Predictive intraday correlations in stable and volatile market environments:Evidence from deep learning

Standard methods and theories in finance can be ill-equipped to capture highly non-linear interactions in financial prediction problems based on large-scale datasets, with deep learning offering a way to gain insights into correlations in markets as complex systems. In this paper, we apply deep learning to econometrically constructed gradients to learn and exploit lagged correlations among S&P 500 stocks to compare model behaviour in stable and volatile market environments, and under the exclusion of target stock information for predictions. In order to measure the effect of time horizons, we predict intraday and daily stock price movements in varying interval lengths and gauge the complexity of the problem at hand with a modification of our model architecture. Our findings show that accuracies, while remaining significant and demonstrating the exploitability of lagged correlations in stock markets, decrease with shorter prediction horizons. We discuss implications for modern finance theory and our work's applicability as an investigative tool for portfolio managers. Lastly, we show that our model's performance is consistent in volatile markets by exposing it to the environment of the recent financial crisis of 2007/2008.Comment: 15 pages, 6 figures, preprint submitted to Physica

Using international diversification to enhance predicted equity index performance: a South African perspective

In the weak form, the Efficient Market Hypothesis (EMH) states that it is not possible to forecast the future price of an asset based on the information contained in the historical prices of that same asset. Under this assumption, the market behaves as a random walk and as a result, price forecasting is impossible. Furthermore, financial forecasting is a difficult task due to the intrinsic complexities of any financial system. The purpose of this study is to examine the potential of developing an international investment strategy using future index price predictions and offsetting predicted price declines by investing in negatively correlated international markets. Therefore, the first objective of this study was to examine the feasibility and accuracy of using a machine learning technique to model and predict the future price of stock market indices of South Africa (All Share Index) and a variety of other developed and developing international markets, which included South Africa, Brazil, Russia, India and China of the BRIC countries and Italy, France, Netherlands, Switzerland, Germany, Nigeria, Australia, Hong Kong, Saudi Arabia, Japan, the U.S., Turkey and the U.K., which were identified as South Africa’s major trading partners. Secondly, an analysis of market correlation between each country’s equity index and South Africa’s ALSI was conducted to determine which of these international indices were positively and negatively correlated to the South African ALSI. This allowed an extrapolation of potential international diversification opportunities. By using machine learning to predict future price trends of the South African All Share Index (ALSI) within a specified time period, the market correlation aspect of this study was able to suggest possible negatively correlated safe haven markets to invest in to offset predicted losses in an expected declining local market. The study’s major limitations include a single method for regression analysis (GARCH(1, 1)) and a limited number of variables in the feature space when predicting future prices. Additional parameters could prove a more robust modelling technique. The data used was a series of past closing prices of each country’s major index. The data was split into five periods, where each period was assigned an overarching theme based on the prevailing market conditions at the time. The ALSI data set was subjected to a unit root test and found to be non-stationary. The analysis thereafter followed a two-step test, with the first being the determination of market correlation of the South African equity market with other markets, using a generalised autoregressive conditional heteroskedasticity (GARCH (1: 1)) approach given the non-stationary nature of the ALSI historic data. The results showed strong positive market correlations between South Africa and China, India, Nigeria, Russia and Saudi Arabia, and strong negative correlation between South Africa and Australia, Germany, the Netherlands, and the United Kingdom. Secondly, the specific area of machine learning employed in this study was support vector machines, as implemented using Python programming. The results compare the actual index price with those predicted by the model and showed that this technique has the ability to predict the future price of the Index within an acceptable accuracy. The accuracy measure used was the mean relative error which in most cases was calculated to be between 95 and 98 which is considered relatively high. However, the results of the investment approach described above was considered to be too inconsistent to consider this diversification strategy viable. From a South African perspective, this approach has not been documented previously

Analysis and modeling a distributed co-operative multi agent system for scaling-up business intelligence

Modeling A Distributed Co-Operative Multi Agent System in the area of Business Intelligence is the newer topic. During the work carried out a software Integrated Intelligent Advisory Model (IIAM) has been develop, which is a personal finance portfolio ma

Training Neural Networks for Financial Forecasting: Backpropagation vs Particle Swarm Optimization

