turned to outside experts for help in finding solutions to pressing issues confronting the multilateral trading system. In 1957 the Contracting Parties decided to create a panel of three (later four) internationally recognized experts in international trade and finance to consider trends in world trade, andin particular the failure of the trade of the less developed countries to develop as rapidly as that of industrialized countries, excessive short-term fluctuations in prices of primary products, and widespread resort to agricultural protection.

scholarly journals Short-Term Stock Market Fuzzy Trading System with Fuzzy Capital Management

2018 ◽  
Vol 11 (3) ◽  
pp. 49-58
Author(s):  
Ahmed Tealab ◽  
◽  
Hesham Hefny ◽  
Amr Badr ◽  
◽  
...  
Keyword(s):  
Stock Market ◽  
Capital Management ◽  
Short Term ◽  
Trading System

scholarly journals Forward Forecast of Stock Price Using LSTM Machine Learning Algorithm

2020 ◽  
Vol 12 (3) ◽  
pp. 74-79
Author(s):  
Kavitha Esther Rajakumari ◽  
◽  
M. Srinivasa Kalyan ◽  
M. Vijay Bhaskar
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Neural System ◽  
Short Term ◽  
Trading System ◽  
Term Memory ◽  
The Future ◽  
Long Short Term Memory

Stock market prediction is the demonstration of attempting to decide the future estimation of an organization stock or other monetary instrument exchanged on a trade. This paper will exhibit how to perform stock expectations utilizing Machine Learning calculations. Foreseeing securities exchange costs is an intricate assignment that generally includes broad human-PC communication. Because of the connected idea of stock costs, customary bunch preparing techniques can't be used productively for securities exchange examination. In the current framework, the Sliding window calculation is used. This calculation investigates the information, with a window pushing ahead, in the wake of examining the information. It is very tedious for expectation of stocks. While, in the proposed framework, the utilization of LSTM (Long Short Term Memory) calculation, gives compelling outcomes. While analyzing, the superfluous information is overlooked. The current framework is additionally not viable, in taking care of non-straight information. What's more, it is less proficient contrasted with LSTM algorithm. So, to help defeat these, LSTM helps in dealing with the information in a productive way. Indeed, speculators are exceptionally intrigued by the exploration zone of stock value expectations. For decent and fruitful speculation, numerous financial specialists are sharp in knowing the future circumstance of the share trading system. Great and viable expectation frameworks for securities exchange encourage brokers, financial specialists, and investigators by giving steady data like the future course of the share trading system. In this work, an intermittent neural system (RNN) and Long Short-Term Memory (LSTM) are presented, a way to deal with anticipate securities exchange lists. The proposed model is a promising prescient procedure for a very non-direct time arrangement, whose designs are hard to catch by customary models.

scholarly journals A Novel Synergetic LSTM-GA Stock Trading Suggestion System in Internet of Things

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Jimmy Ming-Tai Wu ◽  
Lingyun Sun ◽  
Gautam Srivastava ◽  
Jerry Chun-Wei Lin
Keyword(s):  
Genetic Algorithms ◽  
Stock Prices ◽  
Short Term Memory ◽  
Leading Indicators ◽  
Short Term ◽  
Trading System ◽  
Stock Trading ◽  
Data Set ◽  
Term Memory ◽  
Long Short Term Memory

The Internet of Things (IoT) play an important role in the financial sector in recent decades since several stock prediction models can be performed accurately according to IoT-based services. In real-time applications, the accuracy of the stock price fluctuation forecast is very important to investors, and it helps investors better manage their funds when formulating trading strategies. It has always been a goal and difficult problem for financial researchers to use predictive tools to obtain predicted values closer to actual values from a given financial data set. Leading indicators such as futures and options can reflect changes in many markets, such as the industry’s prosperity. Adding the data set of leading indicators can predict the trend of stock prices well. In this research, a trading strategy for finding stock trading signals is proposed that combines long short-term memory neural networks with genetic algorithms. This new framework is called long short-term memory neural network with leading index, or LSTMLI for short. We thus take the stock markets of the United States and Taiwan as the research objects and use historical data, futures, and options as data sets to predict the stock prices of these two markets. After that, we use genetic algorithms to find trading signals for the designed stock trading system. The experimental results show that the stock trading system proposed in this research can help investors obtain certain returns.

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