The case for active management from the perspective of Complexity Theory (original) (raw)

Levels of complexity in financial markets

Physica A: Statistical Mechanics and its Applications, 2001

We consider different levels of complexity which are observed in the empirical investigation of financial time series. We discuss recent empirical and theoretical work showing that statistical properties of financial time series are rather complex under several ways. Specifically, they are complex with respect to their (i) temporal and (ii) ensemble properties. Moreover, the ensemble return properties show a behavior which is specific to the nature of the trading day reflecting if it is a normal or an extreme trading day.

Economic Complexity: why we like "Complexity weighted diversification

arXiv (Cornell University), 2019

A recent paper by Hausmann and collaborators (1) reaches the important conclusion that Complexity-weighted diversification is the essential element to predict country growth. We like this result because Complexity-weighted diversification is precisely the first equation of the Fitness algorithm that we introduced in 2012 (2,3). However, contrary to what is claimed in (1), it is incorrect to say that diversification is contained also in the ECI algorithm (4). We discuss the origin of this misunderstanding and show that the ECI algorithm contains exactly zero diversification. This is actually one of the reasons for the poor performances of ECI which leads to completely unrealistic results, as for instance, the derivation that Qatar or Saudi Arabia are industrially more competitive than China (5,6). Another important element of our new approach is the representation of the economic dynamics of countries as trajectories in the GDPpc-Fitness space (7-10). In some way also this has been rediscovered by Hausmann and collaborators and renamed as "Stream plots", but, given their weaker metrics and methods, they propose it to use it only for a qualitative insight, while ours led to quantitative and successful forecasting. The Fitness approach has paved the way to a robust and testable framework for Economic Complexity resulting in a highly competitive scheme for growth forecasting (7-10). According to a recent report by Bloomberg (9): The new Fitness method, "systematically outperforms standard methods, despite requiring much less data".

Optimal and Naive Diversification in Currency Markets

Management Science, 2016

DeMiguel, Garlappi, and Uppal (Review of Financial Studies, 22 (2009), 1915-1953) showed that in the stock market, it is difficult for an optimized portfolio constructed using meanvariance analysis to outperform a simple equally-weighted portfolio because of estimation error. In this paper, we demonstrate that portfolio optimization can be made to work in currency markets. The key difference between the two settings is that in currency markets interest rates provide a predictor of future returns that is free of estimation error, which permits the application of mean-variance analysis. We show that over the last 26 years, a mean-variance efficient portfolio constructed in this fashion has a Sharpe ratio of 0.91, versus only 0.15 for the equally-weighted portfolio. We also consider the practical implementation of this strategy.

Complexity analysis of the stock market

Physica A: Statistical Mechanics and its Applications, 2007

We study the complexity of the stock market by constructing -machines of Standard and Poor's 500 index from February 1983 to April 2006 and by measuring the statistical complexities. It is found that both the statistical complexity and the number of causal states of constructed -machines have decreased for last 20 years and that the average memory length needed to predict the future optimally has become shorter. These results support that the information is delivered to the economic agents and applied to the market prices more rapidly in year 2006 than in year 1983. r

A BASIC THEORY OF INTELLIGENT FINANCE

This paper presents a basic theory of intelligent¯nance as a new paradigm of¯nancial investment. It is assumed that the¯nancial market is always in a state of swing between e±cient and ine±cient modes on multiple levels of time scale; it is possible to go beyond the e±cient market theory to study the dynamic evolving process of the market between equilibrium and far-from-equilibrium; there are robust dynamic patterns in this evolving process, which may be exploitable via intelligent trading systems. On the foundation of the four principlescomprehensive, predictive, dynamic and strategic, the basic theory takes the information sources into the loop as the starting points for all the market analysis, introducing the scale space of time into the pricing process analysis in order to detect and capture trends, cycles and seasonality on multiple intrinsic levels of time scale which are then used as the dynamic basis for constructing and managing portfolios. In stock markets, the theory exhibits itself in the form of an Intelligent Dynamic Portfolio Theory, which integrates predictive modeling of a bullbear market cycle, sector rotation, and portfolio optimization with a reactive trend following trading strategy.