Neural networks (NN) architectures can be effectively used to classify, forecast and recognize quantity of interest in, e.g., computer vision, machine translation, finance, etc. Concerning the financial framework, fore- casting procedures are often used as a part of the decision making process in both trading and portfolio strategy optimization. Unfortunately training a NN is in general a challenging task mainly because of the high number of parameters involved. In particular, a typical NN is based on a large number of layers, each of which may be composed by several neurons , moreover, for every component, normalization as well as training algorithms, have to be performed. One of the most popular method to overcome such difficulties is represented by the so called back propagation algorithm . Other possibilities are represented by genetic algorithms , and, in this family, the swarm particle optimization method seems to be rather promising. In this paper we want to compare canonical back- propagation and the swarm particle optimization algorithm in minimizing the error on surface created by financial time series, particularly concerning the task of forecast up/down movements for the assets we are interested in

Investments unwrapped : demystifying and automating technical analysis and hedge-fund strategies

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.Includes bibliographical references (p. 563-570).In this thesis we use nonlinear and linear estimation techniques to model two common investment strategies: hedge funds and technical analysis. Our models provide transparent and low-cost alternatives to these two nontransparent, and in some cases prohibitively costly, financial approaches. In the case of hedge funds, we estimate linear factor models to create passive replicating portfolios of common exchange-traded instruments, that provide similar risk exposures as hedge funds, but at lower cost and with greater transparency. While the performance of linear clones is generally inferior to their hedge-fund counterparts, in some cases the clones perform well enough to warrant serious consideration as low-cost passive alternatives to hedge funds. In the case of technical analysis - also known as "charting" - we develop an algorithm based on neural networks that formalizes and automates the highly subjective technical practice of detecting, with the naked eye, certain geometric patterns that appear on price charts and that are believed to have predictive value. We then evaluate the predictive ability of these technical patterns by applying our algorithm to stocks and exchange rates data for a number of stocks and currencies over many time periods, and comparing the unconditional distribution of returns to the return distribution conditional on the occurrence of technical patterns.(cont.) We find that several technical patterns do provide incremental information, suggesting that technical analysis may add value to the investment process. To further demystify the highly controversial practice of technical analysis, we complement our implementation and validation study with a historical overview of the field and interviews with its leading practitioners.by Jasmina Hasanhodzic.Ph.D

The Obstinate Passion of Foreign Exchange Professionals : Technical Analysis

Technical analysis involves the prediction of future exchange rate (or other assetprice) movements from an inductive analysis of past movements. A reading of the large literature on this topic allows us to establish a set of stylised facts, including the facts that technical analysis is an important and widely used method of analysis in the foreign exchange market and that applying certain technical trading rules over a sustained period may lead to significant positive excess returns. We then analyze four arguments that have been put forward to explain the continuing widespread use of technical analysis and its apparent profitability: that the foreign exchange market may be characterised by not-fully-rational behaviour; that technical analysis may exploit the influence of central bank interventions; that technical analysis may be an efficient form of information processing ; and finally that it may provide information on nonfundamental influences on foreign exchange movements. Although all of these positions may be relevant to some degree, neither non-rationality nor official interventions seem to be widespread and persistent enough to explain the obstinate passion of foreign exchange professionals for technical analysis.foreign exchange market ; technical analysis ; market microstructure

The statistical properties of technical trading rules

A portfolio of 200 heterogeneous technical trading rules is tested for their directional predictabilities on the DJIAI from 1988 to 1999. We also explore several nonparametric techniques designed for brain research, and detected possibly other forms of dependencies more significant than the traditional linear autocorrelation for the time series. The overall conditional mean directional predictability is 46%. 36 percent of the rules have more than 50% directional predictability, and the top 20 percent rules has a 73% directional predictability, whereas the bottom 80 percent has a directional predictability of 40%. Buy signals consistently generate higher predictability than sell signals but do not commensurate with their respective risk levels. The relationship between two sub-periods is not stable, while the difference between the conditional mean directional predictability of buy only and sell only signals is highly significance. The belief that most successful rules have a directional predictability of 25% to 50% coincides with the mode of distribution. We observe counter intuitive relationship between volatility and directional predictability. The results of directional predictability in a downtrend concur with the argument that buy-and-hold strategy is not a suitable benchmark. Attempts are made to tackle the issues of small sample bias, data snooping, size of test window, bootstrap or t-test, and homogeneity. Issues are discussed on empirical testing for their real world applications, statistical and non-statistical interpretations; also randomness test; physical or biological science approach