Filtering techniques for asset allocation using a Discrete Time Micro-structure model: a comparative study

This paper is a comparative study of different approaches to using a Discrete Time Micro-structure model. By using the three filtering techniques Extended Kalman, Unscented Kalman and Bootstrap Particle, the hidden variables; excess demand and market liquidity, were estimated and used in an asset allocation strategy that invested in the asset when the excess demand as estimated as positive, due to the assumption that positive excess demand would make the price go up. Two different strategies were used—one based on threshold values of excess demand and one binary approach simply using the sign of the excess demand—to try to outperform a passive allocation strategy on 12 different stock indices. They were then evaluated in terms of average daily returns and market timing. The results showed favourable average daily returns for the Extended and Unscented Kalman filtering techniques using both kinds of strategies, though none of the results were statistically significant at the 5% confidence level. The Bootstrap Particle was deemed generally unreliable. The market timing tests rejected the null hypothesis of no market ability for most data sets using all three filtering techniques, with the two Kalman filters yielding the best results. Nothing was concluded about which filtering technique was superior, though the study indicates that Kalman filtering techniques can be used successfully in many cases while the Bootstrap Particle filter as used in this thesis is not reliable. The threshold-based strategy got slightly worse results in general than those of the binary approach, but this was tested without taking transaction costs into account

Modeling Financial Time Series with Artificial Neural Networks

Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001

Enhancing the Toolbox of Fixed Income Active Portfolio Management

AbstractMany central banks adopt an active investment style for reserve management. This paper discusses various possible enhancements to active management tools and processes to generate extra returns in an increasingly challenging environment. The proposed framework is based on an affine model, which includes macroeconomic and market sentiment indicators among the explanatory variables. Using estimates of expected excess returns drawn from the model, an operational indicator produces input highlighting the portfolio's exposure to duration risk. This indicator is incorporated within a broader framework, in which a scorecard considers a range of qualitative elements, including consensus figures on macroeconomic data, monetary policy and interest rates. These elements are then combined with the model output to produce a comprehensive indication with respect to portfolio deviation from the benchmark. It should be noted that the approach presented in this paper is experimental; it has not yet been used in an active portfolio. Finally, consideration is given to the governance of the central bank investment process in order to assess how the proposed enhancements could strengthen the decision-making process. The analysis suggests that the scorecard with model-based input may address some weaknesses inherent in tactical decision- making.The views expressed herein are solely our own and do not necessarily reflect those of the Bank of Italy or the European System of Central Banks

Limits of Policy Intervention in a World of Neoliberal Mechanism Designs: Paradoxes of the Global Crisis

The current global context poses several paradoxes: the recovery from the 2009 recession was not a recovery; investment, normally driven by profit rates, is lagging and not leading economic activity; the crisis is global but debate involves sub-global levels; and public safety-nets, which have helped to stabilize national income, are being cut. These paradoxes can be traced, in part, to the impact of the “truce” that followed the Keynesian-Monetarist controversy on economists’ ideas about policy activism. This implicit “truce” has removed activist macro policy from discussion, and shifted attention toward institutions as mechanisms for solving game-theoretic coordination problems. Policy activism then centers on how the “agents” (nations) can achieve optimal use of their available resources (or optimal access to resources) at the global level; and this involves creating and fine-tuning compacts – neoliberal mechanism designs – that can capture rents and attract globally mobile capital. This approach leads economists to see the key problem in the current global crisis as fixing broken neoliberal mechanisms. However, a global economy dominated by mechanisms that feed on aggregate demand without generating it faces the prospect of stagnation or collapse.Neoliberal mechanism design, Policy activism, Keynesian- Monetarist controversy, Globalization, Capital mobility, Hyman Minsky, Bradford De Long

Architecting system of systems: artificial life analysis of financial market behavior

This research study focuses on developing a framework that can be utilized by system architects to understand the emergent behavior of system architectures. The objective is to design a framework that is modular and flexible in providing different ways of modeling sub-systems of System of Systems. At the same time, the framework should capture the adaptive behavior of the system since evolution is one of the key characteristics of System of Systems. Another objective is to design the framework so that humans can be incorporated into the analysis. The framework should help system architects understand the behavior as well as promoters or inhibitors of change in human systems. Computational intelligence tools have been successfully used in analysis of Complex Adaptive Systems. Since a System of Systems is a collection of Complex Adaptive Systems, a framework utilizing combination of these tools can be developed. Financial markets are selected to demonstrate the various architectures developed from the analysis framework --Introduction, page 3

Machine Learning and Portfolio Optimization: an application to Italian FTSE-MIB Stocks

A model that combines econometric ARMA model with new machine learning techniques will be developed to build an efficient portfolio, composed of Italian FTSE-MIB stocks. The goal of this portfolio is to over-perform a benchmark portfolio obtained throw traditional Markowitz optimisation.A model that combines econometric ARMA model with new machine learning techniques will be developed to build an efficient portfolio, composed of Italian FTSE-MIB stocks. The goal of this portfolio is to over-perform a benchmark portfolio obtained throw traditional Markowitz optimisation