An Artificial Market Model of a Foreign Exchange Market

1998

In this study, we proposed a new approach to foreign exchange market studies, an artificial market approach. The artificial market approach integrated fieldwork studies and multiagent models in order to explain the micro and macro relation in markets. The artificial market approach has the three steps: First, in order to investigate the learning patterns of actual dealers, we carried out both interviews and questionnaires. These field data made it clear that each dealer improved his or her prediction method by replacing (a part of) his or her opinions about factors with other dealers' opinion which can forecast more accurately. Second, we constructed a multiagent model of a foreign exchange market. Considering the result of the analysis of the field data, the interaction of agents' learning is described with genetic algorithms in our model. Finally, the emergent phenomena at the market level were analyzed on the basis of the simulation results of the model. The results showed that rate bubbles were caused by the interaction between the agents' forecasts and the relationship of demand and supply. The other emergent phenomena were explained by the concept of the phase transition of forecast variety. The filed data also supported this simulation results. This approach therefore integrates the fieldwork and the multiagent model, and provides quantitative explanation of the micro-macro relation in markets.

Framing Complexity in Financial Markets An Example of Portfolio Management

2008

Financial markets are complex: Factors that affect their development and the asso- ciations between these factors are not clearly assignable. Nevertheless, every day thousands of investors face the task of mastering the complexity of the markets. How do they solve this problem in their daily practice? This paper discusses this question by investigating a special area of the capital markets, namely, portfolio management. Two possible methods of handling the complexity of financial mar- kets are presented: first, the qualitative methods (heuristics), and second, the for- mal (mostly computer-assisted) models. The central finding of the paper is the high heterogeneity of the applied methods. There are free spaces that portfolio manag- ers can use while framing complex market situations. It could be suggested that this diversity contributes to the emergence and self-preservation of the market complexity. This contribution is dedicated to the empirical argument for these mechanisms.

Reinforcement Learning Applied to a Cryptocurrency Portfolio in a Complexity Environment

Revista Economia Ensaios, 2020

Recently, cryptocurrencies have been used as financial assets and have presented positive returns, albeit their volatility is high. This paper aims to elaborate a hypothetical cryptocurrency portfolio and to do so, employs machine learning and an optimization algorithm to define the ideal amount to be allocated in each asset. The results show the hypothetical portfolio presents superior returns and lesser volatility compared to other allocation strategies.

Hybrid Model Approach to the Complexity of Stock Trading Decisions in Turkey

The Journal of Asian Finance, Economics and Business, 2020

The aim of this paper is to predict the Borsa Istanbul (BIST) 30 index movements to determine the most accurate buy and sell decisions using the methods of Artificial Neural Networks (ANN) and Genetic Algorithm (GA). We combined these two methods to obtain a hybrid intelligence method, which we apply. In the financial markets, over 100 technical indicators can be used. However, several of them are preferred by analysts. In this study, we employed nine of these technical indicators. They are moving average convergence divergence (MACD), relative strength index (RSI), commodity channel index (CCI), momentum, directional movement index (DMI), stochastic oscillator, on-balance volume (OBV), average directional movement index (ADX), and simple moving averages (3-day moving average, 5-day moving average, 10-day moving average, 14-day moving average, 20-day moving average, 22-day moving average, 50-day moving average, 100-day moving average, 200-day moving average). In this regard, we combined these two techniques and obtained a hybrid intelligence method. By applying this hybrid model to each of these indicators, we forecast the movements of the Borsa Istanbul (BIST) 30 index. The experimental result indicates that our best proposed hybrid model has a successful forecast rate of 75%, which is higher than the single ANN or GA forecasting models.

Volatility Modeling for Currency Pairs and Stock Indices by Means of Complex Networks

Ekonomika, 2020

Financial markets are complex systems. Network analysis is an innovative method for improving data sharing and knowledge discovery in financial data. Oriented weighted networks were created for the Shanghai Composite, S&P500, DAX30, CAC40, Nikkei225, FTSE100, IBEX35 indexes, for CNY-JPY, EUR-USD, GBP-EUR, RUB-CNY and for cryptocurrency BTC-USD. We considered data since January 6, 2006 to September 6, 2019. The complex networks had a similar structure for both types of markets, which was divided into the central part (core) and the outer one (loops). The emergence of such a structure reflects the fact that, for the most part, the stock and currency markets develop around some significant state of volatility, but occasionally anomalies occur when the states of volatility deviate from the core. Comparing the topology of evolutionary networks and the differences found for the stock and currency markets networks, we can conclude that stock markets are characterized by a greater variety ...